Abstract
Since the advent of generative models, deep learning-based methods for generating high-resolution, photorealistic 2D images have made significant successes. However, it is still difficult to create precise 3D image data with 12-bit depth used in clinical settings that capture the anatomy and pathology of CT and MRI scans. Using a score-based diffusion model, we propose a slice-based method that generates 3D images from previous 2D CT slices along the inferior direction. We call this method stochastic differential equations with adjacent slice-based conditional iterative inpainting (ASCII). We also propose an intensity calibration network (IC-Net) that adjusts the among slices intensity mismatch caused by 12-bit depth image generation. As a result, Frechet Inception Distance (FIDs) scores of FID-Ax, FID-Cor and FID-Sag of ASCII(2) with IC-Net were 14.993, 19.188 and 19.698, respectively. Anatomical continuity of the generated 3D image along the inferior direction was evaluated by an expert radiologist with more than 15 years of experience. In the analysis of eight anatomical structures, our method was evaluated to be continuous for seven of the structures.
J. Jeong and K. D. Kim—Contributed equally.
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Jeong, J. et al. (2023). Generating High-Resolution 3D CT with 12-Bit Depth Using a Diffusion Model with Adjacent Slice and Intensity Calibration Network. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_35
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