Interestingly, CSF adiponectin positively correlated with CSF IgG, and CSF/serum albumin directly correlated with CSF/serum adiponectin. Our data demonstrated that CSF adiponectin predicts a worse prognosis: patients with the progressive form of MS had higher levels compared with the relapsing remitting form; patients with higher EDSS at baseline and a higher MS severity score at 4.5-year follow-up had significantly elevated adiponectin levels with respect to patients with a less severe phenotype. Finally, the adiponectin oligomerization profile was altered in CSF from MS patients, with a significant increase in HMW and MMW. The correlation of CSF adiponectin with the severity and prognosis of MS disease confirmed the role of this adipokine in the inflammatory/immune processes of MS and suggested its use as a complementary tool to assess the severity, progression and prognosis of the disease. Further studies on larger MS cohorts are needed to clarify the contribution of adiponectin to the etiopathogenesis of MS.Adiponectin exerts relevant actions in immunity and is modulated in several disorders, such as multiple sclerosis (MS). In this study, we characterized adiponectin expression and profiles in cerebrospinal fluid (CSF) from MS patients to investigate its potential relationship with the severity and progression of the disease. Total adiponectin in CSF was measured by ELISA in 66 unrelated CSF MS patients and compared with 24 age- and sex-matched controls. Adiponectin oligomer profiles were analysed by Western blotting and FPLC chromatography. Total CSF adiponectin was significantly increased in MS patients compared with controls (9.91 ng/mL vs 6.02 ng/mL) ( Multiple sclerosis (MS) is an autoimmune disease of the human central nervous system (CNS) that is very often accompanied by unpredictable clinical relapses and remissions and/or by disability progression over time. The aetiology remains unclear, but the main MS pathological features are due to inflammatory attacks that lead to neurodegenerative processes , 2. CereThe main trademarks of MS are triggered by the interaction between genetic and environmental risk factors. In particular, it is well known that overweight and obesity are risk factors for MS, and an increase in their prevalence has been reported among patients with MS . In thisUntil now, few data have been reported about adiponectin expression in CSF either in normal subjects or in MS subjects –24.The aim of the present study was to characterize adiponectin expression levels and its oligomerization profile in CSF from MS patients compared with those of normal controls. Moreover, the relationship between CSF adiponectin levels and clinical-biochemical MS phenotypes was analysed to investigate the association of this adipokine with the activity and severity of the disease.Sixty-six MS patients  were recruited at the moment of diagnosis at the Multiple Sclerosis Center, Second Division of Neurology of University of Campania “Luigi Vanvitelli” and observed prospectively over time (4.5 years).The inclusion criteria were MS diagnosis according to the revised McDonald criteria , age ≥ 1As controls, 24 subjects  and age-, body weight- and body mass index (BMI)-matched patients who underwent lumbar puncture for other suspected diseases (but with no confirmation of MS or other autoimmune or infectious diseases at the end of the diagnostic protocol) were recruited. Healthy controls followed the same inclusion and exclusion criteria. CSF was collected from patients at the moment of MS diagnosis before starting any treatment. According to clinical practice, demographic and clinical characteristics and body mass index (BMI) of all participants were recorded; every patient underwent extensive clinical and disability evaluation with the Expanded Disability Status Scale (EDSS), brain and spinal cord magnetic resonance imaging (MRI), lumbar puncture with oligoclonal band evaluation, extensive autoimmune panel and metabolic evaluation . The tot2 (m2). Adiponectin concentrations were measured in CSF by enzyme-linked immunosorbent assay (ELISA) . Each CSF sample, diluted 1:10, was assayed three times in duplicate. Serum adiponectin data were obtained in a previous study [For each subject, height and weight were obtained using standard techniques; body mass index (BMI) was calculated as body weight (kg)/heightus study .http://rsbweb.nih.gov.ij/). Each sample was tested three times in duplicate.Five microlitres of CSF was treated with 1× Laemmli buffer, heated at 95 °C for 2 min, loaded on a 10% SDS-PAGE gel and transferred as previously described . The bloThe oligomeric distribution of adiponectin in CSF samples was analysed by gel filtration chromatography (FPLC) on a Superdex 200 10/300 GL column connected to a Fast Protein Liquid Chromatography system . In detail, 500 μL CSF was eluted at 0.5 mL/min using PBS 100 mmol/L, pH 7.4. Fractions (500 μL) were collected, and the occurrence of adiponectin oligomers in each fraction was tested using both ELISA and Western blotting analysis. The column was calibrated using ferritin (440 kDa), aldolase (158 kDa) and ovalbumin (44 kDa) .t test for independent samples) and nonparametric statistics (Mann-Whitney U test) for continuous variables. Fisher’s exact test was performed for categorical variables. The potential association between higher and low levels of adiponectin in determining clinical outcome in MS patients was assessed using a univariate model. Odds ratios (ORs), with 95% confidence intervals (CIs), were estimated by a logistic regression model with the significant variables of the univariate model as covariates. Spearman correlation was used to investigate the relationship between CSF adiponectin and other inflammatory CSF markers. A p value of < 0.05 was considered statistically significant.Continuous variables are given as the mean and standard deviation, and categorical variables are given as absolute and relative frequencies. Univariate analysis was performed using parametric . Subsequently, we investigated the relationship between CSF adiponectin and the clinical phenotype/severity of the disease: we found significantly higher CSF adiponectin levels in progressive patients than in relapsing patients (15.51 ± 11.42 vs 8.91 ± 6.25 p = 0.01), with no differences for sex  or active/inactive patients.As shown in Fig. p = 0.67).Taking into account that the CSF albumin/serum albumin ratio (barrier index) is a marker of blood–CSF barrier integrity , MS patip = 0.04).In addition, the median value of CSF adiponectin (9.91 μg/mL) was used as an arbitrary cut-off to divide patients at the end of the follow-up period into two subgroups. Using the univariate model and comparing the two MS patient subgroups, we found that patients with CSF adiponectin levels above the arbitrary cut-off had significantly higher BMI, serum adiponectin, MSSS and EDSS at the end of the 4.5-year follow-up (see Table p = 0.02).In addition, the multivariate analysis (see Table p = 0.02) but not with other parameters .Adiponectin oligomer distributions were analysed by both Western blotting and FPLC analysis. The first analysis showed three bands corresponding to HMW ≥ 250 kDa), MMW (≥ 180 kDa) and LMW (≥ 70 kDa) in CSF from both controls and MS patients Fig. . In addi50 kDa, MSuccessively, the adiponectin oligomeric profile was further characterized by FPLC under native conditions. Both ELISA Fig. a and Wesp = 0.02); furthermore, the CSF adiponectin/serum adiponectin ratio correlates with the CSF albumin/serum albumin ratio, a measure of the integrity of the blood–brain barrier. Altogether, our results support the pivotal role of adiponectin in the inflammatory/immune processes of MS.In this study, we analysed the expression of adiponectin and characterized its oligomeric profile in CSF from MS patients. We found that CSF levels of adiponectin and its oligomers were increased in MS patients compared with control subjects matched for age, sex and BMI. In addition, we found that CSF adiponectin is correlated with the severity and progression of the disease and that CSF adiponectin levels are positively associated with MSSS, EDSS and CSF IgG  and worse prognosis (PI and MSSS) . The preAdiponectin expression in CSF has also been studied in other neurodegenerative diseases, such as Alzheimer’s disease and mild cognitive impairment; it has been found to be positively correlated with disease markers such as Aβ42 and cognitive function , 35. RegTo our knowledge, this is the first study analysing the implication of adiponectin oligomers in CSF from MS patients. Our data show a major involvement of HMW and MMW oligomers, which are increased in patients compared with controls. Previously published papers described the presence of adiponectin in CSF with contrasting results regarding the expression of different oligomers; medium- and low-molecular-weight complexes seem to be found in CSF. It is important to emphasize that adiponectin circulates as a full-length version and as a globular domain, which has shown very high biological activity . In addiHowever, the biological role of adiponectin in MS is still a matter of debate. In vitro studies demonstrated that adiponectin biologically functions in the brain in processes related to MS, such as synaptic activity and regulation of the immune response . RegardiIn conclusion, we demonstrated that the baseline levels of adiponectin in CSF from MS patients are correlated with the severity and progression of disease. This finding, together with the strong correlation of CSF adiponectin with CSF IgG, confirms the pivotal role of adiponectin in the inflammation/immune responses of MS. Further studies are required to clarify the contribution of adiponectin to the complex cytokine network working in MS, but altogether, our data outline the importance of this adipokine in the etiopathology as well as in the outcome of MS disease.Adding new knowledge to larger MS cohorts could reinforce adiponectin as a tool to assess the severity, progression and prognosis of this disorder.


Osteopontin (OPN) is a proinflammatory marker produced by systemic immune and central nervous system (CNS) resident cells. We examined, if the level of OPN in the cerebrospinal fluid (CSF) and blood is associated with late-time regional brain volumes and white matter (WM) lesion load in MS. Concentrations of OPN in blood and CSF were related to MRI findings 10.1 ± 2.0 years later in 46 patients with MS. OPN concentration was measured by ELISA, while regional brain volumes and lesion load was assessed by magnetic resonance imaging (MRI) using 3D MPRAGE sequence and automated MR volumetry. OPN measured in the CSF was associated with several regional brain volumes and WM lesion load measured 10.1 ± 2.0 years later. CSF OPN concentration correlated with long-term enlargement of lateral- and inferior lateral ventricles and the elevation of gross CSF volume, in conjunction with the reduction of several cortical/subcortical gray matter and WM volumes. Serum OPN showed no long-term association with regional brain volumes. OPN measured from the CSF but not from the serum was associated with lower regional brain volumes measured a decade later, indicating the primary role of inflammation within the CNS in developing long-term brain related alterations. The main driver of the pathology is central nervous system (CNS) inflammation in both the white matter (WM) and gray matter (GM) that induces a number of pathological events ultimately leading to progressive disability in part of the patients4. Magnetic resonance imaging (MRI) is a distinguished paraclinical investigation in the process of clinical diagnosis of MS, along with cerebrospinal fluid- (CSF) and blood tests, and MRI is also basic in defining the clinical course of MS5.Multiple sclerosis (MS) is an inflammatory, demyelinating, and neurodegenerative disease of the central nervous system. It is the most frequent non-traumatic cause of permanent neurological disability in young and middle-aged adults7. Brain atrophy was shown to be extensive in MS, with nearly 0.5–1.35% brain volume loss/year, much higher than that of normal aging (0.1–0.5%/year)8. It arises early in the course of the disease and accelerates along with disease progression9.MRI is a sensitive tool for detecting MS related tissue abnormalities in the CNS, especially the brain-related focal white matter (WM) and gray matter (GM) lesions, as well as the diffuse, or localized tissue loss (atrophy)10 and MS11. OPN is produced by various immune cells, including T cells, B cells, macrophages, dendritic cells, and natural killer cells.Osteopontin (OPN), also known as early T cell-activation gene 1 or secreted phosphoprotein 1 (SPP1) was originally identified as a bone matrix protein. OPN was shown to act as a pro-inflammatory cytokine in several autoimmune diseases, most notably in neuromyelitis optica spectrum disease12. Enhanced OPN expression was found in active MS lesions13, in microvascular endothelial cells and macrophages of plaques and also in the white matter surrounding the plaques14. OPN has a prominent role in secondary neurodegeneration: microglia secrete OPN into the extracellular matrix, which activates and recruits macrophages and CNS resident cells that modulate inflammatory responses16. A recent meta-analysis by Agah et al. summarizes the findings on OPN levels in multiple sclerosis; OPN level in the CSF is higher in MS compared to healthy controls17. Serum OPN levels were shown to be elevated in all MS subtypes, except for clinically isolated syndrome17. The higher concentration of OPN in CSF compared to serum suggests OPN expression by CNS cells18 . The elevated CSF and serum OPN levels were shown in both relapsing–remitting MS (RRSM) and secondary progressive MS (SPMS) patients19, moreover, higher OPN levels in CSF were measured in patients with active disease, as compared to patients with stable disease17. The highest OPN concentration in CSF was measured in RRSM patients17. OPN levels in the CSF correlate with development of microstructural abnormalities and functional connectivity within 10 years20. Therefore, we here examined the long-term effect of OPN on regional brain volumes in patients with MS.Proinflammatory Th17 immune responses induced by OPN have been indicated in the pathogenesis of MS40 reliably segmented structures were extracted from Freesurfer’s segmentation output, omitting brainstem. Figure OPN measured from serum samples showed no associations with the segmented volumes, regardless of the time of collection [median OPN concentration 12.52(9.0–16.7) ng/mL at the time of CSF collection and 9.8(6.2–18.8) ng/mL at the time of MRI].OPN measured from CSF collected 10.1 ± 2.0 years before MRI [median 246.4(164.5–439.5) ng/mL] showed highly significant and diverse associations with the segmented brain volumes. CSF OPN levels were positively associated with the volumes of the ventricles and CSF; left- and right inferior lateral ventricles , left- and right lateral ventricles , T1 derived total WM lesion volume , and CSF volume . Besides, inverse associations were found between CSF OPN levels and the following regional brain volumes: subcortical GM volume , left- and right ventral diencephalon , ventricle-free supratentorial volume , total cerebral cortex and WM volumes . More details and further associations are shown in Table 21. In our longitudinal cohort, CIS patients were not included, and we did not observe such associations in our MS populations. Moreover, cross-sectional statistical analyses yielded no significant associations at all, and all significant correlations surviving FDR correction corresponded to OPN concentration from CSF and MRI acquired 10.1 ± 2.0 years later. The suggestion that OPN may be associated with WM damage21 is well supported by our previous study in the same cohort, demonstrating that CSF OPN levels are related to wide-spread WM alterations localized to the normal appearing white matter (NAWM) of left superior and inferior longitudinal fasciculi, external capsule, forceps minor  and anterior corona radiata, indicating myelin loss and axonal degenerations20. These previous and the current data may thus suggest that patients with higher OPN CSF levels developed more extensive WM damage accompanied by the association with reduced WM and GM regional volumes. Indeed, GM atrophy is strongly associated with WM injury in MS patients, particularly with injury to association fibers22.OPN measured from the CSF was associated with reduced brain volume in several regions within 10.1 ± 2.0 years, indicating that the level of CSF OPN was associated with regional brain volumesmeasured a decade later. Results showed that an elevated CSF OPN concentration predicted the late-time enlargement of lateral- and inferior lateral ventricles and the elevation of gross CSF volume, in conjunction with the reduction of several cortical and subcortical GM volumes. Tortorella et al. conducted a cross-sectional study on patients with clinically isolated syndrome (CIS), measuring OPN concentrations from CSF and gross brain volumetry, including total GM volume, peripheral GM volume, total brain volume, ventricular volume and manually assessed corpus callosal index. Their results showed that OPN levels were only weakly associated with corpus callosum index23. Recently, several phase III trials defined brain atrophy as an outcome in both relapsing and progressive MS, and a number of disease modifying treatments significantly reduced atrophy rate24. If confirmed in other independent cohorts, CSF OPN concentration may be a potential marker for screening patients for high risk of accelerated atrophy rate in the long-term. Despite the fact that due to study design, we cannot state that the observed association with smaller regional brain volumes (and larger ventricles) indicate atrophy, our study may still indicate that in the development of brain volume losses reported earlier9, OPN produced within the CNS plays an important role. This also emphasizes the role of inflammation within the CNS compartment in the evolution of atrophy.It is conceivable that soluble biomarkers, specific for late-time regional brain volumes, alone or in combination with MRI biomarkers, may be clinically valuable in prognostic evaluation at the beginning of MS disease. Brain atrophy has clinically relevant impact on MS pathogenesis: higher atrophy rate leads to the worsening of expanded disability status scale (EDSS) and progression to disability25. In case of relapsing MS, the MRI measurements were taken in the remission phase. Most of the patients were on chronic disease modifying treatment  have participated in the study. Serum and CSF samples were collected 10.1 ± 2.0 years before MRI and aliquots were kept at − 80 °C until further processing. A new serum sample was taken at the day of MRI acquisition. All patients participating in the study had MS fulfilling the 2017 modified McDonald diagnostic criteriaThe study was conducted according to the World Medical Association Declaration of Helsinki and approved by the Regional Ethical Committee of the University of Pecs (7068-PTE 2018). All patients signed written informed consent prior to study procedures.After centrifugation, supernatants were stored at −80 °C until further processing. For quantitative detection of OPN concentrations in the serum and CSF samples, a commercially available sandwich enzyme-linked immunosorbent assay (ELISA) kit was used . All preparations were performed according to the manufacturer’s instructions. Samples were diluted for analysis . All samples were run in duplicates. An iEMS MF microphotometer was used for optical density detection at 450 nm . The detection limit for the assay was 62.5 pg/mL.Fit, Siemens AG, Erlangen, Germany) with a standard 20-channel head-neck coil. Brain volumetry was based on a 3D T1 magnetization-prepared rapid acquisition with gradient echo (MPRAGE) sequence acquired according to the Freesurfer’s Morphometry Protocols Guideline .All subjects were scanned using the same 3 T MRI scanner  as grey matter. The final volumetric results from Freesurfer were fed into statistical analysis.3D T1 images were fed into volumetric segmentation performed with FreeSurfer v6.0. Details of the procedures are described in previous publicationshttps://www.statistical-modelling.de/lst.html) was used to automatically segment cerebral white matter lesions on 3D FLAIR images using the lesion prediction algorithm28.LST toolbox version 3.0.0 . For volumetric analysis, multiple linear regression models were employed with the volumes of the segmented brain structures as dependent variable and OPN concentration, age, gender and estimated total intracranial volume as independent variables. The assumptions of multiple linear regression were satisfied, as judged by testing for linearity, independence of errors, outliers, normality assumptions of the residuals, homoscedasticity and multi-collinearity. Significance level was set at p < 0.05. Given the large number of segmented structures in the volumetric analysis, multiple comparisons correction with Benjamini–Hochberg procedure was applied with a conservative q = 5%.The present study bears limitations inherent to the study design. The main limitation is the lack of initial (baseline) MRI measurements. Without the baseline measurement we cannot state that the association with lower regional brain volumes (and larger ventricles) correspond to brain atrophy per se.


The anatomical routes for the clearance of cerebrospinal fluid (CSF) remain incompletely understood. However, recent evidence has given strong support for routes leading to lymphatic vessels. A current debate centers upon the routes through which CSF can access lymphatics, with evidence emerging for either direct routes to meningeal lymphatics or along cranial nerves to reach lymphatics outside the skull. Here, a method was established to infuse contrast agent into the ventricles using indwelling cannulae during imaging of mice at 2 and 12 months of age by magnetic resonance imaging. As expected, a substantial decline in overall CSF turnover was found with aging. Quantifications demonstrated that the bulk of the contrast agent flowed from the ventricles to the subarachnoid space in the basal cisterns. Comparatively little contrast agent signal was found at the dorsal aspect of the skull. The imaging dynamics from the 2 cohorts revealed that the contrast agent was cleared from the cranium through the cribriform plate to the nasopharyngeal lymphatics. On decalcified sections, we confirmed that fluorescently labeled ovalbumin drained through the cribriform plate and could be found within lymphatics surrounding the nasopharynx. In conclusion, routes leading to nasopharyngeal lymphatics appear to be a major efflux pathway for cranial CSF.  Cerebrospinal fluid (CSF) is produced within the ventricles by the choroid plexuses and circulates within the subarachnoid spaces around the brain and spinal cord. Historically, it was concluded that CSF leaves the central nervous system (CNS) via direct pathways through outcroppings of arachnoid tissue into the venous sinuses of the dura mater , 2. HoweImaging techniques have long been utilized to assess CSF outflow of tracers . TraditiOne valid criticism of tracer injection studies is that they may inherently introduce artifacts because of relatively large acute volumes introduced into the CSF. In the mouse, with an estimated CSF volume of 35 μL , injectiAlterations in CSF circulation may have significant effects on the pathogenesis of neurological conditions associated with the aging process, including dementia and stroke. Many research groups have now demonstrated that a slower overall efflux of CSF occurs during the aging process , 11, 14.Since CSF is principally produced by the choroid plexuses that are located in the ventricles, we decided to analyze the flow of tracer following an intraventricular low-rate infusion of a 17 kDa dendritic gadolinium-based contrast agent, GadoSpin D . We choshttps://doi.org/10.1172/jci.insight.150881DS1). An initial filling of the ventricular system was apparent, spreading ventrally to the basal cisterns and under the olfactory bulbs within the first 15 minutes after the start of the infusion. With increasing time, the signal intensity of the tracers was progressively detectable in the nasal cavity, in the nasopharyngeal region, and in the deep and superficial lymph nodes  or 12 months of age (n = 6). While the low-rate infusion of contrast agent took place during the first 60 minutes, we observed that the signal intensity in the contralateral ventricles reached a peak at around 40 minutes and then progressively decreased in both age groups  showed a substantial increase in maximum change in signal intensity compared with baseline , suggesting that this area is a major site of contrast agent bulk flow , C and DBecause CSF leaving the ventricles has free communication with the subarachnoid space around the spinal cord, we also quantified the dynamics in the ventral aspect of the cervical spine . We obseRecent work has introduced the concept of glymphatic flow that would theoretically aid in CSF/solute penetration into the brain parenchyma along para-arterial spaces. Recent MRI studies have shown that low–molecular weight contrast agents, such as gadoteric acid, do indeed demonstrate signal enhancement within the parenchyma –36. HoweThus, we conclude from this study that bulk flow routes to the basal cisterns and the subarachnoid space of the spinal canal are major pathways for CSF macromolecular distribution from the ventricular system.After observing that a substantial portion of the tracer reached the basal cisterns, we aimed to elucidate the potential anatomical pathways from this location that might be used to reach the cervical draining lymph nodes. Based on our observation that the bulk of the contrast agent appeared to flow continuously from the region of the olfactory bulbs to the nasopharynx and to the draining lymph nodes, we first quantified the changes in signal intensity in ROIs localized in the nasal turbinates and the nasopharynx , A and BAt other regions suggested to be efflux sites from the skull, CSF contrast agent signal could be detected in the jugular region below the skull and around the optic nerves , C and DThus, because the expected differences in signal dynamics between young and older animals were only detectable in the nasal turbinates and the nasopharyngeal areas, we conclude that this route is a major pathway for CSF clearance from the cranium to the cervical lymph nodes.Interestingly, the signal quantifications revealed that only a minimal increase of signal  could be detected within the turbinates themselves in either group, whereas much larger signal increases were detected at the nasopharynx . This may suggest that the contrast agent distributes to a large area throughout the nasal cavity after effluxing through the cribriform plate before draining to the lymphatic vessels near the nasopharynx leading to the lymph nodes, as appears to be evident from the sagittal view in + lymphatic vessels exists laterally on both sides of the nasopharynx, and these vessels could be found to contain AF647-OVA signal  using the same slow infusion protocol employed within the MRI and then assessed the presence of tracer on sections from decalcified craniums and deep cervical lymph nodes. Lymphatic vessels were identified using anti–lymphatic vessel endothelial hyaluronan receptor 1 (anti–LYVE-1) antibodies. As seen in A signal . Deep ce sinuses . Thus, tIn this study, we have assessed CSF outflow by MRI in 2 age groups (2–3 months and 12 months) of mice by utilizing an indwelling catheter system, allowing low-rate infusion of a macromolecular contrast agent during image acquisitions. Similar to previous reports, we found slower dynamics of CSF circulation in the older cohort of mice compared with the younger mice. Imaging of the contrast agent dynamics in the cranial region revealed that the bulk of the CSF flowed ventrally under the brain through the basal cisterns and exited through the cribriform plate to be collected by lymphatics in the nasopharyngeal region.Our methods are similar to those in recent publications utilizing MRI for examination of CSF flow in rodents , 12, 15.We observed minimal contrast agent signal in the dorsal region near the dural sinuses or above the cortical hemispheres. Thus, this is contrary to a concept of a major pathway of CSF outflow at the dorsal dura, as proposed in several recent studies in mice , 28, 29 As we anticipated, our data indicate a reduction in CSF outflow in older mice consistent with previous reports , 14, 41.We took advantage of the differences in contrast agent dynamics between the 2 age groups of mice to attempt to elucidate the relative importance of several potential efflux routes. We hypothesized that only along major bulk flow pathways for CSF egress would the patterns of contrast agent dynamics between the 2 age groups mirror those seen at the downstream lymph nodes. Since we found that limited contrast agent signal was apparent along the dorsal aspect of the skull, we focused these efforts on potential efflux routes from the basal cisterns. From this location, evidence exists in the literature for outflow to the lymphatic system through the cribriform plate , 19, 47,This conclusion appears to conflict with that of Ahn et al. in rats , who detThe situation in humans remains unresolved . StudiesIn sum, through establishment of a technique allowing dynamic CE-MRI under low-rate infusion of gadolinium contrast agents, we conclude that CSF distributes from the ventricles to the subarachnoid space ventral to the brain and in a caudal direction down the spine. Under our experimental conditions, a major outflow route from the cranium appeared to be through the nasal region to reach lymphatic vessels near the nasopharynx before draining to the cervical lymph nodes. With aging, the dynamics of clearance from the ventricles and flow through the nasal turbinates and nasopharyngeal lymphatics to the lymph nodes were reduced. These experiments have set the stage for further MRI evaluation of CSF outflow in mouse models of neurological disorders.Female wild-type mice (Janvier) on a C57BL/6 background were kept under specific pathogen–free conditions until they were used for experimental studies.2 to keep the breathing rate less than 140 breaths/min.Mice were anesthetized by intraperitoneal injection of 100 mg/kg ketamine and 20 mg/kg xylazine and fixed in a stereotaxic frame (Kopf Instruments). Under this anesthesia, the skull was thinned with a Proxxon GG 12 engraving drill. A 28 G, 2.5 mm long, MRI-compatible microcannula  was inserted stereotactically 0.95 mm lateral and 0.22 mm caudal to the bregma and 2.50 mm ventral to the skull surface . The micAnimals were examined in a horizontal-bore 9.4 T animal scanner (BioSpec Avance III 94/20) with a BGA12S gradient system with ParaVision 6.0.1 and a linearly polarized coil with an inner diameter of 40 mm . Contrast-enhanced imaging was achieved with a 3D time-of-flight gradient recalled echo sequence originally adapted for imaging of peripheral lymph vessels  with a rhttps://www.slicer.org). All the Digital Imaging and Communications in Medicine (DICOM) image files were imported into 3DSlicer for segmentation and 3D modeling. The ROI was first defined and segmented with the segment editor module. The model maker module was used to create the 3D model. Finally, the volume was determined via the segment statistics module.ROIs were manually drawn around the different anatomical regions investigated with Horos . Signal intensity was normalized using the reference phantom. The normalized ROI value  dissolved in artificial CSF (Harvard Apparatus) at a concentration of 5 mg/mL was infused at a constant rate of 0.1 μL/min for 60 minutes; the 2- to 3-month-old wild-type mice were sacrificed at the end of the infusion.For decalcification, intracardiac perfusion with PBS and 4% paraformaldehyde (PFA) was performed. Afterward, the mice were decapitated followed by the removal of skin, muscles, incisors, and the lower jaw from the cranium. The cranium was then immersed in 4% PFA for overnight fixation before being placed in 14% EDTA for 7 days (refreshed daily) at 4°C. Decalcified tissue was then immersed in 30% sucrose for 3 days for cryoprotection before OCT embedding. Then, 20 μm thick coronal sections were cut from a cryostat  and stored at –80°C. For immunofluorescence staining of LYVE-1, frozen tissues were first hydrated with PBS for 10 minutes, then permeabilized by 0.1% Triton X-100 for 10 minutes. For blocking, 10% goat serum was used for 1 hour at room temperature. Tissues were incubated with primary antibody  for 3 hours at room temperature and then washed with PBS before incubating with secondary antibody  for 2 hours at room temperature. Imaging of the nasal region was done under a Zeiss Axio Zoom.V16 microscope equipped with a Teledyne Photometrics Prime BSI Scientific CMOS camera combined with a CoolLED pE-4000 illumination system and ZEN 2 software (Zeiss). Higher magnification images were then acquired using a Zeiss LSM800 confocal microscope.For processing of draining lymph nodes, cervical lymph nodes were postfixed in 4% PFA at 4°C overnight. Lymph nodes were further immersed in 30% sucrose for 2 days at 4°C before being snap-frozen in melting isopentane with liquid nitrogen. The frozen tissue was cut serially into 15 μm sections with a cryostat microtome (Leica Microsystems). Sections were incubated with an anti–LYVE-1  primary antibody for 2 hours at room temperature before incubation with a donkey anti-rat  secondary antibody conjugated with Alexa Fluor 488 for 1 hour at room temperature. Sections were finally counterstained with DAPI. ROIs were acquired with a Zeiss LSM 880 Axio Observer.t test. Two-way ANOVAs were used for comparison, with time points being a within-subject factor and age being a between-subject factor, followed by Bonferroni’s post hoc test. A P value less than 0.05 was considered statistically significant.Statistical analyses were performed with GraphPad Prism 5. Graphs represent mean ± SEM. Means of 2 groups were compared using an unpaired 2-tailed Student’s All mouse experiments were approved by the Landesamt für Gesundheit und Verbraucherschutz, Saarbrücken, Germany (license numbers 31/2018 and 45/2019).YD and STP conceived and designed the study. YD performed the MRI experiments. YD, LX, and AS performed the histology experiments. JK, YD, and STP analyzed the data. AM and YD applied for approval of animal experiments. AM maintained the MRI facility. KF made substantial contributions to the conception of the study. YD and STP drafted the manuscript. All authors have approved the final version of the manuscript and have agreed to be accountable for all aspects of the work.


This effect was also detectable in deep white matter hyperintensities (p = 0.024). Total brain volumes and total gray or white matter volumes did not differ between the groups. Periventricular cap free water fraction was highly discriminative regarding patients with presumed iNPH and controls with an ROC AUC of 0.933. Quantitative diffusion microstructure imaging shows elevated water content in periventricular hyperintensities in patients with imaging features of iNPH, which could be the imaging correlate for pathologic fluid accumulation and may be used as an imaging biomarker in the future.Periventricular white matter changes are common in patients with idiopathic normal pressure hydrocephalus (iNPH) and considered to represent focally elevated interstitial fluid. We compared diffusion measures in periventricular hyperintensities in patients with imaging features of iNPH to patients without. The hypothesis is that periventricular hyperintensities in patients with presumed iNPH show higher water content than in patients without imaging features of iNPH. 21 patients with iNPH Radscale 7–12 (“high probability of iNPH”) and 10 patients with iNPH Radscale 2–4 (“low probability of iNPH”) were examined with a neurodegeneration imaging protocol including a diffusion microstructure imaging sequence. Periventricular hyperintensities and deep white matter hyperintensities were segmented and diffusion measures were compared. In patients with imaging features of iNPH, the free water content in periventricular hyperintensities was significantly higher compared to the control group ( The ependymal lining is regarded as a bidirectional barrier and transport system for cerebrospinal fluid (CSF) and fluid exchange. Age-related ependymal denudation might play a pathophysiological role in age-related impaired fluid transport, which may result in parenchymal periventricular fluid accumulation2. In patients with iNPH, chronic CSF circulation disturbance has been demonstrated, e.g. by intrathecal Gadolinium administration and delayed clearance of intrathecal Gadolinium in perivascular spaces. This disturbance is considered to lead to increased capping due to CSF accumulation in PVHs in patients with iNPH4. As both age-related capping and periventricular interstitial edema show PVH with an increased T2 signal, they can usually not be differentiated from one another in conventional MRI1. In contrast, deep white matter hyperintensities (DWMH) are always considered pathological and occur in both iNPH and small vessel disease. Histopathologically, DWMH are considered primarily ischemic because of their similarity to thromboembolic infarcts, although blood–brain barrier disruption with interstitial fluid accumulation is also discussed as a cause5.Pathologically, they represent areas of finely textured myelin associated with denudation of the ventricular ependymal lining rather than ischemic or gliotic changes7 or decreased FA in the corpus callosum and increased FA in the internal capsule8 as reviewed by Siasios and colleagues9. In a recent study using a quantitative multi-echo, gradient-echo water mapping sequence in a cohort of patients with neurovascular symptoms an increased water content in periventricular caps compared to DWMH was described10. Though to date, focused evaluation of iNPH-associated T2w hyperintense white matter changes based on current diffusion sequences is missing.In iNPH, DTI was used to evaluate changes in white matter tracts and found increased fractional anistropy (FA), reduced mean diffusivity and increased oriental coherence in the corticospinal tract11. DMI is based on advanced multi-shell diffusion protocols that allow distinguishing different anatomical compartments by their diffusion properties. The random motion of water within axons is purely one-dimensional. On the other hand, motion in the extra-cellular matrix is along all directions in space, but hindered by organelles and membranes . It has recently gained attention in the context of the glymphatic system17, but also as a confounder of classical DTI studies18. Typically, this compartment is specific to ventricles and perivascular spaces, which is of particular interest in this study. In this work we use the Bayesian approach of Reisert and colleagues13 to estimate DMI parameters, which is a robust generalization of neurite orientation dispersion and density imaging (NODDI). Compared to classical diffusion tensor imaging (DTI) indices, the DMI indices offer more specific and interpretable measures of tissue integrity.Novel multi-compartment diffusion microstructure imaging (DMI) techniques have been used to characterize white matter meso-/microstructure. They promise to bridge the gap between classic in vivo MR imaging and post-mortem microscopy methodsBy comparing diffusion metrics in PVHs and DWMH we aim at a more detailed understanding of the underlying pathophysiology  fluid accumulation vs. primary gliotic changes) of PVHs in iNPH. Our hypothesis is (1) that PVHs in patients with imaging features of iNPH have different diffusion metrics which can be depicted with DMI, i.e. to show a higher water content than PVHs in patients without imaging features of iNPH. In addition (2), we suspect a centripetal gradient of these diffusion metric alterations originating from the ventricles.19 on a 3 Tesla scanner  using a 64 channel head and neck coil including a diffusion tensor/microstructure imaging (DTI/DMI) sequence (Supplementary Table Within a two-year period 2018–2019) 228 patients were referred due to a suspected neurodegenerative disease. Imaging was performed with a dedicated protocol for neurodegenerative diseases8–2019 2220 including typical iNPH imaging features such as: Increased Evans-Index (> 0.3)21, decreased callosal angle, measured at the level of the posterior commissure (< 90°)22, Disproportionately Enlarged Subarachnoid Space Hydrocephalus (DESH)23, narrowed sulci and a tight high convexity24, dilation of the temporal horns, focally enlarged sulci and periventricular white matter changes—an example is given in Fig. Patients were classified based on the iNPH Radscalendex > 0.21, decre25 using a prediction score of 0.42 [Range 0–1] as cut-off value, whereas > 0.42 corresponds to a high probability of iNPH and < 0.42 to a low probability of iNPH (in our study = control group).In addition, we evaluated this subjective scoring using an SVM-based approachThe study is in accordance with the 1964 Helsinki Declaration and its later amendments and approved by the local ethical committee . Informed written consent was waived by the local ethics committee  due to the purely retrospective analysis. We hereby confirm that all methods were performed in accordance with the relevant guidelines and regulations.www.nora-imaging.org). Preprocessing of diffusion weighted images included a denoising step26 followed by correction of the Gibbs-ringing artifacts27 and final upsampling to isotropic resolution13.The DTI/DMI sequence parameters are given in Supplementary Table 13 to determine the intra-axonal volume fraction (V-intra), the extra-axonal intracellular volume fraction (V-extra) and the extracellular (CSF/ free water = V-CSF) volume fraction, respectively. T1w imaging datasets were automatically segmented into white matter, grey matter and cerebrospinal fluid (CSF) using CAT12 (http://www.neuro.uni-jena.de/cat/). White matter T2w-hyperintensities and periventricular hyperintensities (PVH) were manually segmented on 3D reformatted FLAIR images as depicted in Fig. 28. Furthermore, the lesion load was assessed using the Fazekas score29.Microstructural diffusion metrics based on a three-compartment diffusion-model were estimated using a Bayesian approach30.The volumes of the created ROIs for PVH and DWMH were calculated. In addition, the total intracranial, total brain, gray and white matter volumes, total intracranial CSF and lateral ventricular volumes were calculated using an atlas-based approachhttps://www.R-project.org). The Test ROC module was used to calculate ROC curves, which is built on the cutpointR module version 1.1.1 (https://CRAN.R-project.org/package=cutpointr).Continuous parametric variables are reported as mean and SD or median and ranges/interquartile ranges when appropriate. Normal distribution was tested with a Shapiro–Wilk test. The Mann–Whitney-U test and, when appropriate, 2-sided t-tests were performed to compare age, total brain, total gray and white matter, total intracranial CSF and lateral ventricle volumes, PVH and DWMH volumes between presumed iNPH and control groups. A One-way ANCOVA was conducted between PVH and DWMH comparing presumed iNPH and control groups, adjusting for age and lesion volume as covariates of no interest. A 2-sided Wilcoxon rank test was used to compare V-intra, V-extra and V-CSF within PVHs and DWMH within the groups. The Spearman correlation coefficient was used to relate PVH and DWMH V-CSF to lateral ventricular and lesional volumes. We plotted the ROC curves of PVH and DWMH V-CSF and the AUC and cut-off values were calculated. Values with an α-level of 0.05 were considered statistically significant. All statistical analyses were performed using R statistics version 3.6.3 . Informed written consent was waived by the local ethics committee due to the purely retrospective analysis.p < 0.001). There was no significant difference in terms of age, total brain volume, volume of grey or white matter between the groups. There also was no significant difference regarding PVH (p = 0.142) and DWMH volumes (p = 0.990) between groups with imaging features of iNPH and controls, even though periventricular white matter changes are included in the selection criteria of the iNPH Radscale. The distribution of lesion load assessed by Fazekas score was similar between groups  and 10 patients fulfilled imaging criteria for low iNPH Radscale scores . 6 patients with intermediate iNPH Radscale scores of 5–6 were excluded. Patients and imaging characteristics are given in Table Using SVM analysis with a prediction score of 0.42 as cut-off value, the diagnosis of iNPH pattern was confirmed in 19/21 patients whereas 2/21 patients were assigned to the control group according to the SVM analysis. No patient in the control group was assigned to the iNPH group.p = 0.033, reduced V-extra, F  = 4.89, p = 0.036 and increased V-CSF, F  = 9.40, p = 0.005. In DWMH in presumed iNPH there was a significant reduction in V-intra, F  = 16.81, p < 0.001 with no significant difference in V-extra, F  = 0.16, p = 0.689 but a significant increase in V-CSF, F  = 5.68 p = 0.024. Group related metrics and ranges are presented in Table DMI-parameters in PVH and DWMH were compared between groups with imaging features of iNPH and controls, adjusting for age and lesion volume. In PVHs, there was a significant reduction of V-intra in presumed iNPH compared to controls, F  = 5.06, p < 0.001) and also between lateral ventricular volume and DWMH V-CSF  within the group with imaging features of iNPH. Within the control group, there was a weak positive association between lateral ventricular volume and PVH V-CSF, without reaching significance . No significant correlation between PVH or DWMH volume and PVH/DWMH V-CSF was observed in both groups.Results of the Spearman correlation indicated a strong positive correlation between lateral ventricular volume and PVHs V-CSF  when applying an estimated cutpoint of 0.277. DWMH V-CSF appeared only slightly less discriminative , applying an optimal cutpoint of 0.283. Correlation plots and ROC curves are presented in Fig. The pathogenesis of periventricular and deep white matter changes in iNPH is not fully understood. By analyzing diffusion metrics in periventricular hyperintensities in patients with imaging features of iNPH compared to those without we found a significant increase of the free water fraction (V-CSF). A similar, however less pronounced effect was observed in deep white matter hyperintensities Fig. . Corresp4. The increased free water fraction in PVHs in the group with imaging features of iNPH compared to controls in our study  within the group of patients with image features of an iNPH showed no significant difference. As we applied a pooled analysis of total volume in which we did not correct for individual lesion volume, voxel-based methods with intraindividual statistics and larger numbers of patients might be advantageous to test this hypothesis, especially since it is unknown how pronounced such a gradient could be.Beyond that, our data provide evidence that an increase in free fluid beyond the periventricular zone may be present in DWMH. This raises the question to what extent pathophysiological phenomena in iNPH exceed the immediate periventricular region. In line with our findings, it has been demonstrated that in iNPH reduced cerebral blood flow in the deep white matter extends beyond the periventricular areas37. This delayed clearance as well as the transependymal leakage might potentially result in increased levels of free water in DWMH, as we observed in our study. Whether centripetal CSF propagation or transependymal CSF leakage predominates in iNPH and whether there is a gradient between PVH and DWMH should be the subject of further studies.Through intrathecal administration of gadobutrol it was shown that a tracer applied via the subarachnoid space propagated to deep brain tissue in a centripetal pattern and that clearance was delayed in the iNPH cohort22 and thus are also part of diagnostic imaging criteria. However, dilated lateral ventricles  are a nonspecific phenomenon in older patients. Thus, the finding of ventriculomegaly alone is nonspecific and, for itself, nondiagnostic38.The strong correlation of PVH V-CSF with ventricular width Fig. A,B suggeIn contrast, in our patient cohort, increased V-CSF in PVH and DWMH was strongly predictive for high iNPH Radscale scores. This indicates that a pathophysiological relationship may exist between dilated lateral ventricles and elevated levels of free fluid in the periventricular parenchyma in patients with imaging features of iNPH. The ROC analysis Fig. C reveale39. Nevertheless, objectifiable criteria were applied using the iNPH Radscale, which showed low interrater variability in the evaluation based on CT and MRI and demonstrated a strong correlation with clinical features of iNPH in recent studies41.The main limitation of our study is that due to the retrospective design the patient selection (iNPH) and the control group is based on imaging criteria alone, without considering clinical data , as recommended in current clinical diagnostic guidelines for iNPH43. For this reason, patients with an intermediate iNPH Radscale score of 5–6 were excluded from the analysis. Therefore, the question of how far our findings correlate with clinical features of iNPH remains open and should be addressed in a larger, prospective cohort of iNPH patients. We believe that the diagnostic value of V-CSF for iNPH and shunt-responsiveness in particular should be further investigated. Another technical limitation is, that even though there is no difference in lesion volume between the groups, it cannot be ruled out that e.g., by inclusion of small hyperintensities partial volume effects might occur. This needs to be addressed by further  analyses in larger patient cohorts.Furthermore, based on the current literature, there is a transitional range in which individual imaging features of iNPH are present without clinically confirming the diagnosis of iNPHIn summary, diffusion microstructural imaging seems potentially valuable to further elucidate the role of white matter changes in iNPH. Using diffusion microstructure imaging, increased levels of free fluid in periventricular and deep white matter hyperintensities were observed in patients with imaging features of iNPH, positively correlating with increased lateral ventricular volumes. Whether this change is primarily due to white matter degeneration, malfunction of the cerebrospinal fluid circulation or both cannot be determined with certainty. Quantification of water content and intra-/extraaxonal volume fractions of periventricular T2 hyperintense white matter changes by diffusion microstructure imaging might be a useful additional tool for the differentiation of T2 hyperintensities in iNPH or other diseases.Supplementary Information.


Vision sensing is a key technology to realize on-line detection of welding groove sizes and welding torch relative position and posture parameters during the arc welding process of intelligent production. For the specially designed vision sensor based on combined laser structured lights, an integrated calibration method for its internal parameters is proposed firstly, which improves the efficiency, accuracy and comprehensiveness of internal parameter calibration for a line structured light vision sensor and provides a good foundation for industrial application of the vision sensor. Then, the high precision integrated detection algorithms are derived for the V-groove size parameters and the spatial position and posture (SPP) parameters of the welding torch relative to the welding groove based on a single modulated laser lines image. The algorithms make full use of the data in a single modulated laser lines image, adopting data segmentation and plane fitting to realize the 3D reconstruction of V-groove surfaces and its adjacent workpiece surfaces of planar workpiece, so solving the parameters with high precision. In the verification tests, the relative detection error of V-groove size parameters of planar workpiece is less than 1%, and the relative detection error of SPP parameters of welding torch relative to the welding groove is less than 5%, which separately shows the effectiveness and accuracy of the calibration method and the detection algorithms. This research work provides a good technical support for the practical application of the specially designed vision sensor in the intelligent welding production. For the welding of workpieces with determined groove shape and size located in specified spatial posture, the spatial position and posture (SPP) of the welding torch relative to the welding groove have great influence on weld formation quality that cannot be ignored. Thus, they should be reasonably set prior to implementing the arc welding process in addition to the basic process parameters such as welding current, arc voltage, welding speed and motion trajectory of the welding torch. However, machining and assembly errors for the welding groove sizes usually exist, and thermal deformation when workpieces are being welded inevitably occurs. This will often lead to defects and deficiencies with the weld formation if only relying on the preset SPP as well as the motion trajectory of the welding torch relative to the welding groove. Thus, it is necessary during the actual welding procedure to carry out high-precision welding groove sizes detection and weld tracking, that is, the motion trajectory control of the welding torch and synchronous control on the SPP of the welding torch relative to the welding groove. In other words, this important direction in the welding field is worthy of research and development to realize intelligent welding by on-line sensing detection and real-time feedback control, which can not only improve the efficiency of welding production but also effectively ensure the quality of the welded joint and product ,2.Vision sensing is a key technology to realize intelligent welding. Compared with other vision sensing technologies, the line structured light vision sensing based on the perspective projection principle has the comprehensive advantages of simple operation, high detection accuracy and low system cost and has been widely used in modern industrial production .The premise of a vision sensor based on line structured light successfully applied in realizing high precision detection is firstly to calibrate its internal parameters effectively, including camera calibration and structured light plane calibration . In the Meanwhile, the line structured light vision sensor has been widely used in the welding field, such as weld tracking, welding torch location determining and so on ,13,14. HIn terms of the welding groove size parameters detection based on line structured light vision sensing, some typical research works are as follows. He et al.  and Zhu In terms of the relative position and posture detection based on line structured light vision sensing, some research works have also been carried out. Xue et al.  and ZengFrom the above brief review and analysis, we can see that the current calibration method of the line structured light vision sensor is complex and inconvenient. Meanwhile, in the detection process, the mentioned detection methods for the welding groove sizes and welding torch relative position and posture parameters depend on sensor installation and welding equipment motion parameters at different degrees. There are few studies that have completed the integrated detection of welding groove sizes and welding torch relative position and posture parameters with high detection accuracy, robustness and adaptability at the same time.fx, fy, u0, v0, k1, k2) and the structured light plane equation parameters  in a camera coordinate system. Next, based on the processing of single modulated laser lines image captured by camera, the 3D point cloud data of the laser lines can be obtained. Then, the V-groove surfaces and its adjacent workpiece surfaces of the planar workpiece are reconstructed by data segmentation and plane fitting. Finally, the integrated detection of the V-groove size parameters  of the planar workpiece and the relative position and posture parameters  of the welding torch in any relative position and posture is realized. This research work effectively improves the robustness and applicability of detection for the vision sensor and has important application value in intelligent welding production.Aiming at the existing problems mentioned above, on the basis of the deduced detection mathematical model of the vision sensor based on combined laser structured lights, this paper firstly proposes an integrated calibration method using only an ordinary checkerboard calibration board for internal parameters of the vision sensor, including camera internal parameters  and o-uv is a 2D pixel coordinate system, which are used to characterize the 2D coordinates in the image plane; CO-CYCZCX is the 3D camera coordinate system  and WO-WYWZWX is the 3D world coordinate system , which are used to characterize the 3D coordinates of space points in the actual physical space. Point P is the sampling point in the modulated laser lines projected on the surfaces of the measured object and point p is the perspective imaging point of the sampling point P on the 2D image plane.In order to convert the 2D image data of the modulated laser lines captured by the camera into 3D space data through the perspective projection model, four rectangular coordinate systems are established, as shown in P from the 3D world coordinate system W-XWYWZWO to the 3D camera coordinate system C-XCYCZCO is a position and posture transformation of rigid body (including rotation and translation). This transformation matrix is called the external parameter matrix of the camera and it can be expressed as follows [CR is a 3 × 3 matrix and T is a 3 × 1 matrix, which represent the posture and position of the camera coordinate system C-XCYCZCO in the world coordinate system W-XWYWZWO, respectively.In  follows :(1)[XCYCP in the 3D camera coordinate system C-XCYCZCO and the coordinates of the imaging point p in the 2D image coordinate system O-xy is as follows:According to the perspective projection principle, the matrix relationship between the coordinates of the sampling point x and y directions are dx and dy (mm/pixel), respectively, and u0, v0 (pixel) are the position coordinates of the origin of the 2D image coordinate system O-xy in the 2D pixel coordinate system o-uv. Then, the matrix relationship of the imaging point p between its image coordinates  and pixel coordinates  is:On the image plane, the pixel widths along the P, the imaging point p and the camera optical center CO meet geometric collinear constraint. According to the conversion matrix of Equations (1)–(3), the conversion relationship between the 3D world coordinates of sampling point P and the 2D pixel coordinates of imaging point p can be obtained:fx = f/dx and fy = f/dy represent the dimensionless scale factor of the camera in the x direction and y direction, respectively.Ignoring the camera imaging distortion, the sampling point k1 and k2 represent the radial distortion coefficient of the camera, r represents the normalized distance between the distortion point and the main point of the image and cx and cy represent the image coordinates after radial distortion correction. So, k1, k2, xf, yf, u0 and v0 are called internal parameters of the camera system.However, the camera lens always has radial distortion and tangential distortion. In general, the tangential distortion is small and can be ignored, and only the radial distortion needs to be considered. The mathematical model used frequently for removing radial distortion of the camera is the Brown model , which iP on the surfaces of the measured object is also a point on the structured light plane projected by the line laser transmitter, a complete vision sensing detection mathematical model can be established by taking the structured light plane equation in the 3D camera coordinate system C-XCYCZCO as a supplementary condition. The general expression of the laser structured light plane equation in the 3D camera coordinate system C-XCYCZCO is:lA, lB, lC and lD are the coefficients of the laser structured light plane equation.Only relying on the constraint provided by Equation (4), the detection mathematical model of the vision sensor is incomplete. Considering that the sampling point XC, YC, ZC) of the sampling point P in the 3D camera coordinate system C-XCYCZCO can be solved by the pixel coordinates  of the imaging point p in the image plane.lA1, lB1, lC1, lD1 and lA2, lB2, lC2, lD2 are the plane equation parameters of the structured light planes projected by the line laser transmitters 1 and 2 in the 3D camera coordinate system C-XCYCZCO.Combining the above equations, the detection mathematical model of the vision sensor based on combined laser structured lights are obtained, as shown in Equation (7). Through this equation, the 3D coordinates  and the structured light plane equation parameters  of two line laser transmitters in the camera coordinate system. This method is only based on the ordinary planar checkerboard calibration board , and the images of the calibration board and the laser lines are collected before and after the two line laser transmitters project the laser lines onto the calibration board by using different exposure times, respectively, as shown in For the vision sensor based on combined laser structured lights, an integrated calibration method is proposed and applied for the calibration of vision sensor internal parameters, including the camera internal parameters , the caRC, T of the chessboard calibration board relative to the 3D camera coordinate system C-XCYCZCO under different positions and postures and Equation (4), the 20 sets of 2D coordinate data of the laser centerlines in the laser lines image are converted into the 3D coordinate data of their corresponding point in the laser lines projected on the calibration board in the 3D camera coordinate system C-XCYCZCO. Finally, according to the 20 sets of 3D coordinate data of laser lines projected on the calibration board under different positions and postures, the two laser structured light plane equation parameters can be obtained by fitting the two structured light planes separately, which are the actual structured light planes projected from the two line laser transmitters.For the calibration of laser structured light plane equation parameters, its detailed steps are as follows. Firstly, the laser lines image is preprocessed by median filtering, binary segmentation and morphological processing. Next, the laser centerlines in the image are extracted by skeleton thinning and Hough line transform. Then, according to the obtained external parameter matrices The calibration results of the internal parameters for the vision sensor are shown in The mathematical algorithms of vision detection are different for vision sensors with different arrangement structures between camera and laser structured light. For the designed vision sensor based on combined laser structured lights, the 3D reconstruction method for the V-groove surfaces and its adjacent workpiece surfaces of planar workpiece is primarily studied after the single modulated laser lines image processing effectively. Then, the detection algorithms of the V-groove size parameters and the SPP parameters of the welding torch relative to the welding groove are deduced. Finally, some experiments are carried out for the verification of the effectiveness and accuracy of the deduced detection algorithms.The laser lines projected on the V-groove surfaces and its adjacent workpiece surfaces are modulated, and then the CMOS camera captures the modulated laser lines image. The flow of processing and feature extraction of modulated laser lines image is shown in Firstly, the modulated laser lines image a is prepConsidering the requirements of real-time detection accuracy and anti-interference ability of image processing, the Zhang-Suen thinning algorithm  is selecρ and θ in polar coordinates for each line segment.Then, for the obtained single pixel laser centerline image, the probabilistic Hough transform method is applied to determine the lines in the image. In the actual detection, the detection results of multi-group points of line segment will appear. The final detected values of the laser centerline equations can be obtained by calculating the average values of Finally, the intersection points of the two adjacent lines detected can be calculated, which are the image feature points of the V-groove of the planar workpiece e. For thThe obtained intersections of the fitting lines after image processing and feature extraction, which are the characteristic points of the modulated laser lines image, will inevitably have pixel errors. If the size parameters of the V-groove are directly solved by using image coordinates of these points, it will inevitably produce large detection deviation, reduce the robustness of the detection algorithm and decrease the utilization of the laser lines image data.CO-CYCZCX, and then to realize the high precision calculation of the welding groove sizes and welding torch relative position and posture parameters. The flow of algorithm for solving the welding groove sizes and welding torch relative position and posture parameters is shown in In order to improve the accuracy and robustness of the detection results of welding groove size parameters, a plane fitting method is proposed, that is, to reconstruct the V-groove surfaces and its adjacent workpiece surfaces based on the 3D data points of the modulated laser lines in the camera coordinate system ix, iy; i = 1, 2,..., 6) according to their serial number. Each modulated laser line has three image feature points. Taking them as breakpoints, the data of each modulated laser line are divided into four segments, which are recorded as datai , as shown in The coordinates of the six feature points in o-xy are mapped into the camera coordinate system C-XCYCZCO based on the perspective projection principle. Then, the 3D point cloud segmented data of two single pixel modulated laser lines can be obtained, as shown in According to the detection mathematical model of Equation (7), the 2D segmented data of two single pixel modulated laser lines in the image coordinate system iS  can be obtained by fitting the datai  points with the same serial number in the two modulated laser lines, respectively.According to the structural characteristics of V-groove of planar workpiece, the data points with the same serial number in the two modulated laser lines are on the same welding groove surfaces or its adjacent workpiece surfaces. Theoretically, the welding groove surfaces and its adjacent workpiece surfaces are plane, so the 3D point cloud segmented data can be used for plane fitting. Four planes Considering that the obtained six image feature points may have pixel errors, their corresponding 3D feature points are not completely accurate segment interval points. Therefore, to ensure the accuracy of plane fitting data points and reduce the influence of the data error caused by segment interval points, the segment interval points and their neighborhood data points are removed during the process of data segmentation.M and column matrix X can be constructed as follows:C, C, C) is the average coordinates of one 3D segmented data point  which is used to fit corresponding plane, and column matrix X represents the normal vector of the plane.The singular value decomposition (SVD) is used to plane fitting for the different segmented 3D data points separately. The coefficient matrix The purpose of plane fitting is to minimize the sum of the distances between the fitting plane and all fitting points; thus, the objective function is established as follows:M is decomposed by the method of SVD. It can be obtained that the eigenvector corresponding to the minimum singular value of the coefficient matrix M is the optimal solution X of the objective function in Equation (9). According to D = −, all of the parameter values of the fitting plane are obtained. Taking the equation parameters of four fitting planes iS as Ai, iB, Ci and iD , the fitting plane equations of the four planes S1, S2, S3 and S4 associated with the V-groove are:m1 , m2 , m3 , m4 , respectively.The constraint condition is The welding groove surfaces and its adjacent workpiece surfaces obtained by fitting the segmented 3D point cloud data are shown in C-XCYCZCO, the welding groove size parameters and the SPP parameters of the welding torch relative to the welding groove can be detected and solved.Further, according to the reconstructed 3D welding groove and the position and posture of the welding torch in the camera coordinate system h, groove width b1 and b2. Their detection algorithms are as follows.The main size parameters of V-groove of planar workpiece include groove depth l1, which is the intersection line of the left groove surface S2 and the right groove surface S3, to the left workpiece surface S1 or the right workpiece surface S4 is called the groove depth.The distance from the groove bottom intersection line l1 is:The linear equation of the intersection line l1 to the left workpiece surface S1 and the right workpiece surface S4 are recorded as the groove depth h1 and the groove depth h2, respectively; The difference between h1 and h2 reflects the groove misalignment caused by planar workpiece assembly or thermal deformation. When the value of misalignment is small enough or negligible, take the average value of h1 and h2 as the groove depth h, as shown in The average distance from the points on the intersection line S1 and left groove surface S2, the intersection line l2 of the two planes can be obtained, and its linear equation is:According to the fitted left workpiece surface n2 of line l2 is:The direction vector n2 of the line l2 and the normal vector m1 of the fitting left workpiece surface S1, the normal vector m5 of the virtual vertical plane S5, which is perpendicular to the left workpiece surface S1 and passing through the line l2, can be obtained, expressed as:According to the direction vector l2, the plane equation of S5 can be obtained:m5 = , D5 = −(A5CXq + B5CYq + C5ZCq).  are the coordinates of any point on the line l2.Take any point of line S6 can also be obtained. Thus, the average distance from the points on the intersection line l1 to the virtual vertical planes S5 and S6 are the groove width b1 and b2, respectively, and the sum of b1 and b2 is the total groove width B, as shown in Similarly, the plane equation of the virtual vertical plane W-XWYWZWO is the world coordinate system fixedly connected with the planar workpiece, WX is the welding direction (groove direction) vector, WY is the welding groove width direction vector and WZ is the normal vector of the upper surface of the planar workpiece. The vector tor is the direction vector of the central axis of welding torch, which is used to characterize the posture of welding torch.e, angular deviation γ and welding torch height H.The relative position parameters of welding torch include transverse deviation S7, which is a plane passing through the groove bottom intersection line l1 and parallel to the upper surface of the planar welded workpiece. The point Pj0 and point Pj1 are the intersections of the central axis of welding torch and the optical axis of CMOS camera with the bottom plane S7, respectively. The transverse deviation e of welding torch is defined as the distance from point Pj0 to the groove bottom intersection line l1, and the angular deviation γ is the included angle between the line Pj0Pj1 and the intersection line l1.Construct the groove bottom plane H, camera height H1 are, respectively, the distances from the end of welding torch conductive nozzle PE, the camera focus along their respective axes to the upper surface of planar welded workpiece. The sensor installation height H0 is the distance from the camera focus along the central axis of welding torch to the end of welding torch conductive nozzle PE, which is a fixed value after the sensor being installed. When the central axis of welding torch is perpendicular to the upper surface of planar welded workpiece, the relationship among them is:The welding torch height α, left and right tilt angle β. The front and rear tilt angle α is the included angle between the welding torch direction vector tor and the normal vector WZ of planar workpiece upper surface along the welding direction. The left and right tilt angle β is the included angle between the welding torch direction vector tor and the normal vector WZ of planar workpiece upper surface in the plane of groove cross section.The relative posture parameters of welding torch include front and rear tilt angle γ. When welding process for different welding beads (layers) of V-groove are being implemented, the transverse deviation e and the height H of welding torch also need to be adjusted appropriately.The moving direction of welding torch during arc welding process may not always coincide with the direction of welding groove, which often leads to an angular deviation e, angular deviation γ and welding torch height H are solved as follows:1.Transverse deviation eAccording to the plane equations of the welding groove surfaces and its adjacent workpiece surfaces and the definition of position parameters of welding torch relative to the welding groove, the transverse deviation S7.(1). Solve the plane equation of the bottom plane S1 and S4 is taken as the value of the normal vector m7  of the bottom plane S7, which are given as follows:When the misalignment of the welding groove can be ignored, the average value of the normal vectors of planar workpiece upper surfaces P of the intersection line l1, the plane equation of the groove bottom plane S7 can be determined as:D7 = −(A7CXp + B7CYp + C7ZCp), and  are the coordinates of any point P on the intersection line l1.Take any point Pj0 of the central axis of welding torch and the plane S7.(2). Find the intersection point CX = D0, CY = 0. where D0 is the distance between the central axis of welding torch and the optical axis of camera, which are two parallel lines, and D0 is a fixed value after the sensor has been installed. The coordinates of the intersection point Pj0 of the welding torch central axis and plane S7 can be solved as /C7).The linear equation of the central axis of welding torch is: e.(3). Calculate the transverse deviation n1 of the groove bottom intersection line l1 is:The value of the direction vector Pj0 to intersection l1 is the transverse deviation e, which is:The distance from point 2.Angular deviation γPj1 between the camera optical axis and plane S7.(1). Find the intersection point XC = 0, YC = 0. The coordinates of the intersection point Pj1 of the camera optical axis and plane S7 can be obtained, which are .The linear equation of the camera optical axis is: γ.(2). Calculate the angular deviation Pj0Pj1 and the intersection line l1 are both in plane S7, their included angle is the angular deviation γ, which is:Since the straight line 3.HWelding torch height Pj of the central axis of welding torch and plane S1 can be solved by combining two corresponding equations, which are /C1). In the camera coordinate system C-XCYCZCO, the coordinates of the end of welding torch conductive nozzle PE are . where the sensor installation height H0 can be determined by calibration.The coordinates of the intersection point S1 of planar welded workpiece, the welding torch height H is the distance between point Pj and PE, which is:Thus, for the upper surface S4 of planar workpiece, the welding torch height H can also be calculated similarly. In practical application, the welding torch heights H relative to the two upper surfaces of S1 and S4, respectively, can be used alone or used with their mean value according to actual needs.For the upper surface α, left and right tilt angle β) affect the shape of weld pool and its fluidity, then will affect the quality of weld formation, including penetration, width and reinforcement. The values of relative posture parameters of welding torch are closely related to the absolute spatial posture of welding groove.During the arc welding process, the relative position parameters of welding torch affect the accuracy of the weld forming position and the relative posture parameters of welding torch . Take the value of the direction vector of the central axis of welding torch as: tor = . Therefore, the front and rear tilt angle α of welding torch can be solved, as follows:The direction vector of the line α is generally expressed as an acute angle during the actual welding process. A positive value of α indicates the welding torch tilts forward relatively along welding direction, and a negative value of α indicates the welding torch tilts backward relatively along welding direction.Considering the front and rear tilt angle 2.Left and right tilt angle β of the welding torchtor of welding torch and the vector q2, which is simultaneously perpendicular to the normal vector m7 and the vector q1, can be used to characterize the left and right tilt angle β of welding torch relative to the welding groove. The vector q2 is:The relationship between the direction vector β of the welding torch is:Therefore, the left and right tilt angle β is generally expressed as an acute angle during the actual welding process. A positive value of β indicates the welding torch slants to the left relatively in the welding groove cross section, and a negative value of β indicates the welding torch slants to the right relatively in the welding groove cross section.At the same, considering the left and right tilt angle H0 and D0  are used in the solutions of the relative SPP parameters of the welding torch, the solutions of other parameters only depend on the internal parameters of the designed vision sensor, which effectively improve its detection applicability.According to the above derived detection algorithm of welding groove sizes and relative position and posture parameters of welding torch, in the process of practical application of the designed vision sensor based on combined laser structured lights, there is no need to preset special or fixed relative posture between the welding torch (or the vision sensor) and the welding groove, that is, it can be arbitrary. The whole process only requires the modulated laser lines can be imaged completely, and the integrated detection of the welding groove sizes and the relative SPP parameters of the welding torch can be realized. At the same time, it can be seen that the derived detection algorithms have few application restrictions and do not depend on other devices. Except that the sensor installation parameters H0, the object distance of the camera is controlled at 140–160 mm in the verification tests, which can ensure that the magnification of the camera is similar in the verification tests, and the resolution of the camera is about 0.03–0.04 (mm/pixel) in the width direction and 0.07–0.10 (mm/pixel) in the depth direction in this case. These three tests were used to verify the detection accuracy of welding groove size parameters  and the spatial position parameters  and posture parameters  of the welding torch relative to the welding groove separately.In order to verify the correctness of the internal parameter calibration method for the designed vision sensor proposed in this paper and the accuracy of calibration results, as well as the effectiveness of the above derived detection algorithms of welding groove sizes and the SPP parameters of the welding torch relative to the welding groove, some verification tests under three different conditions were carried out on the test platform shown in h, groove width b1 and b2, respectively. T1: In test 1, symmetric and asymmetric V-grooves were used to verify the effectiveness and accuracy of detection algorithms of groove depth During the detection test of T1, on the premise of ensuring that the modulated laser lines are completely located in the field of vision of the camera, the workpiece was placed on the platform in any position and posture. The feature extraction diagram of the laser lines image modulated by typical V-groove and the 3D reconstruction diagram of the welding groove surfaces and its adjacent workpiece surfaces are shown in From e, γ, H1) relative to the welding groove. In order to facilitate the setting of position parameters and test verification, this test placed the planar workpiece with V-groove vertically under the camera, that is, the upper surface of planar workpiece was perpendicular to the optical axis of the camera. Under this condition, the camera height H1 can be expressed as: H1 = |D1/C1|, and the relationship between the torch height H and the camera height H1 is shown in Equation (14). As shown in e, the rotary precision displacement platform was used to set the angular deviation γ and the vertical precision displacement platform was used to set the relative height H1 of the camera.T2: Since the central axis of the welding torch is coplanar with the optical axis of the CMOS camera and the central axes of two line laser emitters, and the central axis of the welding torch is parallel to the optical axis of the camera, the SPP of the camera relative to the welding groove can be used to characterize the SPP of the welding torch relative to the welding groove. In test 2, a symmetric V-groove was used in the detection test of the camera (characterizing welding torch) position parameters  relative to the welding groove. The dual-axis tilt sensor was placed on the upper surface of the planar workpiece to real-time display the posture parameter values of the planar workpiece.T3: In test 3, a symmetric V-groove was used in the detection test of the camera (characterizing welding torch) posture parameters  relative to the welding groove is not an ideal vertical condition, which leads to the coupling among these parameters, and the measured values cannot fully represent the actual values of the parameters in the detection process of the relative position and posture parameters of the camera (characterizing welding torch).h, b1, b2) as well as the SPP parameters  of the welding torch relative to the welding groove. Their detection accuracy can fully meet the needs of actual welding production, which indicates the algorithms can provide strong support for on-line detection and feedback control of intelligent welding production.In short, based on the processing of a single modulated laser lines image, the derived detection algorithms can effectively realize the integrated detection of the welding groove size parameters ((1)fx, fy, u0, v0, k1, k2 and laser structured light plane equation parameters of Al1, Bl1, Cl1, Dl1 and Al2, Bl2, Cl2, Dl2 in the camera coordinate system) of the vision sensor can be integrated, calibrated effectively by the proposed calibration method. This reduces the requirement for high installation accuracy of two laser transmitters to a great extent, avoids the influence of non-parallel error in two laser structured light projection planes on detection results and eliminates the cumulative error in stepwise calibration. Thus, the proposed integrated calibration method improves the efficiency, accuracy and comprehensiveness of internal parameter calibration for a line structured light vision sensor and provides a good foundation for industrial application of the vision sensor.For the specially designed vision sensor based on combined laser structured lights, an integrated calibration method of vision sensor internal parameters is proposed, which uses only an ordinary planar checkerboard calibration board. The internal parameters b1, b2 and groove depth h) of the planar workpiece and the SPP parameters  of the welding torch relative to the welding groove can be applied under any SPP of the welding torch (or vision sensor). Based on the 3D data of modulated laser lines obtained by the processing of the single modulated laser lines image, the algorithms reconstruct the 3D surfaces of V-groove surfaces and its adjacent surfaces of planar workpiece by data segmentation and plane fitting. This improves the utilization of modulated laser lines image data and reduces the interference of image processing error on the parameter detection.The derived high precision integrated detection algorithms for the V-groove size parameters (groove width (3)According to the proposed integrated calibration method and derived high precision integrated detection algorithms, some verification tests were carried out. The experimental results show that the derived integrated detection algorithms can be applied under any position and posture of the welding torch (or vision sensor) and has good detection accuracy and robustness, which improves the applicability of the vision sensor and the integration of detection algorithms. This work has important value for the application of the vision sensor in intelligent welding production.There are three main research works accomplished in this paper. Some valuable conclusions could be obtained as follows: