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Background: Imaging of peritoneal malignancies using conventional cross-sectional imaging is challenging, but accurate assessment of peritoneal disease burden could guide better selection for definitive surgery. Here we demonstrate feasibility of high-resolution, high-contrast magnetic resonance imaging (MRI) of peritoneal mesothelioma and explore optimal timing for delayed post-contrast imaging. Methods: Prospective data from inpatients with malignant peritoneal mesothelioma (MPM), imaged with a novel MRI protocol, were analyzed. The new sequences augmenting the clinical protocol were (I) pre-contrast coronal high-resolution T2-weighted single-shot fast spin echo (COR hr T2w SSH FSE) of abdomen and pelvis; and (II) post-contrast coronal high-resolution three-dimensional (3D) T1-weighted modified Dixon (COR hr T1w mDIXON) of abdomen, acquired at five delay times, up to 20 min after administration of a double dose of contrast agent. Quantitative analysis of contrast enhancement was performed using linear regression applied to normalized signal in lesion regions of interest (ROIs). Qualitative analysis was performed by three blinded radiologists. Results: MRI exams from 14 participants (age: mean ± standard deviation, 60±12 years; 71% male) were analyzed. The rate of lesion contrast enhancement was strongly correlated with tumor grade (cumulative nuclear score) (r=-0.65, P<0.02), with 'early' delayed phase (12 min post-contrast) and 'late' delayed phase (19 min post-contrast) performing better for higher grade and lower grade tumors, respectively, in agreement with qualitative scoring of image contrast. Conclusions: High-resolution, high-contrast MRI with extended post-contrast imaging is a viable modality for imaging peritoneal mesothelioma. Multiple, extended (up to 20 min post-contrast) delayed phases are necessary for optimal imaging of peritoneal mesothelioma, depending on the grade of disease.
RESUMEN
PURPOSE: This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions. METHODS: We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans. RESULTS: Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p < $$ < $$ 0.005). In blind qualitative experiments, 96 . 1 % $$ 96.1\% $$ of super-resolution images were assessed to have superior diagnostic quality compared to interpolated images. CONCLUSION: High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.
