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Dai L, Md Johar MG, Alkawaz MH. The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning. Sci Rep 2024; 14:28885. [PMID: 39572780 PMCID: PMC11582322 DOI: 10.1038/s41598-024-80441-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Accepted: 11/19/2024] [Indexed: 11/24/2024] Open
Abstract
This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is developed by optimizing and integrating the AlexNet and U-Net algorithms for the segmentation of MRI images of the shoulder joint. The model is evaluated using metrics such as the Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity (SE). A cohort of 52 swimmers with SJIs from Guangzhou Hospital serve as the subjects for this study, wherein the accuracy of the developed shoulder joint MRI IS model in diagnosing swimmers' SJIs is analyzed. The results reveal that the DSC for segmenting joint bones in MRI images based on the MSFFN algorithm is 92.65%, with PPV of 95.83% and SE of 96.30%. Similarly, the DSC for segmenting humerus bones in MRI images is 92.93%, with PPV of 95.56% and SE of 92.78%. The MRI IS algorithm exhibits an accuracy of 86.54% in diagnosing types of SJIs in swimmers, surpassing the conventional diagnostic accuracy of 71.15%. The consistency between the diagnostic results of complete tear, superior surface tear, inferior surface tear, and intratendinous tear of SJIs in swimmers and arthroscopic diagnostic results yield a Kappa value of 0.785 and an accuracy of 87.89%. These findings underscore the significant diagnostic value and potential of the MRI IS technique based on the MSFFN algorithm in diagnosing SJIs in swimmers.
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Affiliation(s)
- Lina Dai
- School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou, China.
- School of Graduate Studies, Management and Science University, Shah Alam, 40100, Selangor, Malaysia.
| | - Md Gapar Md Johar
- Software Engineering and Digital Innovation Center, Management and Science University, Shah Alam, 40100, Selangor, Malaysia
| | - Mohammed Hazim Alkawaz
- Department of Computer Science, College of Education for Pure Science, University of Mosul, Mosul, Nineveh, Iraq
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Gao S, Peng C, Wang G, Deng C, Zhang Z, Liu X. Cartilage T2 mapping-based radiomics in knee osteoarthritis research: Status, progress and future outlook. Eur J Radiol 2024; 181:111826. [PMID: 39522425 DOI: 10.1016/j.ejrad.2024.111826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Revised: 10/09/2024] [Accepted: 11/06/2024] [Indexed: 11/16/2024]
Abstract
Osteoarthritis (OA) affects more than 500 millions people worldwide and places an enormous economic and medical burden on patients and healthcare systems. The knee is the most commonly affected joint. However, there is no effective early diagnostic method for OA. The main pathological feature of OA is cartilage degeneration. Owing to the poor regenerative ability of chondrocytes, early detection of OA and prompt intervention are extremely important. The T2 relaxation time indicates changes in cartilage composition and responds to alterations in the early cartilage matrix. T2 mapping does not require contrast agents or special equipment, so it is widely used. Radiomics analysis methods are used to construct diagnostic or predictive models based on information extracted from clinical images. Owing to the development of artificial intelligence methods, radiomics has made excellent progress in segmentation and model construction. In this review, we summarize the progress of T2 mapping radiomics research methods in terms of T2 map acquisition, image postprocessing, and OA diagnosis or predictive model construction.
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Affiliation(s)
- Shi Gao
- Department of Rehabilitation, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chengbao Peng
- Platform Engineering Research Center, Neusoft Research Institute of Healthcare Technology, Shenyang, Liaoning Province, China
| | - Guan Wang
- Platform Engineering Research Center, Neusoft Research Institute of Healthcare Technology, Shenyang, Liaoning Province, China
| | - Chunbo Deng
- Department of Orthopedics, Central Hospital of Shenyang Medical College, Shenyang, China
| | - Zhan Zhang
- Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xueyong Liu
- Department of Rehabilitation, Shengjing Hospital of China Medical University, Shenyang, China.
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Wen D, Zhou X, Hou B, Zhang Q, Raithel E, Wang Y, Wu G, Li X. 3D-DESS MRI with CAIPIRINHA two- and fourfold acceleration for quantitatively assessing knee cartilage morphology. Skeletal Radiol 2024; 53:1481-1494. [PMID: 38347270 DOI: 10.1007/s00256-024-04605-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 06/25/2024]
Abstract
OBJECTIVES This study aimed to assess the diagnostic image quality and compare the knee cartilage segmentation results using a controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-accelerated 3D-dual echo steady-state (DESS) research package sequence in the knee. MATERIALS AND METHODS A total of 64 subjects underwent both two- and fourfold CAIPIRINHA-accelerated 3D-DESS and DESS without parallel acceleration technique of the knee on a 3.0 T system. Two musculoskeletal radiologists evaluated the images independently for image quality and diagnostic capability following randomization and anonymization. The consistency of automatic segmentation results between sequences was explored using an automatic knee cartilage segmentation research application. The descriptive statistics and inter-observer and inter-method concordance of various acceleration sequences were investigated. P values < .05 were considered significant. RESULTS For image quality evaluation, the image signal-to-noise ratio and contrast-to-noise ratio decreased with the decrease in scanning time. However, it is accompanied by the reduction of artifacts. Using 3D-DESS without parallel acceleration technique as the standard for cartilage grading diagnosisand the diagnostic agreement of two- and fourfold CAIPIRINHA-accelerated 3D-DESS was good, kappa value was 0.860 (P < .001) and 0.804 (p < 0.001), respectively. Regarding cartilage defects, the sensitivity and specificity of the twofold acceleration 3D-CAIPIRINHA-DESS were 95.56% and 97.70%, and the fourfold CAIPIRINHA-accelerated 3D-DESS were 91.49% and 97.65%, respectively. The intraclass correlation coefficients of various sequences in cartilage segmentation were almost all greater than 0.9. CONCLUSION The CAIPIRINHA-accelerated 3D-DESS sequence maintained comparable diagnostic and segmentations performance of knee cartilage after a 60% scan time reduction.
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Affiliation(s)
- Donglin Wen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan City, 430030, Hubei Province, China
| | - Xiaoyue Zhou
- MR Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | - Bowen Hou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan City, 430030, Hubei Province, China
| | - Qiong Zhang
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | | | - Yi Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan City, 430030, Hubei Province, China
| | - Gang Wu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan City, 430030, Hubei Province, China.
| | - Xiaoming Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan City, 430030, Hubei Province, China.
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Katano H, Nagai K, Kaneko H, Sasaki E, Hashiguchi N, Kuroda R, Ishijima M, Ishibashi Y, Adachi N, Tomita M, Masumoto J, Sekiya I. Variations in knee cartilage thickness: Fully automatic three-dimensional analysis of MRIs from five manufacturers. Eur J Radiol 2024; 176:111528. [PMID: 38815306 DOI: 10.1016/j.ejrad.2024.111528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND Measurements of knee cartilage thickness derived from MR images are attractive biomarkers for osteoarthritis research. Although some cross-sectional multivendor studies exist, none have employed fully automatic three-dimensional MRI analysis. Our objective was to evaluate the variations in knee cartilage thickness measurements obtained using automated methods and MRI instruments from five different vendors. METHODS The subjects were 10 healthy volunteers aged 22-60 years. MRI models with 3 Tesla strength from five different companies were used. Cartilage thickness was quantified fully automatically for seven regions. We hypothesized that "the MRI model influences cartilage thickness measurements." Inter-measurement error, defined as the absolute difference between the targeted and median thicknesses determined by the five MRI models, was analyzed using histograms. The factors generating the largest inter-measurement error were also examined. RESULTS No exceptional trends attributable to a specific instrument model were observed, and the p-value from the Kruskal-Wallis test exceeded 0.05 in all seven regions. Therefore, the study hypothesis was rejected. Of the 350 measurements, the inter-measurement error was ≤0.05 mm in 53 %, ≤0.10 mm in 75 %, and ≤0.20 mm in 95 %. Analysis of the medial tibial cartilage, which had the largest inter-measurement error, revealed mis-extraction of synovial fluid as cartilage. CONCLUSIONS The choice of MRI model did not influence cartilage thickness measurements. Overall, 95 % of the inter-measurement errors were within 0.20 mm. The greatest error resulted from mis-extracting synovial fluid as cartilage.
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Affiliation(s)
- Hisako Katano
- Center for Stem Cell and Regenerative Medicine, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Kanto Nagai
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, 7‑5‑2, Kusunoki‑cho, Chuo‑ku, Kobe, Hyogo 650‑0017, Japan
| | - Haruka Kaneko
- Department of Medicine for Orthopedics and Motor Organ, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Eiji Sasaki
- Department of Orthopaedic Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-Cho, Hirosaki, Aomori 036-8562, Japan
| | - Naofumi Hashiguchi
- Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima 734-8551, Japan
| | - Ryosuke Kuroda
- Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, 7‑5‑2, Kusunoki‑cho, Chuo‑ku, Kobe, Hyogo 650‑0017, Japan
| | - Muneaki Ishijima
- Department of Medicine for Orthopedics and Motor Organ, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Yasuyuki Ishibashi
- Department of Orthopaedic Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifu-Cho, Hirosaki, Aomori 036-8562, Japan
| | - Nobuo Adachi
- Department of Orthopaedic Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima 734-8551, Japan
| | - Makoto Tomita
- School of Data Science, Graduate School of Data Science, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama 236-0027, Japan
| | - Jun Masumoto
- Fujifilm Corporation, 7-3, Akasaka 9-chome, Minato-ku, Tokyo 107-0052, Japan
| | - Ichiro Sekiya
- Center for Stem Cell and Regenerative Medicine, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
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Lee HY, Kim JM, Lee BS, Bin SI, Kim SM, Lee SJ. Lateral Meniscal Allograft Transplantation Shows a Long-Term Chondroprotective Effect on Quantitative Magnetic Resonance Imaging T2 Mapping at 7-Year Minimum Follow-Up. Arthroscopy 2024; 40:1568-1574. [PMID: 37813204 DOI: 10.1016/j.arthro.2023.09.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 09/11/2023] [Accepted: 09/14/2023] [Indexed: 10/11/2023]
Abstract
PURPOSE To assess the long-term chondroprotective effect of lateral meniscal allograft transplantation (MAT) using quantitative magnetic resonance imaging (MRI) T2 mapping. METHODS In patients who underwent isolated lateral MAT, quantitative MRI T2 mapping was conducted preoperatively and postoperatively with at minimum follow-up of 7 years to assess the articular cartilage status. On the sagittal section image bisecting the lateral femoral condyle, the weight-bearing portions of the femoral and tibial articular cartilage were divided into 3 segments each-6 segments in total-based on the meniscal coverage area. The regions-of-interest analyses were performed on the 6 segments to measure the mean T2 value. Then the whole layer was divided into deep and superficial layers for further zonal analysis. The longitudinal change in T2 values was statistically analyzed using paired t-tests. Clinical outcome was evaluated using the Lysholm score. RESULTS A total of 31 patients were included in the study, with the MRI follow-up period of a minimum of 7 years (mean: 8.9 ± 1.3 years; range: 7.0-11.2 years). The mean T2 value of the whole layer showed significant improvement in all segments of the femoral cartilage and the posterior segment of tibial cartilage. In the zonal analysis, the mean T2 value of the tibial cartilage showed significant improvement in the superficial layer of the mid to posterior portion, while the deep layer remained stable. In contrast, the mean T2 value of the femoral cartilage showed significant improvement in the superficial and deep layers in all segments. The mean Lysholm score significantly improved from 62.6 ± 12.8 to 90.9 ± 10.5 (P < .001). CONCLUSIONS This study suggests that MAT appears to have a long-term chondroprotective effect on the articular cartilage as judged by quantitative T2 mapping. LEVEL OF EVIDENCE Level Ⅳ, case series.
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Affiliation(s)
- Hyo Yeol Lee
- Department of Orthopaedic Surgery, Eulji Medical Center Daejeon Hospital, Eulji University College of Medicine, Daejeon, Republic of Korea; Department of Orthopaedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jong-Min Kim
- Department of Orthopaedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Bum-Sik Lee
- Department of Orthopaedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seong-Il Bin
- Department of Orthopaedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung-Min Kim
- Department of Orthopaedic Surgery, Wiltse Memorial Hospital, Anyang, Republic of Korea
| | - Seon-Jong Lee
- Department of Orthopaedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Zhang R, Zhou X, Raithel E, Ren C, Zhang P, Li J, Bai L, Zhao J. A reproducibility study of knee cartilage volume and thickness values derived by fully automatic segmentation based on three-dimensional dual-echo in steady state data from 1.5 T and 3 T magnetic resonance imaging. MAGMA (NEW YORK, N.Y.) 2024; 37:69-82. [PMID: 37815638 DOI: 10.1007/s10334-023-01122-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023]
Abstract
OBJECTIVE To evaluate the repeatability of cartilage volume and thickness values at 1.5 T MRI using a fully automatic cartilage segmentation method and reproducibility of the method between 1.5 T and 3 T data. METHODS The study included 20 knee joints from 10 healthy subjects with each subject having undergone double-knee MRI. All knees were scanned at 1.5 T and 3 T MR scanners using a three-dimensional (3D) high-resolution dual-echo in steady state (DESS) sequence. Cartilage volume and thickness of 21 subregions were quantified using a fully automatic cartilage segmentation research application (MR Chondral Health, version 3.0, Siemens Healthcare, Erlangen, Germany). The volume and thickness values derived from fully automatically computed segmentation masks were analyzed for the scan-rescan data from the same volunteers. The accuracy of the automatic segmentation of the cartilage in 1.5 T images was evaluated by the dice similarity coefficient (DSC) and Hausdorff distance (HD) using the manually corrected segmentation as a reference. The volume and thickness values calculated from 1.5 T and 3 T were also compared. RESULTS No statistically significant differences were found for cartilage thickness or volume across all subregions between the scan-rescanned data at 1.5 T (P > 0.05). The mean DSC between the fully automatic and manually corrected knee cartilage segmentation contours at 1.5 T was 0.9946. The average value of HD was 2.41 mm. Overall, there was no statistically significant difference in the cartilage volume or thickness in most-subregions between the two field strengths (P > 0.05) except for the medial region of femur and tibia. Bland-Altman plot and intraclass correlation coefficient (ICC) showed high consistency between results obtained based on same and different scanning sequences. CONCLUSION The cartilage segmentation software had high repeatability for DESS images obtained from the same device. In addition, the overall reproducibility of the images obtained from equipment of two different field strengths was satisfactory.
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Affiliation(s)
- Ranxu Zhang
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Xiaoyue Zhou
- MR Collaboration, Siemens Healthineers Ltd, Shanghai, 200126, China
| | | | - Congcong Ren
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Ping Zhang
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Junfei Li
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Lin Bai
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Jian Zhao
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China.
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Oei EHG, Hirvasniemi J, van Zadelhoff TA, van der Heijden RA. Osteoarthritis year in review 2021: imaging. Osteoarthritis Cartilage 2022; 30:226-236. [PMID: 34838670 DOI: 10.1016/j.joca.2021.11.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/16/2021] [Accepted: 11/11/2021] [Indexed: 02/02/2023]
Abstract
PURPOSE To provide a narrative review of original articles on imaging of osteoarthritis (OA) published between January 1, 2020 and March 31, 2021, with a special focus on imaging of inflammation, imaging of bone, cartilage and bone-cartilage interactions, imaging of peri-articular tissues, imaging scoring methods for OA, and artificial intelligence (AI) applied to OA imaging. METHODS The Embase, Pubmed, Medline, Cochrane databases were searched for original research articles in the English language on human, in vivo, imaging of OA published between January 1, 2020 and March 31, 2021. Search terms related to osteoarthritis combined with all imaging modalities and artificial intelligence were applied. A selection of articles reporting on one of the focus topics was discussed further. RESULTS The search resulted in 651 articles, of which 214 were deemed relevant to human OA imaging. Among the articles included, the knee joint (69%) and magnetic resonance imaging (MRI) (52%) were the predominant anatomical area and imaging modality studied. There were also a substantial number of papers (n = 46) reporting on AI applications in the field of OA imaging. CONCLUSION Imaging continues to play an important role in the assessment of OA. Recent advances in OA imaging include quantitative, non-contrast, and hybrid imaging techniques for improved characterization of multiple tissue processes in OA. In addition, an increasing effort in AI techniques is undertaken to enhance OA imaging acquisition and analysis.
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Affiliation(s)
- E H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
| | - J Hirvasniemi
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
| | - T A van Zadelhoff
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
| | - R A van der Heijden
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
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Chen E, Hou W, Wang H, Li J, Lin Y, Liu H, Du M, Li L, Wang X, Yang J, Yang R, Zhou C, Chen P, Zeng M, Yao Q, Chen W. Quantitative MRI evaluation of articular cartilage in patients with meniscus tear. Front Endocrinol (Lausanne) 2022; 13:911893. [PMID: 35966082 PMCID: PMC9372396 DOI: 10.3389/fendo.2022.911893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 06/30/2022] [Indexed: 11/25/2022] Open
Abstract
PURPOSE The aim of this study was to assess quantitatively articular cartilage volume, thickness, and T2 value alterations in meniscus tear patients. MATERIALS AND METHODS The study included 32 patients with meniscus tears (17 females, 15 males; mean age: 40.16 ± 11.85 years) and 24 healthy controls (12 females; 12 males; mean age: 36 ± 9.14 years). All subjects were examined by 3 T magnetic resonance imaging (MRI) with 3D dual-echo steady-state (DESS) and T2 mapping images. All patients underwent diagnostic arthroscopy and treatment. Cartilage thickness, cartilage volume and T2 values of 21 subregions of knee cartilage were measured using the prototype KneeCaP software (version 2.1; Siemens Healthcare, Erlangen, Germany). Mann-Whitney-U tests were utilized to determine if there were any significant differences among subregional articular cartilage volume, thickness and T2 value between patients with meniscus tear and the control group. RESULTS The articular cartilage T2 values in all subregions of the femur and tibia in the meniscus tear group were significantly higher (p< 0.05) than in the healthy control group. The cartilage thickness of the femoral condyle medial, femur trochlea, femur condyle lateral central, tibia plateau medial anterior and patella facet medial inferior in the meniscus tear group were slightly higher than in the control group (p< 0.05). In the femur trochlea medial, patella facet medial inferior, tibia plateau lateral posterior and tibia plateau lateral central, there were significant differences in relative cartilage volume percentage between the meniscus tear group and the healthy control group (p< 0.05). Nineteen patients had no cartilage abnormalities (Grade 0) in the meniscus tear group, as confirmed by arthroscopic surgery, and their T2 values in most subregions were significantly higher (p< 0.05) than those of the healthy control group. CONCLUSION The difference in articular cartilage indexes between patients with meniscus tears and healthy people without such tears can be detected by using quantitative MRI. Quantitative T2 values enable early and sensitive detection of early cartilage lesions.
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Affiliation(s)
- Enqi Chen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Wenjing Hou
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Hu Wang
- Department of Radiology, Sichuan Science City Hospital, Mianyang, China
| | - Jing Li
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Yangjing Lin
- Centre of Joint Surgery, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - He Liu
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Mingshan Du
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Lian Li
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Xianqi Wang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Jing Yang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Rui Yang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Changru Zhou
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Pinzhen Chen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Meng Zeng
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Qiandong Yao
- Department of Radiology, Sichuan Science City Hospital, Mianyang, China
- *Correspondence: Wei Chen, ; Qiandong Yao,
| | - Wei Chen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
- *Correspondence: Wei Chen, ; Qiandong Yao,
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Thomas KA, Krzemiński D, Kidziński Ł, Paul R, Rubin EB, Halilaj E, Black MS, Chaudhari A, Gold GE, Delp SL. Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning. Cartilage 2021; 13:747S-756S. [PMID: 34496667 PMCID: PMC8808775 DOI: 10.1177/19476035211042406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVE We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically. DESIGN We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison. RESULTS Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values. CONCLUSIONS Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.
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Affiliation(s)
- Kevin A. Thomas
- Department of Biomedical Data Science,
Stanford University, Stanford, CA, USA
| | - Dominik Krzemiński
- Cardiff University Brain Research
Imaging Centre, Cardiff University, Cardiff, Wales, UK
| | - Łukasz Kidziński
- Department of Bioengineering, Stanford
University, Stanford, CA, USA
| | - Rohan Paul
- Department of Biomedical Data Science,
Stanford University, Stanford, CA, USA
| | - Elka B. Rubin
- Department of Radiology, Stanford
University, Stanford, CA, USA
| | - Eni Halilaj
- Department of Mechanical Engineering,
Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Akshay Chaudhari
- Department of Biomedical Data Science,
Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford
University, Stanford, CA, USA
| | - Garry E. Gold
- Department of Bioengineering, Stanford
University, Stanford, CA, USA
- Department of Radiology, Stanford
University, Stanford, CA, USA
- Department of Orthopaedic Surgery,
Stanford University, Stanford, CA, USA
| | - Scott L. Delp
- Department of Bioengineering, Stanford
University, Stanford, CA, USA
- Department of Orthopaedic Surgery,
Stanford University, Stanford, CA, USA
- Department of Mechanical Engineering,
Stanford University, Stanford, CA, USA
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