ArticleFrontiers in oncology2022
Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 15 citations in OpenAlex.
- Application of synthetic computed tomography based on a deep learning model in pelvic radiotherapy planning: A feasibility study.Journal of applied clinical medical physics · 2026Article
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Article
- Quality evaluation of synthetic CT using a gamma analysis for prostate cancer MR-only radiotherapy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Article
- Results of 2023 survey on the use of synthetic computed tomography for magnetic resonance Imaging-only radiotherapy: Current status and future steps.Physics and imaging in radiation oncology · 2024Article
- Translation of Color Fundus Photography into Fluorescein Angiography Using Deep Learning for Enhanced Diabetic Retinopathy Screening.Ophthalmology science · 2023Article
- Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy.Physical and engineering sciences in medicine · 2023Article
- Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning.Physics and imaging in radiation oncology · 2023Article
- MRI-Guided Radiation Therapy for Prostate Cancer: The Next Frontier in Ultrahypofractionation.Cancers · 2023Review
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
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Authors and funding
12 authors at 6 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system. Then, the voxel-based process was applied on two sCT generation methods: a bulk-density method and a generative adversarial network. The CT and MRI pairs of 39 patients treated by radiotherapy for prostate cancer were used for sCT generation, and 26 of them with delineated structures were selected for analysis. Voxel-wise errors in sCT compared to CT were assessed for image intensities and dose calculation, and a population-based statistical test was applied to identify the regions where discrepancies were significant. The cumulative histograms of the mean absolute dose error per volume of tissue were computed to give a quantitative indication of the error for each generation method. Accurate interpatient registration was achieved, with mean Dice scores higher than 0.91 for all organs. The proposed method produces three-dimensional maps that precisely show the location of the major discrepancies for both sCT generation methods, highlighting the heterogeneity of image and dose errors for sCT generation methods from MRI across the pelvic anatomy. Hence, this method provides additional information that will assist with both sCT development and quality control for MRI-based planning radiotherapy.
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