Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB
Detection of early knee osteoarthritis remains a driving force in the search for more promising quantitative assessment approaches. Apart from other conventional methods such as radiography, computed tomography, and sonography, magnetic resonance imaging has become more widely available and has made...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2021
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| Online Access: | http://journalarticle.ukm.my/18941/ http://journalarticle.ukm.my/18941/1/10.pdf |
| _version_ | 1848814706699010048 |
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| author | Sia, Joyce Sin Yin Tan, Tian Swee Khairil Amir Sayuti, Ahmad Tarmizi Musa, Azli Yahya, Tiong, Matthias Foh Thye Sameen Ahmed Malik, Jahanzeb Sheikh, Sia, Jeremy Yik Xian |
| author_facet | Sia, Joyce Sin Yin Tan, Tian Swee Khairil Amir Sayuti, Ahmad Tarmizi Musa, Azli Yahya, Tiong, Matthias Foh Thye Sameen Ahmed Malik, Jahanzeb Sheikh, Sia, Jeremy Yik Xian |
| author_sort | Sia, Joyce Sin Yin |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Detection of early knee osteoarthritis remains a driving force in the search for more promising quantitative assessment approaches. Apart from other conventional methods such as radiography, computed tomography, and sonography, magnetic resonance imaging has become more widely available and has made it essential to visualize the knee's entire anatomy. Biomarkers such as joint space narrowing, articular cartilage thickness, cartilage volume, cartilage surface curvature, lesion depth, and others are used to determine disease progression in non-invasive manner. In this research, a regional cartilage normal thickness approximation (RCN-ta) model was developed with MATLAB to enable rapid cartilage thickness assessment with a simple click. The model formulated was compared to the FDA-cleared software measurements. A reasonable range of 0.135-0.214 mm of root-mean-square error may be predicted from the model. With a high ICC > 0.975, the model was highly accurate and reproducible. A good agreement between the proposed model and the medically used software can be found with a high Pearson correlation of r > 0.90. |
| first_indexed | 2025-11-15T00:38:21Z |
| format | Article |
| id | oai:generic.eprints.org:18941 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:38:21Z |
| publishDate | 2021 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:189412022-07-13T07:07:31Z http://journalarticle.ukm.my/18941/ Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB Sia, Joyce Sin Yin Tan, Tian Swee Khairil Amir Sayuti, Ahmad Tarmizi Musa, Azli Yahya, Tiong, Matthias Foh Thye Sameen Ahmed Malik, Jahanzeb Sheikh, Sia, Jeremy Yik Xian Detection of early knee osteoarthritis remains a driving force in the search for more promising quantitative assessment approaches. Apart from other conventional methods such as radiography, computed tomography, and sonography, magnetic resonance imaging has become more widely available and has made it essential to visualize the knee's entire anatomy. Biomarkers such as joint space narrowing, articular cartilage thickness, cartilage volume, cartilage surface curvature, lesion depth, and others are used to determine disease progression in non-invasive manner. In this research, a regional cartilage normal thickness approximation (RCN-ta) model was developed with MATLAB to enable rapid cartilage thickness assessment with a simple click. The model formulated was compared to the FDA-cleared software measurements. A reasonable range of 0.135-0.214 mm of root-mean-square error may be predicted from the model. With a high ICC > 0.975, the model was highly accurate and reproducible. A good agreement between the proposed model and the medically used software can be found with a high Pearson correlation of r > 0.90. Penerbit Universiti Kebangsaan Malaysia 2021 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/18941/1/10.pdf Sia, Joyce Sin Yin and Tan, Tian Swee and Khairil Amir Sayuti, and Ahmad Tarmizi Musa, and Azli Yahya, and Tiong, Matthias Foh Thye and Sameen Ahmed Malik, and Jahanzeb Sheikh, and Sia, Jeremy Yik Xian (2021) Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB. Jurnal Kejuruteraan, 33 (4). pp. 875-882. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-334-2021/ |
| spellingShingle | Sia, Joyce Sin Yin Tan, Tian Swee Khairil Amir Sayuti, Ahmad Tarmizi Musa, Azli Yahya, Tiong, Matthias Foh Thye Sameen Ahmed Malik, Jahanzeb Sheikh, Sia, Jeremy Yik Xian Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB |
| title | Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB |
| title_full | Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB |
| title_fullStr | Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB |
| title_full_unstemmed | Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB |
| title_short | Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB |
| title_sort | magnetic resonance imaging-based estimation of knee cartilage thickness with matlab |
| url | http://journalarticle.ukm.my/18941/ http://journalarticle.ukm.my/18941/ http://journalarticle.ukm.my/18941/1/10.pdf |