Analysis and Evaluation of a Deep Learning Reconstruction Approach with Denoising for Orthopedic MRI.

PURPOSE: To evaluate two settings (noise reduction of 50% or 75%) of a deep learning (DL) reconstruction model relative to each other and to conventional MR image reconstructions on clinical orthopedic MRI datasets. MATERIALS AND METHODS: This retrospective study included 54 patients who underwent two-dimensional fast spin-echo MRI for hip (n = 22; mean age, 44 years ± 13 [standard deviation]; nine men) or shoulder (n = 32; mean age, 56 years ± 17; 17 men) conditions between March 2019 and June 2020. MR images were reconstructed with conventional methods and the vendor-provided and commercially available DL model applied with 50% and 75% noise reduction settings (DL 50 and DL 75, respectively). Quantitative analytics, including relative anatomic edge sharpness, relative signal-to-noise ratio (rSNR), and relative contrast-to-noise ratio (rCNR) were computed for each dataset. In addition, the image sets were randomized, blinded, and presented to three board-certified musculoskeletal radiologists for ranking based on overall image quality and diagnostic confidence. Statistical analysis was performed with a nonparametric hypothesis comparing derived quantitative metrics from each reconstruction approach. In addition, inter- and intrarater agreement analysis was performed on the radiologists' rankings. RESULTS: Both denoising settings of the DL reconstruction showed improved edge sharpness, rSNR, and rCNR relative to the conventional reconstructions. The reader rankings demonstrated strong agreement, with both DL reconstructions outperforming the conventional approach (Gwet agreement coefficient = 0.98). However, there was lower agreement between the readers on which DL reconstruction denoising setting produced higher-quality images (Gwet agreement coefficient = 0.31 for DL 50 and 0.35 for DL 75). CONCLUSION: The vendor-provided DL MRI reconstruction showed higher edge sharpness, rSNR, and rCNR in comparison with conventional methods; however, optimal levels of denoising may need to be further assessed.Keywords: MRI Reconstruction Method, Deep Learning, Image Analysis, Signal-to-Noise Ratio, MR-Imaging, Neural Networks, Hip, Shoulder, Physics, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021.

Multivariate use of MRI biomarkers to classify histologically confirmed necrosis in symptomatic total hip arthroplasty.

The failure of total hip arthroplasty (THA) is commonly associated with the necrosis of the periprosthetic tissue. To date, there is no established method to noninvasively quantify the progression of such necrosis. Magnetic resonance imaging (MRI) of soft tissues near implants has undergone a recent renaissance due to the development of multispectral metal-artifact reduction techniques. Advanced analysis of multispectral MRI has been shown capable of detecting small magnetism effects of metallic debris in periprosthetic tissue. The purpose of this study is to demonstrate the diagnostic utility of these MRI-based tissue-magnetism signatures. Together with morphological MRI metrics, such as synovial volume and thickness, these measurements are utilized as biomarkers to noninvasively detect soft-tissue necrosis in symptomatic THA patients ( N=78 ). All subjects underwent an advanced MRI scan before revision surgery and tissue biopsies utilized for necrosis grading. Statistical analyses demonstrated a weak, but significant positive correlation (P = .04) between MRI magnetism signatures and necrosis scores, while indicating no meaningful association between the latter and serum cobalt and chromium ion levels. Receiver-operating characteristic (ROC) analyses were then performed based on uni- and multivariate logistic regression models utilizing the measured MRI biomarkers as predictors of severe necrosis. The area under the curve of the ROC plots for MRI biomarkers as combined predictors were found to be 0.70 and 0.84 for cross-validation and precision-recall tests, respectively.

Origin of colossal magnetoresistance in LaMnO3 manganite.

Phase separation is a crucial ingredient of the physics of manganites; however, the role of mixed phases in the development of the colossal magnetoresistance (CMR) phenomenon still needs to be clarified. We report the realization of CMR in a single-valent LaMnO3 manganite. We found that the insulator-to-metal transition at 32 GPa is well described using the percolation theory. Pressure induces phase separation, and the CMR takes place at the percolation threshold. A large memory effect is observed together with the CMR, suggesting the presence of magnetic clusters. The phase separation scenario is well reproduced, solving a model Hamiltonian. Our results demonstrate in a clean way that phase separation is at the origin of CMR in LaMnO3.