Ellisontemple0723

fect on clinical cognitive domains. After ascertaining the ApoE ε 4 status, specific MRI regions can be correlated to the cognitive domain and will be helpful for precise assessment in prodromal AD. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.Background To study the predictive value of semi-quantitative pleural effusion and pulmonary consolidation for acute pancreatitis (AP) severity. Methods Thorax-abdominal computed tomography (CT) examinations were performed on 309 consecutive AP patients in a single center. Among them, 196 were male, and 113 were female, and the average age was 50±16 years. The etiology of AP was biliary in 43.7% (n=135), hyperlipidemia in 22.0% (n=68), alcoholic in 7.4% (n=23), trauma in 0.6% (n=2), and postoperative status in 1.6% (n=5) cases; 24.6% (n=76) of patients did not have specified etiologies. The prevalence of pleural effusion and pulmonary consolidation was noted. The pleural effusion volume was quantitatively derived from a CT volume evaluation software tool. The pulmonary consolidation score was based on the number of lobes involved in AP. Each patient's CT severity index (CTSI), acute physiology and chronic health evaluation II (APACHE II) scoring system, and bedside index for severity in acute pancreatitis (BI of the CTSI score (P=0.503), APACHE II score (P=0.343), and BISAP score (P=0.669). In predicting organ failure, the accuracy (AUC 0.783) of pleural effusion volume was similar to that of the CTSI score (P=0.473), APACHE II score (P=0.119), and BISAP score (P=0.980), and the accuracy (AUC 0.808) of the pulmonary consolidation score was also similar to that of the CTSI score (P=0.236), APACHE II score (P=0.293), and BISAP score (P=0.612). Conclusions Pleural effusion and pulmonary consolidation are common in AP and correlated to the severity of AP. Furthermore, the pleural effusion volume and pulmonary consolidation lobes can provide early prediction of severe AP and organ failure. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.Background The purpose of this study is to improve on-board volumetric cine magnetic resonance imaging (VC-MRI) using multi-slice undersampled cine images reconstructed using spatio-temporal k-space data, patient prior 4D-MRI, motion modeling (MM) and free-form deformation (FD) for real-time 3D target verification of liver and lung radiotherapy. Methods A previous method was developed to generate on-board VC-MRI by deforming prior MRI images based on a MM and a single-slice on-board 2D-cine image. The two major improvements over the previous method are (I) FD was introduced to estimate VC-MRI to correct for inaccuracies in the MM; (II) multi-slice undersampled 2D-cine images reconstructed by a k-t SLR reconstruction method were used for FD-based estimation to maintain the temporal resolution while improving the accuracy of VC-MRI. The method was evaluated using XCAT lung simulation and four liver patients' data. Results For XCAT, VC-MRI estimated using ten undersampled sagittal 2D-cine MRIs resulted in volume percent difference/volume dice coefficient/center-of-mass shift of 9.77%±3.71%/0.95±0.02/0.75±0.26 mm among all scenarios based on estimation with MM and FD. BI-3812 nmr Adding FD optimization improved VC-MRI accuracy substantially for scenarios with anatomical changes. For patient data, the mean tumor tracking errors were 0.64±0.51, 0.62±0.47 and 0.24±0.24 mm along the superior-inferior (SI), anterior-posterior (AP) and lateral directions, respectively, across all liver patients. Conclusions It is feasible to improve VC-MRI accuracy while maintaining high temporal resolution using FD and multi-slice undersampled 2D cine images for real-time 3D target verification. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.Background The goal of this study is to systematically evaluate the magnetic resonance imaging (MRI) signal characteristics and size of cataracts that may be encountered in pediatric and young adult patients. Methods A retrospective analysis of the MRI features with cataracts in a series of cases, including characterization of signal intensity on T2-weighted and T1-weighted sequences, as well as measuring the thickness of the lens. Results Among nine cataracts in seven patients, three lenses were thickened and hyperintense on T2-weighted sequences, presumably related to osmotic effects. The rest of the lenses were either normal in size and signal characteristics, such as in the cases of neurofibromatosis type 2 or small in cases of microphthalmos, with signal characteristics related to calcifications. Conclusions There are several different types of cataracts that can occur in pediatric and young adult patients, which may or may not be conspicuous on MRI. The findings in this study can serve as a guide for what abnormalities of the lens may be encountered on MRI. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.Background Recently, the paradigm of computed tomography (CT) reconstruction has shifted as the deep learning technique evolves. In this study, we proposed a new convolutional neural network (called ADAPTIVE-NET) to perform CT image reconstruction directly from a sinogram by integrating the analytical domain transformation knowledge. Methods In the proposed ADAPTIVE-NET, a specific network layer with constant weights was customized to transform the sinogram onto the CT image domain via analytical back-projection. With this new framework, feature extractions were performed simultaneously on both the sinogram domain and the CT image domain. The Mayo low dose CT (LDCT) data was used to validate the new network. In particular, the new network was compared with the previously proposed residual encoder-decoder (RED)-CNN network. For each network, the mean square error (MSE) loss with and without VGG-based perceptual loss was compared. Furthermore, to evaluate the image quality with certain metrics, the noise correlation was quantified via the noise power spectrum (NPS) on the reconstructed LDCT for each method. Results CT images that have clinically relevant dimensions of 512×512 can be easily reconstructed from a sinogram on a single graphics processing unit (GPU) with moderate memory size (e.g., 11 GB) by ADAPTIVE-NET. With the same MSE loss function, the new network is able to generate better results than the RED-CNN. Moreover, the new network is able to reconstruct natural looking CT images with enhanced image quality if jointly using the VGG loss. Conclusions The newly proposed end-to-end supervised ADAPTIVE-NET is able to reconstruct high-quality LDCT images directly from a sinogram. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.