Dual-energy (DE) chest radiographs provide greater diagnostic information than standard radiographs by separating the image into bone and soft tissue, revealing suspicious lesions which may otherwise be obstructed from view. However, acquisition of DE images requires two physical scans, necessitating specialized hardware and processing, and images are prone to motion artifact. Generation of virtual DE images from standard, single-shot chest radiographs would expand the diagnostic value of standard radiographs without changing the acquisition procedure. We present a Multi-scale Conditional Adversarial Network (MCA-Net) which produces high-resolution virtual DE bone images from standard, single-shot chest radiographs. Our proposed MCA-Net is trained using the adversarial network so that it learns sharp details for the production of high-quality bone images. Then, the virtual DE soft tissue image is generated by processing the standard radiograph with the virtual bone image using a cross projection transformation. Experimental results from 210 patient DE chest radiographs demonstrated that the algorithm can produce high-quality virtual DE chest radiographs. Important structures were preserved, such as coronary calcium in bone images and lung lesions in soft tissue images. The average structure similarity index and the peak signal to noise ratio of the produced bone images in testing data were 96.4 and 41.5, which are significantly better than results from previous methods. Furthermore, our clinical evaluation results performed on the publicly available dataset indicates the clinical values of our algorithms. Thus, our algorithm can produce high-quality DE images that are potentially useful for radiologists, computer-aided diagnostics, and other diagnostic tasks.
Besides from the learning-based methods, several statistical analysis based methods for suppressing bone structures without the supervision from training data have been studied. @cite_1 proposed a clavicle suppression algorithm which works by first generating a bone image from a gradient map modified along the bone border direction and then creating a soft-tissue image by subtraction of the bone image from the standard image. Later, @cite_26 @cite_15 proposed blind-source signal separation algorithms for suppression of bone structures in standard chest radiography. @cite_22 presented a ribcage segmentation algorithm based on Active Appearance Model that can accurately estimate the rib border and suppress the bone signal from standard radiography based on this prediction. In general, these statistical based methods require accurate segmentation and border annotations for the target structures, which is challenging to acquire. Although all have shown improvement in thoracic disease detection, many image details are suppressed and these methods require substantial hyper-parameter tuning.
@cite_15 @cite_26 @cite_1 @cite_22
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Chest radiographs play an important role in the diagnosis of lung cancer. Detection of pulmonary nodules in chest radiographs forms the basis of early detection. Due to its sparse bone structure and overlapping of the nodule with ribs and clavicles the nodule is difficult to detect in conventional chest radiographs. We present a technique based on independent component analysis (ICA) for the suppression of posterior ribs and clavicles which will enhance the visibility of the nodules and aid the radiologist in diagnosis. Projection images, such as those routinely acquired in radiological practice, are difficult to analyze because multiple 3-D structures superimpose at a single point in the 2-D image. Removal of particular superimposed structures may improve interpretation of these images, both by humans and by computers. This work therefore presents a general method to isolate and suppress structures in 2-D projection images. The focus is on elongated structures, which allows an intensity model of a structure of interest to be extracted using local information only. The model is created from profiles sampled perpendicular to the structure. Profiles containing other structures are detected and removed to reduce the influence on the model. Subspace filtering, using blind source separation techniques, is applied to separate the structure to be suppressed from other structures. By subtracting the modeled structure from the original image a structure suppressed image is created. The method is evaluated in four experiments. In the first experiment ribs are suppressed in 20 artificial radiographs simulated from 3-D lung computed tomography (CT) images. The proposed method with blind source separation and outlier detection shows superior suppression of ribs in simulated radiographs, compared to a simplified approach without these techniques. Additionally, the ability of three observers to discriminate between patches containing ribs and containing no ribs, as measured by the area under the receiver operating characteristic curve (AUC), reduced from 0.99-1.00 on original images to 0.75-0.84 on suppressed images. In the second experiment clavicles are suppressed in 253 chest radiographs. The effect of suppression on clavicle visibility is evaluated using the clavicle contrast and border response, showing a reduction of 78 and 34 , respectively. In the third experiment nodules extracted from CT were simulated close to the clavicles in 100 chest radiographs. It was found that after suppression contrast of the nodules was higher than of the clavicles (1.35 and 0.55, respectively) than on original images (1.83 and 2.46, respectively). In the fourth experiment catheters were suppressed in chest radiographs. The ability of three observers to discriminate between patches originating from 36 images with and 21 images without catheters, as measured by the AUC, reduced from 0.98-0.99 on original images to 0.64-0.74 on suppressed images. We conclude that the presented method can markedly reduce the visibility of elongated structures in chest radiographs and shows potential to enhance diagnosis. Lung nodule detection is one of the most important goals of large scale screening of chest radiographs. The success of nodule detection can be increased if it is possible to suppress the bony structures from the chest radiographs. While one possible way to do this is to use dual-energy imaging, most of the commercial X-ray machines do not offer this technology. Finding an alternative approach is an important task. This paper proposes a new solution for it, where first the contours of bones -especially the contours of clavicles- are detected then the shadow of the bones is removed. The performance of the clavicle suppression algorithm is then evaluated by the use of a nodule detection system. The algorithms were tested on images from the JSRT database. The nodule detection algorithm was looking for up to 20 suspicious areas in the original images. According to our first results in the images with suppressed clavicle shadows 20 of 380 false positives were removed, while all true positives were preserved. 69 of the original 380 false positives were around the clavicle, which would mean 24 decrease in the concerned region. Coronary artery calcification (CAC) as assessed with CT calcium score is the best biomarker for predicting future cardiac events. Dual energy (DE) chest radiography offers an inexpensive, low radiation-dose alternative to CT. We have shown CAC can be visualized using non-gated, 2-shot, DE chest x-ray imaging. However, calcium signal from the ribs, superimposed on the CAC signal, can interfere with both image registration and reader detection of CAC. To improve the registration algorithm and the detectability of CAC, we created an enhanced CAC visualization algorithm with automatic rib segmentation and suppression. In this paper, we trained an active appearance model to detect the spatial location of rib in the DE bone images. Then, we suppressed the rib signal in the single kVp images using a kernel-filter based method. After the rib suppression, we used affine and non-rigid registration to align the CAC in low and high kVp image. 1800 dual energy cases were used in our experiment after data augmentation on 60 cases. We evaluated the segmentation from the rib AAM using six-fold cross validation. The average dice coefficient of the rib segmentation in the heart region is 0.92. The reduction of CAC mis-registration by the enhanced CAC registration algorithm is evaluated using both simulated and clinical images. Adding rib suppression improves the image registration significantly. Our results show that in both simulated synthetic images and clinical images, the CAC overlap improves from 0p to > 50p after rib suppression. Results suggest that CAC visualization and registration can be significantly improved using the rib suppression when rib signal confounds CAC signal in DE chest radiography.