DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding

Abstract

Reducing the radiation dose in computed tomography (CT) is important to mitigate radiation-induced risks. One option is to employ a well-trained model to compensate for incomplete information and map sparse-view measurements to the CT reconstruction. However, reconstruction from sparsely sampled measurements is insufficient to uniquely characterize an object in CT, and a learned prior model may be inadequate for unencountered cases. Medical modal translation from magnetic resonance imaging (MRI) to CT is an alternative but may introduce incorrect information into the synthesized CT images in addition to the fact that there exists no explicit transformation describing their relationship. To address these issues, we propose a novel framework called the denoising diffusion model for medical image synthesis (DDMM-Synth) to close the performance gaps described above. This framework combines an MRI-guided diffusion model with a new CT measurement embedding reverse sampling scheme. Specifically, the null-space content of the one-step denoising result is refined by the MRI-guided data distribution prior, and its range-space component derived from an explicit operator matrix and the sparse-view CT measurements is directly integrated into the inference stage. DDMM-Synth can adjust the projection number of CT a posteriori for a particular clinical application and its modified version can even improve the results significantly for noisy cases. Our results show that DDMM-Synth outperforms other state-of-the-art supervisedlearning-based baselines under fair experimental conditions.

An Empirical Analysis of Diffusion, Autoencoders, and Adversarial Deep Learning Models for Predicting Dementia Using High-Fidelity MRI

“…The paper [11] depicted the utility of a Cascaded Diffusion model specifically for high-fidelity image generation, showed thorough experimentation, and proposed the diffusion model paradigm for the aforementioned statement. The article [17] introduces a new approach to medical image synthesis called the denoising diffusion model for medical image synthesis (DDMM-Synth). The primary goal of this framework is to decrease radiation exposure during computed tomography (CT) scans while addressing several issues such as incomplete information, limitations of learned prior models, and errors in translating medical modalities from magnetic resonance imaging (MRI) to CT.…”

“…The primary goal of this framework is to decrease radiation exposure during computed tomography (CT) scans while addressing several issues such as incomplete information, limitations of learned prior models, and errors in translating medical modalities from magnetic resonance imaging (MRI) to CT. This approach [17] can be used to optimize the projection number of CT for specific clinical applications and can significantly enhance results for cases with noise. In the paper [18], a novel method named Light and Effective Generative Adversarial Network (LEGAN) is introduced to generate high-quality medical images in a lightweight manner.…”