Deep Learning-based Prescription of Cardiac MRI Planes.

PurposeTo develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks.Materials and methodsAnnotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.ResultsOn LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.ConclusionDL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019 Supplemental material is available for this article

Restriction Spectrum Imaging Differentiates True Tumor Progression From Immune-Mediated Pseudoprogression: Case Report of a Patient With Glioblastoma.

Immunotherapy is increasingly used in the treatment of glioblastoma (GBM), with immune checkpoint therapy gaining in popularity given favorable outcomes achieved for other tumors. However, immune-mediated (IM)-pseudoprogression is common, remains poorly characterized, and renders conventional imaging of little utility when evaluating for treatment response. We present the case of a 64-year-old man with GBM who developed pathologically proven IM-pseudoprogression after initiation of a checkpoint inhibitor, and who subsequently developed true tumor progression at a distant location. Based on both qualitative and quantitative analysis, we demonstrate that an advanced diffusion-weighted imaging (DWI) technique called restriction spectrum imaging (RSI) can differentiate IM-pseudoprogression from true progression even when conventional imaging, including standard DWI/apparent diffusion coefficient (ADC), is not informative. These data complement existing literature supporting the ability of RSI to estimate tumor cellularity, which may help to resolve complex diagnostic challenges such as the identification of IM-pseudoprogression

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Ectopic expression of miRNA-21 and miRNA-205 in non-small cell lung cancer

This research is a part of the efforts of the professors and colleagues of Masih Daneshvari Hospital of Shahid Beheshti University of Medical Sciences and Mashhad Medical Sciences University. All involved are sincerely thanked.Peer reviewedPublisher PD

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

Modeling Hyperpolarized 13C Pyruvate And Urea Concentration Kinetics With Multiband RF Excitation MRI In Prostate Cancer

The accurate detection and characterization of cancerous tissue is still a major problem for the clinical management of individual prostate cancer patients and for monitoring their response to therapy. ρ_1 (TR times to times points per second over T1 ratio) of urea, pyruvate, lactate, and alanine, also the amount urea and pyruvate perfusion, and conversion constant between pyruvate to lactate(Kpl) and pyruvate to alanine(Kpa) are important parameters in different organs including cancerous and healthy tissues. ρ_1 of urea in kidneys, prostate cancerous tissues, and liver are measured 0.13(1/s), 0.15(1/s), and 0.075(1/s), respectively and ρ_1 of pyruvate in kidneys, liver cancer and healthy part of liver is 0.08(1/s), 0.13(1/s), and 0.064(1/s), respectively with TR=250ms. Kpl in cancerous tissues are more than 0.44(1/s) which is significantly higher than Kpl of metabolites in healthy tissues (Kpl = 0.028(1/s)) with p value less than 0.001. This Kpl is proportional to the lactate signal to pyruvate signal ratio with Correlation Coefficient=0.95. High perfusion amount of the accumulation of pyruvate, lactate, and alanine in compare to urea perfusion has been seen in cancerous tissues (liver cancer and prostate cancer) significantly (p<0.001) less than in healthy tissues

Modeling Hyperpolarized 13C Pyruvate And Urea Concentration Kinetics With Multiband RF Excitation MRI In Prostate Cancer

The accurate detection and characterization of cancerous tissue is still a major problem for the clinical management of individual prostate cancer patients and for monitoring their response to therapy. ρ_1 (TR times to times points per second over T1 ratio) of urea, pyruvate, lactate, and alanine, also the amount urea and pyruvate perfusion, and conversion constant between pyruvate to lactate(Kpl) and pyruvate to alanine(Kpa) are important parameters in different organs including cancerous and healthy tissues. ρ_1 of urea in kidneys, prostate cancerous tissues, and liver are measured 0.13(1/s), 0.15(1/s), and 0.075(1/s), respectively and ρ_1 of pyruvate in kidneys, liver cancer and healthy part of liver is 0.08(1/s), 0.13(1/s), and 0.064(1/s), respectively with TR=250ms. Kpl in cancerous tissues are more than 0.44(1/s) which is significantly higher than Kpl of metabolites in healthy tissues (Kpl = 0.028(1/s)) with p value less than 0.001. This Kpl is proportional to the lactate signal to pyruvate signal ratio with Correlation Coefficient=0.95. High perfusion amount of the accumulation of pyruvate, lactate, and alanine in compare to urea perfusion has been seen in cancerous tissues (liver cancer and prostate cancer) significantly (p<0.001) less than in healthy tissues