arXiv subjects

Anders Garpebring

Publications and source records attributed to Anders Garpebring.

5 recordsLinked to original sources

arXiv2025

A physics-informed, plug-and-play dose engine for gradient-based radiotherapy treatment planning

Radiotherapy treatment planning remains a time-intensive iterative process requiring expert intervention in commercial treatment planning system (TPS). While machine learning approaches have demonstrated promise, most remain depedent on TPS-based dose calculation or surrogate dose models, preventing direct optimization of deliverable treatment plan parameters. We propose PyDoseRT (PDRT), a physics-informed, GPU-accelerated dose engine implemented in PyTorch that computes dose distributions directly from treatment delivery parameters (i.e., MLC leaf positions, jaw positions, gantry angles, and monitor units). The engine preserves gradient information throughout the dose computation pipeline, enabling gradient-based optimization of hardware-constrained treatment plans without the reliance on a commercial TPS. PDRT was evaluated on 19 and 162 clinical VMAT prostate cancer plans from two hospitals (with different treatment machines). When recalculating clinical plans, PDRT achieved high 3D gamma pass rates (mean 96.8% for 2%/2 mm and 98.9% for 3%/3 mm, depending on cohort). All optimized plans converged to clinically acceptable solutions and passed deliverability verification when imported into a commercial TPS. This physics-informed framework eliminates TPS dependency for radiotherapy optimization research by enabling gradient-based planning while ensuring that delivery parameters remain in the machine-feasible range. The gradient-enabled dose engine allows exploration of novel optimization strategies and objective functions while maintaining clinical validity. The proposed approach provides a research platform for investigating real-time adaptive radiotherapy concepts, automated planning workflows, and TPS-independent optimization strategies, and democratizing radiotherapy research, by exposing gradient-enabled, hardware-aware, open-source dose computation.

physics.med-ph↗

arXiv2022

Reproducibility of the Methods in Medical Imaging with Deep Learning

Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The relevance of machine learning research can only be improved if we also employ empirical rigor that incorporates reproducibility guidelines, especially so in the medical imaging field. The Medical Imaging with Deep Learning (MIDL) conference has made advancements in this direction by advocating open access, and recently also recommending authors to make their code public - both aspects being adopted by the majority of the conference submissions. This helps the reproducibility of the methods, however, there is currently little or no support for further evaluation of these supplementary material, making them vulnerable to poor quality, which affects the impact of the entire submission. We have evaluated all accepted full paper submissions to MIDL between 2018 and 2022 using established, but slightly adjusted guidelines on reproducibility and the quality of the public repositories. The evaluations show that publishing repositories and using public datasets are becoming more popular, which helps traceability, but the quality of the repositories has not improved over the years, leaving room for improvement in every aspect of designing repositories. Merely 22% of all submissions contain a repository that were deemed repeatable using our evaluations. From the commonly encountered issues during the evaluations, we propose a set of guidelines for machine learning-related research for medical imaging applications, adjusted specifically for future submissions to MIDL.

cs.LG↗

arXiv2019

Statistical Methods in Computed Tomography Image Estimation

Purpose: There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images can be utilised for attenuation correction, patient positioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introduce a novel statistical learning approach for improving CT estimation from MR images and to compare the performance of our method with the existing model based CT image estimation methods. Methods: The statistical learning approach proposed here consists of two stages. At the training stage, prior knowledges about tissue-types from CT images were used together with a Gaussian mixture model (GMM) to explore CT image estimations from MR images. Since the prior knowledges are not available at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimate the tissue-types from MR images. For a new patient, the trained classifier and GMMs were used to predict CT image from MR images. The classifier and GMMs were validated by using voxel level 10-fold cross-validation and patient-level leave-one-out cross-validation, respectively. Results: The proposed approach has outperformance in CT estimation quality in comparison with the existing model based methods, especially on bone tissues. Our method improved CT image estimation by 5% and 23% on the whole brain and bone tissues, respectively. Conclusions: Evaluation of our method shows that it is a promising method to generate CT image substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications.

stat.ME↗

arXiv2017

Sparsity estimation in compressive sensing with application to MR images

The theory of compressive sensing (CS) asserts that an unknown signal $\mathbf{x} \in \mathbb{C}^N$ can be accurately recovered from $m$ measurements with $m\ll N$ provided that $\mathbf{x}$ is sparse. Most of the recovery algorithms need the sparsity $s=\lVert\mathbf{x}\rVert_0$ as an input. However, generally $s$ is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. In the simulation study and real data study, magnetic resonance image data is used as input signal, which becomes sparse after sparsified transformation. The results from the simulation study confirm the theoretical properties of the estimator. In practice, the estimate from a real MR image can be used for recovering future MR images under the framework of CS if they are believed to have the same sparsity level after sparsification.

stat.ME↗

arXiv2017

Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI

The purpose of this study is to investigate a method, using simulations, to improve contrast agent quantification in Dynamic Contrast Enhanced MRI. Bayesian hierarchical models (BHMs) are applied to smaller images ($10\times10\times10$) such that spatial information can be incorporated. Then exploratory analysis is done for larger images ($64\times64\times64$) by using maximum a posteriori (MAP). For smaller images: the estimators of proposed BHMs show improvements in terms of the root mean squared error compared to the estimators in existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperforms Besag models. For larger images: MAP estimators also show improvements by assigning Leroux prior.

stat.AP↗