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Training a deep neural network coping with diversities in abdominal and pelvic images of children and young adults for CBCT-based adaptive proton therapy.
- Source :
-
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology [Radiother Oncol] 2021 Jul; Vol. 160, pp. 250-258. Date of Electronic Publication: 2021 May 13. - Publication Year :
- 2021
-
Abstract
- Purpose: To train a deep neural network for correcting abdominal and pelvic cone-beam computed tomography (CBCT) of children and young adults in the presence of diverse patient size, anatomic extent, and scan parameters.<br />Materials and Methods: Pretreatment CBCT and planning/repeat CT image pairs from 64 children and young adults treated with proton therapy (aged 1-23 years) were analyzed. To evaluate the impact of anatomic extent in CBCT and data size in the training data, we compared the performance of three cycle-consistent generative adversarial network models that were separately trained by three datasets comprising abdominal (n = 21), pelvic (n = 29), and combined abdominal-pelvic image pairs (n = 50), respectively. The maximum body width of each patient was normalized to a fixed width before training and model application to reduce the impact of variations in body size. The corrected CBCT images by the three models were comparatively evaluated against the repeat CT closest in time to the CBCT (median gap, 0 days; range, 0-6 days) in HU accuracy, estimated dose distribution, and proton range.<br />Results: The network model trained by the combined dataset significantly outperformed the abdomen and pelvis models in mean absolute HU error of the corrected CBCT from 14 testing patients (47 ± 7 HU versus 51 ± 8 HU; paired Wilcoxon signed-rank test, P < 0.01). The larger error (60 ± 7 HU) without the body-size normalization confirmed the efficacy of the preprocessing. The model trained with the combined dataset resulted in gamma passing rates of 98.5 ± 1.9% (2%/2 mm criterion) and the range (80% distal fall-off) differences from the reference within ±3 mm for 91.2 ± 11.5% beamlets.<br />Conclusion: Combining data from adjacent anatomic sites and normalizing age-dependent body sizes in children and young adults were beneficial in training a neural network to accurately estimate proton dose from CBCT despite limited training data size and anatomic diversities.<br />Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2021 Elsevier B.V. All rights reserved.)
- Subjects :
- Abdomen diagnostic imaging
Adaptation, Psychological
Child
Cone-Beam Computed Tomography
Humans
Image Processing, Computer-Assisted
Neural Networks, Computer
Pelvis diagnostic imaging
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted
Young Adult
Proton Therapy
Spiral Cone-Beam Computed Tomography
Subjects
Details
- Language :
- English
- ISSN :
- 1879-0887
- Volume :
- 160
- Database :
- MEDLINE
- Journal :
- Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
- Publication Type :
- Academic Journal
- Accession number :
- 33992626
- Full Text :
- https://doi.org/10.1016/j.radonc.2021.05.006