Faculty Bibliography

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.

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 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:

Purpose To investigate the diagnostic performance of three-dimensional (3D) MRI for depicting meniscal injuries of the knee by using surgery as the standard of reference. Materials and Methods A literature search was performed to identify original studies published between 1985 and 2017. Summary receiver operating characteristic curve and sensitivity analyses were performed to compare the diagnostic performance of 3D versus two-dimensional (2D) MRI for the assessment of knee meniscal injuries and to evaluate the impact of relevant covariates on the diagnostic performance for assessment of knee meniscal injuries. Results Of identified records, 31 studies (1743 3D knee MRI examinations) were included (23 studies also reported the results of 2D MRI). All studies before 2008 used gradient-echo (GRE) sequences, whereas all studies after 2011 used fast spin-echo (FSE) sequences. By comparing FSE and GRE sequences with 2D MRI, pooled estimate of sensitivity (90.0%; P = .2 and 90.1%; P = .2 vs 88.5%) and pooled estimate of specificity (91%; P = .3 and 89.8% vs 90.1%; P = .7) were comparable. The 3D FSE sequences demonstrated similar diagnostic performance as 3D GRE sequences, except for slightly improved sensitivity for depicting lateral meniscal injuries (FSE, 84.6%; GRE, 75%; P = .01). The specificity of 3D sequences improved when multiplanar reformatting was performed (P = .02). Conclusion Both three-dimensional (3D) fast spin-echo (FSE) and 3D gradient-echo (GRE) sequences had similar diagnostic performance as two-dimensional sequences, with slight superior sensitivity of 3D FSE sequences compared with 3D GRE sequences for depicting lateral meniscal injuries of the knee. © RSNA, 2018 Online supplemental material is available for this article.

OBJECTIVE:The objective of our study was to use a T2 mapping sequence performed at 3 T to investigate changes in the composition and microstructure of the cartilage and menisci of the pediatric knee joint during maturation. MATERIALS AND METHODS:This retrospective study was performed of MRI examinations of 76 pediatric knees without internal derangement in 72 subjects (29 boys [mean age, 12.5 years] and 43 girls [mean age, 13.0 years]) who were evaluated with a sagittal T2 mapping sequence. T2 relaxation time values were quantitatively measured in eight cartilage subregions and in the medial and lateral menisci. Wilcoxon rank sum and Kruskal-Wallis tests were used to analyze the relationship between cartilage and meniscus T2 relaxation time values and sex and skeletal maturation, respectively. A multivariate linear regression model was used to investigate the independent association between cartilage T2 relaxation time values and age, weight, and body mass index (BMI [weight in kilograms divided by the square of height in meters]). RESULTS:There were no significant sex differences (p = 0.26-0.91) in T2 relaxation time values for cartilage or meniscus. T2 relaxation time values in each individual cartilage subregion significantly decreased (p < 0.001) with progressive maturation. T2 relaxation time values in the lateral meniscus significantly increased (p = 0.001) with maturation, whereas T2 relaxation time values in the medial meniscus did not significantly change (p = 0.82). There was a significant association (p < 0.001) between cartilage T2 relaxation time values and age independent of weight and BMI, but no significant association between cartilage T2 relaxation time values and weight (p = 0.06) and BMI (p = 0.20) independent of age. CONCLUSION:Cartilage T2 relaxation time values significantly decreased in all cartilage subregions and meniscus T2 relaxation time values significantly increased in the lateral meniscus during maturation. These changes in T2 relaxation time values reflect age-related changes in tissue composition and microstructure.

PURPOSE:To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissue segmentation. METHODS:A segmentation pipeline was built by combining a semantic segmentation CNN, 3D fully connected CRF, and 3D simplex deformable modeling. A convolutional encoder-decoder network was designed as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification for 12 different joint structures. The 3D fully connected CRF was applied to regularize contextual relationship among voxels within the same tissue class and between different classes. The 3D simplex deformable modeling refined the output from 3D CRF to preserve the overall shape and maintain a desirable smooth surface for joint structures. The method was evaluated on 3D fast spin-echo (3D-FSE) MR image data sets. Quantitative morphological metrics were used to evaluate the accuracy and robustness of the method in comparison to the ground truth data. RESULTS:The proposed segmentation method provided good performance for segmenting all knee joint structures. There were 4 tissue types with high mean Dice coefficient above 0.9 including the femur, tibia, muscle, and other non-specified tissues. There were 7 tissue types with mean Dice coefficient between 0.8 and 0.9 including the femoral cartilage, tibial cartilage, patella, patellar cartilage, meniscus, quadriceps and patellar tendon, and infrapatellar fat pad. There was 1 tissue type with mean Dice coefficient between 0.7 and 0.8 for joint effusion and Baker's cyst. Most musculoskeletal tissues had a mean value of average symmetric surface distance below 1 mm. CONCLUSION:The combined CNN, 3D fully connected CRF, and 3D deformable modeling approach was well-suited for performing rapid and accurate comprehensive tissue segmentation of the knee joint. The deep learning-based segmentation method has promising potential applications in musculoskeletal imaging.

BACKGROUND:F-FDG) PET images. A deep convolutional encoder-decoder network was trained to identify tissue contrast in volumetric uncorrected PET images co-registered to CT data. A set of 100 retrospective 3D FDG PET head images was used to train the model. The model was evaluated in another 28 patients by comparing the generated pseudo-CT to the acquired CT using Dice coefficient and mean absolute error (MAE) and finally by comparing reconstructed PET images using the pseudo-CT and acquired CT for attenuation correction. Paired-sample t tests were used for statistical analysis to compare PET reconstruction error using deepAC with CT-based attenuation correction. RESULTS:F-FDG PET results with average errors of less than 1% in most brain regions. CONCLUSIONS:F-FDG non-attenuation-corrected (NAC) PET image and evaluated it in PET/CT brain imaging.