Compressed radiotherapy treatment planning (CompressRTP): A new paradigm for rapid and high-quality treatment planning optimization

TL;DR

This paper introduces a novel approach to radiotherapy treatment planning that leverages the high compressibility of the dose influence matrix, enabling faster and more accurate optimization of treatment plans.

Contribution

The study demonstrates that the scattering component of the dose influence matrix is low-rank and can be efficiently compressed, improving optimization speed and quality.

Findings

The scattering matrix exhibits exponential decay in singular values, indicating low-rank structure.

Compressed influence matrices produce treatment plans with comparable dosimetric quality to uncompressed methods.

The proposed algorithm effectively decomposes the influence matrix without direct access to scattering data.

Abstract

Background: Radiotherapy treatment planning involves solving large-scale optimization problems that are often approximated and solved sub-optimally due to time constraints. Central to these problems is the dose influence matrix which quantifies the radiation dose delivered from each beamlet to each voxel. Our findings demonstrate that this matrix is highly compressible, enabling a compact representation of the optimization problems and allowing them to be solved more efficiently and accurately. Methods: We precompute the primary (S) and scattering (L) dose contributions of the dose influence matrix A separately for photon therapy, expressed as: A = S + L. Our analysis reveals that the singular values of the scattering matrix L exhibit exponential decay, indicating that L is a low-rank matrix. This allows us to compress L into two smaller matrices: L=HW, where r is relatively small (approximately 5 to 10). Since the primary dose matrix S is sparse, this supports the use of the well-established "sparse-plus-low-rank" decomposition technique for the influence matrix A, approximated as: A = S + H * W. We introduce an efficient algorithm for sparse-plus-low-rank matrix decomposition, even without direct access to the scattering matrix. This algorithm is applied to optimize treatment plans for ten lung and ten prostate patients, using both compressed and sparsified versions of matrix A. We then evaluate the dose discrepancy between the optimized and final plans. We also integrate this compression technique with our in-house automated planning system, ECHO, and evaluate the dosimetric quality of the generated plans with and without compression.