ArticlePhysics in medicine and biology2019
Conversion of computational human phantoms into DICOM-RT for normal tissue dose assessment in radiotherapy patients.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Estimation of Detailed Cardiac Doses for Pediatric Radiation Therapy Patients in National Wilms Tumor Study.International journal of radiation oncology, biology, physics · 2026Article
- Realistic extension of partial-body pediatric CT for whole-body organ dose estimation in radiotherapy patients.Radiation physics and chemistry (Oxford, England : 1993) · 2025Article
- Development of whole-body representation and dose calculation in a commercial treatment planning system.Zeitschrift fur medizinische Physik · 2022Article
- Body region-specific 3D age-scaling functions for scaling whole-body computed tomography anatomy for pediatric late effects studies.Biomedical physics & engineering express · 2022Article
- Fetal dose from proton pencil beam scanning craniospinal irradiation during pregnancy: a Monte Carlo study.Physics in medicine and biology · 2022Article
- Development of a novel program for conversion from tetrahedral-mesh-based phantoms to DICOM dataset for radiation treatment planning: TET2DICOM.Journal of applied clinical medical physics · 2022Article
- Development and validation of an age-scalable cardiac model with substructures for dosimetry in late-effects studies of childhood cancer survivors.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2020Article
- A dose voxel kernel method for rapid reconstruction of out-of-field neutron dose of patients in pencil beam scanning (PBS) proton therapy.Physics in medicine and biology · 2020Article
- Validation and Comparison of Radiograph-Based Organ Dose Reconstruction Approaches for Wilms Tumor Radiation Treatment Plans.Advances in radiation oncologyArticle
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Radiotherapy (RT) treatment planning systems (TPS) are designed for the fast calculation of dose to the tumor bed and nearby organs at risk using x-ray computed tomography (CT) images. However, CT images for a patient are typically available for only a small portion of the body, and in some cases, such as for retrospective epidemiological studies, no images may be available at all. When dose to organs that lie out-of-scan must be estimated, a convenient alternative for the unknown patient anatomy is to use a matching whole-body computational phantom as a surrogate. The purpose of the current work is to connect such computational phantoms to commercial RT TPS for retrospective organ dose estimation. A custom software with graphical user interface (GUI), called the DICOM-RT Generator, was developed in MATLAB to convert voxel computational phantoms into the digital imaging and communications in medicine radiotherapy (DICOM-RT) format, compatible with commercial TPS. DICOM CT image sets for the phantoms are created via a density-to-Hounsfield unit (HU) conversion curve. Accompanying structure sets containing the organ contours are automatically generated by tracing binary masks of user-specified organs on each phantom CT slice. The software was tested on a library of body size-dependent phantoms, the International Commission on Radiological Protection reference phantoms, and a canine voxel phantom, taking only a few minutes per conversion. The resulting DICOM-RT files were tested on several commercial TPS. As an example application, a library of converted phantoms was used to estimate organ doses for members of the National Wilms Tumor Study (NWTS) cohort. The converted phantom library, in DICOM format, and a standalone MATLAB-compiled executable of the DICOM-RT Generator are available for others to use for research purposes (http://ncidose.cancer.gov).
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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.