Arttu Peuna1,2,3, Joonas Hekkala1, Marianne Haapea1,2,3, Jana Podlipská1, Ali Guermazi4, Simo Saarakkala1,2,3, Miika T Nieminen1,2,3, Eveliina Lammentausta1,2. 1. Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland. 2. Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland. 3. Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland. 4. Quantitative Imaging Center, Department of Radiology, Boston University School of Medicine, Boston, Massachusetts, USA.
Abstract
BACKGROUND: Texture analysis methods based on gray level co-occurrence matrices (GLCM) can be optimized to probe the spatial correspondence information from knee MRI T2 maps and the changes caused by osteoarthritis, and thus possibly leading to a more sensitive characterization of osteoarthritic cartilage. Curvature of the cartilage surfaces combined with the low image resolution in relation to cartilage thickness set special requirements for an effective texture analysis tool. PURPOSE/HYPOTHESIS: To introduce a novel implementation of GLCM algorithm optimized for cartilage texture analysis; to evaluate the performance of the designed algorithm against mean T2 relaxation time analysis; and to identify the most suitable texture features for discerning osteoarthritic subjects and asymptomatic controls. STUDY TYPE: Case control. POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL: Eighty symptomatic osteoarthritis patients and 64 asymptomatic controls. FIELD STRENGTH/SEQUENCE: Multislice multiecho spin echo sequence on a 3T MRI system. ASSESSMENT: The T2 relaxation time maps were manually segmented by an operator trained for the task. Texture analysis was performed using an in-house algorithm developed in MATLAB. STATISTICAL TESTS: Symptomatic and asymptomatic subjects were compared using Mann-Whitney U-test. Repeatability of different features was addressed using the concordance correlation coefficient (CCC). Spearman's correlations between the features were determined. RESULTS: The algorithm displayed excellent performance in discerning symptomatic and asymptomatic subjects. Fifteen features provided a significant difference between the groups (P ≤ 0.05) and 12 of those had P values smaller than the mean T2 differences. Most of the studied texture features were highly repeatable with CCC over 90%. For the features with significant differences, correlation with mean T2 was low or moderate (|r| ≤ 0.5). DATA CONCLUSION: With careful parameter and feature selection and algorithm optimization, texture analysis provides a powerful tool for assessing knee osteoarthritis with more sensitive detection of cartilage degeneration compared to the mean value of the T2 relaxation times in an identical region of interest. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2018;47:1316-1327.
BACKGROUND: Texture analysis methods based on gray level co-occurrence matrices (GLCM) can be optimized to probe the spatial correspondence information from knee MRI T2 maps and the changes caused by osteoarthritis, and thus possibly leading to a more sensitive characterization of osteoarthritic cartilage. Curvature of the cartilage surfaces combined with the low image resolution in relation to cartilage thickness set special requirements for an effective texture analysis tool. PURPOSE/HYPOTHESIS: To introduce a novel implementation of GLCM algorithm optimized for cartilage texture analysis; to evaluate the performance of the designed algorithm against mean T2 relaxation time analysis; and to identify the most suitable texture features for discerning osteoarthritic subjects and asymptomatic controls. STUDY TYPE: Case control. POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL: Eighty symptomatic osteoarthritispatients and 64 asymptomatic controls. FIELD STRENGTH/SEQUENCE: Multislice multiecho spin echo sequence on a 3T MRI system. ASSESSMENT: The T2 relaxation time maps were manually segmented by an operator trained for the task. Texture analysis was performed using an in-house algorithm developed in MATLAB. STATISTICAL TESTS: Symptomatic and asymptomatic subjects were compared using Mann-Whitney U-test. Repeatability of different features was addressed using the concordance correlation coefficient (CCC). Spearman's correlations between the features were determined. RESULTS: The algorithm displayed excellent performance in discerning symptomatic and asymptomatic subjects. Fifteen features provided a significant difference between the groups (P ≤ 0.05) and 12 of those had P values smaller than the mean T2 differences. Most of the studied texture features were highly repeatable with CCC over 90%. For the features with significant differences, correlation with mean T2 was low or moderate (|r| ≤ 0.5). DATA CONCLUSION: With careful parameter and feature selection and algorithm optimization, texture analysis provides a powerful tool for assessing knee osteoarthritis with more sensitive detection of cartilage degeneration compared to the mean value of the T2 relaxation times in an identical region of interest. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2018;47:1316-1327.
Authors: Marc Sebastian Huppertz; Justus Schock; Karl Ludger Radke; Daniel Benjamin Abrar; Manuel Post; Christiane Kuhl; Daniel Truhn; Sven Nebelung Journal: Life (Basel) Date: 2021-03-05
Authors: Vladimir Juras; Pavol Szomolanyi; Markus M Schreiner; Karin Unterberger; Andrea Kurekova; Benedikt Hager; Didier Laurent; Esther Raithel; Heiko Meyer; Siegfried Trattnig Journal: Cartilage Date: 2020-09-29 Impact factor: 4.634
Authors: Vladimir Juras; Pavol Szomolanyi; Veronika Janáčová; Alexandra Kirner; Peter Angele; Siegfried Trattnig Journal: Cartilage Date: 2021-07-16 Impact factor: 4.634