ArticleJournal of applied clinical medical physics2026
Pulmonary ventilation images from planning CT images using deep learning on locally-advanced NSCLC patients.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPulmonary ventilation imaging has become an increasingly important component in thoracic radiotherapy, as it is applied to functional avoidance radiotherapy, where radiation doses are strategically minimized to preserve functional lung regions. PURPOSE: We developed a framework that generates computed tomography ventilation images (CTVI) from planning CT (PCT) images and demonstrated its estimation performance.
methodsThe subjects were a patient cohort consisting of 102 locally-advanced non-small-cell lung cancer (NSCLC) patients who received radiotherapy from 2014 to 2023. The PCT images were acquired while ensuring the absence of baseline drift, frequency variation, amplitude changes, and additive random observation noise using a real-time position management (RPM) system to minimize the introduction of inaccuracies into PCT images. On the day of PCT scan, CTVI based on four-dimensional computed tomography images (
resultsThe CTVI based on PCT (
conclusionsWe successfully developed a framework that estimates
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