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Background: In external beam radiation therapy (EBRT) for prostate cancer, both MRI and CT are typically used-CT provides electron density for dose calculation and visualization of bony anatomy and fiducial markers, while MRI offers superior soft tissue contrast. With recent advances in deep learning, synthetic CT (sCT) images can now be generated from MRI, potentially eliminating the need for separate CT scans.
Purpose: To clinically implement an MRI-only planning (MROP) workflow for both X-ray and MRI-guided systems.
Methods: MROP implementation involved optimizing MRI simulation protocols, developing a deep learning-based sCT generation method, and automating fiducial marker detection. A multichannel CycleGAN model was trained using T1-weighted Dixon images to generate sCT. For SBRT cases, the immobilization device was auto-inserted into sCT images. Fiducial markers were detected using a quantitative susceptibility mapping (QSM) method. Validation included retrospective comparison of Hounsfield units and dosimetric plans to those based on planning CT for 11 patients, followed by prospective evaluation of ten patients.
Results: The MRI protocol included T2-weighted 3D imaging for contouring, T1-weighted Dixon for sCT, and T2*-weighted GRE for fiducial detection. The MROP workflow was prospectively applied in 10 patients (three SBRT, seven conventional). Dosimetric comparisons showed <1% difference between MROP and CT-based plans across all PTVs. The QSM-based fiducial detection achieved a 95.5% success rate (63/66 markers) and reduced detection time from ∼10 to 2 min per patient. All MROP components were integrated into the treatment planning system via scripting. Implementation reduced simulation scheduling time by 7.1 days.
Conclusion: We successfully implemented an MROP workflow for prostate cancer EBRT, eliminating the need for CT, reducing radiation exposure, and improving clinical efficiency. Over 500 patients have been treated to date using this approach on both C-arm and MR-guided systems.
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http://dx.doi.org/10.1002/acm2.70228 | DOI Listing |
Rep Pract Oncol Radiother
August 2025
Department of Oncology and Radiotherapy, University Hospital Hradec Kralove, Hradec Kralove, Czech Republic.
Background: This study evaluates the quality of synthetic computed tomography (sCT) images for MR-only radiotherapy in prostate cancer using gamma analysis. A software tool, MRGamma, was developed to address challenges like the absence of electron density maps and registration uncertainties between magnetic resonance imaging (MRI) and planning CT (pCT).
Materials And Methods: Aplication developed in MATLAB assesses Hounsfield units (HU) discrepancies between CT and sCT images via 2D and 3D gamma analysis (GA).
J Appl Clin Med Phys
September 2025
Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.
Background: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy. While methods exist to generate voxel-wise uncertainty maps from DL-based auto-segmentation models, these maps are rarely presented to clinicians.
Purpose: This study aimed to evaluate the impact of DL-generated uncertainty maps on experienced radiation oncologists during the manual correction of DL-based auto-segmentation for prostate radiotherapy.
J Appl Clin Med Phys
September 2025
Department of Radiation Oncology, University of Texas Southwestern Medical Center, Texas, USA.
Background: In external beam radiation therapy (EBRT) for prostate cancer, both MRI and CT are typically used-CT provides electron density for dose calculation and visualization of bony anatomy and fiducial markers, while MRI offers superior soft tissue contrast. With recent advances in deep learning, synthetic CT (sCT) images can now be generated from MRI, potentially eliminating the need for separate CT scans.
Purpose: To clinically implement an MRI-only planning (MROP) workflow for both X-ray and MRI-guided systems.
Med Phys
July 2025
Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.
Purpose: Medical imaging is crucial in modern radiotherapy, aiding diagnosis, treatment planning, and monitoring. The development of synthetic imaging techniques, particularly synthetic computed tomography (sCT), continues to attract interest in radiotherapy. The SynthRAD2025 dataset and the accompanying SynthRAD2025 Grand Challenge aim to stimulate advancements in synthetic CT generation algorithms by providing a platform for comprehensive evaluation and benchmarking of synthetic CT generation algorithms based on cone-beam CTs (CBCT) and magnetic resonance images (MRI).
View Article and Find Full Text PDFBiomed Phys Eng Express
May 2025
Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA, United States of America.
This study aims to investigate the feasibility of a single general model to synthesize CT images across body sites, thorax, abdomen, and pelvis, to support treatment planning for MRI-only radiotherapy. A total of 157 patients who received MRI-guided radiation therapy in the thorax, abdomen, and pelvis on a 0.35T MRIdian Linac were included.
View Article and Find Full Text PDF