An MR-only deep learning inference model-based dose estimation algorithm for MR-guided adaptive radiation therapy.

Abstract

Background: Magnetic resonance-guided adaptive radiation therapy (MRgART) systems combine Magnetic resonance imaging (MRI) technology with linear accelerators (LINAC) to enhance the precision and efficacy of cancer treatment. These systems enable real-time adjustments of treatment plans based on the latest patient anatomy, creating an urgent need for accurate and rapid dose calculation algorithms. Traditional CT-based dose calculations and ray-tracing (RT) processes are time-consuming and may not be feasible for the online adaptive workflow required in MRgART. Recent advancements in deep learning (DL) offer promising solutions to overcome these limitations.

Purpose: This study aims to develop a DL-based dose calculation engine for MRgART that relies solely on MR images. This approach addresses the critical need for accurate and rapid dose calculations in the MRgART workflow without relying on CT images or time-consuming RT processes.

Methods: We used a deep residual network inspired by U-Net to establish a direct connection between distance-corrected conical (DCC) fluence maps and dose distributions in the image domain. The study utilized data from 30 prostate cancer patients treated with fixed-beam Intensity-Modulated Radiation Therapy (IMRT) on an MR-guided LINAC system. We trained, validated, and tested the model using a total of 120 online treatment plans, which encompassed 1080 individual beams. We extensively evaluated the network's performance by comparing its dose calculation accuracy against Monte Carlo (MC)-based methods using metrics such as mean absolute error (MAE) of pixel-wise dose differences, 3D gamma analysis, dose-volume histograms (DVHs), dosimetric indices, and isodose line similarity.

Results: The proposed DL model demonstrated high accuracy in dose calculations. The median MAE of pixel-wise dose differences was 1.2% for the whole body, 1.9% for targets, and 1.1% for organs at risk (OARs). The median 3D gamma passing rates for the 3%/3  mm criterion were 94.8% for the whole body, 95.7% for targets, and 98.7% for OARs. Additionally, the Dice similarity coefficient (DSC) of isodose lines between the DL-based and MC-based dose calculations averaged 0.94 ± 0.01. There were no big differences between the DL-based and MC-based calculations in the DVH curves and clinical dosimetric indices. This proved that the two methods were clinically equivalent.

Conclusion: This study presents a novel MR-only dose calculation engine that eliminates the need for CT images and complex RT processes. By leveraging DL, the proposed method significantly enhances the efficiency and accuracy of the MRgART workflow, particularly for prostate cancer treatment. This approach holds potential for broader applications across different cancer types and MR-linac systems, paving the way for more streamlined and precise radiation therapy planning.