Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy
Purpose: Accurate cardiac chamber segmentation is crucial for improving cardiac sparing in magnetic resonance (MR)-guided adaptive radiation therapy, especially in patients at risk for radiation-induced cardiotoxicity. Here, we developed and evaluated automatic segmentation models for cardiac chambe...
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Elsevier
2025-09-01
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| Series: | Advances in Radiation Oncology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2452109425001320 |
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| author | Xinru Chen, PhD Yao Ding, PhD Julius Weng, MD Carol C. Wu, MD Yao Zhao, PhD Angela Sobremonte, BS Mustefa Mohammedsaid, MS Zhan Xu, PhD Xiaodong Zhang, PhD Joshua S. Niedzielski, PhD Sanjay S. Shete, PhD Laurence E. Court, PhD Zhongxing Liao, MD Jihong Wang, PhD Ergys Subashi, PhD Percy P. Lee, MD Jinzhong Yang, PhD |
| author_facet | Xinru Chen, PhD Yao Ding, PhD Julius Weng, MD Carol C. Wu, MD Yao Zhao, PhD Angela Sobremonte, BS Mustefa Mohammedsaid, MS Zhan Xu, PhD Xiaodong Zhang, PhD Joshua S. Niedzielski, PhD Sanjay S. Shete, PhD Laurence E. Court, PhD Zhongxing Liao, MD Jihong Wang, PhD Ergys Subashi, PhD Percy P. Lee, MD Jinzhong Yang, PhD |
| author_sort | Xinru Chen, PhD |
| collection | DOAJ |
| description | Purpose: Accurate cardiac chamber segmentation is crucial for improving cardiac sparing in magnetic resonance (MR)-guided adaptive radiation therapy, especially in patients at risk for radiation-induced cardiotoxicity. Here, we developed and evaluated automatic segmentation models for cardiac chambers that use daily MR images acquired on a 1.5-T MR-Linac system. Methods and Materials: Twenty healthy volunteers underwent daily MR scanning on a 1.5-T MR-Linac, with 2 radial sequences: T2/T1 3DVaneXD balanced fast field echo with spectral attenuated inversion recovery (bFFE-SPAIR) and T1 3DVaneXD mDixon. Three flip angles were tested for each sequence to determine optimal image quality for chamber segmentation. Full-resolution 3D nnU-Net models were trained for the following: (1) bFFE-SPAIR (bFFE model); (2) T1 mDixon (mDixon model); and (3) both sequences (hybrid model). Models were evaluated based on Dice similarity coefficient (DSC) and mean surface distance against manual contours. Clinical acceptance of the automatic segmentation was assessed with a 5-point Likert scale. An in-silico planning study was performed to assess cardiac chamber sparing during plan adaptation. Results: The average contrast-to-noise ratios in bFFE-SPAIR were 8.7 (20°), 34.2 (50°), and 37.3 (80°); for T1 mDixon, these values were 3.6 (5°), 5.9 (10°), and 4.9 (20°). The bFFE model achieved the highest segmentation performance (average DSC 0.85 ± 0.05 and mean surface distance 2.2 ± 0.6 mm). The T1 mDixon sequence, despite lower contrast-to-noise ratios, provided similar segmentation accuracy (DSC 0.83 ± 0.06). A hybrid model combining both sequences showed no significant improvement over the bFFE model. Clinical evaluation indicated that 95% of the autosegmented contours from the bFFE model were acceptable for clinical use (score ≥4). Adaptive plan greatly reduced individual cardiac chamber dose while maintaining similar target coverage. Conclusions: This study demonstrated the feasibility of using bFFE-SPAIR and T1 mDixon sequences to accurately segment cardiac chambers on a 1.5-T MR-Linac. These models offer potential for improved cardiac sparing in MR-guided adaptive radiation therapy. |
| format | Article |
| id | doaj-art-e4e095d69fe64f828af83b683364ef67 |
| institution | Kabale University |
| issn | 2452-1094 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Advances in Radiation Oncology |
| spelling | doaj-art-e4e095d69fe64f828af83b683364ef672025-08-20T03:44:27ZengElsevierAdvances in Radiation Oncology2452-10942025-09-0110910184510.1016/j.adro.2025.101845Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation TherapyXinru Chen, PhD0Yao Ding, PhD1Julius Weng, MD2Carol C. Wu, MD3Yao Zhao, PhD4Angela Sobremonte, BS5Mustefa Mohammedsaid, MS6Zhan Xu, PhD7Xiaodong Zhang, PhD8Joshua S. Niedzielski, PhD9Sanjay S. Shete, PhD10Laurence E. Court, PhD11Zhongxing Liao, MD12Jihong Wang, PhD13Ergys Subashi, PhD14Percy P. Lee, MD15Jinzhong Yang, PhD16Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TexasDepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Oncology, City of Hope National Medical Center, Los Angeles, CaliforniaDepartment of Thoracic Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TexasDepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TexasThe University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TexasDepartment of Thoracic Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TexasDepartment of Radiation Oncology, City of Hope National Medical Center, Los Angeles, CaliforniaDepartment of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas; Corresponding author: Jinzhong Yang, PhDPurpose: Accurate cardiac chamber segmentation is crucial for improving cardiac sparing in magnetic resonance (MR)-guided adaptive radiation therapy, especially in patients at risk for radiation-induced cardiotoxicity. Here, we developed and evaluated automatic segmentation models for cardiac chambers that use daily MR images acquired on a 1.5-T MR-Linac system. Methods and Materials: Twenty healthy volunteers underwent daily MR scanning on a 1.5-T MR-Linac, with 2 radial sequences: T2/T1 3DVaneXD balanced fast field echo with spectral attenuated inversion recovery (bFFE-SPAIR) and T1 3DVaneXD mDixon. Three flip angles were tested for each sequence to determine optimal image quality for chamber segmentation. Full-resolution 3D nnU-Net models were trained for the following: (1) bFFE-SPAIR (bFFE model); (2) T1 mDixon (mDixon model); and (3) both sequences (hybrid model). Models were evaluated based on Dice similarity coefficient (DSC) and mean surface distance against manual contours. Clinical acceptance of the automatic segmentation was assessed with a 5-point Likert scale. An in-silico planning study was performed to assess cardiac chamber sparing during plan adaptation. Results: The average contrast-to-noise ratios in bFFE-SPAIR were 8.7 (20°), 34.2 (50°), and 37.3 (80°); for T1 mDixon, these values were 3.6 (5°), 5.9 (10°), and 4.9 (20°). The bFFE model achieved the highest segmentation performance (average DSC 0.85 ± 0.05 and mean surface distance 2.2 ± 0.6 mm). The T1 mDixon sequence, despite lower contrast-to-noise ratios, provided similar segmentation accuracy (DSC 0.83 ± 0.06). A hybrid model combining both sequences showed no significant improvement over the bFFE model. Clinical evaluation indicated that 95% of the autosegmented contours from the bFFE model were acceptable for clinical use (score ≥4). Adaptive plan greatly reduced individual cardiac chamber dose while maintaining similar target coverage. Conclusions: This study demonstrated the feasibility of using bFFE-SPAIR and T1 mDixon sequences to accurately segment cardiac chambers on a 1.5-T MR-Linac. These models offer potential for improved cardiac sparing in MR-guided adaptive radiation therapy.http://www.sciencedirect.com/science/article/pii/S2452109425001320 |
| spellingShingle | Xinru Chen, PhD Yao Ding, PhD Julius Weng, MD Carol C. Wu, MD Yao Zhao, PhD Angela Sobremonte, BS Mustefa Mohammedsaid, MS Zhan Xu, PhD Xiaodong Zhang, PhD Joshua S. Niedzielski, PhD Sanjay S. Shete, PhD Laurence E. Court, PhD Zhongxing Liao, MD Jihong Wang, PhD Ergys Subashi, PhD Percy P. Lee, MD Jinzhong Yang, PhD Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy Advances in Radiation Oncology |
| title | Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy |
| title_full | Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy |
| title_fullStr | Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy |
| title_full_unstemmed | Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy |
| title_short | Deep Learning-Based Cardiac Chamber Segmentation in Magnetic Resonance-Guided Adaptive Radiation Therapy |
| title_sort | deep learning based cardiac chamber segmentation in magnetic resonance guided adaptive radiation therapy |
| url | http://www.sciencedirect.com/science/article/pii/S2452109425001320 |
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