Best practices for MRI systematic reviews and metaâ analyses

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149543/1/jmri26198.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149543/2/jmri26198_am.pd

MRI safety and devices: An update and expert consensus

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154533/1/jmri26909_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154533/2/jmri26909.pd

Automated segmentation of prostate zonal anatomy on T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images using U-Nets

Purpose: Accurate regional segmentation of the prostate boundaries on magnetic resonance (MR) images is a fundamental requirement before automated prostate cancer diagnosis can be achieved. In this paper, we describe a novel methodology to segment prostate whole gland (WG), central gland (CG), and peripheral zone (PZ), where PZ + CG = WG, from T2W and apparent diffusion coefficient (ADC) map prostate MR images. Methods: We designed two similar models each made up of two U-Nets to delineate the WG, CG, and PZ from T2W and ADC map MR images, separately. The U-Net, which is a modified version of a fully convolutional neural network, includes contracting and expanding paths with convolutional, pooling, and upsampling layers. Pooling and upsampling layers help to capture and localize image features with a high spatial consistency. We used a dataset consisting of 225 patients (combining 153 and 72 patients with and without clinically significant prostate cancer) imaged with multiparametric MRI at 3 Tesla. Results and conclusion: Our proposed model for prostate zonal segmentation from T2W was trained and tested using 1154 and 1587 slices of 100 and 125 patients, respectively. Median of Dice similarity coefficient (DSC) on test dataset for prostate WG, CG, and PZ were 95.33 ± 7.77%, 93.75 ± 8.91%, and 86.78 ± 3.72%, respectively. Designed model for regional prostate delineation from ADC map images was trained and validated using 812 and 917 slices from 100 and 125 patients. This model yielded a median DSC of 92.09 ± 8.89%, 89.89 ± 10.69%, and 86.1 ± 9.56% for prostate WG, CG, and PZ on test samples, respectively. Further investigation indicated that the proposed algorithm reported high DSC for prostate WG segmentation from both T2W and ADC map MR images irrespective of WG size. In addition, segmentation accuracy in terms of DSC does not significantly vary among patients with or without significant tumors. Significance: We describe a method for automated prostate zonal segmentation usin

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Background: Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis: To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U-Net-based model. Study Type: Retrospective, case–control. Population: In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. Field Strength: 3T, ADC maps. Sequence: ADC map. Assessment: The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI-radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per-slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t-test was used to test the differences between the performances of constituent networks of the ensemble model. Results: Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. Data Conclusion: An ensemble U-Net-based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. Level of Evidence: 4. Technical Efficacy: Stage 1. J. Magn. Reson. Imaging 2019

Update on MRI of Cystic Renal Masses Including Bosniak Version 2019

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168456/1/jmri27364.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168456/2/jmri27364_am.pd