An approach for estimating dosimetric uncertainties in deformable dose accumulation in pencil beam scanning proton therapy for lung cancer

Abstract

Deformable image registration (DIR) is an important component for dose accumulation and associated clinical outcome evaluation in radiotherapy. However, the resulting deformation vector field (DVF) is subject to unavoidable discrepancies when different algorithms are applied, leading to dosimetric uncertainties of the accumulated dose. We propose here an approach for proton therapy to estimate dosimetric uncertainties as a consequence of modeled or estimated DVF uncertainties. A patient-specific DVF uncertainty model was built on the first treatment fraction, by correlating the magnitude differences of five DIR results at each voxel to the magnitude of any single reference DIR. In the following fractions, only the reference DIR needs to be applied, and DVF geometric uncertainties were estimated by this model. The associated dosimetric uncertainties were then derived by considering the estimated geometric DVF uncertainty, the dose gradient of fractional recalculated dose distribution and the direction factor from the applied reference DIR of this fraction. This estimated dose uncertainty was respectively compared to the reference dose uncertainty when different DIRs were applied individually for each dose warping. This approach was validated on seven NSCLC patients, each with nine repeated CTs. The proposed model-based method is able to achieve dose uncertainty distribution on a conservative voxel-to-voxel comparison within ±5% of the prescribed dose to the 'reference' dosimetric uncertainty, for 77% of the voxels in the body and 66%-98% of voxels in investigated structures. We propose a method to estimate DIR induced uncertainties in dose accumulation for proton therapy of lung tumor treatments.

Clinical necessity of multi-image based (4DMIB) optimization for targets affected by respiratory motion and treated with scanned particle therapy – A comprehensive review

“…Some 4D MIB optimization implementations as well as 4D evaluation relied on deformable image registration (DIR). Deformation vector fields are subject to discrepancies when different algorithms are applied, leading to dosimetric uncertainties of accumulated dose distributions [78], [79], [80], [81].…”

The dose accumulation and the impact of deformable image registration on dose reporting parameters in a moving patient undergoing proton radiotherapy

“…Compared to similar studies in the field, the novelty lies in the simultaneous evaluation of landmark associated errors and uncertainties of the dose associated quantitative measures. 46 …”

Deformable Image Registration Uncertainty Quantification Using Deep Learning for Dose Accumulation in Adaptive Proton Therapy

“…We train networks to predict the uncertainty associated with three existing DIR algorithms: a b-spline and a demon implementation in Plastimatch and a non-diffeomorphic VoxelMorph predicting both µ z|f,m and Σ z|f,m . The parameters for b-spline and demon are taken from [17,2]. Furthermore, we verify whether these networks can be used to predict the uncertainty of other DIR algorithms by evaluating them on the results of a commercial DIR in Velocity.…”

“…Verifying whether the dosimetric uncertainty is realistic is non-trivial. Previous work [17,2] quantified it by warping the dose with several DIR algorithms and calculating the dose differences between the results. Similarly, here we verify whether the warped dose with three conventional DIR algorithms falls in between our predicted lower and upper bound (Fig.…”