Efficient Radiation Treatment Planning based on Voxel Importance
- URL: http://arxiv.org/abs/2405.03880v1
- Date: Mon, 6 May 2024 21:55:19 GMT
- Title: Efficient Radiation Treatment Planning based on Voxel Importance
- Authors: Sebastian Mair, Anqi Fu, Jens Sjölund,
- Abstract summary: We propose to reduce the optimization problem by only using a representative subset of informative voxels.
By solving a reduced version of the original optimization problem using this subset, we effectively reduce the problem's size and computational demands.
Empirical experiments on open benchmark data highlight substantially reduced optimization times, up to 50 times faster than the original ones.
- Score: 1.9712632719704106
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Optimization is a time-consuming part of radiation treatment planning. We propose to reduce the optimization problem by only using a representative subset of informative voxels. This way, we improve planning efficiency while maintaining or enhancing the plan quality. To reduce the computational complexity of the optimization problem, we propose to subsample the set of voxels via importance sampling. We derive a sampling distribution based on an importance score that we obtain from pre-solving an easy optimization problem involving a simplified probing objective. By solving a reduced version of the original optimization problem using this subset, we effectively reduce the problem's size and computational demands while accounting for regions in which satisfactory dose deliveries are challenging. In contrast to other stochastic (sub-)sampling methods, our technique only requires a single sampling step to define a reduced optimization problem. This problem can be efficiently solved using established solvers. Empirical experiments on open benchmark data highlight substantially reduced optimization times, up to 50 times faster than the original ones, for intensity-modulated radiation therapy (IMRT), all while upholding plan quality comparable to traditional methods. Our approach has the potential to significantly accelerate radiation treatment planning by addressing its inherent computational challenges. We reduce the treatment planning time by reducing the size of the optimization problem rather than improving the optimization method. Our efforts are thus complementary to much of the previous developments.
Related papers
- Analyzing and Enhancing the Backward-Pass Convergence of Unrolled
Optimization [50.38518771642365]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form.
This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method.
A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
arXiv Detail & Related papers (2023-12-28T23:15:18Z) - Refined Coreset Selection: Towards Minimal Coreset Size under Model
Performance Constraints [69.27190330994635]
Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms.
We propose an innovative method, which maintains optimization priority order over the model performance and coreset size.
Empirically, extensive experiments confirm its superiority, often yielding better model performance with smaller coreset sizes.
arXiv Detail & Related papers (2023-11-15T03:43:04Z) - Optimal Guarantees for Algorithmic Reproducibility and Gradient
Complexity in Convex Optimization [55.115992622028685]
Previous work suggests that first-order methods would need to trade-off convergence rate (gradient convergence rate) for better.
We demonstrate that both optimal complexity and near-optimal convergence guarantees can be achieved for smooth convex minimization and smooth convex-concave minimax problems.
arXiv Detail & Related papers (2023-10-26T19:56:52Z) - Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic
Optimization [1.1612308609123565]
We develop a prescriptive dimensionality reduction framework that aims to minimize the degree of suboptimality in the optimization phase.
For the case where the downstream optimization problem has an expected value objective, we show that prescriptive dimensionality reduction can be performed via solving a distributionally-robust optimization problem.
Our approach significantly outperforms principal component analysis with real and synthetic data sets.
arXiv Detail & Related papers (2023-06-04T00:50:35Z) - A Data-Driven Evolutionary Transfer Optimization for Expensive Problems
in Dynamic Environments [9.098403098464704]
Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems.
This paper proposes a simple but effective transfer learning framework to empower data-driven evolutionary optimization to solve dynamic optimization problems.
Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm.
arXiv Detail & Related papers (2022-11-05T11:19:50Z) - Outlier-Robust Sparse Estimation via Non-Convex Optimization [73.18654719887205]
We explore the connection between high-dimensional statistics and non-robust optimization in the presence of sparsity constraints.
We develop novel and simple optimization formulations for these problems.
As a corollary, we obtain that any first-order method that efficiently converges to station yields an efficient algorithm for these tasks.
arXiv Detail & Related papers (2021-09-23T17:38:24Z) - Latent Space Arc Therapy Optimization [1.1186291300604743]
arc therapy planning is a challenging problem in high-dimensional, non-informed optimization.
In this paper we address the issue of arc therapy optimization with unsupervised deep learning.
An engine is built based on low-dimensional arc representations which facilitates faster planning times.
arXiv Detail & Related papers (2021-05-24T19:06:00Z) - Decomposition and Adaptive Sampling for Data-Driven Inverse Linear
Optimization [12.610576072466895]
This work addresses inverse linear optimization where the goal is to infer the unknown cost vector of a linear program.
We introduce a new formulation of the problem that, compared to other existing methods, allows the recovery of a less restrictive and generally more appropriate admissible set of cost estimates.
arXiv Detail & Related papers (2020-09-16T22:25:31Z) - Automatically Learning Compact Quality-aware Surrogates for Optimization
Problems [55.94450542785096]
Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.
Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making.
We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
arXiv Detail & Related papers (2020-06-18T19:11:54Z) - Effective Dimension Adaptive Sketching Methods for Faster Regularized
Least-Squares Optimization [56.05635751529922]
We propose a new randomized algorithm for solving L2-regularized least-squares problems based on sketching.
We consider two of the most popular random embeddings, namely, Gaussian embeddings and the Subsampled Randomized Hadamard Transform (SRHT)
arXiv Detail & Related papers (2020-06-10T15:00:09Z) - Tiering as a Stochastic Submodular Optimization Problem [5.659969270836789]
Tiering is an essential technique for building large-scale information retrieval systems.
We show that the optimal tiering as an optimization problem can be cast as a submodular minimization problem with a submodular knapsack constraint.
arXiv Detail & Related papers (2020-05-16T07:39:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.