Faculty Bibliography

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.

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.

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.

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.

Purpose To determine the diagnostic performance of three-dimensional (3D) MRI for the depiction and characterization of cartilage defects within the knee joint by using arthroscopy and/or open surgery as the standard of reference. Materials and Methods A systematic literature search was performed to extract diagnostic studies published between January 1985 and October 2017. Two independent investigators assessed the methodologic quality of each study by using Quality Assessment of Diagnostic Accuracy Studies 2. Bivariate random-effects model was used to compare the diagnostic odds ratio (DOR) of 3D and two-dimensional (2D) MRI for helping to detect knee cartilage defects and to assess the effect of relevant covariates on diagnostic performance of 3D MRI. Meta-regression analysis was performed to assess DOR of 3D MRI during the last 3 decades. Results Twenty-seven studies (composed of 1710 MRI examinations) were included. Of those, 16 (59%) studies compared the diagnostic performance of 3D and 2D MRI. The diagnostic performance of 3D MRI statistically significantly improved over the last 3 decades (P = .003). Three-dimensional MRI obtained by using 3.0-T field strength had higher DOR relative to 1.5-T or lower field strength (relative DOR, 4.05; P = .01). Three-dimensional multiplanar reformation was associated with higher specificity (P = .001) compared with conventional axial, sagittal, and coronal 2D MRI planes. Three-dimensional fast-spin-echo sequences provided higher sensitivity and specificity (P < .05) than did 2D MRI. Conclusion Three-dimensional MRI currently provides comparable diagnostic performance to two-dimensional MRI, with improvement in diagnostic performance achieved by using 3.0-T field strength, three-dimensional fast-spin-echo sequences, and multiplanar reformation. © RSNA, 2018 Online supplemental material is available for this article.

Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. Materials and Methods A fully automated deep learning-based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system. Results The sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1% and 85.2%, respectively, for evaluation 1 and 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76. Conclusion This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury. © RSNA, 2018 Online supplemental material is available for this article .

Purpose To determine whether a T2 mapping sequence could depict early changes in the composition and microstructure of cartilage overlying stable lesions of the medial femoral condyle in patients with juvenile osteochondritis dissecans (JOCD). Materials and Methods This retrospective study analyzed a sagittal T2 mapping sequence performed between September 1, 2015, and March 31, 2017, on 16 patients (10 boys and six girls; median age, 11.5 years) with 18 stable medial femoral condyle JOCD lesions and 18 age-, sex-, and skeletal maturation-matched control participants (11 boys and seven girls; median age, 11.5 years). Cartilage T2 values were quantitatively measured within regions of interest placed around the cartilage within and overlying the JOCD lesion in patients with JOCD and around the cartilage on the weight-bearing medial femoral condyle in patients with JOCD and controls. Wilcoxon signed rank and Wilcoxon rank sum tests were used to compare T2 values. Results T2 values were significantly higher (P < .001) for cartilage within the JOCD lesion than for cartilage overlying the JOCD lesion in patients with JOCD. However, there were no significant differences in T2 values between cartilage overlying the JOCD lesion and cartilage on the weight-bearing medial femoral condyle in patients with JOCD (P = .67) or in T2 values of the cartilage on the weight-bearing medial femoral condyle between patients with JOCD and controls (P = .30). Conclusion There were no significant quantifiable differences in T2 values of cartilage overlying stable JOCD lesions and normal cartilage on the medial femoral condyle, suggesting no substantial changes in cartilage composition and microstructure.

BACKGROUND:Abnormal knee mechanics may contribute to early cartilage degeneration following anterior cruciate ligament reconstruction. Anterior cruciate ligament graft geometry has previously been linked to abnormal tibiofemoral kinematics, suggesting this parameter may be important in restoring normative cartilage loading. However, the relationship between graft geometry and cartilage contact is unknown. METHODS:Static MR images were collected and segmented for eighteen subjects to obtain bone, cartilage, and anterior cruciate ligament geometries for their reconstructed and contralateral knees. The footprint locations and orientation of the anterior cruciate ligament were calculated. Volumetric, dynamic MR imaging was also performed to measure tibiofemoral kinematics, cartilage contact location, and contact sliding velocity while subjects performed loaded knee flexion-extension. Multiple linear regression was used to determine the relationship between non-anatomic graft geometry and asymmetric knee mechanics. FINDINGS: = 0.54). INTERPRETATION:This study provides evidence that non-anatomic graft geometry is linked to asymmetric knee mechanics, suggesting that restoring native anterior cruciate ligament geometry may be important to mitigate the risk of early cartilage degeneration in these patients.

PURPOSE:To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. METHODS:A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. RESULTS:The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. CONCLUSION:The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379-2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

OBJECTIVE:The purpose of this study is to determine whether marginal osteophytes in compartments with normal cartilage would be more frequently observed in knees with cartilage lesions and osteophytes in other compartments. MATERIALS AND METHODS/METHODS:This retrospective study reviewed 500 consecutive knee MRI examinations performed within 6 months of arthroscopic knee surgery conducted for 497 patients with symptoms (289 male patients and 208 female patients; age range, 17-74 years; median age, 43 years). The highest grade of cartilage lesion detected at MRI and arthroscopy was recorded. Marginal osteophytes were graded on MRI with use of a standardized scoring system, with grade 0 denoting no osteophyte; grade 1, small osteophyte; grade 2, medium-size osteophyte; and grade 3, large osteophyte). The frequency of false-positive osteophytes, defined as osteophytes present in compartments (the patellofemoral, medial tibiofemoral, and lateral tibiofemoral compartments) with normal cartilage observed on MRI and arthroscopy, was calculated. The Goodman and Kruskal gamma statistic was used to test the association of osteophyte size between compartments. Logistic regression was used to test the association between osteophyte size and the severity of the cartilage lesions. RESULTS:Marginal osteophytes were seen in compartments with normal cartilage on MRI and arthroscopy in 60.5% of knees (75 of 124) with cartilage lesions and osteophytes in other compartments and accounted for all false-positive grade 2 and grade 3 osteophytes. Marginal osteophytes were seen in 12.7% of knees (13 of 102) that had no cartilage lesions in any compartment on MRI or arthroscopy, and all of these were grade 1 osteophytes. The presence of larger sized osteophytes in the compartments with cartilage lesions was associated with the presence of larger sized osteophytes in the compartments with normal cartilage. More severe cartilage lesions were associated with larger osteophyte size. CONCLUSION/CONCLUSIONS:Compartments with marginal osteophytes and normal cartilage are commonly seen in knees that have other compartments with osteophytes and cartilage lesions.