Evidence map›Paper›PMID 39946267›Full record

ArticleJournal of applied clinical medical physics2025

Robust automated method of spatial resolution measurement in radiotherapy CT simulation images.

Pavel Govyadinov, Rick R Layman, Tucker Netherton, Raymond Mumme, Aaron K Jones, Laurence E Court, Moiz Ahmad

Abstract read
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Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Pavel GovyadinovDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Rick R LaymanDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Tucker NethertonDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Raymond MummeDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Aaron K JonesDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Laurence E CourtDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Moiz AhmadDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVariation in imaging protocol, patient positioning, and the presence of artifacts can vary image quality in CT images used for radiotherapy planning. Automated methods for spatial resolution (SR) estimation exist but require further investigation and validation for wider adoption. PURPOSE: To validated previously existing algorithm for SR estimation and introduce improvements that make it robust to patient positioning, CT protocol, site, and artifacts.

methodA reference algorithm based on the previous gold standard was recreated and modified to improve robustness. The algorithms were tested on three different datasets: (1) a cylindrical ACR CT QC phantom scanned using a Siemens SOMATOM Definition Edge scanner and reconstructed using 61 different kernels, (2) a set of anthropomorphic phantoms scanned with the presence of artifacts common to clinical acquisitions such as blankets and immobilization devices, and (3) a clinical patient dataset of head and neck (HN) CT scans (nine patients) and spine/pelvis (10 patients). The robustness of both algorithms was tested on the clinical patient data.

resultsOver the range of tested kernels, both algorithms were accurate when the ground truth MTF f

conclusionThe SR algorithm was validated cylindrical/anthropomorphic phantoms and clinical CT scans. Further modifications were tested and showed improved accuracy in more challenging CT acquisitions.

Indexed as

AlgorithmsHead and Neck NeoplasmsImage Processing, Computer-AssistedPhantoms, ImagingRadiotherapy Planning, Computer-AssistedTomography, X-Ray ComputedArtifactsHumansRadiotherapy DosageRadiotherapy, Intensity-ModulatedCTimage quality assessmentimage reconstructionquality controlspatial resolution

Identifiers

PMID39946267
PMCPMC11905253

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