Resolution-dependent MRI-to-CT translation for orthotopic breast cancer models using deep learning.

Objective.This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity computed tomography (CT) images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiation while maintaining treatment accuracy and accelerating MR image acquisition. The primary focus is to determine the extent to which low-resolution MR images can be utilized to generate high-quality CT images through a systematic study of spatial resolution-dependent magnetic resonance imaging (MRI)-to-CT image conversion.Approach.Paired MRI-CT images were acquired from healthy control and tumor models, generated by injecting MDA-MB-231 and 4T1 tumor cells into the mammary fat pad of nude and BALB/c mice to ensure model diversification. To explore various MRI resolutions, we downscaled the highest-resolution MR image into three lower resolutions. Using a customized U-Net model, we automated region of interest masking for both MRI and CT modalities with precise alignment, achieved through three-dimensional affine paired MRI-CT registrations. Then our customized models, Nested U-Net GAN and Attention U-Net GAN, were employed to translate low-resolution MR images into high-resolution CT images, followed by evaluation with separate testing datasets.Main Results.Our approach successfully generated high-quality CT images (0.14²mm²) from both lower-resolution (0.28²mm²) and higher-resolution (0.14²mm²) MR images, with no statistically significant differences between them, effectively doubling the speed of MR image acquisition. Our customized GANs successfully preserved anatomical details, addressing the typical loss issue seen in other MRI-CT translation techniques across all resolutions of MR image inputs.Significance.This study demonstrates the potential of using low-resolution MR images to generate high-quality CT images, thereby reducing radiation exposure and expediting MRI acquisition while maintaining accuracy for radiotherapy.