Affiliation:
1. Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology University of Texas Southwestern Medical Center Dallas Texas USA
Abstract
AbstractBackgroundMRI‐only radiotherapy planning (MROP) is beneficial to patients by avoiding MRI/CT registration errors, simplifying the radiation treatment simulation workflow and reducing exposure to ionizing radiation. MRI is the primary imaging modality for soft tissue delineation. Treatment planning CTs (i.e., CT simulation scan) are redundant if a synthetic CT (sCT) can be generated from the MRI to provide the patient positioning and electron density information. Unsupervised deep learning (DL) models like CycleGAN are widely used in MR‐to‐sCT conversion, when paired patient CT and MR image datasets are not available for model training. However, compared to supervised DL models, they cannot guarantee anatomic consistency, especially around bone.PurposeThe purpose of this work was to improve the sCT accuracy generated from MRI around bone for MROP.MethodsTo generate more reliable bony structures on sCT images, we proposed to add bony structure constraints in the unsupervised CycleGAN model's loss function and leverage Dixon constructed fat and in‐phase (IP) MR images. Dixon images provide better bone contrast than T2‐weighted images as inputs to a modified multi‐channel CycleGAN. A private dataset with a total of 31 prostate cancer patients were used for training (20) and testing (11).ResultsWe compared model performance with and without bony structure constraints using single‐ and multi‐channel inputs. Among all the models, multi‐channel CycleGAN with bony structure constraints had the lowest mean absolute error, both inside the bone and whole body (50.7 and 145.2 HU). This approach also resulted in the highest Dice similarity coefficient (0.88) of all bony structures compared with the planning CT.ConclusionModified multi‐channel CycleGAN with bony structure constraints, taking Dixon‐constructed fat and IP images as inputs, can generate clinically suitable sCT images in both bone and soft tissue. The generated sCT images have the potential to be used for accurate dose calculation and patient positioning in MROP radiation therapy.