A Unified Generation-Registration Framework for Improved MR-based CT Synthesis in Proton Therapy
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916885476409344 |
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| author | Li, Xia Bellotti, Renato Bachtiary, Barbara Hrbacek, Jan Weber, Damien C. Lomax, Antony J. Buhmann, Joachim M. Zhang, Ye |
| author_facet | Li, Xia Bellotti, Renato Bachtiary, Barbara Hrbacek, Jan Weber, Damien C. Lomax, Antony J. Buhmann, Joachim M. Zhang, Ye |
| contents | Background: In MR-guided proton therapy planning, aligning MR and CT images is key for MR-based CT synthesis, especially in mobile regions like the head-and-neck. Misalignments here can lead to less accurate synthetic CT (sCT) images, impacting treatment precision. Purpose: This study introduces a novel network that cohesively unifies image generation and registration processes to enhance the quality and anatomical fidelity of sCTs derived from better-aligned MR images. Methods: The approach synergizes a generation network (G) with a deformable registration network (R), optimizing them jointly in MR-to-CT synthesis. This goal is achieved by alternately minimizing the discrepancies between the generated/registered CT images and their corresponding reference CT counterparts. The generation network employs a UNet architecture, while the registration network leverages an implicit neural representation of the Deformable Vector Fields (DVFs). We validated this method on a dataset comprising 60 Head-and-Neck patients, reserving 12 cases for holdout testing. Results: Compared to the baseline Pix2Pix method with MAE 124.95\pm 30.74 HU, the proposed technique demonstrated 80.98\pm 7.55 HU. The unified translation-registration network produced sharper and more anatomically congruent outputs, showing superior efficacy in converting MR images to sCTs. Additionally, from a dosimetric perspective, the plan recalculated on the resulting sCTs resulted in a remarkably reduced discrepancy to the reference proton plans. Conclusions: This study conclusively demonstrates that a holistic MR-based CT synthesis approach, integrating both image-to-image translation and deformable registration, significantly improves the precision and quality of sCT generation, particularly for the challenging body area with varied anatomic changes between corresponding MR and CT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12878 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Unified Generation-Registration Framework for Improved MR-based CT Synthesis in Proton Therapy Li, Xia Bellotti, Renato Bachtiary, Barbara Hrbacek, Jan Weber, Damien C. Lomax, Antony J. Buhmann, Joachim M. Zhang, Ye Medical Physics Background: In MR-guided proton therapy planning, aligning MR and CT images is key for MR-based CT synthesis, especially in mobile regions like the head-and-neck. Misalignments here can lead to less accurate synthetic CT (sCT) images, impacting treatment precision. Purpose: This study introduces a novel network that cohesively unifies image generation and registration processes to enhance the quality and anatomical fidelity of sCTs derived from better-aligned MR images. Methods: The approach synergizes a generation network (G) with a deformable registration network (R), optimizing them jointly in MR-to-CT synthesis. This goal is achieved by alternately minimizing the discrepancies between the generated/registered CT images and their corresponding reference CT counterparts. The generation network employs a UNet architecture, while the registration network leverages an implicit neural representation of the Deformable Vector Fields (DVFs). We validated this method on a dataset comprising 60 Head-and-Neck patients, reserving 12 cases for holdout testing. Results: Compared to the baseline Pix2Pix method with MAE 124.95\pm 30.74 HU, the proposed technique demonstrated 80.98\pm 7.55 HU. The unified translation-registration network produced sharper and more anatomically congruent outputs, showing superior efficacy in converting MR images to sCTs. Additionally, from a dosimetric perspective, the plan recalculated on the resulting sCTs resulted in a remarkably reduced discrepancy to the reference proton plans. Conclusions: This study conclusively demonstrates that a holistic MR-based CT synthesis approach, integrating both image-to-image translation and deformable registration, significantly improves the precision and quality of sCT generation, particularly for the challenging body area with varied anatomic changes between corresponding MR and CT. |
| title | A Unified Generation-Registration Framework for Improved MR-based CT Synthesis in Proton Therapy |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2401.12878 |