Cross-modality synthesis represent nowadays a promising application in medical image processing to manage the problem of paired data scarcity. In this work we designed and trained a CycleGAN model to generate PET/CT data from 2D slices collected from the liver body region of twelve patients. The results obtained from the six test patients show how our model can outperform baseline CycleGAN framework and effectively be used for synthesizing artificial images to be used for data augmentation or dataset completion.
Ruptured coronary atherosclerotic plaques commonly cause acute myocardial infarction. It has been recently shown that active microcalcification in the coronary arteries, one of the features that characterizes vulnerable plaques at risk of rupture, can be imaged using cardiac gated 18F-sodium fluoride (18F-NaF) PET. We have shown in previous work that a motion correction technique applied to cardiac-gated 18F-NaF PET images can enhance image quality and improve uptake estimates. In this study, we further investigated the applicability of different algorithms for registration of the coronary artery PET images. In particular, we aimed to compare demons vs. level-set nonlinear registration techniques applied for the correction of cardiac motion in coronary 18F-NaF PET. To this end, fifteen patients underwent 18F-NaF PET and prospective coronary CT angiography (CCTA). PET data were reconstructed in 10 ECG gated bins; subsequently these gated bins were registered using demons and level-set methods guided by the extracted coronary arteries from CCTA, to eliminate the effect of cardiac motion on PET images. Noise levels, target-to-background ratios (TBR) and global motion were compared to assess image quality. Compared to the reference standard of using only diastolic PET image (25% of the counts from PET acquisition), cardiac motion registration using either level-set or demons techniques almost halved image noise due to the use of counts from the full PET acquisition and increased TBR difference between 18F-NaF positive and negative lesions. The demons method produces smoother deformation fields, exhibiting no singularities (which reflects how physically plausible the registration deformation is), as compared to the level-set method, which presents between 4 and 8% of singularities, depending on the coronary artery considered. In conclusion, the demons method produces smoother motion fields as compared to the level-set method, with a motion that is physiologically plausible. Therefore, level-set technique will likely require additional post-processing steps. On the other hand, the observed TBR increases were the highest for the level-set technique. Further investigations of the optimal registration technique of this novel coronary PET imaging technique are warranted.
CT attenuation correction (CTAC) images acquired with PET/CT visualize coronary artery calcium (CAC) and enable CAC quantification. CAC scores acquired with CTAC have been suggested as a marker of cardiovascular disease (CVD). In this work, an algorithm previously developed for automatic CAC scoring in dedicated cardiac CT was applied to automatic CAC detection in CTAC. The study included 134 consecutive patients undergoing 82-Rb PET/CT. Low-dose rest CTAC scans were acquired (100 kV, 11 mAs, 1.4mm×1.4mm×3mm voxel size). An experienced observer defined the reference standard with the clinically used intensity level threshold for calcium identification (130 HU). Five scans were removed from analysis due to artifacts. The algorithm extracted potential CAC by intensity-based thresholding and 3D connected component labeling. Each candidate was described by location, size, shape and intensity features. An ensemble of extremely randomized decision trees was used to identify CAC. The data set was randomly divided into training and test sets. Automatically identified CAC was quantified using volume and Agatston scores. In 33 test scans, the system detected on average 469mm3/730mm3 (64%) of CAC with 36mm3 false positive volume per scan. The intraclass correlation coefficient for volume scores was 0.84. Each patient was assigned to one of four CVD risk categories based on the Agatston score (0-10, 11-100, 101-400, <400). The correct CVD category was assigned to 85% of patients (Cohen's linearly weighted κ0.82). Automatic detection of CVD risk based on CAC scoring in rest CTAC images is feasible. This may enable large scale studies evaluating clinical value of CAC scoring in CTAC data.
Disorders of consciousness (DOC) may be characterized by the degree at which consciousness is impaired, and include for example vegetative state (VS) and minimally conscious state (MCS) patients. Using a reliable marker as a measure of the level of consciousness in such patients is of utmost necessity and importance for their appropriate diagnosis and prognosis. Identification of VS and MCS states based on their behaviors sometimes leads to incorrect inferences due to the influence of a range of factors like motor impairment, fluctuating arousal levels and rapidly habituating responses to name a few.1 The extent of damage in the thalamus, a structure known for its role in arousal regulation, may provide an imaging biomarker to better differentiate between VS and MCS. In this study, we manually segmented the thalamus from T1-weighted brain MRI images in a large cohort of 19 VS and 23 MCS subjects that were examined using the French version of the Coma Recovery Scale Revised (CRS-R).2 This scale is the most trustworthy behavioural diagnosis tool3 for patients with DOC available. The aim was to determine whether a relationship between thalamus volume and consciousness level exists. Results show that total thalamic volume tends to decrease over time after a severe brain injury. Moreover, for subjects in chronic state, the thalamic volume seems to differ with respect to the degree of consciousness that was diagnosed. Finally, for these same chronic patients, the total thalamic volume is varying linearly as a function of the CRS-R score obtained, indicating that thalamic volume may be used as a biomarker to measure the level of consciousness.
Image-Guided Radiation Therapy (IGRT) aims at increasing the precision of radiation dose delivery. In the context of prostate cancer, a planning Computed Tomography (CT) image with manually defined prostate and organs at risk (OAR) delineations is usually associated with daily Cone Beam Computed Tomography (CBCT) follow-up images. The CBCT images allow to visualize the prostate position and to reposition the patient accordingly. They also should be used to evaluate the dose received by the organs at each fraction of the treatment. To do so, the first step is a prostate and OAR segmentation on the daily CBCTs, which is very timeconsuming. To simplify this task, CT to CBCT non-rigid registration could be used in order to propagate the original CT delineations in the CBCT images. For this aim, we compared several non-rigid registration methods. They are all based on the Mutual Information (MI) similarity measure, and use a BSpline transformation model. But we add different constraints to this global scheme in order to evaluate their impact on the final results. These algorithms are investigated on two real datasets, representing a total of 70 CBCT on which a reference delineation has been realized. The evaluation is led using the Dice Similarity Coefficient (DSC) as a quality criteria. The experiments show that a rigid penalty term on the bones improves the final registration result, providing high quality propagated delineations.