Patient-specific 3D volumetric CBCT image reconstruction with single x-ray projection using denoising diffusion probabilistic model

Shaoyan Pan; Shao-Yuan Lo; Chih-Wei Chang; Ellahheh Salari; Tonghe Wang; Justin Roper; Aparna H. Kesarwala; Xiaofeng Yang

doi:10.1117/12.3006561

2 April 2024 Patient-specific 3D volumetric CBCT image reconstruction with single x-ray projection using denoising diffusion probabilistic model

Shaoyan Pan, Shao-Yuan Lo, Chih-Wei Chang, Ellahheh Salari, Tonghe Wang, Justin Roper, Aparna H. Kesarwala, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 129310P (2024) https://doi.org/10.1117/12.3006561
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

This study proposes an innovative 3D diffusion-based model called the Cycle-consistency Geometric-integrated X-ray to Computed Tomography Denoising Diffusion Probabilistic Model (X-CBCT-DDPM). The X-CBCT-DDPM is designed to effectively reconstruct volumetric Cone-Beam CBCTs (CBCTs) from a single X-ray projection from any angle, reducing the number of required projections and minimizing patient radiation exposure in acquiring volumetric images. In contrast to the traditional DDPMs, the X-CBCT-DDPM utilizes dual DDPMs: one for generating full-view x-ray projections and another for volumetric CBCT reconstruction. These dual networks synergistically enhance each other's learning capabilities, leading to improved reconstructed CBCT quality with high anatomical accuracy. The proposed patient-specific X-CBCT-DDPM was tested using 4DCBCT data from ten patients, with each patient's dataset comprising ten phases of 3D CBCTs to simulate CBCTs and Cone-Beam X-ray projections. For model training, eight phases of 3D CBCTs from each patient were utilized, with one for validation purposes and the remaining one reserved for final testing. The X-CBCT-DDPM exhibits superior performance to DDPM, conditional Generative Adversarial Networks (GAN), and Vnet, in terms of various metrics, including a Mean Absolute Error (MAE) of 36.36±4.04, Peak Signal-to-Noise Ratio (PSNR) of 32.83±0.98, Structural Similarity Index (SSIM) of 0.91±0.01, and Fréchet Inception Distance (FID) of 0.32±0.02. These results highlight the model's potential for ultra-sparse projection-based CBCT reconstruction.

Conference Presentation

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Shaoyan Pan, Shao-Yuan Lo, Chih-Wei Chang, Ellahheh Salari, Tonghe Wang, Justin Roper, Aparna H. Kesarwala, and Xiaofeng Yang "Patient-specific 3D volumetric CBCT image reconstruction with single x-ray projection using denoising diffusion probabilistic model", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 129310P (2 April 2024); https://doi.org/10.1117/12.3006561

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