Multicriterial CNN based beam generation for robotic radiosurgery of the prostate

Theresa Hofmann,Stefan Gerlach,Christoph Fürweger,Alexander Schlaefer

doi:10.1515/cdbme-2020-0030

Theresa Hofmann, Stefan Gerlach + Show 2 more

Open Access

https://doi.org/10.1515/cdbme-2020-0030

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Abstract

Abstract Although robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner. Furthermore, different clinical goals have to be considered during planning and generally different sets of beams correspond to different clinical goals. Typically, candidate beams sampled from a randomized heuristic form the basis for treatment planning. We propose a new approach to generate candidate beams based on deep learning using radiological features as well as the desired constraints. We demonstrate that candidate beams generated for specific clinical goals can improve treatment plan quality. Furthermore, we compare two approaches to include information about constraints in the prediction. Our results show that CNN generated beams can improve treatment plan quality for different clinical goals, increasing coverage from 91.2 to 96.8% for 3,000 candidate beams on average. When including the clinical goal in the training, coverage is improved by 1.1% points.

Highlights

Robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner
One system used in clinical practice is the CyberKnife system [1] where a linear accelerator is mounted on a robotic arm
We investigate the multicriterial aspect of treatment planning on the CNN based candidate beam generation

Summary

Introduction

Robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner. Other knowledge based methods are employed to optimize beam related parameters in intensity modulated radiation therapy (IMRT) [4, 5] or to optimize beam orientations, positions, shapes, and weights directly (direct aperture optimization) The latter either requires solving a computationally demanding mixed integer problem [6] or they combine the dose in the target, dose constraints, and apertures in the objective function [7], not allowing setting hard constraints on the doses of critical organ structures. We extend an earlier approach [9] and present different setups to train CNNs using radiological features for predicting each beam’s influence on the dose with various clinical goals We use these predictions to select new candidate beams, improving plan quality while using fewer candidate beams. We train and evaluate the CNNs on different subsets of 50 patients previously treated for prostate tumor

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Results

Conclusion