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Synthetic computed tomography generation using deep-learning for female pelvic radiotherapy planning

Tulip, R.
; 
Andersson, S.
; 
Chuter, R.
; 
Manolopoulos, S.
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Abstract
Synthetic Computed Tomography (sCT) is required to provide electron density information for MR-only radiotherapy. Deep-learning (DL) methods for sCT generation show improved dose congruence over other sCT generation methods (e.g. bulk density). Using 30 female pelvis datasets to train a cycleGAN-inspired DL model, this study found mean dose differences between a deformed planning CT (dCT) and sCT were 0.2 % (D98 %). Three Dimensional Gamma analysis showed a mean of 90.4 % at 1 %/1mm. This study showed accurate sCTs (dose) can be generated from routinely available T2 spin echo sequences without the need for additional specialist sequences.
Authors
Tulip, R.
Andersson, S.
Chuter, R.
Manolopoulos, S.
Affiliation
Northern Centre for Cancer Care - North Cumbria, Newcastle upon Tyne Hospitals NHS Foundation Trust, Carlisle, Cumbria CA2 7HY, UK. RaySearch Laboratories, Stockholm, Sweden. Christie Medical Physics and Engineering (CMPE), The Christie NHS Foundation Trust, Wilmslow Road, Manchester M20 4BX, UK. Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK.
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2025
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All Christie Publications
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Tulip R, Andersson S, Chuter R, Manolopoulos S. Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning. Physics and imaging in radiation oncology. 2025 Jan;33:100719. PubMed PMID: 40008279. Pubmed Central PMCID: PMC11851199. Epub 2025/02/26. eng.
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https://christie.openrepository.com/handle/10541/628133
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