CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to International Application No. PCT/EP2018/054488, filed Feb. 23, 2018 and titled “MRI METHOD FOR CALCULATING A PROTON DENSITY FAT FRACTION,” which in turn claims priority from a European Patent Application having serial number 17181641.6, filed Jul. 17, 2017, titled “CALCULATION OF T2* CORRECTED PDFF,” both of which are incorporated herein by reference in their entireties.

TECHNICAL FIELD

The present disclosure relates to calculation of proton density fat fraction (PDFF) magnetic resonance imaging (MRI) based on fat-referenced lipid quantification.

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD), a range of diseases characterized by steatosis, is associated with metabolic syndrome, diabetes, and obesity (Ekstedt et al., 2006; Ertle et al., 2011) and can lead to advanced fibrosis, cirrhosis, and hepatocellular carcinoma (Ekstedt et al., 2006; Wattacheril et al., 2012). Non-alcoholic steatohepatitis, a more serious form of NAFLD, is now the single most common cause of liver disease in developed countries (Sanyal, 2011; Misra et al., 2009) and is associated with high rates of morbidity and mortality. The evaluation and grading of hepatocellular fat in patients with NAFLD usually requires a liver biopsy and histology. However, as liver biopsy is an expensive, invasive, and painful procedure that is sensitive to sampling variability (Hubscher, 2006; Wieckowska et al. 2008), imaging modalities, including magnetic resonance spectroscopy (MRS) and MRI, are frequently being adopted to determine proton density fat fraction (PDFF). PDFF-MRS is a noninvasive and accurate method for quantification of hepatic fat content, but it has proven difficult to implement in clinical practice owing to its expense and dependence on specific expertise. Further, the method only gives a local estimate of the liver fat content (Reeder et al., 2010). Multi-echo MRI-determined PDFF imaging provides non-local, quantitative, standardized measurements of hepatic fat that is reproducible and correlates closely with MRS (Noureddin 2013; Kamg 2011), liver biopsy (Tang et al., 2013) and ex vivo measurements (Bannas et al., 2015).

Traditional quantitative fat-fraction analysis using the two point Dixon (2PD) technique, which is based on acquisition of out-of-phase and in-phase images, has been shown to be useful to evaluate hepatic fat (Dixon, 1984; Glover, 1991; Qayyum et al., 2005; Pilleul et al., 2005; Reeder et al., 2005; Reeder & Sirlin, 2010). The disadvantage of 2PD in relation to multi-echo Dixon is that T2* has to be determined in a separate experiment. But, in essence, both dual and multi echo Dixon shares the same confounding factors that influence MRI signal intensity, including T2* decay, spectral complexity of hepatic fat, and T1-saturation bias (Reeder et al., 2011; Chebrolu et al., 2010). While the first three factors can be reduced through signal modeling, T1-saturation bias is commonly avoided using a low flip angle since the alternative, long repetition times, results in unfeasible breath-hold times. However, a low flip angle is associated with a low signal-to-noise-ratio (SNR), leading to reduced sensitivity in liver PDFF imaging (Johnson et al., 2014). This complicates the trade-off between image voxel size, breath-hold length, and SNR. Furthermore, as the SNR decreases, proper coil positioning becomes more important, especially in obese patients, and this makes clinical translation of the technique more difficult.

By increasing the flip angle, and thereby transforming the acquisition into a T1-saturated state, the trade-off between voxel size, breath-hold length, and desired level of SNR becomes less critical (Kühn et al., 2014). However, the T1weighting causes a bias in the measured signals unless corrected (Fleysher et al., 2009). It is possible to adaptively measure and compensate for the T1bias using a more complex experiment, as has been demonstrated by Kuhn and colleagues (Kuhn et al., 2014). Applying such correction to three-dimensional (3D) acquisitions is straightforward if the T1values of the fat and water tissue are well characterized. Nevertheless, the specific characteristics of the MR pulse sequence implementation and uncertainty in quantification of the T1of water and fat may influence the validity of the signal equations and lead to residual T1bias.

Fat-referenced lipid quantification allows fat quantification in T1-weighted Dixon imaging, and was originally introduced by Hu and colleagues and Dahlqvist Leinhard and colleagues (Hu and Nayak, 2008; Dahlqvist Leinhard et al., 2008). This quantification method calibrates the observed signal intensities of the water and fat images using the lipid signal in pure adipose tissue. This transforms the Dixon images into a common intensity scale where a value of 1 in the fat image corresponds to an adipose tissue concentration of 100%. The invariability to the T1 weighting has been shown by Peterson et al. (Peterson et al., 2016). In that study, intramuscular adipose tissue quantification using the fat-referenced technique was validated against conventional low-flip-angle PDFF estimation with a very high agreement between the methods. Recently, Andersson and colleagues further validated the fat-referenced technique in whole-body imaging at both 1.5 T and 3.0 T for bias field estimation in skeletal muscle and liver tissue (Andersson et al., 2015). Further, Heba and colleagues determined that the accuracy of magnitude-based MRI for estimating hepatic PDFF using MRS as a reference was unchanged when using different numbers of echoes and was unaffected by possible subject-based confounders (Heba et al., 2016).

SUMMARY

As discussed above, there is a need for a framework for calculation of PDFF of for instance a liver. It is an object of the present invention to provide such framework for calculation of PDFF based on T1-weighted two-point and multipoint Dixon imaging according to the appended independent claims. Embodiments of the present invention are provided by the accompanying independent claims. The present invention thereby provides technology for accurate estimation of organ PDFF using fat-referenced Dixon imaging, either via correction using the saturation ratio between the fat and water signal, or by direct use of fat estimates obtained following fat-referencing. This is especially relevant for estimation of liver PDFF for diagnosis and treatment of decreases as discussed above.

By calculating a PDFF it may be meant that in the method according to the present invention the reconstruction of fat and water images may, or may not, be corrected for T2* effects and/or spectral dispersion effects due to characteristics of the lipid spectrum, or the reconstruction.

The PDFF calculation apparatus configured to perform the PDFF calculations may be constituted by a computer comprising the necessary computer executable program and provided with the necessary input for the calculations.

The present invention and its embodiments provide that accurate proton density fat fraction (PDFF) estimation may be achieved in T1-weighted fat- and water-separated imaging using the presented framework based on fat-referenced fat quantification. The present invention provides that two-point Dixon (2PD) magnetic resonance imaging (MRI) using simplistic reconstruction without a multispectral lipid model may be used for accurate liver PDFF estimation using fixed T2* correction. This may further be improved by taking the individual T2* values of the liver water signal into account. But this is also applicable to other organs in the human body.

The fat-referenced quantification technique shows much lower sensitivity to T2* effects in 2PD PDFF calculations compared to the 2PD fat fraction technique. This lowered sensitivity to T2* relaxation is achieved because the fat referenced calculations do not include the water signal in the denominator.

The present invention provides that PDFF may be accurately estimated using T1 saturation corrected 10PD acquisitions using the suggested approach. Limits of agreement of ±1.41% for liver PDFF acquired with different sequences, in different breath holds, and with different acquisition coils in the datasets fulfilling strict quality control and ±1.44% in the analysis including all datasets may be achieved with the present invention, which are lower than what is commonly observed using other state of the art implementations.

The findings of using the present invention may be compared with those of the recent study by Heba and colleagues, in their retrospective analysis of 506 adults with non-alcoholic fatty liver disease (NAFLD), where liver PDFF was estimated using unenhanced 3.0T MRI, using right liver lobe magnetic resonance spectroscopy (MRS) as a reference (Heba et al. 2016). In this previous study, PDFF MRI findings were in close agreement with magnetic resonance spectroscopy (MRS), with the two-echo method based on fat fraction measurement with spectral correction but without T2* correction being least accurate. (Heba et al. 2016).

The present invention provides an alternative way to compensate for effects caused by the hepatic lipid spectrum. Correction of the lipid spectrum based on the acquired data, is normally a complex process, especially as the analysis also involves estimation of lipid T2* and water T2* relaxation (Qayyum et al. 2005; Reeder et al. 2011; Hu et al. 2011). Here, no assumptions have been made about the details of the lipid spectrum model. Using the methods described herein, the only basic assumptions made are that in-phase and opposite-phase imaging creates a highly specific contrast for fat and water, and that the effects on the observed lipid signal caused by the lipid spectrum are similar in both the reference adipose tissue and in the liver tissue.

According to a first aspect of the invention, a method of calculating a proton density fat fraction, PDFF, from a water, W, and fat, F, separated magnetic resonance imaging, MRI, based on fat-referenced lipid quantification in a region of interest (ROI) and using determination of a reference tissue is provided. The method comprises the step of determining PDFF as:

F·βfR

wherein

F is the fat signal in the ROI provided from the MRI,

βfis a function providing a ratio between T1 saturation values of the fat signals in the reference tissue and in the ROI; and

R is a representation of the sum of fat and water signals on an intensity scale where the saturation of each of the fat and water signals equals the saturation of fat in the reference tissue.

The method may be described as comprising the fat signal in the ROI provided from the MRI multiplied with βfbeing a function providing a ratio between T1 saturation value of the fat signals in the reference tissue and in the ROI, wherein the product thereof is divided with R being a representation of the signal intensity of the sum of fat and water on an intensity scale where fat and water are saturated with the saturation level only affecting the fat signal extrapolated from the reference tissue.

In one embodiment, the method may further comprise a step of determining the T1 saturation value of the fat signal in the reference tissue. Alternatively, such determination is premade and the method may comprise a step of receiving a T1 saturation value of the fat signal in the reference tissue. Further, the method may in one embodiment comprise a step of determining a T1 saturation value of the fat signal in the ROI, to be used for βfin the determination of PDFF, or alternatively a step of receiving such T1 saturation value of the fat signal in the ROI.

In one embodiment, the method may comprise a step of receiving a value of R as defined above, for the determination of PDFF. Alternatively, the method may comprise a step of receiving input for the determination of R, and a step of determining R based on said input.

In one embodiment, R may provide a quota between Frefand PDFFrefsuch that the method comprises the step of determining PDFF as:

FFref·βf·PDFFref

wherein

Frefis the fat signal in the reference tissue; and

PDFFrefis the PDFF of the reference tissue provided by a separate experiment of the reference tissue or by a predetermined constant.

The method according to this embodiment may further be described as the quota between F and Frefbeing the fat signal in the reference tissue, multiplied with βfand PDFFrefbeing the PDFF of the reference tissue provided by a separate experiment of the reference tissue or by a predetermined constant. The values of PDFFrefand Frefmay in one embodiment be received in a step of the method to be used for the determination of PDFF.

In another embodiment, the T1 saturation values of the fat signal in the reference tissue and in the ROI may be equal, providing βf=1.

In a further embodiment, the PDFF may be determined from a fat-referenced two-point Dixon acquisition without previous correction for T2* relaxation effects, and the water signal in the reference tissue, Wref, may be low such that a resulting value when Wrefis multiplied with a resulting T2* relaxation effect provides an approximation that the water signal in the ROI equals an observed water signal in the ROI, W2PD, being a reconstruction of the water signal from the MRI in the ROI using two-point Dixon acquisition, providing the PDFF to be determined as

F2⁢⁢PD-W2⁢⁢PD·e-Tip/T2,w*-e-Top/T2,w*2F2⁢⁢PD,ref·βf·PDFFref

wherein

F2PDis the observed fat signal in the ROI, being a reconstruction of the fat signal from the MRI in the ROI using two-point Dixon acquisition;

Tipis a constant of the echo time of the in-phase (IP) component comprising water plus fat signal of the water and fat signals from the MRI in the ROI;

T2*wis the T2* relaxation effect of water in the ROI resulting from the two-point Dixon analysis;

Topis a constant of the echo time of the out-of-phase (OP) component comprising the difference between the water and fat signals from the MRI in the ROI; and

F2PD, refis the observed fat signal of the reference tissue, being a reconstruction of the fat signal from the MRI in the reference tissue using two-point Dixon acquisition. In one embodiment, the method may comprise a step of receiving the F2PDand F2PD, refsignal values to be used for the PDFF determination. In one embodiment, the method may comprise a step of receiving the Topand Tiptime values to be used for the PDFF determination. In one embodiment, the method may comprise a step of receiving the T2*wvalue to be used for the PDFF determination.

In one embodiment, the T2* relaxation effect value may be determined in a separate experiment.

In another embodiment, the T2* relaxation effect value may be set as a constant based on a population mean.

In a further embodiment, βfmay be the quota of

sin⁡(α)⁢1-e-TR/T1⁢⁢f,ref1-cos⁢⁢α⁡(1-e-TR/T1⁢⁢f,ref)⁢⁢and⁢⁢sin⁡(α)⁢1-e-TR/T1⁢⁢f,ROI1-cos⁢⁢α⁡(1-e-TR/T1⁢⁢f,ROI),
wherein α is the effective flip angle in the MRI acquisition, TRis the repetition time, T1f,refis the T1 value for the fat signal in the reference tissue, and T1f,ROIis the T1 value for the fat signal in the ROI. In one embodiment, the method may comprise a step of receiving the TR, T1f,refand T1f,ROIvalues to be used for the PDFF determination.

In another embodiment, the Frefmay be determined as a weighted interpolation of the fat signal in the reference tissue. Further, to provide such weighted interpolation of the fat signal in the reference tissue, a method as presented in “Romu T, Borga M, Dahlqvist Leinhard O. MANA—multiscale adaptive normalized averaging. In: Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, Ill., USA, 2011. pp 361-364” may be used.

In one embodiment, R may be defined as F·βf+W·βw, and wherein the T1 saturation value of the fat signal in the reference tissue and in the ROI is equal, providing βf=1, providing the method comprising the step of determining PDFF as

FF+W·βw

wherein

W is the water signal in the ROI provided by the MRI; and

βwis a function providing a ratio between the T1 saturation value of the fat signal in the reference tissue and the T1 saturation value of the water signal in the ROI. In one embodiment, the method may further comprise a step of determining the T1 saturation value of the fat signal in the reference tissue. Alternatively, such determination is premade and the method may comprise a step of receiving a T1 saturation value of the fat signal in the reference tissue. Further, the method may in one embodiment comprise a step of determining a T1 saturation value of the fat signal in the ROI, to be used for βwin the determination of PDFF. Alternatively, such determination is premade and the method may comprise a step of receiving a T1 saturation value of the fat signal in the ROI.

In a further embodiment, βwmay be determined in a separate experiment by determining

minβw⁢(∑Measurements⁢⁢(FF+W·βw-PDFFex)2)
wherein PDFFexis the proton density fat fraction measured in a separate experiment. The PDFFexmeasured in a separate experiment may preferably, but not necessarily, be made in the same subject as the PDFF calculation in which the βwto be determined is to be used.

In a yet further embodiment, PDFF may be provided by a separate 2-point Dixon experiment. The equation to determine βwmay then be provided by determining

minβw⁢(∑Measurements⁢⁢(FF+W·βw-PDFF2⁢⁢PD)2)

According to a second aspect of the invention, a proton density fat fraction, PDFF, calculation apparatus is provided, wherein the calculation apparatus comprises a receiver and a processor. The receiver may be configured to receive a water, W, and fat, F, separated magnetic resonance imaging, MRI. The processor may be configured to, based on the received water and fat separated MRI, and based on fat-referenced lipid quantification in a region of interest (ROI) and using determination of a reference tissue, determine the PDFF as

F·βfR

wherein

F is the fat signal in the ROI provided from the MRI,

βfis a function providing a ratio between T1 saturation values of the fat signals in the reference tissue and in the ROI; and

R is a representation of the sum of fat and water signals on an intensity scale where the saturation of each of the fat and water signals equals the saturation of fat in the reference tissue. The receiver and the processor may further be configured to receive entities and perform determinations according to any of the embodiments described above. As seen inFIG. 3, the PDFF calculation apparatus10may receive input from a MRI source20. The MRI source20may provide water and fat separated MR data for the ROI and the reference tissue. The PDFF calculation apparatus10may be a computer configured to perform the calculations according to any of the embodiments above.

