ArticleInternational journal of radiation oncology, biology, physics2016
Image Guided Radiation Therapy Using Synthetic Computed Tomography Images in Brain Cancer.
Article in International journal of radiation oncology, biology, physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Adoption of Hybrid MRI-Linac Systems for the Treatment of Brain Tumors: A Systematic Review of the Current Literature Regarding Clinical and Technical Features.Technology in cancer research & treatmentPooled it
- Clinical feasibility of a commercially available MRI-only method for radiotherapy treatment planning of the brain.Journal of applied clinical medical physics · 2023Article
- On the effect of training database size for MR-based synthetic CT generation in the head.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2023Article
- Feasibility of artificial-intelligence-based synthetic computed tomography in a magnetic resonance-only radiotherapy workflow for brain radiotherapy: Two-way dose validation and 2D/2D kV-image-based positioning.Physics and imaging in radiation oncology · 2022Article
- Head and neck cancer patient positioning using synthetic CT data in MRI-only radiation therapy.Journal of applied clinical medical physics · 2022Article
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Clinical validation of a commercially available deep learning software for synthetic CT generation for brain.Radiation oncology (London, England) · 2021Article
- Performance of deep learning synthetic CTs for MR-only brain radiation therapy.Journal of applied clinical medical physics · 2021Article
- MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning.Frontiers in oncology · 2021Article
- Assessment of MRI-Based Attenuation Correction for MRI-Only Radiotherapy Treatment Planning of the Brain.Diagnostics (Basel, Switzerland) · 2020Article
- MRIgRT head and neck anthropomorphic QA phantom: Design, development, reproducibility, and feasibility study.Medical physics · 2020Article
- The rationale for MR-only treatment planning for external radiotherapy.Clinical and translational radiation oncology · 2019Review
- MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.Medical physics · 2019Article
- Feasibility of generating synthetic CT from T1-weighted MRI using a linear mixed-effects regression model.Biomedical physics & engineering express · 2019Article
- Generating synthetic CTs from magnetic resonance images using generative adversarial networks.Medical physics · 2018Article
- MRI-only treatment planning: benefits and challenges.Physics in medicine and biology · 2018Review
- Article
- Do we need a new CT scan for retreatment of intracranial SRS patients?Journal of applied clinical medical physics · 2017Article
- MR-guided radiation therapy: transformative technology and its role in the central nervous system.Neuro-oncology · 2017Review
- Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach.International journal of radiation oncology, biology, physics · 2017Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeThe development of synthetic computed tomography (CT) (synCT) derived from magnetic resonance (MR) images supports MR-only treatment planning. We evaluated the accuracy of synCT and synCT-generated digitally reconstructed radiographs (DRRs) relative to CT and determined their performance for image guided radiation therapy (IGRT). METHODS AND MATERIALS: Magnetic resonance simulation (MR-SIM) and CT simulation (CT-SIM) images were acquired of an anthropomorphic skull phantom and 12 patient brain cancer cases. SynCTs were generated using fluid attenuation inversion recovery, ultrashort echo time, and Dixon data sets through a voxel-based weighted summation of 5 tissue classifications. The DRRs were generated from the phantom synCT, and geometric fidelity was assessed relative to CT-generated DRRs through bounding box and landmark analysis. An offline retrospective analysis was conducted to register cone beam CTs (n=34) to synCTs and CTs using automated rigid registration in the treatment planning system. Planar MV and KV images (n=37) were rigidly registered to synCT and CT DRRs using an in-house script. Planar and volumetric registration reproducibility was assessed and margin differences were characterized by the van Herk formalism.
resultsBounding box and landmark analysis of phantom synCT DRRs were within 1 mm of CT DRRs. Absolute planar registration shift differences ranged from 0.0 to 0.7 mm for phantom DRRs on all treatment platforms and from 0.0 to 0.4 mm for volumetric registrations. For patient planar registrations, the mean shift differences were 0.4 ± 0.5 mm (range, -0.6 to 1.6 mm), 0.0 ± 0.5 mm (range, -0.9 to 1.2 mm), and 0.1 ± 0.3 mm (range, -0.7 to 0.6 mm) for the superior-inferior (S-I), left-right (L-R), and anterior-posterior (A-P) axes, respectively. The mean shift differences in volumetric registrations were 0.6 ± 0.4 mm (range, -0.2 to 1.6 mm), 0.2 ± 0.4 mm (range, -0.3 to 1.2 mm), and 0.2 ± 0.3 mm (range, -0.2 to 1.2 mm) for the S-I, L-R, and A-P axes, respectively. The CT-SIM and synCT derived margins were <0.3 mm different.
conclusionDRRs generated by synCT were in close agreement with CT-SIM. Planar and volumetric image registrations to synCT-derived targets were comparable with CT for phantom and patients. This validation is the next step toward MR-only planning for the brain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.