Faculty Bibliography

PURPOSE/OBJECTIVE:To develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping. METHODS:mapping of the brain and the knee at an acceleration rate R = 8 and was compared with other state-of-the-art reconstruction methods. Global and regional quantitative assessments were performed to demonstrate the reconstruction performance of the proposed method. RESULTS:estimation. The quantitative metrics were normalized root mean square error of 3.6% for brain and 7.3% for knee, structural similarity index of 85.1% for brain and 83.2% for knee, and tenengrad measures of 9.2% for brain and 10.1% for the knee. The adversarial approach also achieved better performance for maintaining greater image texture and sharpness in comparison to the CNN approach without adversarial learning. CONCLUSION/CONCLUSIONS:The proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.

The purpose of this study is to determine the feasibility of using a single scan ramped hybrid-encoding (RHE) method for rapid bicomponent T2* analysis of the human knee joint. The proposed method utilizes RHE to acquire ultrashort echo time (UTE) and subsequent gradient echo images at 16 different echo times ranging between 40 μs and 30 ms in a single scan. In the proposed RHE technique, UTE imaging was followed by acquisition of 14 gradient recalled echo images, where an additional UTE image was obtained within the first readout by oversampling single point imaging (SPI) encoding. The single scan RHE method with a 9-minute scan time was performed on human cadaveric knee joints from six donors and in vivo knee joints from four healthy volunteers at 3 T. A bicomponent signal model was used to characterize the short T2* and long T2* water components. Mean bicomponent T2* parameters for patellar tendon, anterior cruciate ligament (ACL), posterior cruciate ligament (PCL) and meniscus were calculated. In the experimental results, the RHE technique provided bicomponent T2* parameter estimations of tendon, ACL, PCL and meniscus, which were similar to previously reported values in the literature. In conclusion, the proposed single scan RHE technique provides rapid bicomponent T2* analysis of the human knee joint with a total scan time of less than 9 minutes.

Osteoarthritis (OA) is a highly prevalent chronic condition with marked implications for affected individuals and public health care. There are available treatments to manage pain and symptoms but no effective treatment for OA. In the past 10 years, joint imaging, particularly MRI, has evolved rapidly due to technical advances and their application to clinical research, which has led to abundant evidence regarding the natural history of the disease. Radiography remains the primary imaging modality in clinical practice for the diagnosis and follow-up of OA. The many developments in MRI techniques capable of assessing cartilage morphologic features and the methods for evaluating its biochemical composition will be discussed. Advances in quantitative morphologic cartilage assessment and semiquantitative whole-organ assessment will be reviewed, as will other modalities such as US, CT and CT arthrography, and nuclear medicine techniques that play a complementary role. Various therapeutic approaches and ongoing developments, including the impact of artificial intelligence on the field of OA imaging, will also be discussed.

BACKGROUND:Despite the advantages of 3D MRI in evaluation of cruciate ligament injuries, its use in clinical practice is still a matter of debate due to controversy regarding its diagnostic performance. PURPOSE/OBJECTIVE:To evaluate the diagnostic performance of 3D MRI for detecting cruciate ligament injuries, using surgery or arthroscopy as the reference standard. STUDY TYPE/METHODS:Meta-analysis. POPULATION/METHODS:Patients with knee pain. FIELD STRENGTH/SEQUENCE/UNASSIGNED:3D and 2D MRI. ASSESSMENT/RESULTS:Four databases were reviewed according to PRISMA guidelines. STATISTICAL TESTS/UNASSIGNED:Pooled values of sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model. To investigate the effect of relevant covariates on the diagnostic performance of 3D MRI, sensitivity analysis was performed using meta-regression to calculate relative DOR. RESULTS:Of 731 initially identified reports, 22 (1298 3D MRI examinations) met our criteria and were included. Pooled estimates of sensitivity and specificity for 3D sequences were 91.4% (95% confidence interval [CI]: 87.4-94.2%) and 96.1% (95% CI: 93.8-97.6%), respectively. Fourteen studies also reported the results of 2D MRI, with pooled sensitivity of 90.6% (95% CI: 84.1-94.6%) and specificity of 97.1% (95% CI: 94.7-98.4%), which were not significantly different from 3D sequences. 3D MRI sequences performed using 3T scanners had significantly higher DOR compared with 3D sequences performed on 1.5T or lower scanners (relative DOR: 6.04, P = 0.01). DATA CONCLUSION/UNASSIGNED:3D MRI is equivalent to 2D MRI in the diagnosis of cruciate ligament injuries. The use of 3T scanners improves the performance of 3D MRI for detecting cruciate ligament injuries. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1545-1560.

PURPOSE:To develop and evaluate a novel deep learning-based reconstruction framework called SANTIS (Sampling-Augmented Neural neTwork with Incoherent Structure) for efficient MR image reconstruction with improved robustness against sampling pattern discrepancy. METHODS:With a combination of data cycle-consistent adversarial network, end-to-end convolutional neural network mapping, and data fidelity enforcement for reconstructing undersampled MR data, SANTIS additionally utilizes a sampling-augmented training strategy by extensively varying undersampling patterns during training, so that the network is capable of learning various aliasing structures and thereby removing undersampling artifacts more effectively and robustly. The performance of SANTIS was demonstrated for accelerated knee imaging and liver imaging using a Cartesian trajectory and a golden-angle radial trajectory, respectively. Quantitative metrics were used to assess its performance against different references. The feasibility of SANTIS in reconstructing dynamic contrast-enhanced images was also demonstrated using transfer learning. RESULTS:Compared to conventional reconstruction that exploits image sparsity, SANTIS achieved consistently improved reconstruction performance (lower errors and greater image sharpness). Compared to standard learning-based methods without sampling augmentation (e.g., training with a fixed undersampling pattern), SANTIS provides comparable reconstruction performance, but significantly improved robustness, against sampling pattern discrepancy. SANTIS also achieved encouraging results for reconstructing liver images acquired at different contrast phases. CONCLUSION:By extensively varying undersampling patterns, the sampling-augmented training strategy in SANTIS can remove undersampling artifacts more robustly. The novel concept behind SANTIS can particularly be useful for improving the robustness of deep learning-based image reconstruction against discrepancy between training and inference, an important, but currently less explored, topic.

OBJECTIVE. The purpose of this study was to investigate the performance of MRI criteria for predicting instability of osteochondritis dissecans (OCD) lesions of the elbow in children. MATERIALS AND METHODS. This retrospective study included 41 children with 43 OCD lesions of the elbow who underwent an MRI examination between April 1, 2010, and May 31, 2018. Two radiologists blinded to clinical outcomes reviewed MRI studies to determine the presence or absence of joint effusion, osteochondral defect, intraarticular body, overlying cartilage changes, subchondral bone disruption, rim of high signal intensity on T2-weighted images, cysts, marginal sclerosis, and perilesional bone marrow edema. The stability of OCD lesions was determined with clinical follow-up and surgical findings as reference standards. Mann-Whitney U, chi-square, Fisher exact, and Cochran-Armitage tests were used to compare MRI findings between stable and unstable OCD lesions. RESULTS. There were 20 stable and 23 unstable OCD lesions. An osteochondral defect (p = 0.01), intraarticular body (p < 0.001), overlying cartilage changes (p = 0.001), subchondral bone plate disruption (p = 0.02), and hyperintense rim (p = 0.01) were significantly more common in unstable than stable OCD lesions. However, only osteochondral defect and intraarticular body were 100% specific for OCD instability. There was no significant difference between stable and unstable OCD lesions in the presence of joint effusion (p = 0.10), cysts (p = 0.45), marginal sclerosis (p = 0.70), or perilesional bone marrow edema (p = 1.00). CONCLUSION. MRI findings of OCD instability of the elbow include an osteochondral defect, intraarticular body, overlying cartilage changes, subchondral bone disruption, and rim of high signal intensity on T2-weighted MR images.

PURPOSE:To develop and evaluate a novel deep learning-based image reconstruction approach called MANTIS (Model-Augmented Neural neTwork with Incoherent k-space Sampling) for efficient MR parameter mapping. METHODS:analysis for the cartilage and meniscus were performed to demonstrate the reconstruction performance of MANTIS. RESULTS:estimation. MANTIS also achieved superior performance compared to direct CNN mapping and a 2-step CNN method. CONCLUSION:The MANTIS framework, with a combination of end-to-end CNN mapping, signal model-augmented data consistency, and incoherent k-space sampling, is a promising approach for efficient and robust estimation of quantitative MR parameters.

Background Investigation of the use of preoperative MRI for providing prognostic information regarding clinical outcome following rotator cuff repair has been limited. Purpose To determine whether patients with more severe rotator cuff tears of the shoulder at preoperative MRI have a greater degree of residual pain and disability after rotator cuff repair. Materials and Methods This retrospective study included a cohort of 141 patients who underwent surgical repair of a full-thickness rotator cuff tear at a single institution between April 16, 2012, and September 3, 2015. The mean patient age was 56.8 years, and there were 100 men (mean age, 56.1 years) and 41 women (mean age, 56.3 years). Patients completed the Disabilities of the Arm, Shoulder, and Hand (DASH) survey (lower score indicates less pain and disability) before and 1 year after surgery. One musculoskeletal radiologist blinded to the DASH scores measured the maximal anterior-posterior width and medial-lateral retraction of the rotator cuff tear on the preoperative MRI and assessed tendon degeneration and composite muscle atrophy and fatty infiltration using categorical grading scales (grade 0 indicates no tendon degeneration or muscle atrophy and fatty infiltration, and higher grades indicate incrementally more severe tendon degeneration or muscle atrophy and fatty infiltration). Generalized estimating equation models were used to determine the association between preoperative MRI findings and the postoperative DASH score. Results There was a significant positive association (P < .05) between the measured tear width (estimate, 2.05), measured tear retraction (estimate, 3.52), and tendon degeneration grade (estimate, 1.59) and the postoperative DASH score. There was no significant association (P = .49) between the composite muscle atrophy and fatty infiltration grade (estimate, 0.31) and the postoperative DASH score. Conclusion Patients with larger rotator cuff tears, more tendon retraction, and more severe tendon degeneration have worse clinical outcome scores 1 year after rotator cuff repair. © RSNA, 2019.

Purpose/UNASSIGNED:To investigate the feasibility of using a deep learning-based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard. Materials and Methods/UNASSIGNED:A fully automated deep learning-based diagnosis system was developed by using two deep convolutional neural networks (CNNs) to isolate the ACL on MR images followed by a classification CNN to detect structural abnormalities within the isolated ligament. With institutional review board approval, sagittal proton density-weighted and fat-suppressed T2-weighted fast spin-echo MR images of the knee in 175 subjects with a full-thickness ACL tear (98 male subjects and 77 female subjects; average age, 27.5 years) and 175 subjects with an intact ACL (100 male subjects and 75 female subjects; average age, 39.4 years) were retrospectively analyzed by using the deep learning approach. Sensitivity and specificity of the ACL tear detection system and five clinical radiologists for detecting an ACL tear were determined by using arthroscopic results as the reference standard. Receiver operating characteristic (ROC) analysis and two-sided exact binomial tests were used to further assess diagnostic performance. Results/UNASSIGNED:< .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy. Conclusion/UNASSIGNED: