Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.12600 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916400970334208 |
|---|---|
| author | Zhong, Junru Yao, Yongcheng Xiao, Fan Ong, Tim-Yun Michael Ho, Ki-Wai Kevin Li, Siyue Huang, Chaoxing Chan, Queenie Griffith, James F. Chen, Weitian |
| author_facet | Zhong, Junru Yao, Yongcheng Xiao, Fan Ong, Tim-Yun Michael Ho, Ki-Wai Kevin Li, Siyue Huang, Chaoxing Chan, Queenie Griffith, James F. Chen, Weitian |
| contents | Objective: To establish an automated pipeline for post-processing of quantitative spin-lattice relaxation time constant in the rotating frame ($T_{1ρ}$) imaging of knee articular cartilage. Design: The proposed post-processing pipeline commences with an image standardisation procedure, followed by deep learning-based segmentation to generate cartilage masks. The articular cartilage is then automatically parcellated into 20 subregions, where $T_{1ρ}$ quantification is performed. The proposed pipeline was retrospectively validated on a dataset comprising knee $T_{1ρ}$ images of 10 healthy volunteers and 30 patients with knee osteoarthritis. Three experiments were conducted, namely an assessment of segmentation model performance (using Dice similarity coefficients, DSCs); an evaluation of the impact of standardisation; and a test of $T_{1ρ}$ quantification accuracy (using paired t-tests; root-mean-square deviations, RMSDs; and coefficients of variance of RMSDs, $CV_{RMSD}$). Statistical significance was set as p<0.05. Results: There was a substantial agreement between the subregional $T_{1ρ}$ quantification from the model-predicted masks and those from the manual segmentation labels. In patients, 17 of 20 subregions, and in healthy volunteers, 18 out of 20 subregions, demonstrated no significant difference between predicted and reference $T_{1ρ}$ quantifications. Average RMSDs were 0.79 ms for patients and 0.56 ms for healthy volunteers, while average $CV_{RMSD}$ were 1.97% and 1.38% for patients and healthy volunteers. Bland-Altman plots showed negligible bias across all subregions for patients and healthy volunteers. Conclusion: The proposed pipeline can perform automatic and reliable post-processing of quantitative $T_{1ρ}$ images of knee articular cartilage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12600 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Systematic Post-Processing Approach for Quantitative $T_{1ρ}$ Imaging of Knee Articular Cartilage Zhong, Junru Yao, Yongcheng Xiao, Fan Ong, Tim-Yun Michael Ho, Ki-Wai Kevin Li, Siyue Huang, Chaoxing Chan, Queenie Griffith, James F. Chen, Weitian Image and Video Processing Objective: To establish an automated pipeline for post-processing of quantitative spin-lattice relaxation time constant in the rotating frame ($T_{1ρ}$) imaging of knee articular cartilage. Design: The proposed post-processing pipeline commences with an image standardisation procedure, followed by deep learning-based segmentation to generate cartilage masks. The articular cartilage is then automatically parcellated into 20 subregions, where $T_{1ρ}$ quantification is performed. The proposed pipeline was retrospectively validated on a dataset comprising knee $T_{1ρ}$ images of 10 healthy volunteers and 30 patients with knee osteoarthritis. Three experiments were conducted, namely an assessment of segmentation model performance (using Dice similarity coefficients, DSCs); an evaluation of the impact of standardisation; and a test of $T_{1ρ}$ quantification accuracy (using paired t-tests; root-mean-square deviations, RMSDs; and coefficients of variance of RMSDs, $CV_{RMSD}$). Statistical significance was set as p<0.05. Results: There was a substantial agreement between the subregional $T_{1ρ}$ quantification from the model-predicted masks and those from the manual segmentation labels. In patients, 17 of 20 subregions, and in healthy volunteers, 18 out of 20 subregions, demonstrated no significant difference between predicted and reference $T_{1ρ}$ quantifications. Average RMSDs were 0.79 ms for patients and 0.56 ms for healthy volunteers, while average $CV_{RMSD}$ were 1.97% and 1.38% for patients and healthy volunteers. Bland-Altman plots showed negligible bias across all subregions for patients and healthy volunteers. Conclusion: The proposed pipeline can perform automatic and reliable post-processing of quantitative $T_{1ρ}$ images of knee articular cartilage. |
| title | A Systematic Post-Processing Approach for Quantitative $T_{1ρ}$ Imaging of Knee Articular Cartilage |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2409.12600 |