Uncertainty-aware MR-based CT synthesis for robust proton therapy planning of brain tumour.
Publisher DOI
PubMed ID
38104781
Abstract
BACKGROUND AND PURPOSE
Deep learning techniques excel in MR-based CT synthesis, but missing uncertainty prediction limits its clinical use in proton therapy. We developed an uncertainty-aware framework and evaluated its efficiency in robust proton planning.
MATERIALS AND METHODS
A conditional generative-adversarial network was trained on 64 brain tumour patients with paired MR-CT images to generate synthetic CTs (sCT) from combined T1-T2 MRs of three orthogonal planes. A Bayesian neural network predicts Laplacian distributions for all voxels with parameters (μ, b). A robust proton plan was optimized using three sCTs of μ and μ±b. The dosimetric differences between the plan from sCT (sPlan) and the recalculated plan (rPlan) on planning CT (pCT) were quantified for each patient. The uncertainty-aware robust plan was compared to conventional robust (global ±3%) and non-robust plans.
RESULTS
In 8-fold cross-validation, sCT-pCT image differences (Mean-Absolute-Error) were 80.84±9.84HU (body), 35.78±6.07HU (soft tissues) and 221.88±31.69HU (bones), with Dice scores of 90.33±2.43%, 95.13±0.80%, and 85.53±4.16%, respectively. The uncertainty distribution positively correlated with absolute prediction error (Correlation Coefficient: 0.62±0.01). The uncertainty-conditioned robust optimisation improved the rPlan-sPlan agreement, e.g., D95 absolute difference (CTV) was 1.10±1.24% compared to conventional (1.64±2.71%) and non-robust (2.08±2.96 %) optimisation. This trend was consistent across all target and organs-at-risk indexes.
CONCLUSION
The enhanced framework incorporates 3D uncertainty prediction and generates high-quality sCTs from MR images. The framework also facilitates conditioned robust optimisation, bolstering proton plan robustness against network prediction errors. The innovative feature of uncertainty visualisation and robust analyses contribute to evaluating sCT clinical utility for individual patients.
Deep learning techniques excel in MR-based CT synthesis, but missing uncertainty prediction limits its clinical use in proton therapy. We developed an uncertainty-aware framework and evaluated its efficiency in robust proton planning.
MATERIALS AND METHODS
A conditional generative-adversarial network was trained on 64 brain tumour patients with paired MR-CT images to generate synthetic CTs (sCT) from combined T1-T2 MRs of three orthogonal planes. A Bayesian neural network predicts Laplacian distributions for all voxels with parameters (μ, b). A robust proton plan was optimized using three sCTs of μ and μ±b. The dosimetric differences between the plan from sCT (sPlan) and the recalculated plan (rPlan) on planning CT (pCT) were quantified for each patient. The uncertainty-aware robust plan was compared to conventional robust (global ±3%) and non-robust plans.
RESULTS
In 8-fold cross-validation, sCT-pCT image differences (Mean-Absolute-Error) were 80.84±9.84HU (body), 35.78±6.07HU (soft tissues) and 221.88±31.69HU (bones), with Dice scores of 90.33±2.43%, 95.13±0.80%, and 85.53±4.16%, respectively. The uncertainty distribution positively correlated with absolute prediction error (Correlation Coefficient: 0.62±0.01). The uncertainty-conditioned robust optimisation improved the rPlan-sPlan agreement, e.g., D95 absolute difference (CTV) was 1.10±1.24% compared to conventional (1.64±2.71%) and non-robust (2.08±2.96 %) optimisation. This trend was consistent across all target and organs-at-risk indexes.
CONCLUSION
The enhanced framework incorporates 3D uncertainty prediction and generates high-quality sCTs from MR images. The framework also facilitates conditioned robust optimisation, bolstering proton plan robustness against network prediction errors. The innovative feature of uncertainty visualisation and robust analyses contribute to evaluating sCT clinical utility for individual patients.
Date Issued
2024-02
Publication Type
Article
Subject(s)
Subjects
Brain Tumours MR-based CT Synthesis Proton Therapy Robust Planning Uncertainty Estimation
Language(s)
en
Author(s)
Li, Xia | |
Bellotti, Renato | |
Meier, Gabriel | |
Bachtiary, Barbara | |
Lomax, Antony | |
Buhmann, Joachim | |
Zhang, Ye |
Additional Credits
Journal
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Publisher
Elsevier Scientific Publ. Ireland
ISSN
1879-0887
Access(Rights)
open.access