Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours:
Publication date: Available online 7 October 2020
Source: Radiotherapy and Oncology
Author(s): Mateusz C. Florkow, Filipa Guerreiro, Frank Zijlstra, Enrica Seravalli, Geert O. Janssens, John H. Maduro, Antje C. Knopf, René M. Castelein, Marijn van Stralen, Bas W. Raaymakers, Peter R. Seevinck
Highlights
- •
Satisfactory synthetic CT images were derived from planning T1w and T2w MR images.
- •
Deep learning-based MRI-only radiotherapy is feasible in pediatric abdominal tumors.
- •
CT-sCT dose differences were clinically acceptable (<2%) for photon & proton plans.
- •
Larger differences were caused by existing interscan differences (eg bowel filling)
Abstract
Purpose
To assess the feasibility of magnetic resonance imaging (MRI)-only treatment planning for photon and proton radiotherapy in children with abdominal tumours.
Materials and methods
The study was conducted on 66 paediatric patients with Wilms’ tumour or neuroblastoma (age 4±2 years) who underwent MR and computed tomography (CT) acquisition on the same day as part of the clinical protocol. MRI intensities were converted to CT Hounsfield units (HU) by means of a UNet-like neural network trained to generate synthetic CT (sCT) from T1- and T2-weighted MR images. The CT-to-sCT image similarity was evaluated by computing the mean error (ME), mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and Dice score coefficient (DSC). Synthetic CT dosimetric accuracy was verified against CT-based dose distributions for volumetric-modulated arc therapy (VMAT) and intensity-modulated pencil-beam scanning (PBS). Relative dose differences (Ddiff) in the internal target volume and organs-at-risk were computed and a three-dimensional gamma analysis (2mm, 2%) was performed.
Results
The average ± standard deviation ME was -5±12 HU, MAE was 57±12 HU, PSNR was 30.3±1.6 dB and DSC was 76±8% for bones and 92±9% for lungs. Average Ddiff were <0.5% for both VMAT (range [-2.5;2.4]%) and PBS (range [-2.7;3.7]%) dose distributions. The average gamma pass-rates were >99% (range [85;100]%) for VMAT and >96% (range [87;100]%) for PBS.
Conclusion
The deep learning-based model generated accurate sCT from planning T1w- and T2w-MR images. Most dosimetric differences were within clinically acceptable criteria for photon and proton radiotherapy, demonstrating the feasibility of an MRI-only workflow for paediatric patients with abdominal tumours.
Keywords
Synthetic CT
MRI only
Paediatric
Abdomen
Deep learning
Proton therapy
Photon therapy
Planning
MRI
Wilms Tumour
Neuroblastoma
Δεν υπάρχουν σχόλια:
Δημοσίευση σχολίου