Quantitative MR evaluation of the infrapatellar fat pad for knee osteoarthritis: using proton density fat fraction and T2* relaxation based on DIXON.

OBJECTIVES: To investigate the efficacy of fat fraction (FF) and T2* relaxation based on DIXON in the assessment of infrapatellar fat pad (IFP) for knee osteoarthritis (KOA) progression in older adults. METHODS: Ninety volunteers (age range 51-70 years, 65 females) were enrolled in this study. Participants were grouped based on the Kellgren-Lawrence grading (KLG). The FF and T2* values were measured based on the 3D-modified DXION technique. Cartilage defects, bone marrow lesions, and synovitis were assessed based on a modified version of whole-organ magnetic resonance imaging score (WORMS). Knee pain was assessed by self-administered Western Ontario and McMaster Osteoarthritis Index (WOMAC) questionnaire. The differences of FF and T2* measurement and the correlation with WORMS and WOMAC assessments were analyzed. Diagnostic efficiency was analyzed by using receiver operating characteristic (ROC) curves. RESULTS: A total of 60 knees were finally included (n = 20 in each group). The values were 82.6 ± 3.7%, 74.7 ± 5.4%, and 60.5 ± 14.1% for FF is the no OA, mild OA, and advanced OA groups, and were 50.7 ± 6.6 ms, 44.1 ± 6.6 ms, and 39.1 ± 4.2 ms for T2*, respectively (all p values < 0.001). The WORMS assessment and WOMAC pain assessment showed negative correlation with FF and T2* values. The ROC showed the area under the curve (AUC), sensitivity, and specificity for diagnosing OA were 0.93, 77.5%, and 100% using FF, and were 0.86, 75.0%, and 90.0% using T2*, respectively. CONCLUSIONS: FF and T2* alternations in IFP are associated with knee structural abnormalities and clinical symptoms cross-sectionally and may have the potential to predict the severity of KOA. KEY POINTS: • Fat fraction (FF) and T2* relaxation based on DIXON imaging are novel methods to quantitatively assess the infrapatellar fat pad for knee osteoarthritis (KOA) progression in older adults. • The alterations of FF and T2* using mDIXON technique in IFP were associated with knee structural abnormalities and clinical symptoms. • FF and T2* alternations in IFP can serve as the new imaging biomarkers for fast, simple, and noninvasive assessment in KOA.

Prediction of Abnormal Bone Density and Osteoporosis From Lumbar Spine MR Using Modified Dixon Quant in 257 Subjects With Quantitative Computed Tomography as Reference.

BACKGROUND: Bone marrow fat increases when bone mass decreases, which could be attributed to the fact that adipogenesis competes with osteogenesis. Bone marrow fat has the potential to predict abnormal bone density and osteoporosis. PURPOSE: To investigate the predictive value of using vertebral bone marrow fat fraction(BMFF) obtained from modified Dixon(mDixon) Quant in the determination of abnormal bone density and osteoporosis. STUDY TYPE: Prospective. POPULATION: 257 subjects (age: 20-79 years old; BMI: 16.6-32.9 kg/m2 ;181 females,76 males) without known spinal tumor, history of trauma, dysplasia, spinal surgery or hormone therapy. FIELD STRENGTH/SEQUENCE: 3.0T/mDixon. ASSESSMENT: BMFF was measured at the L1, L2 and L3 vertebral body on fat fraction maps of the lumbar spine. Bone mineral density (BMD) was obtained using quantitative computed tomography, which served as the reference standard. STATISTICAL TESTS: The BMFF between the three groups (normal bone density, osteopenia and osteoporosis) was tested using one-way analysis of variance in SPSS. The correlation and partial correlation of BMFF and BMD were analyzed before and after controlling for age, sex and BMI. Logistic regression analysis using independent training and validation data was conducted to evaluate the performance of predicting abnormal BMD or osteoporosis using BMFF. RESULTS: There was a significant difference in vertebral BMFF between the three groups (P < 0.001). Moderate inverse correlation was found between vertebral BMFF and BMD after controlling age, sex and BMI (r = -0.529; P < 0.001). The mean area under the curve, sensitivity, specificity and negative predictive value (NPV) for predicting abnormal bone density were 0.940, 0.877, 0.896, and 0.890, respectively. The corresponding results for predicting subjects with osteoporosis were 0.896, 0.848, 0.853, and 0.969, respectively. DATA CONCLUSION: mDixon Quant is a fast, simple, noninvasive and nonionizing method to access vertebral BMFF and has a high predictive power for identifying abnormal bone density and osteoporosis. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:390-399.

One-stop detection of anterior cruciate ligament injuries on magnetic resonance imaging using deep learning with multicenter validation.

Background: Anterior cruciate ligament (ACL) injuries are closely associated with knee osteoarthritis (OA). However, diagnosing ACL injuries based on knee magnetic resonance imaging (MRI) has been subjective and time-consuming for clinical doctors. Therefore, we aimed to devise a deep learning (DL) model leveraging MRI to enable a comprehensive and automated approach for the detection of ACL injuries. Methods: A retrospective study was performed extracting data from the Osteoarthritis Initiative (OAI). A total of 1,589 knees (comprising 1,443 intact, 90 with partial tears, and 56 with full tears) were enrolled to construct the classification model. This one-stop detection pipeline was developed using a tailored YOLOv5m architecture and a ResNet-18 convolutional neural network (CNN) to facilitate tasks based on sagittal 2-dimensional (2D) intermediate-weighted fast spin-echo sequence at 3.0T. To ensure the reliability and robustness of the classification system, it was subjected to external validation across 3 distinct datasets. The accuracy, sensitivity, specificity, and the mean average precision (mAP) were utilized as the evaluation metric for the model performance by employing a 5-fold cross-validation approach. The radiologist's interpretations were employed as the reference for conducting the evaluation. Results: The localization model demonstrated an accuracy of 0.89 and a sensitivity of 0.93, achieving a mAP score of 0.96. The classification model demonstrated strong performance in detecting intact, partial tears, and full tears at the optimal threshold on the internal dataset, with sensitivities of 0.941, 0.833, and 0.929, specificities of 0.925, 0.947, and 0.991, and accuracies of 0.940, 0.941, and 0.989, respectively. In comparison, on a subset consisting of 171 randomly selected knees from the OAI, the radiologists demonstrated a sensitivity ranging between 0.660 and 1.000, specificity ranging between 0.691 and 1.000, and accuracy ranging between 0.689 and 1.000. On a subset consisting of 170 randomly selected knees from the Chinese dataset, the radiologists exhibited a sensitivity ranging between 0.711 and 0.948, specificity ranging between 0.768 and 0.977, and accuracy ranging between 0.683 and 0.917. After retraining, the model achieved sensitivities ranging between 0.630 and 0.961, specificities ranging between 0.860 and 0.961, and accuracies ranging between 0.832 and 0.951, respectively, on the external validation dataset. Conclusions: The proposed model utilizing knee MRI showcases robust performance in the domains of ACL localization and classification.

Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.

Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supervised networks based on MRI images. In this retrospective study, data were from the osteoarthritis initiative (OAI). The MR images were reconstructed using a sagittal intermediate-weighted fat-suppressed turbo spin-echo sequence. (1) We used 130 knees from the OAI to develop the LGSA-UNet model which fuses the features of adjacent slices and adjusts the blocks in Siam to enable the central slice to obtain rich contextual information. (2) One thousand seven hundred and fifty-six knees from the OAI were included to establish segmentation and classification models. The segmentation model achieved a DICE coefficient ranging from 0.84 to 0.93. The AUC values ranged from 0.85 to 0.95 in the binary models. The accuracy for the three types of menisci (normal, tear, and maceration) ranged from 0.60 to 0.88. Furthermore, 206 knees from the orthopedic hospital were used as an external validation data set to evaluate the performance of the model. The segmentation and classification models still performed well on the external validation set. To compare the diagnostic performances between the deep learning (DL) models and radiologists, the external validation sets were sent to two radiologists. The binary classification model outperformed the diagnostic performance of the junior radiologist (0.82-0.87 versus 0.74-0.88). This study highlights the potential of DL in knee meniscus segmentation and injury classification which can help improve diagnostic efficiency.

Quantitative evaluation of the characteristic of infrapatellar fat pad Fat Content and Unsaturation Index by using hydrogen proton MR spectroscopy.

OBJECTIVE: To investigate the characteristics of fat content and component in IFP using hydrogen proton MR spectroscopy (1H-MRS), and to explore the correlation with the severity of OA, Hoffa-synovitis, and knee pain. MATERIALS AND METHODS: 80 volunteers were enrolled. Subjects were grouped based on Kellgren-Lawrence (K-L) grading. Fat fraction (FF) and unsaturation index (UI) of IFP were measured using 1H-MRS. Hoffa-synovitis was evaluated based on the MRI Osteoarthritis Knee Score system (MOAKS). Knee pain was assessed by a self-administered Western Ontario and McMaster Osteoarthritis Index (WOMAC) questionnaire. One-way ANOVA or Kruskal-Wallis test and Spearman's correlation tests were applied for statistical analysis. RESULTS: After matching BMI, waistline, and K-L grade, a total of 64 knees were included and divided into 23 normal, 25 mild OA, and 16 advanced OA. The mean values were 76.79% ± 7.24%, 70.35% ± 7.42%, and 58.29% ± 10.32% for FF in the healthy controls, mild OA, and advanced OA group, and 6.36 ± 1.19%, 6.08 ± 1.35%, and 5.69 ± 1.78% for UI, respectively, the statistical difference was found for FF (p < 0.01). A good negative correlation was observed between the FF and the severity of OA, Hoffa-synovitis (r = -0.625, -0.758, respectively, p < 0.0001), and a weak inverse correlation with knee pain. CONCLUSION: FF alteration in IFP is associated with the severity of OA, Hoffa synovitis, and knee pain, and has the potential to be a new quantitative imaging biomarker in knee OA.

Fatty infiltration of paraspinal muscles is associated with bone mineral density of the lumbar spine.

A total of 88 subjects were enrolled to investigate the relationship between paraspinal muscle fatty infiltration and lumbar bone mineral density (BMD) using chemical shift encoding-based water-fat MRI and quantitative computed tomography (QCT), respectively. A moderate inverse correlation between paraspinal muscle proton density fat fraction and lumbar QCT-BMD was found with age, sex, and BMI controlled. PURPOSE: To investigate the relationship between paraspinal muscle fatty infiltration and lumbar bone mineral density (BMD). METHODS: A total of 88 subjects were enrolled in this study (52 females, 36 males; age, 46.6 ± 14.2 years old; BMI, 23.2 ± 3.49 kg/m2). Proton density fat fractions (PDFF) of paraspinal muscles (erector spinae, multifidus, and psoas) were measured at L2/3, L3/4, and L4/5 levels using chemical shift encoding-based water-fat MRI. Quantitative computed tomography (QCT) was used to assess BMD of L1, L2, and L3. The differences in paraspinal muscle PDFF among subjects with normal bone density, osteopenia, and osteoporosis were tested using one-way ANOVA. The relationship between paraspinal muscle PDFF and QCT-BMD was analyzed using linear regression with age, sex, and BMI variables. RESULTS: PDFF of the erector spinae, multifidus, and psoas of subjects with normal bone density were all significantly less than those with osteopenia and those with osteoporosis (all p < 0.001). There was an inverse correlation between paraspinal muscle PDFF and BMD after controlling for age, sex, and BMI (standardized beta coefficient, - 0.21~- 0.29; all p < 0.05). CONCLUSIONS: Paraspinal muscle fatty infiltration increased while lumbar BMD decreased after adjusting for age, sex, and BMI. Paraspinal muscles and vertebrae are interacting tissues. Paraspinal muscle fatty infiltration may be a marker of low lumbar BMD. Chemical shift imaging is an efficient and fast quantitative method and can be easily added to the clinical protocol to measure paraspinal muscle PDFF when the patient underwent the routine lumbar MRI with low-back pain.

The Value of Percutaneous Shoulder Puncture with Contrast-enhanced Ultrasound in Differentiation of Rotator Cuff Tear Subtypes: A Preliminary Prospective Study.

Imaging tests perform relatively well in the detection of rotator cuff tears (RCTs), exhibiting high sensitivity and specificity, mainly among larger full-thickness tears (tear width >1 cm). However, these tests are relatively less accurate in the detection of small full-thickness tears and partial-thickness tears. The purpose of this study was to determine the feasibility of percutaneous ultrasound-guided tendon lesionography (PUTL) using the SonoVue and the value of percutaneous shoulder puncture via contrast-enhanced ultrasound (CEUS)-a combination of percutaneous ultrasound-guided subacromial bursography (PUSB) and PUTL-in the detection of RCT subtypes. Conventional ultrasound (US), CEUS and magnetic resonance imaging (MRI) were performed and prospectively evaluated in 97 patients who had undergone arthroscopy because of suspected RCTs. The rates of detection of the various subtypes of RCTs using CEUS, PUSB, PUTL, US and MRI were evaluated. The RCT subtype detection rate via CEUS was significantly higher than the rates via US and MRI (96.9%, 74.2% and 76.3%, respectively), as were the detection rates for small full-thickness tears combined with partial-thickness tears (98.2%, 60.0% and 61.8%, respectively). The detection rate with PUSB was significantly higher than those with US and MRI in assessing full-thickness tears combined with bursal-side partial-thickness tears (93.9%, 65.3% and 65.3%, respectively). The detection rate with PUTL was significantly higher than those with US and MRI in assessing the corresponding subtypes (100.0%, 69.2% and 76.9%, respectively). On the basis of our findings, we consider PUTL a tolerable and feasible procedure. Percutaneous shoulder puncture using CEUS can be an effective alternative method with better diagnostic performance than US and MRI for the detection of RCT subtypes.