ArticleQuantitative imaging in medicine and surgery2026
Novel extended field-of-view reconstruction algorithms in computed tomography simulation for imaging and volumetric modulated arc therapy dosimetry.
Article in Quantitative imaging in medicine and surgery, 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
Background: Accurate computed tomography (CT) reconstruction is essential for effective radiotherapy, particularly when the patient anatomy extends beyond the standard scanning field of view (sFOV). Extended-FOV algorithms address this challenge, but their performance in clinical CT simulations remains unclear. This study systematically evaluated two algorithms, the extended-FOV reconstruction algorithm (eFOV) and the deep learning-enhanced eFOV (AI-eFOV), on the uCT610 Sim system (United Imaging Healthcare), with CT image quality and volumetric modulated arc therapy (VMAT) dosimetric accuracy being the outcomes. Methods: A whole-body phantom simulated off-center displacements (5-20 cm) for extended FOV reconstruction. Isocentric CT images served as the ground truth for the assessment of geometric distortion, Hounsfield unit (HU) stability, and autocontouring accuracy. VMAT plans for the lung, chest wall, and liver were analyzed for dosimetric impact, and four patient cases provided clinical validation. Results: With increasing displacement, eFOV showed large HU deviations [root mean squared error (RMSE) 96 HU; maximum error 275 HU for bone and 293 HU for soft tissue], whereas AI-eFOV reduced errors (RMSE 81 HU; maximum 65 HU for bone and 25 HU for soft tissue; P<0.01) and achieved higher autocontouring accuracy (Dice ≥0.973 for AI-eFOV and ≥0.926 for eFOV). In VMAT plans, both algorithms achieved >96.66% gamma passing rates (3%/3 mm) within a 10-cm offset. At 20 cm, AI-eFOV maintained a rate above 93.37%, while eFOV dropped to 82.05% in hypofractionated plans using the flattening-filter-free high dose rate delivery mode. Clinical data further confirmed AI-eFOV's advantage in artifact suppression and tissue boundary delineation. Conclusions: Image distortion is determined by anatomy and lateral displacement beyond the sFOV. AI-eFOV provided superior image quality and dosimetric accuracy, especially beyond 70 cm and should be preferred for cases with large offsets.
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