Faculty Bibliography

BACKGROUND/UNASSIGNED:Graft placement is a modifiable and often discussed surgical factor in anterior cruciate ligament (ACL) reconstruction (ACLR). However, the sensitivity of functional knee mechanics to variability in graft placement is not well understood. PURPOSE/UNASSIGNED:To (1) investigate the relationship of ACL graft tunnel location and graft angle with tibiofemoral kinematics in patients with ACLR, (2) compare experimentally measured relationships with those observed with a computational model to assess the predictive capabilities of the model, and (3) use the computational model to determine the effect of varying ACL graft tunnel placement on tibiofemoral joint mechanics during walking. STUDY DESIGN/UNASSIGNED:Controlled laboratory study. METHODS/UNASSIGNED:Eighteen participants who had undergone ACLR were tested. Bilateral ACL footprint location and graft angle were assessed using magnetic resonance imaging (MRI). Bilateral knee laxity was assessed at the completion of rehabilitation. Dynamic MRI was used to measure tibiofemoral kinematics and cartilage contact during active knee flexion-extension. Additionally, a total of 500 virtual ACLR models were created from a nominal computational knee model by varying ACL footprint locations, graft stiffness, and initial tension. Laxity tests, active knee extension, and walking were simulated with each virtual ACLR model. Linear regressions were performed between internal knee mechanics and ACL graft tunnel locations and angles for the patients with ACLR and the virtual ACLR models. RESULTS/UNASSIGNED:= 0.56, 0.26, and 0.13). These effects extended to simulations of walking, with a more vertical ACL graft inducing greater anterior tibial translation, ACL loading, and posterior migration of contact on the tibial plateaus. CONCLUSION/UNASSIGNED:This study provides clinical evidence from patients who underwent ACLR and from complementary modeling that functional postoperative knee mechanics are sensitive to graft tunnel locations and graft angle. Of the factors studied, the sagittal angle of the ACL was particularly influential on knee mechanics. CLINICAL RELEVANCE/UNASSIGNED:Early-onset osteoarthritis from altered cartilage loading after ACLR is common. This study shows that postoperative cartilage loading is sensitive to graft angle. Therefore, variability in graft tunnel placement resulting in small deviations from the anatomic ACL angle might contribute to the elevated risk of osteoarthritis after ACLR.

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.

Deep learning is one of the most exciting new areas in medical imaging. This review article provides a summary of the current clinical applications of deep learning for lesion detection, progression, and prediction of musculoskeletal disease on radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Deep-learning methods have shown success for estimating pediatric bone age, detecting fractures, and assessing the severity of osteoarthritis on radiographs. In particular, the high diagnostic performance of deep-learning approaches for estimating pediatric bone age and detecting fractures suggests that the new technology may soon become available for use in clinical practice. Recent studies have also documented the feasibility of using deep-learning methods for identifying a wide variety of pathologic abnormalities on CT and MRI including internal derangement, metastatic disease, infection, fractures, and joint degeneration. However, the detection of musculoskeletal disease on CT and especially MRI is challenging, as it often requires analyzing complex abnormalities on multiple slices of image datasets with different tissue contrasts. Thus, additional technical development is needed to create deep-learning methods for reliable and repeatable interpretation of musculoskeletal CT and MRI examinations. Furthermore, the diagnostic performance of all deep-learning methods for detecting and characterizing musculoskeletal disease must be evaluated in prospective studies using large image datasets acquired at different institutions with different imaging parameters and different imaging hardware before they can be implemented in clinical practice. Level of Evidence: 5 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019.

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.

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.

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.

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:

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.