Technical note: A method to synthesize magnetic resonance images in different patient rotation angles with deep learning for gantry-free radiotherapy.

Abstract

Recently, patient rotating devices for gantry-free radiotherapy, a new approach to implement external beam radiotherapy, have been introduced. When a patient is rotated in the horizontal position, gravity causes anatomic deformation. For treatment planning, one feasible method is to acquire simulation images at different horizontal rotation angles. This study aimed to investigate the feasibility of synthesizing magnetic resonance (MR) images at patient rotation angles of 180° (prone position) and 90° (lateral position) from those at a rotation angle of 0° (supine position) using deep learning. This study included 23 healthy male volunteers. They underwent MR imaging (MRI) in the supine position and then in the prone (23 volunteers) and lateral (16 volunteers) positions. T1-weighted fast spin echo was performed for all positions with the same parameters. Two two-dimensional deep learning networks, pix2pix generative adversarial network (pix2pix GAN) and CycleGAN, were developed for synthesizing MR images in the prone and lateral positions from those in the supine position, respectively. For the evaluation of the models, leave-one-out cross-validation was performed. The mean absolute error (MAE), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were used to determine the agreement between the prediction and ground truth for the entire body and four specific organs. For pix2pix GAN, the synthesized images were visually bad, and no quantitative evaluation was performed. The quantitative evaluation metrics of the body outlines calculated for the synthesized prone and lateral images using CycleGAN were as follows: MAE, 35.63±3.98 and 40.45±5.83, respectively; DSC, 0.97±0.01 and 0.94±0.01, respectively; and HD (in pixels), 16.74±3.55 and 31.69±12.03, respectively. The quantitative metrics of the bladder and prostate performed were also promising for both the prone and lateral images, with mean values>0.90 in DSC (p>0.05). The mean DSC and HD values of the bilateral femur for the prone images were 0.96 and 3.63 (in pixels), respectively, and 0.78 and 12.65 (in pixels) for the lateral images, respectively (p<0.05). The CycleGAN could synthesize the MRI at lateral and prone positions using images at supine position, and it could benefit gantry-free radiation therapy.