Journal articles: 'Knee segmentation in MRI'

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Riza, Sulaiman, Djasmir Marlinawati, and Mohamad Amran Mohd Fahmi. "COMSeg technique for MRI knee cartilage segmentation." International Review of Applied Sciences and Engineering 10, no. 2 (December 2019): 147–55. http://dx.doi.org/10.1556/1848.2019.0018.

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Segmentation is one of important methods in medical images processing, particularly as it allows images to be analysed. The method used for segmentation depends on the image problem to be resolved. In this research, knee cartilage needs to be segmented to determine the level of the Osteoarthritis (OA) and for further treatment. Knee cartilage is a soft hyline sponge that is located at the end of the femur, tibia and patella bone to release friction during movement. OA is a knee cartilage problem wherein there is a thinning of the cartilage that results in a shift especially happening between femur and tibia bone causing discomfort and pain. Thinning of the knee cartilage is due to many factors such as age, body weight, genetic, accident, sport injury and extreme use such as physical work. OA can occur to a male or female, child or adult. The effects experienced by patients with OA are such as difficulty to walk, limited movement, and pain in the thin cartilage areas. Monitoring of patients' condition needs to be done to help reduce the problem and thereby enable specialists to perform the appropriate treatment. Imaging is a method used today to monitor the condition of patients with OA. Previous studies showed that MRI is a suitable method for monitoring the condition of patients with OA because of its advantages in visualising knee cartilage more clearly than other imaging methods. Thus, for segmenting the knee cartilage which as mentioned before is an important process in medical images processing, the MR images were selected based on many factors. Segmentation in this study was aimed to obtain the cartilage region to diagnose patient OA level. Various segmentation techniques have been developed by researchers in segmenting the knee cartilage region but they have been unable to segment precisely due to the thin structure of the knee cartilage, especially for patients with intermediate and severe OA. COMSeg technique was developed to segment knee cartilage, especially for those experiencing a normal and intermediate OA and try to implement it to severe OA. The development of this new technique takes into account the imaging method used, the images feature obtained so it can be suitable to process knee image and then selection of an appropriate technique to be applied to the selected images as input.

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Oei, Edwin H. G., Tijmen A. van Zadelhoff, Susanne M. Eijgenraam, Stefan Klein, Jukka Hirvasniemi, and Rianne A. van der Heijden. "3D MRI in Osteoarthritis." Seminars in Musculoskeletal Radiology 25, no. 03 (June 2021): 468–79. http://dx.doi.org/10.1055/s-0041-1730911.

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AbstractOsteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification.

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More, Sujeet, Jimmy Singla, Ahed Abugabah, and Ahmad Ali AlZubi. "Machine Learning Techniques for Quantification of Knee Segmentation from MRI." Complexity 2020 (December 7, 2020): 1–13. http://dx.doi.org/10.1155/2020/6613191.

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Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed.

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Barendregt, Anouk M., Valentina Mazzoli, J. Merlijn van den Berg, Taco W. Kuijpers, Mario Maas, Aart J. Nederveen, and Robert Hemke. "T1ρ-mapping for assessing knee joint cartilage in children with juvenile idiopathic arthritis — feasibility and repeatability." Pediatric Radiology 50, no. 3 (November 9, 2019): 371–79. http://dx.doi.org/10.1007/s00247-019-04557-4.

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Abstract Background Ongoing arthritis in children with juvenile idiopathic arthritis (JIA) can result in cartilage damage. Objective To study the feasibility and repeatability of T1ρ for assessing knee cartilage in JIA and also to describe T1ρ values and study correlation between T1ρ and conventional MRI scores for disease activity. Materials and methods Thirteen children with JIA or suspected JIA underwent 3-tesla (T) knee MRI that included conventional sequences and a T1ρ sequence. Segmentation of knee cartilage was carried out on T1ρ images. We used intraclass correlation coefficient to study the repeatability of segmentation in a subset of five children. We used the juvenile arthritis MRI scoring system to discriminate inflamed from non-inflamed knees. The Mann-Whitney U and Spearman correlation compared T1ρ between children with and without arthritis on MRI and correlated T1ρ with the juvenile arthritis MRI score. Results All children successfully completed the MRI examination. No images were excluded because of poor quality. Repeatability of T1ρ measurement had an intraclass correlation coefficient (ICC) of 0.99 (P<0.001). We observed no structural cartilage damage and found no differences in T1ρ between children with (n=7) and without (n=6) inflamed knees (37.8 ms vs. 31.7 ms, P=0.20). However, we observed a moderate correlation between T1ρ values and the juvenile arthritis MRI synovitis score (r=0.59, P=0.04). Conclusion This pilot study suggests that T1ρ is a feasible and repeatable quantitative imaging technique in children. T1ρ values were associated with the juvenile arthritis MRI synovitis score.

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Zhang, Ying, Mo Ruan, Hongbo Tan, Ming Chen, and Yongqing Xu. "Analysis of the Effect of Intra-Articular Injection of Platelet-Rich Plasma on Knee Arthritis Pain Based on MRI Image Segmentation Algorithm." Journal of Medical Imaging and Health Informatics 11, no. 1 (January 1, 2021): 192–96. http://dx.doi.org/10.1166/jmihi.2021.3441.

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Objective: To study the effect of bone marrow platelet-rich plasma (PRP) injection on osteoarthritis using a level set-based MRI image segmentation algorithm. Methods: 180 patients with knee osteoarthritis (206 knees) were randomly divided into observation group (92 knees) and control group (88 knees). The observation group was injected with 2 mL of enriched stem cells and PRP suspension in the joint cavity, and then injected once again after 6 weeks. The control group was injected with 2 mL of sodium hyaluronate in the joint cavity, once a week for a total of 5 times. After 3 months and 6 months of treatment, the patients were followed up, and the clinical efficacy of the two groups was evaluated by the WOMAC score. The MRI image segmentation of the patients was analyzed using the level set-based MRI image segmentation method. Results: The WOMAC score observation group was 45.88 ± 9.54 points before treatment, 25.26 ± 6.67 points 3 months after treatment, 22.44 ± 5.19 points 6 months after treatment; the control group was 46.76 ± 8.06 points before treatment and 32.12 ± 5.35 3 months after treatment. Points, 33.34 ± 6.32 points 6 months after treatment. The WOMAC score of the observation group and the control group was improved after treatment, and the difference was statistically significant (P < 0.05) compared with that before treatment. Conclusion: The observation group has a clear effect on the treatment of knee osteoarthritis and is worthy of clinical application.

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Kashyap, S., H. Zhang, and M. Sonka. "Accurate Fully Automated 4D Segmentation of Osteoarthritic Knee MRI." Osteoarthritis and Cartilage 25 (April 2017): S227—S228. http://dx.doi.org/10.1016/j.joca.2017.02.391.

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Saygili, Ahmet, and Songül Albayrak. "Knee Meniscus Segmentation and Tear Detection from MRI: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (January 6, 2020): 2–15. http://dx.doi.org/10.2174/1573405614666181017122109.

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Background: Automatic diagnostic systems in medical imaging provide useful information to support radiologists and other relevant experts. The systems that help radiologists in their analysis and diagnosis appear to be increasing. Discussion: Knee joints are intensively studied structures, as well. In this review, studies that automatically segment meniscal structures from the knee joint MR images and detect tears have been investigated. Some of the studies in the literature merely perform meniscus segmentation, while others include classification procedures that detect both meniscus segmentation and anomalies on menisci. The studies performed on the meniscus were categorized according to the methods they used. The methods used and the results obtained from such studies were analyzed along with their drawbacks, and the aspects to be developed were also emphasized. Conclusion: The work that has been done in this area can effectively support the decisions that will be made by radiology and orthopedics specialists. Furthermore, these operations, which were performed manually on MR images, can be performed in a shorter time with the help of computeraided systems, which enables early diagnosis and treatment.

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Kumar, Deepak, and Jitendra Bhaskar. "A Review on Modelling of Knee Joint Using Medical Imaging Methods." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 5, no. 4 (October 5, 2020): 84–89. http://dx.doi.org/10.35121/ijapie202001146.

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The accuracy of the 3D CAD model of the knee joint is based on various factors like imaging method i.e CT scan, MRI data, modelling software and different algorithms for segmentation. For generating geometrical and CAD model techniques like CT scan, Co-ordinate Measuring Machine (CMM) and 3D laser scanner is used. So in this paper efforts have been made to study the different factors which affect the accuracy of a 3D CAD and additively manufactured knee model. Accuracy of the knee joint is important for anatomical study, implant modeling, and pre-surgical planning. The segmentation technique is another important factor that affects the accuracy of a 3D CAD model so each segmentation technique has its pros and cons therefore evaluation of segmentation technique is also studied and compared with each other.

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Dam, E. B., and J. Marques. "422 AUTOMATIC SEGMENTATION OF BONE AND CARTILAGE FROM KNEE MRI." Osteoarthritis and Cartilage 19 (September 2011): S196. http://dx.doi.org/10.1016/s1063-4584(11)60449-4.

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Aprovitola, Andrea, and Luigi Gallo. "Knee bone segmentation from MRI: A classification and literature review." Biocybernetics and Biomedical Engineering 36, no. 2 (2016): 437–49. http://dx.doi.org/10.1016/j.bbe.2015.12.007.

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Mohammadi, Ali, Katariina A. H. Myller, Petri Tanska, Jukka Hirvasniemi, Simo Saarakkala, Juha Töyräs, Rami K. Korhonen, and Mika E. Mononen. "Rapid CT-based Estimation of Articular Cartilage Biomechanics in the Knee Joint Without Cartilage Segmentation." Annals of Biomedical Engineering 48, no. 12 (November 11, 2020): 2965–75. http://dx.doi.org/10.1007/s10439-020-02666-y.

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AbstractKnee osteoarthritis (OA) is a painful joint disease, causing disabilities in daily activities. However, there is no known cure for OA, and the best treatment strategy might be prevention. Finite element (FE) modeling has demonstrated potential for evaluating personalized risks for the progression of OA. Current FE modeling approaches use primarily magnetic resonance imaging (MRI) to construct personalized knee joint models. However, MRI is expensive and has lower resolution than computed tomography (CT). In this study, we extend a previously presented atlas-based FE modeling framework for automatic model generation and simulation of knee joint tissue responses using contrast agent-free CT. In this method, based on certain anatomical dimensions measured from bone surfaces, an optimal template is selected and scaled to generate a personalized FE model. We compared the simulated tissue responses of the CT-based models with those of the MRI-based models. We show that the CT-based models are capable of producing similar tensile stresses, fibril strains, and fluid pressures of knee joint cartilage compared to those of the MRI-based models. This study provides a new methodology for the analysis of knee joint and cartilage mechanics based on measurement of bone dimensions from native CT scans.

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Han, Yue Mei. "Study on 3D Model Reconstruction of Human Knee Joint Based on MRI." Applied Mechanics and Materials 333-335 (July 2013): 934–37. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.934.

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Reconstruction of a 3D model for human knee joint is the basic step for its kinematics and dynamics analysis. To make further research on knee joint modeling, we present a new method to reconstruct 3D knee joint models based on magnetic resonance image (MRI). This method consists of steps as pretreatment of the images, the region growing for segmentation and the contour interpolation or the grey value interpolation and so on. The resulting 3D knee joint model are used for dynamics analysis of human knee joint after being imported into the finite-element platform which includes the tibia, the femur, the meniscus and the cartilages. The 3D model provides the possibility for the research on the movement roles and mechanics characteristics of the knee joint.

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Wang, Lijuan, and Gaoyuan Cui. "The Value of Computed Tomography Three-Dimensional Imaging Technology in the Diagnosis and Treatment of Sports Knee Ligament Strain." Journal of Medical Imaging and Health Informatics 10, no. 9 (August 1, 2020): 2067–72. http://dx.doi.org/10.1166/jmihi.2020.3123.

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Objective: To improve the efficiency and accuracy of the diagnosis and treatment of sports knee ligament strain, the application value of computerized tomography (CT) three-dimensional (3D) imaging technology in the diagnosis and treatment of sports knee ligament strain is studied. Methods: This study proposes a method for CT 3D image reconstruction based on the model clustering algorithm. First, the knee joint CT images of research objects are preprocessed. Second, based on the preprocessing, the healthy adult male knee ligament distribution structure map is used as a reference model. The model clustering segmentation algorithm proposed in this study is used for detailed segmentation, and the results are input into Materialise’s interactive medical control system (Mimics) software. According to the process, the 3D CT reconstructed images are derived. Finally, the 3D reconstruction results of knee ligament CT are optimized by using Geomagic Studio 2012 software. The application of 3D CT images and magnetic resonance imaging (MRI) images obtained by the algorithm in this study in the diagnosis and treatment of knee ligament strains are compared. Results: The CT 3D image reconstruction method based on the model clustering algorithm proposed in this study can clearly show the ligament structure of the knee joint. The optimized CT 3D image has a smoother surface and a clearer display, which is more conducive for observing the knee joint ligament structure more clearly. The comparative experiments have found that the diagnostic accuracy of 3D CT images is 95%, and the diagnostic accuracy of MRI images is 85%. The diagnostic accuracy of the 3D reconstructed images proposed in this study is significantly higher than that of MRI images, and the difference is statistically significant (P < 0.05). Conclusion: The proposed algorithm has an excellent effect on the 3D reconstruction of CT images. Also, it has high efficiency and accuracy in the diagnosis and treatment of sports knee ligament strains.

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Navkar, Nikhil. "Feasibility study on MRI segmentation of knee structures for computer-assisted surgery." Qatar Foundation Annual Research Forum Proceedings, no. 2013 (November 2013): BIOP 028. http://dx.doi.org/10.5339/qfarf.2013.biop-028.

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Pang, Jianfei, PengYue Li, Mingguo Qiu, Wei Chen, and Liang Qiao. "Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images." Journal of Digital Imaging 28, no. 6 (February 21, 2015): 695–703. http://dx.doi.org/10.1007/s10278-015-9780-x.

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Almajalid, Rania, Juan Shan, Yaodong Du, and Ming Zhang. "Identification of Knee Cartilage Changing Pattern." Applied Sciences 9, no. 17 (August 22, 2019): 3469. http://dx.doi.org/10.3390/app9173469.

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This paper studied the changing pattern of knee cartilage using 3D knee magnetic resonance (MR) images over a 12-month period. As a pilot study, we focused on the medial tibia compartment of the knee joint. To quantify the thickness of cartilage in this compartment, we utilized two methods: one was measurement through manual segmentation of cartilage on each slice of the 3D MR sequence; the other was measurement through cartilage damage index (CDI), which quantified the thickness on a few informative locations on cartilage. We employed the artificial neural networks (ANNs) to model the changing pattern of cartilage thickness. The input feature space was composed of the thickness information at a cartilage location as well as its neighborhood from baseline year data. The output categories were ‘changed’ and ‘no-change’, based on the thickness difference at the same location between the baseline year and the 12-month follow-up data. Different ANN models were trained by using CDI features and manual segmentation features. Further, for each type of feature, individual models were trained at different subregions of the medial tibia compartment, i.e., the bottom part, the middle part, the upper part, and the whole. Based on the experiment results, we found that CDI features generated better prediction performance than manual segmentation, on both whole medial tibia compartment and any subregion. For CDI, the best performance in term of AUC was obtained using the central CDI locations (AUC = 0.766), while the best performance for manual segmentation was obtained using all slices of the 3D MR sequence (AUC = 0.656). As experiment results showed, the CDI method demonstrated a stronger pattern of cartilage change than the manual segmentation method, which required up to 6-hour manual delineation of all MRI slices. The result should be further validated by extending the experiment to other compartments.

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Liu, Fang. "Improving Quantitative Magnetic Resonance Imaging Using Deep Learning." Seminars in Musculoskeletal Radiology 24, no. 04 (August 2020): 451–59. http://dx.doi.org/10.1055/s-0040-1709482.

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AbstractDeep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parametric maps, allowing efficient and accurate T2 and T1ρ relaxometry analysis for monitoring and predicting MSK diseases. Deep learning methods have shown promising results for disease detection on quantitative MRI with diagnostic performance superior to conventional machine-learning methods for identifying knee osteoarthritis.

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Noorveriandi, Henry, Matthew J. Parkes, Michael J. Callaghan, David T. Felson, Terence W. O'Neill, and Richard Hodgson. "Assessment of bone marrow oedema-like lesions using MRI in patellofemoral knee osteoarthritis: comparison of different MRI pulse sequences." British Journal of Radiology 94, no. 1124 (August 1, 2021): 20201367. http://dx.doi.org/10.1259/bjr.20201367.

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Objective: To compare bone marrow oedema-like lesion (BML) volume in subjects with symptomatic patellofemoral (PF) knee osteoarthritis (OA) using four different MRI sequences and to determine reliability of BML volume assessment using these sequences and their correlation with pain. Methods: 76 males and females (mean age 55.8 years) with symptomatic patellofemoral knee OA had 1.5 T MRI scans. PD fat suppressed (FS), STIR, contrast-enhanced (CE) T1W FS, and 3D T1W fast field echo (FFE) sequences were obtained. All sequences were assessed by one reader, including repeat assessment of 15 knees using manual segmentation and the measurements were compared. We used random-effects panel linear regression to look for differences in the log-transformed BML volume (due to positive skew in the BML volume distribution) between sequences and to determine associations between BML volumes and knee pain. Results: 58 subjects had PF BMLs present on at least one sequence. Median BML volume measured using T1W FFE sequence was significantly smaller (224.7 mm3, interquartile range [IQR] 82.50–607.95) than the other three sequences. BML volume was greatest on the CE sequence (1129.8 mm3, IQR 467.28–3166.02). Compared to CE sequence, BML volumes were slightly lower when assessed using PDFS (proportional difference = 0.79; 95% confidence interval [CI] 0.62, 1.01) and STIR sequences (proportional difference = 0.85; 95% CI 0.67, 1.08). There were strong correlations between BML volume on PDFS, STIR, and CE T1W FS sequences (ρs = 0.98). Correlations were lower between these three sequences and T1W FFE (ρs = 0.80–0.81). Intraclass correlation coefficients were excellent for proton density fat-suppressed, short-tau inversion recovery, and CE T1W FS sequences (0.991–0.995), while the ICC for T1W FFE was good at 0.88. We found no significant association between BML volumes assessed using any of the sequences and knee pain. Conclusion: T1W FFE sequences were less reliable and measured considerably smaller BML volume compared to other sequences. BML volume was larger when assessed using the contrast enhanced T1W FS though not statistically significantly different from BMLs when assessed using PDFS and STIR sequences. Advances in knowledge: This is the first study to assess BMLs by four different MRI pulse sequences on the same data set, including different fluid sensitive sequences and gradient echo type sequence.

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Gaj, Sibaji, Mingrui Yang, Kunio Nakamura, and Xiaojuan Li. "Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks." Magnetic Resonance in Medicine 84, no. 1 (December 2, 2019): 437–49. http://dx.doi.org/10.1002/mrm.28111.

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Zhang, Ping, Baohai Yu, Ranxu Zhang, Xiaoshuai Chen, Shuying Shao, Yan Zeng, Jianling Cui, and Jian Zhao. "Longitudinal study of the morphological and T2* changes of knee cartilages of marathon runners using prototype software for automatic cartilage segmentation." British Journal of Radiology 94, no. 1119 (March 1, 2021): 20200833. http://dx.doi.org/10.1259/bjr.20200833.

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Objective: To study the effect of long-distance running on the morphological and T2* assessment of knee cartilage. Methods: 3D-DESS and T2* mapping was performed in 12 amateur marathon runners (age: between 21 and 37 years) without obvious morphological cartilage damage. MRI was performed three times: within 24 h before the marathon, within 12 h after the marathon, and after a period of convalescence of two months. An automatic cartilage segmentation method was used to quantitatively assessed the morphological and T2* of knee cartilage pre- and post-marathon. The cartilage thickness, volume, and T2* values of 21 sub-regions were quantitatively assessed, respectively. Results: The femoral lateral central (FLC) cartilage thickness was increased when 12-h post-marathon compared with pre-marathon. The tibial medial anterior (TMA) cartilage thickness was decreased when 2 months post-marathon compared with pre-marathon. The tibial lateral posterior (TLP) cartilage volume was increased when 12-h post-marathon compared with pre-marathon. The cartilage T2* value in most sub-regions had the upward trend when 12-h post-marathon and restored trend when 2 months post-marathon, compared with pre-marathon. The femoral lateral anterior (FLA) and TMA cartilage volumes were decreased 2 months post-marathon compared with pre-marathon. Conclusions: The marathon had some effects on the thickness, volume, and T2* value of the knee cartilages. The thickness and volume of knee cartilage in most sub-regions were without significantly changes post-marathon compared with pre-marathon. T2* value of knee cartilage in most sub-regions was increased right after marathon and recovered 2 months later. The TLP and TMA subregions needed follow-up after marathon. Advances in knowledge: The morphological and T2* changes of knee cartilage after marathon were evaluated by MRI and automatic segmentation software. This study was the first to use cartilage automatic segmentation software to evaluate the effects of marathon on the morphology and biochemical components of articular cartilage, and to predict the most vulnerable articular cartilage subregions, for the convenience of future exercise adjustment and the avoidance of sports cartilage injury.

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Kashyap, S., H. Zhang, and M. Sonka. "Just Enough Interaction for Fast Minimally Interactive Correction of 4D Segmentation of Knee MRI." Osteoarthritis and Cartilage 25 (April 2017): S224—S225. http://dx.doi.org/10.1016/j.joca.2017.02.388.

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Dodin, Pierre, Johanne Martel-Pelletier, Jean-Pierre Pelletier, and François Abram. "A fully automated human knee 3D MRI bone segmentation using the ray casting technique." Medical & Biological Engineering & Computing 49, no. 12 (October 29, 2011): 1413–24. http://dx.doi.org/10.1007/s11517-011-0838-8.

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Niu, Junlong, Xiansheng Qin, Jing Bai, and Haiyan Li. "Reconstruction and optimization of the 3D geometric anatomy structure model for subject-specific human knee joint based on CT and MRI images." Technology and Health Care 29 (March 25, 2021): 221–38. http://dx.doi.org/10.3233/thc-218022.

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BACKGROUND: Nowadays, the total knee arthroplasty (TKA) technique plays an important role in surgical treatment for patients with severe knee osteoarthritis (OA). However, there are still several key issues such as promotion of osteotomy accuracy and prosthesis matching degree that need to be addressed. OBJECTIVE: It is significant to construct an accurate three-dimensional (3D) geometric anatomy structure model of subject-specific human knee joint with major bone and soft tissue structures, which greatly contributes to obtaining personalized osteotomy guide plate and suitable size of prosthesis. METHODS: Considering different soft tissue structures, magnetic resonance imaging (MRI) scanning sequences involving two-dimensional (2D) spin echo (SE) sequence T1 weighted image (T1WI) and 3D SE sequence T2 weighted image (T2WI) fat suppression (FS) are selected. A 3D modeling methodology based on computed tomography (CT) and two sets of MRI images is proposed. RESULTS: According to the proposed methods of image segmentation and 3D model registration, a novel 3D knee joint model with high accuracy is finally constructed. Furthermore, remeshing is used to optimize the established model by adjusting the relevant parameters. CONCLUSIONS: The modeling results demonstrate that reconstruction and optimization model of 3D knee joint can clearly and accurately reflect the key characteristics, including anatomical structure and geometric morphology for each component.

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Hotfiel, Thilo, Svenja Höger, Armin M. Nagel, Michael Uder, Wolfgang Kemmler, Raimund Forst, Martin Engelhardt, Casper Grim, and Rafael Heiss. "Multi-Parametric Analysis of Below-Knee Compression Garments on Delayed-Onset Muscle Soreness." International Journal of Environmental Research and Public Health 18, no. 7 (April 6, 2021): 3798. http://dx.doi.org/10.3390/ijerph18073798.

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To investigate below-knee compression garments during exercise and a post-exercise period of 6 h on clinical, functional, and morphological outcomes in delayed-onset muscle soreness (DOMS). Eighteen volunteers (age: 24.1 ± 3.6, BMI 22.7 ± 2.7 kg/m2) were enrolled. Measures were acquired at baseline, 6 h, and 48 h after eccentric and plyometric exercise, with wearing a compression garment (21–22 mmHg) on a calf during and for the first 6 h after exercise. 3T MRI was performed for quantification of intramuscular edema (T2 signal intensity (SI), T2 time, and manual volume segmentation); jump height, calf circumference, ankle dorsiflexion (DF), creatine kinase (CK), and muscle soreness were assessed. DOMS was confirmed in all participants after 48 h, with an increase in soreness (p < 0.001) and CK (p = 0.001), decrease in jump height (p < 0.01), and the presence of intramuscular edema (p < 0.01) in both the compressed and non-compressed limbs. No differences between the compressed and non-compressed limbs were observed for muscle soreness and jump height. MRI T2 SI, T2 time, soreness, and manual segmentation revealed no effect of the compression treatment. The assessment of calf circumference and DF showed no changes in either the compression or non-compression limb (p = 1.0). Wearing compression garments during combined eccentric and plyometric exercise and for 6 h post-exercise has no effect on clinical signs of DOMS, jump performance, or the development of intramuscular edema.

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Panfilov, E., A. Tiulpin, M. Juntunen, V. Casula, M. Nieminen, and S. Saarakkala. "Automatic knee cartilage and menisci segmentation from 3D-DESS MRI using deep semi-supervised learning." Osteoarthritis and Cartilage 27 (April 2019): S390—S391. http://dx.doi.org/10.1016/j.joca.2019.02.391.

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Chen, Hao, André M. J. Sprengers, Yan Kang, and Nico Verdonschot. "Automated segmentation of trabecular and cortical bone from proton density weighted MRI of the knee." Medical & Biological Engineering & Computing 57, no. 5 (December 5, 2018): 1015–27. http://dx.doi.org/10.1007/s11517-018-1936-7.

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Burton, William, Casey Myers, and Paul Rullkoetter. "Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks." Computer Methods and Programs in Biomedicine 189 (June 2020): 105328. http://dx.doi.org/10.1016/j.cmpb.2020.105328.

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Said, Oliver, Justus Schock, Daniel Benjamin Abrar, Philipp Schad, Christiane Kuhl, Teresa Nolte, Matthias Knobe, Andreas Prescher, Daniel Truhn, and Sven Nebelung. "In-Situ Cartilage Functionality Assessment Based on Advanced MRI Techniques and Precise Compartmental Knee Joint Loading through Varus and Valgus Stress." Diagnostics 11, no. 8 (August 14, 2021): 1476. http://dx.doi.org/10.3390/diagnostics11081476.

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Stress MRI brings together mechanical loading and MRI in the functional assessment of cartilage and meniscus, yet lacks basic scientific validation. This study assessed the response-to-loading patterns of cartilage and meniscus incurred by standardized compartmental varus and valgus loading of the human knee joint. Eight human cadaveric knee joints underwent imaging by morphologic (i.e., proton density-weighted fat-saturated and 3D water-selective) and quantitative (i.e., T1ρ and T2 mapping) sequences, both unloaded and loaded to 73.5 N, 147.1 N, and 220.6 N of compartmental pressurization. After manual segmentation of cartilage and meniscus, morphometric measures and T2 and T1ρ relaxation times were quantified. CT-based analysis of joint alignment and histologic and biomechanical tissue measures served as references. Under loading, we observed significant decreases in cartilage thickness (p < 0.001 (repeated measures ANOVA)) and T1ρ relaxation times (p = 0.001; medial meniscus, lateral tibia; (Friedman test)), significant increases in T2 relaxation times (p ≤ 0.004; medial femur, lateral tibia; (Friedman test)), and adaptive joint motion. In conclusion, varus and valgus stress MRI induces meaningful changes in cartilage and meniscus secondary to compartmental loading that may be assessed by cartilage morphometric measures as well as T2 and T1ρ mapping as imaging surrogates of tissue functionality.

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Lee, Han Sang, and Helen Hong. "Anterior Cruciate Ligament Segmentation in Knee MRI with Locally-aligned Probabilistic Atlas and Iterative Graph Cuts." Journal of KIISE 42, no. 10 (October 15, 2015): 1222–30. http://dx.doi.org/10.5626/jok.2015.42.10.1222.

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Zhou, Zhaoye, Gengyan Zhao, Richard Kijowski, and Fang Liu. "Deep convolutional neural network for segmentation of knee joint anatomy." Magnetic Resonance in Medicine 80, no. 6 (May 17, 2018): 2759–70. http://dx.doi.org/10.1002/mrm.27229.

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Meng, Qingen, John Fisher, and Ruth Wilcox. "The effects of geometric uncertainties on computational modelling of knee biomechanics." Royal Society Open Science 4, no. 8 (August 2017): 170670. http://dx.doi.org/10.1098/rsos.170670.

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The geometry of the articular components of the knee is an important factor in predicting joint mechanics in computational models. There are a number of uncertainties in the definition of the geometry of cartilage and meniscus, and evaluating the effects of these uncertainties is fundamental to understanding the level of reliability of the models. In this study, the sensitivity of knee mechanics to geometric uncertainties was investigated by comparing polynomial-based and image-based knee models and varying the size of meniscus. The results suggested that the geometric uncertainties in cartilage and meniscus resulting from the resolution of MRI and the accuracy of segmentation caused considerable effects on the predicted knee mechanics. Moreover, even if the mathematical geometric descriptors can be very close to the imaged-based articular surfaces, the detailed contact pressure distribution produced by the mathematical geometric descriptors was not the same as that of the image-based model. However, the trends predicted by the models based on mathematical geometric descriptors were similar to those of the imaged-based models.

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Parker, David A., Samuel Grasso, Corey Scholes, Brett Fritsch, and Qing Li. "Quantitative MRI Evaluation of Tunnel placement in ACL Reconstruction." Orthopaedic Journal of Sports Medicine 5, no. 5_suppl5 (May 1, 2017): 2325967117S0018. http://dx.doi.org/10.1177/2325967117s00180.

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Introduction: Positioning of the graft ACL in the native footprint center is important to replicate the anatomy and function of the ACL for each individual patient. It is known that incorrect bone tunnel placement for the reconstructed ligament is a contributor to poor clinical outcomes postoperatively. Currently the success of tunnel placement is determined by training and experience of the treating surgeon and there is no universally accepted quantifiable and objective method to evaluate the execution of these decisions. The goal of this project was to develop a quantitative routine assessment to assist pre-surgical planning and also evaluate the execution of femoral and tibial bone tunnel placement in ACL reconstructed knees. Methods: The study recruited failed primary ACL reconstructed patients (N=25) who consented to undergo revision ACL reconstruction to establish the placement of the graft ACL tunnel apertures in the femur and tibia. Prior to surgery each participant underwent high resolution 3 T MRI of their injured knee and 3D models were generated through segmentation of soft and hard tissue knee structures. During surgery previous graft tunnels and prominent reference landmarks visible on MRI and arthroscopically were registered using intraoperative navigation to act as the reference standard. The placement of the tunnel apertures in the femur and tibia were measured in all three planes using a novel measurement method. Results: Preliminary result show that the measurement method can assess the placement of tunnel apertures in the femur and tibia within 0.1 – 1.0 mm of the intraoperative data, using reference landmarks identifiable in MRI and arthroscopically. Additionally, the area of the tunnel aperture, bone tunnel volume can be evaluated. Reliability and validation of the novel method is ongoing using medical imaging and intraoperative navigation to register the placement of bone tunnels in revision ACL reconstruction patients. Conclusions: Correct placement of graft ACL bone tunnels inside the native ACL footprint is critical to the outcome of ACL reconstruction. Development of an accurate reproducible method for assessment of tunnel placement relative to the anatomical footprint should provide a simple method for objectively assessing ACL reconstructions. Preliminary results of this routine assessment suggests that graft tunnel placement can be objectively assessed to assist clinicians to evaluate and improve ACL reconstruction technique and evaluation of ACL reconstruction outcomes.

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Fotinos‐Hoyer, Amber Kassel, Ali Guermazi, Hernán Jara, Felix Eckstein, Al Ozonoff, Hussain Khard, Alexander Norbash, Klaus Bohndorf, and Frank W. Roemer. "Assessment of synovitis in the osteoarthritic knee: Comparison between manual segmentation, semiautomated segmentation, and semiquantitative assessment using contrast‐enhanced fat‐suppressed T 1 ‐weighted MRI." Magnetic Resonance in Medicine 64, no. 2 (May 25, 2010): 604–9. http://dx.doi.org/10.1002/mrm.22401.

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Hunter, D. J., J. Niu, Y. Zhang, S. Totterman, J. Tamez, C. Dabrowski, R. Davies, et al. "Change in cartilage morphometry: a sample of the progression cohort of the Osteoarthritis Initiative." Annals of the Rheumatic Diseases 68, no. 3 (April 13, 2008): 349–56. http://dx.doi.org/10.1136/ard.2007.082107.

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Objective:The performance characteristics of hyaline articular cartilage measurement on magnetic resonance imaging (MRI) need to be accurately delineated before widespread application of this technology. Our objective was to assess the rate of natural disease progression of cartilage morphometry measures from baseline to 1 year in knees with osteoarthritis (OA) from a subset of participants from the Osteoarthritis Initiative (OAI).Methods:Subjects included for this exploratory analysis are a subset of the approximately 4700 participants in the OAI Study. Bilateral radiographs and 3T MRI (Siemans Trio) of the knees and clinical data were obtained at baseline and annually in all participants. 160 subjects from the OAI Progression subcohort all of whom had both frequent symptoms and, in the same knee, radiographic OA based on a screening reading done at the OAI clinics were eligible for this exploratory analysis. One knee from each subject was selected for analysis. 150 participants were included. Using sagittal 3D DESSwe (double echo, steady-state sequence with water excitation) MR images from the baseline and 12 follow-up month visit, a segmentation algorithm was applied to the cartilage plates of the index knee to compute the cartilage volume, normalised cartilage volume (volume normalised to bone surface interface area), and percentage denuded area (total cartilage bone interface area denuded of cartilage).Results:Summary statistics of the changes (absolute and percentage) from baseline at 1 year and the standardised response mean (SRM), ie, mean change divided by the SD change were calculated. On average the subjects were 60.9 years of age and obese, with a mean body mass index of 30.3 kg/m2. The SRMs for cartilage volume of various locations are: central medial tibia −0.096; central medial femur −0.394; and patella −0.198. The SRMs for normalised cartilage volume of the various locations are central medial tibia −0.044, central medial femur −0.338 and patella −0.193. The majority of participants had a denuded area at baseline in the central medial femur (62%) and central medial tibia (60%). In general, the SRMs were small.Conclusions: These descriptive results of cartilage morphometry and its change at the 1-year time point from the first substantive MRI data release from the OAI Progression subcohort indicate that the annualised rates of change are small with the central medial femur showing the greatest consistent change.

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Ciba, Malin, Eva-Maria Winkelmeyer, Justus Schock, Philipp Schad, Niklas Kotowski, Teresa Nolte, Lena Marie Wollschläger, et al. "Comprehensive Assessment of Medial Knee Joint Instability by Valgus Stress MRI." Diagnostics 11, no. 8 (August 9, 2021): 1433. http://dx.doi.org/10.3390/diagnostics11081433.

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Standard clinical MRI techniques provide morphologic insights into knee joint pathologies, yet do not allow evaluation of ligament functionality or joint instability. We aimed to study valgus stress MRI, combined with sophisticated image post-processing, in a graded model of medial knee joint injury. To this end, eleven human cadaveric knee joint specimens were subjected to sequential injuries to the superficial medial collateral ligament (sMCL) and the anterior cruciate ligament (ACL). Specimens were imaged in 30° of flexion in the unloaded and loaded configurations (15 kp) and in the intact, partially sMCL-deficient, completely sMCL-deficient, and sMCL- and ACL-deficient conditions using morphologic sequences and a dedicated pressure-controlled loading device. Based on manual segmentations, sophisticated 3D joint models were generated to compute subchondral cortical distances for each condition and configuration. Statistical analysis included appropriate parametric tests. The medial compartment opened gradually as a function of loading and injury, especially anteriorly. Corresponding manual reference measurements by two readers confirmed these findings. Once validated in clinical trials, valgus stress MRI may comprehensively quantify medial compartment opening as a functional imaging surrogate of medial knee joint instability and qualify as an adjunct diagnostic tool in the differential diagnosis, therapeutic decision-making, and monitoring of treatment outcomes.

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Owusu-Akyaw, Kwadwo A., Sophia Y. Kim, Charles E. Spritzer, Amber T. Collins, Zoë A. Englander, Gangadhar M. Utturkar, William E. Garrett, and Louis E. DeFrate. "Determination of the Position of the Knee at the Time of an Anterior Cruciate Ligament Rupture for Male Versus Female Patients by an Analysis of Bone Bruises." American Journal of Sports Medicine 46, no. 7 (April 18, 2018): 1559–65. http://dx.doi.org/10.1177/0363546518764681.

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Background: The incidence of anterior cruciate ligament (ACL) ruptures is 2 to 4 times higher in female athletes as compared with their male counterparts. As a result, a number of recent studies have addressed the hypothesis that female and male patients sustain ACL injuries via different mechanisms. The efficacy of prevention programs may be improved by a better understanding of whether there are differences in the injury mechanism between sexes. Hypothesis/Purpose: To compare knee positions at the time of a noncontact ACL injury between sexes. It was hypothesized that there would be no differences in the position of injury. Study Design: Controlled laboratory study. Methods: Clinical T2-weighted magnetic resonance imaging (MRI) scans from 30 participants (15 male and 15 female) with a noncontact ACL rupture were reviewed retrospectively. MRI scans were obtained within 1 month of injury. Participants had contusions associated with an ACL injury on both the medial and lateral articular surfaces of the femur and tibia. Three-dimensional models of the femur, tibia, and associated bone bruises were created via segmentation on MRI. The femur was positioned relative to the tibia to maximize bone bruise overlap, thereby predicting the bone positions near the time of the injury. Flexion, valgus, internal tibial rotation, and anterior tibial translation were measured in the predicted position of injury. Results: No statistically significant differences between male and female patients were detected in the position of injury with regard to knee flexion ( P = .66), valgus ( P = .87), internal tibial rotation ( P = .26), or anterior tibial translation ( P = .18). Conclusion: These findings suggest that a similar mechanism results in an ACL rupture in both male and female athletes with this pattern of bone bruising. Clinical Relevance: This study provides a novel comparison of male and female knee positions at the time of an ACL injury that may offer information to improve injury prevention strategies.

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Berta, Agnes, Matthew S. Shive, Andrew K. Lynn, Alan Getgood, Saara Totterman, Grahame Busby, Jerome Hollenstein, Gábor Vásárhelyi, Imre Kéki, and László Hangody. "Follow-Up Study Evaluating the Long Term Outcome of ChondroMimetic in the Treatment of Osteochondral Defects in the Knee." Applied Sciences 10, no. 16 (August 14, 2020): 5642. http://dx.doi.org/10.3390/app10165642.

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Scaffolds are thought to be a key element needed for successful cartilage repair treatments, and this prospective extension study aimed to evaluate long-term structural and clinical outcomes following osteochondral defect treatment with a cell-free biphasic scaffold. Structural outcomes were assessed using quantitative 3-D magnetic resonance imaging (MRI) and morphological segmentation to determine the percentage of defect filling and repair cartilage T2 relaxation times, and clinical outcomes were determined with the modified Cincinnati Rating System, and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Seventeen subjects with osteochondral defects in the knee were treated with ChondroMimetic scaffolds, from which 15 returned for long-term evaluation at a mean follow-up of 7.9 ± 0.3 years. The defects treated were trochlear donor sites for mosaicplasty in 13 subjects, and medial femoral condyle defects in 2 subjects. MRI analysis of scaffold-treated defects found a mean total defect filling of 95.2 ± 3.6%, and a tissue mean T2 relaxation time of 52.5 ± 4.8 ms, which was identical to the T2 of ipsilateral control cartilage (52.3 ± 9.2 ms). The overall modified Cincinnati Rating System score was statistically significant from baseline (p = 0.0065), and KOOS subscales were equivalent to other cartilage repair techniques. ChondroMimetic treatment resulted in a consistently high degree of osteochondral defect filling with durable, cartilage-like repair tissue at 7.9 years, potentially associated with clinical improvement.

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Winkelmeyer, Eva-Maria, Justus Schock, Lena Marie Wollschläger, Philipp Schad, Marc Sebastian Huppertz, Niklas Kotowski, Andreas Prescher, Christiane Kuhl, Daniel Truhn, and Sven Nebelung. "Seeing Beyond Morphology-Standardized Stress MRI to Assess Human Knee Joint Instability." Diagnostics 11, no. 6 (June 4, 2021): 1035. http://dx.doi.org/10.3390/diagnostics11061035.

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While providing the reference imaging modality for joint pathologies, MRI is focused on morphology and static configurations, thereby not fully exploiting the modality’s diagnostic capabilities. This study aimed to assess the diagnostic value of stress MRI combining imaging and loading in differentiating partial versus complete anterior cruciate ligament (ACL)-injury. Ten human cadaveric knee joint specimens were subjected to serial imaging using a 3.0T MRI scanner and a custom-made pressure-controlled loading device. Emulating the anterior-drawer test, joints were imaged before and after arthroscopic partial and complete ACL transection in the unloaded and loaded configurations using morphologic sequences. Following manual segmentations and registration of anatomic landmarks, two 3D vectors were computed between anatomic landmarks and registered coordinates. Loading-induced changes were quantified as vector lengths, angles, and projections on the x-, y-, and z-axis, related to the intact unloaded configuration, and referenced to manual measurements. Vector lengths and projections significantly increased with loading and increasing ACL injury and indicated multidimensional changes. Manual measurements confirmed gradually increasing anterior tibial translation. Beyond imaging of ligament structure and functionality, stress MRI techniques can quantify joint stability to differentiate partial and complete ACL injury and, possibly, compare surgical procedures and monitor treatment outcomes.

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PIANIGIANI, SILVIA, MARTA D'AIUTO, DAVIDE CROCE, and BERNARDO INNOCENTI. "ARE MRIs NECESSARY TO DEVELOP SUBJECT-SPECIFIC CARTILAGE AND MENISCI GEOMETRIES FOR SUBJECT-SPECIFIC KNEE MODELS?" Journal of Mechanics in Medicine and Biology 17, no. 03 (October 7, 2016): 1750049. http://dx.doi.org/10.1142/s021951941750049x.

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Native subject-specific knee geometries are usually based on CT and MRI images reconstruction. Unfortunately, while the definition of bone geometries using CTs is quite consistent, MRIs are often hardly readable, due to the usual lower resolution, and the final shape of cartilage and menisci is not consequently detailed enough. Moreover, further smoothing techniques, necessary to efficiently use these structures for numerical modeling, could result in bad interfaces and/or geometry inaccuracies. In this study a CAD-based approach to generate 3D cartilages and menisci geometries, avoiding the use of MRIs, was proposed and tested versus the traditional methods that use MRIs segmentation. The femoral, tibial and patellar cartilage layers were generated as offset from the bone geometries, the menisci were obtained by an extrusion based on tibia borders. Such geometries were compared to the reconstructions obtained from MRIs of healthy knee specimens. Overlapping the resulting geometries with the ones traditionally reconstructed, volumes differ from 2% to 14%. By using the new methodology, the geometries are obtained in 75% less time. The CAD-based methods shown in this pilot study is able to generate faster and accurate subject-specific knee cartilage layers and menisci geometries and can be suitable to be applied for numerical modeling.

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Kubicek, Penhaker, Augustynek, Cerny, and Oczka. "Segmentation of Articular Cartilage and Early Osteoarthritis based on the Fuzzy Soft Thresholding Approach Driven by Modified Evolutionary ABC Optimization and Local Statistical Aggregation." Symmetry 11, no. 7 (July 2, 2019): 861. http://dx.doi.org/10.3390/sym11070861.

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Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel’s classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel’s membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.

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Eckstein, F., S. Maschek, W. Wirth, M. Hudelmaier, W. Hitzl, B. Wyman, M. Nevitt, and M.-P. Hellio Le Graverand. "One year change of knee cartilage morphology in the first release of participants from the Osteoarthritis Initiative progression subcohort: association with sex, body mass index, symptoms and radiographic osteoarthritis status." Annals of the Rheumatic Diseases 68, no. 5 (June 2, 2008): 674–79. http://dx.doi.org/10.1136/ard.2008.089904.

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Objective:The Osteoarthritis Initiative (OAI) is a multicentre study targeted at identifying biomarkers for evaluating the progression and risk factors of symptomatic knee OA. Here cartilage loss using 3 Tesla (3 T) MRI is analysed over 1 year in a subset of the OAI, together with its association with various risk factors.Methods:An age- and gender-stratified subsample of the OAI progression subcohort (79 women and 77 men, mean (SD) age 60.9 (9.9) years, body mass index (BMI) 30.3 (4.7)) with both frequent symptoms and radiographic OA in at least one knee was studied. Coronal FLASHwe (fast low angle shot with water excitation) MRIs of the right knee were acquired at 3 T. Seven readers segmented tibial and femoral cartilages blinded to order of acquisition. Segmentations were quality controlled by one expert.Results:The reduction in mean cartilage thickness (ThC) was greater (p = 0.004) in the medial than in the lateral compartment, greater (p = 0.001) in the medial femur (−1.9%) than in the medial tibia (−0.5%) and greater (p = 0.011) in the lateral tibia (−0.7%) than in the lateral femur (0.1%). Multifactorial analysis of variance did not reveal significant differences in the rate of change in ThC by sex, BMI, symptoms and radiographic knee OA status. Knees with Kellgren–Lawrence grade 2 or 3 and with a BMI >30 tended to display greater changes.Conclusions:In this sample of the OAI progression subcohort, the greatest, but overall very modest, rate of cartilage loss was observed in the weight-bearing medial femoral condyle. Knees with radiographic OA in obese participants showed trends towards higher rates of change than those of other participants, but these trends did not reach statistical significance.

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Desai, Arjun D., Francesco Caliva, Claudia Iriondo, Aliasghar Mortazi, Sachin Jambawalikar, Ulas Bagci, Mathias Perslev, et al. "The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset." Radiology: Artificial Intelligence 3, no. 3 (May 1, 2021): e200078. http://dx.doi.org/10.1148/ryai.2021200078.

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Farber, J. M., J. Tamez-Pena, S. Totterman, J. Larkin, B. Holladay, and F. Heis. "Pre-operative evaluation of patients undergoing knee articular cartilage repair: MRI 3D thickness maps derived from a validated, automated segmentation platform - initial results." Osteoarthritis and Cartilage 21 (April 2013): S202. http://dx.doi.org/10.1016/j.joca.2013.02.422.

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Kashyap, Satyananda, Honghai Zhang, Karan Rao, and Milan Sonka. "Learning-Based Cost Functions for 3-D and 4-D Multi-Surface Multi-Object Segmentation of Knee MRI: Data From the Osteoarthritis Initiative." IEEE Transactions on Medical Imaging 37, no. 5 (May 2018): 1103–13. http://dx.doi.org/10.1109/tmi.2017.2781541.

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Zheng, Hai Dong, Rong Ying Huang, Hong Guang Zheng, and Yun Fei Guo. "The Effects of Bony Structure Simplification Methods on the Biomechanics of Tibiofemoral Joint in Series of Flexion Angles." Applied Mechanics and Materials 163 (April 2012): 70–73. http://dx.doi.org/10.4028/www.scientific.net/amm.163.70.

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To investigate the effects of bony structure simplification methods on the biomechanics of tibiofemoral joint under compression and torsion effects in series of flexion angles, the MRI images of the normal human knee at flexion angles of 0°/25°/60°/80° were developed through SONATA MAESTRO 1.5T, then through the technology of threshold segmentation and registration assembly, bone tissue distinguished models and single material models were built based on these images. The results show that: (1) at the flexion angles of 0°/60°/80°, the maximum equivalent stress on femur and femoral cartilage significantly were slightly higher than single material models, only at 25 °, the maximum equivalent stress of the femoral cartilage in single material model was obviously larger; (2) difference of maximum equivalent stress on tibia and tibial cartilage between two kinds of models was not significant, and stress increased with the increase of flexion angle, only at 80 °, the stress on tibia of bone tissue distinguished models reduced; (3) The skeletal load was borne mainly by the cortical bone.

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Jansen, M., S. Mastbergen, T. D. Turmezei, J. W. Mackay, and F. Lafeber. "POS1091 KNEE JOINT DISTRACTION RESULTS IN MRI CARTILAGE THICKNESS INCREASE UP TO TEN YEARS AFTER TREATMENT." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 825.1–825. http://dx.doi.org/10.1136/annrheumdis-2021-eular.1321.

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Background:Knee joint distraction (KJD) is a joint-preserving treatment option for younger (age <65 years) knee osteoarthritis (OA) patients. It has shown clinical improvement for up to nine years after treatment. Radiographs and MRI scans have previously shown cartilage regeneration activity, especially in the first two years after treatment. However, MRIs have not been evaluated more than five years after this treatment.Objectives:To evaluate MRI cartilage thickness up to ten years after KJD treatment.Methods:Patients (n=20) with end-stage knee OA, indicated for total knee arthroplasty (TKA) but <60 years old, were treated with KJD. 3T MRIs with 3D spoiled gradient recalled imaging sequence with fat suppression (SPGR-fs) were acquired before and one, two, five, seven and ten years after surgical treatment. Stradview v6.0 was used for semi-automatic cartilage segmentation; wxRegSurf v18 was used for surface registration. MATLAB R2020a and the SurfStat MATLAB package were used for data analysis and visualization. For changes over time, linear mixed models were used. Two separate linear regression models were used to show the influence of baseline Kellgren-Lawrence grade and sex on the changes over time. Statistical significance was calculated with statistical parametric mapping; a p-value <0.05 was considered statistically significant. Since KJD has previously shown significant results mostly in the patients’ most affected compartment (MAC), patients were separated in two groups based on whether their MAC was the medial or lateral compartment.Results:The MAC was predominantly the medial side (medial MAC n=18; lateral n=2). The 18 patients with a medial MAC all had MRI scans at baseline, one and two years after treatment. After two years, some patients were lost to follow-up, decreasing data availability at five (n=15), seven (n=11) and ten years (n=7). Figure 1 (top) shows the average cartilage thickness at the different time points for all medial MAC patients together. One and two years after treatment the cartilage in the medial weight-bearing region was on average thicker than before treatment. While from five years after treatment the cartilage thickness gradually decreased, even at ten years the medial cartilage thickness seemed slightly higher than pre-treatment. Figure 1 (bottom) shows cartilage thickness changes compared to baseline for patients with a medial MAC. Patients with a lateral MAC showed a similar pattern, with the biggest changes showing on the lateral side. As indicated by the dark blue areas, the medial femoral cartilage thickness increase, which was up to 0.5 mm after one year and 0.6 mm after two years, was largely statistically significant at both these time points. While the medial tibia showed an increase of up to 0.5 mm at these time points as well, this was not statistically significant at two years. Surprisingly, long-term results showed areas of the lateral (less affected) compartment were significantly thicker, up to 0.7 mm, compared to pre-treatment in both the femur and tibia compared to baseline. Kellgren-Lawrence grade and sex were shown to influence the changes, albeit not statistically significantly. Patients with a higher Kellgren-Lawrence grade and male sex showed a higher short-term (one and two year) but a lower long-term (seven and ten year) cartilage thickness increase.Conclusion:KJD treatment results in significant short-term cartilage regeneration in the most affected compartment. While after two years this initial gain in cartilage thickness is gradually lost, likely as a result of natural progression, even ten years after treatment the cartilage is thicker than before treatment. In the less affected compartment, a delayed cartilage response seems to take place, with significantly increased cartilage thickness in the long term. In conclusion, in these young OA patients indicated for TKA, KJD results in femoral and tibial cartilaginous tissue regeneration both short- and long-term and in both sides of the joint.Disclosure of Interests:None declared.

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Kabalyk, M. A. "Opportunities of magnetic resonance imaging in diagnosis of microstructural changes of articular cartilage in osteoarthritis." Perm Medical Journal 35, no. 3 (December 15, 2018): 15–23. http://dx.doi.org/10.17816/pmj35315-23.

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Aim. To estimate the opportunities of proton density-weighed magnetic resonance tomograms in diagnosis of microstructural changes of articular cartilage (AC) in osteoarthritis (OA) on the basis of proton density (PD) variability analyzed. Materials and methods. Sixty two patients with OA and 8 volunteers without OA were examined. All the patients underwent MRI of the knee joints, using tomograph with magnetic field intensity equal to 1.5 tesla. To assess MR images, semiquantitative measurements of articular tissues on the basis of WORMS protocols were used. To estimate the proton density, manual segmentation of PDFS-weighed images of the knee joint medial condyle was implemented. The proton density was estimated, applying 3-D histogram (0–255). Results. At stage I of osteoarthritis, fall in density H+ in the peripheral zone of AC was observed, but it was preserved in the contact part, exposed to maximum statodynamic loadings. At stage II, significant progressing decrease in H+ density peaks in the AC regions, subjected to lesser loads, with preservation of high spectral peaks in the region of elevated friction was stated. Stage III of gonarthrosis was characterized by decrease in H+-spectra as a whole, especially in the loading regions of AC. At stage IV of OA, global reduction in PD intensity was observed along the whole cartilaginous plate surface. Conclusions. The detected patterns of changes in proton density spectra reflect the known degenerative process in AC with osteoarthritis. This property of proton-weight MR-images can be used for assessment of microstructural changes in the articular cartilage with OA.

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Wirth, W., A. S. Chaudhari, J. Kemnitz, C. F. Baumgartner, E. Konukoglu, D. Fürst, and F. Eckstein. "Agreement and accuracy of femorotibial cartilage morphometry in radiographic knee OA using different training sets for automateddeep learning segmentation - comparison between flash and dess MRI." Osteoarthritis and Cartilage 29 (April 2021): S334. http://dx.doi.org/10.1016/j.joca.2021.02.435.

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Trattnig, S., C. Scotti, D. Laurent, V. Juras, S. Hacker, B. Cole, L. Pasa, et al. "POS0277 ANABOLIC EFFECT OF LNA043, A NOVEL DISEASE-MODIFYING OSTEOARTHRITIS DRUG CANDIDATE: RESULTS FROM AN IMAGING-BASED PROOF-OF-CONCEPT TRIAL IN PATIENTS WITH FOCAL ARTICULAR CARTILAGE LESIONS." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 363.2–363. http://dx.doi.org/10.1136/annrheumdis-2021-eular.447.

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Background:LNA043 is a modified, recombinant version of the human angiopoietin-like 3 (ANGPTL3) protein acting directly on cartilage-resident cells to transmit its cartilage anabolic effect. A first-in-human study previously demonstrated the favourable safety profile and the modulation of several pathways involved in cartilage homeostasis and osteoarthritis (OA)1. A previous proof-of-mechanism imaging study used high field (7 Tesla) magnetic resonance imaging (MRI) to show formation of hyaline-like tissue after a single injection of 20 mg LNA043 (unpublished data).Objectives:To evaluate non-invasively the chondro-regenerative capacity of multiple intra-articular (i.a.) injections of LNA043 in patients with articular cartilage lesions in the knee (NCT03275064).Methods:This was a randomised, double-blind, placebo (PBO)-controlled, proof-of-concept study in patients with a partial thickness cartilage lesion. In total, 58 patients (43 [20 mg LNA043]; 15 [PBO]), stratified by lesion type (condylar or patellar) were treated with 4 weekly i.a. injections. The primary endpoint was T2 relaxation time measurement as a marker of collagen fiber network, and cartilage lesion-volume was a secondary endpoint, both using 3-Tesla MRI. Assessments were performed at baseline, weeks (wks) 8, 16, 28 and 52 (the latter in 23/58 patients). While lesion-volume for the secondary endpoint was determined from manually segmented images, the cartilage volume of 21 sub-regions spanning the entire knee was also measured from 3D isotropic MR images employing an automated segmentation prototype software (MR Chondral Health 2.1 [MRCH], Siemens Healthcare)2. An exploratory analysis evaluated the treatment effect for the additive volume of the 3 subregions in the weight-bearing area of the medial femur.Results:No change in T2 relaxation time was detected between treatment and PBO groups. Manual segmentation showed continuous filling of the cartilage lesions up to wk 28 in LNA043-treated patients with femoral lesions (p=0.08, vs PBO) while no effect was detected for patients with patellar lesions. Given the limitations of measuring small, irregularly shaped lesions with manual image-analysis, the MRCH approach was used (Figure 1). In the medial femoral weight-bearing region, refilling was detected over time (Δ=123 mm3 at wk 28, N= 37, p= 0.05). No overgrowth was detected in the lateral femoral condyles without cartilage damage. The overall safety profile was favourable; only mild/moderate local reactions were reported, including a higher incidence of joint swelling (9.3% vs 0%) and arthralgia (11.6% vs 6.7%) for LNA043 vs PBO resolving spontaneously or with paracetamol/NSAIDs. No anti-drug antibodies were detected.Conclusion:Treatment with 4 weekly i.a. injections of 20 mg LNA043 resulted in regeneration of damaged cartilage in patients with femoral articular cartilage lesions. Automated measurement of cartilage volume in the femoral index region was able to detect a relevant treatment effect and was found to be more sensitive than the manual segmentation method. No sign of cartilage overgrowth was observed in healthy femoral regions. A Phase 2b study in patients with mild to moderate knee OA is in preparation.References:[1]Scotti et al. ACR Convergence 2020; Abstract #1483[2]Juras et al. Cartilage 2020; Sep 29:1-12Disclosure of Interests:Siegfried Trattnig: None declared, Celeste Scotti Shareholder of: Novartis, Employee of: Novartis, Didier Laurent Shareholder of: Novartis, Employee of: Novartis, Vladimir Juras: None declared, Scott Hacker Grant/research support from: Novartis, Brian Cole: None declared, Libor Pasa: None declared, Roman Lehovec: None declared, Pavol Szomolanyi: None declared, Esther Raithel Employee of: Siemens Healthcare GmbH, Franziska Saxer Shareholder of: Novartis, Employee of: Novartis, Jens Praestgaard Shareholder of: Novartis, Employee of: Novartis, Fabiola La Gamba Shareholder of: Novartis, Employee of: Novartis, José L. Jiménez Employee of: Novartis, David Sanchez Ramos Shareholder of: Novartis, Employee of: Novartis, Ronenn Roubenoff Shareholder of: Novartis, Employee of: Novartis, Matthias Schieker Shareholder of: Novartis, Employee of: Novartis

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Jaremko, J. L., B. Felfeliyan, A. Rakkunedeth, B. Thejeel, V. Quinn-Laurin, M. Østergaard, P. G. Conaghan, R. Lambert, J. Ronsky, and W. P. Maksymowych. "AB0594 IMPROVING OSTEOARTHRITIS CARE BY AUTOMATIC MEASUREMENT OF HIP EFFUSION USING AI." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 1334.1–1334. http://dx.doi.org/10.1136/annrheumdis-2021-eular.2196.

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Background:Osteoarthritis (OA) is the commonest disease affecting hip joints and has high prevalence across various age groups [1,2]. Effusion is a hallmark of OA and could represent a potential target for therapy [3–5]. Positive correlations of effusion to clinical outcomes are not well established, partly due to variability in manual assessment. Voxel-based volume quantification could reduce this variability [6].Objectives:We examine the inter-observer agreement of manual assessment of voxel-based effusion volume from coronal STIR MRI sequences at two time points and examine the feasibility of using Artificial Intelligence (AI) for standalone volume assessment.Methods:Our algorithm is based on Mask R-CNN [7] and was trained on labeled effusion regions in MRI slices from 68 patients with hip osteoarthritis. For validation, 2 human readers measured effusion from MRI STIR sequences of 25 patients at baseline and at 8 weeks follow-up. AI was used to measure effusion volume as an independent reader. Agreement between human readers and AI was assessed using absolute difference in volume (DV), Coefficients of Variation (CoV) and intraclass correlation coefficient (ICC).Results:Effusion regions detected by AI closely correlated with manual segmentation (Figure 1) for all samples. Differences in volumes measured by each pair of readers are summarized in Table 1. Agreement was excellent between human readers (ICC=0.99) and for each reader vs AI (ICC = 0.85-0.87).Figure 1.Mask overlays of regions of joint fluid detected by human readers (green, column 2) and AI (red, column 3) from 3 different patients. Raw MRI images are shown in column 1.Table 1.Comparison of volumes measured in cubic millimeters and agreement between each pair of readers (with AI as the 3rd reader)Volumes measured by readersAgreement between reader pairsReaderOverall VolumeMean ± Standard DeviationReader PairDifference in VolumeMean ± Standard DeviationCoVICCReader 16943 ± 5845Reader 1-21127 ± 9000.210.99 [0.98, 1.0]Reader 27638 ± 5619Reader 1-AI3311 ±16430.350.87 [0.7, 0.94]AI11014 ± 4454Reader 2-AI4151 ± 49860.270.85 [0.66,0.94]Conclusion:Initial results of automatic effusion measurement using AI show high agreement with human experts. This has potential to reduce variability and save expert time in OA MRI assessment, and to lead to improved OA care.References:[1]Sharif B, Garner R, Hennessy D, Sanmartin C, Flanagan WM, Marshall DA. Productivity costs of work loss associated with osteoarthritis in Canada from 2010 to 2031. Osteoarthritis Cartilage. 2017 Feb;25(2):249–58.[2]Sharif B, Kopec J, Bansback N, Rahman MM, Flanagan WM, Wong H, et al. Projecting the direct cost burden of osteoarthritis in Canada using a microsimulation model. Osteoarthritis Cartilage. 2015 Oct;23(10):1654–63.[3]Loeuille D, Chary-Valckenaere I, Champigneulle J, Rat A-C, Toussaint F, Pinzano-Watrin A, et al. Macroscopic and microscopic features of synovial membrane inflammation in the osteoarthritic knee: correlating magnetic resonance imaging findings with disease severity. Arthritis Rheum. 2005 Nov;52(11):3492–501.[4]Fernandez-Madrid F, Karvonen RL, Teitge RA, Miller PR, An T, Negendank WG. Synovial thickening detected by MR imaging in osteoarthritis of the knee confirmed by biopsy as synovitis. Magn Reson Imaging. 1995;13(2):177–83.[5]Atukorala I, Kwoh CK, Guermazi A, Roemer FW, Boudreau RM, Hannon MJ, et al. Synovitis in knee osteoarthritis: a precursor of disease? Ann Rheum Dis. 2016 Feb;75(2):390–5.[6]Quinn-Laurin V, Thejeel B, Chauvin NA, Brandon TG, Weiss PF, Jaremko JL. Normal hip joint fluid volumes in healthy children of different ages, based on MRI volumetric quantitative measurement. Pediatr Radiol. 2020 Oct;50(11):1587–93.[7]He K, Gkioxari G, Dollár P, Girshick R. Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. openaccess.thecvf.com; 2017. p. 2961–9.Acknowledgements:Jacob Jaremko is supported by the AHS Chair in Diagnostic Imaging at the University of Alberta. Medical Imaging Consultants (MIC) funds musculoskeletal radiology fellowships for Vanessa Quinn-Laurin at the University of Alberta, and provides Jacob Jaremko and Robert Lambert with protected academic time. Banafshe Felfeliyan is supported by an Alberta Innovates Graduate Student Scholarship for Data-Enabled Innovation.Disclosure of Interests:None declared.

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Journal articles on the topic 'Knee segmentation in MRI'

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