Double U-Net CycleGAN for 3D MR to CT image synthesis.

PURPOSE: CycleGAN and its variants are widely used in medical image synthesis, which can use unpaired data for medical image synthesis. The most commonly used method is to use a Generative Adversarial Network (GAN) model to process 2D slices and thereafter concatenate all of these slices to 3D medical images. Nevertheless, these methods always bring about spatial inconsistencies in contiguous slices. We offer a new model based on the CycleGAN to work out this problem, which can achieve high-quality conversion from magnetic resonance (MR) to computed tomography (CT) images. METHODS: To achieve spatial consistencies of 3D medical images and avoid the memory-heavy 3D convolutions, we reorganized the adjacent 3 slices into a 2.5D slice as the input image. Further, we propose a U-Net discriminator network to improve accuracy, which can perceive input objects locally and globally. Then, the model uses Content-Aware ReAssembly of Features (CARAFE) upsampling, which has a large field of view and content awareness takes the place of using a settled kernel for all samples. RESULTS: The mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) for double U-Net CycleGAN generated 3D image synthesis are 74.56±10.02, 27.12±0.71 and 0.84±0.03, respectively. Our method achieves preferable results than state-of-the-art methods. CONCLUSION: The experiment results indicate our method can realize the conversion of MR to CT images using ill-sorted pair data, and achieves preferable results than state-of-the-art methods. Compared with 3D CycleGAN, it can synthesize better 3D CT images with less computation and memory.

An MRI-only Three-dimensional Cephalometry Protocol based on the Integrated and Modular Architecture of the Human Head.

BACKGROUND: Currently, three-dimensional cephalometry measurements are mainly based on cone beam computed tomography (CBCT), which has limitations of ionizing radiation, lack of soft tissue information, and lack of standardization of median sagittal plane establishment. OBJECTIVES: This study investigated magnetic resonance imaging (MRI)-only based 3D cephalometry measurement based on the integrated and modular characteristics of the human head. METHODS: Double U-Net CycleGAN was used for CT image synthesis from MRI. This method enabled the synthesis of a CT-like image from MRI and measurements were made using 3D slicer registration and fusion. RESULTS: A protocol for generating and optimizing MRI-based synthetic CT was described and found to meet the precision requirements of 3D head measurement using MRI midline positioning methods reported in neuroscience to establish the median sagittal plane. An MRI-only reference frame and coordinate system were established enabling an MRI-only cephalometric analysis protocol that combined the dual advantages of soft and hard tissue display. The protocol was devised using data from a single volunteer and validation data from a larger sample remains to be collected. CONCLUSION: The reported method provided a new protocol for MRI-only cephalometric analysis of craniofacial growth and development, malformation occurrence, treatment planning, and outcomes.