MRI data consistency guided conditional diffusion probabilistic model for MR imaging acceleration

The long acquisition time required for high-resolution Magnetic Resonance Imaging (MRI) leads to patient discomfort, increased likelihood of voluntary and involuntary movements, and reduced throughput in imaging centers. This study proposed a novel method that leverages MRI physics to incorporate data consistency during the training of a conditional diffusion probabilistic model, which we refer to as the data consistency-guided conditional diffusion probabilistic model (DC-CDPM). This model aimed to reconstruct high-resolution contrast enhanced T1W MRI from partially sampled data. The DC-CDPM utilized the conjugate gradient optimization method to minimize data consistency loss between reconstructed MRI images and fully sampled unknown MRI images. Further, a diffusion probabilistic model conditioned on the optimization's output was trained to reconstruct the fully sampled MRI. The publicly available dataset of 230 post-surgery patients with different brain tumors was used in this study to train the model. The equidistant under-sampling method was implemented to simulate four different under-sampling levels. The qualitative and quantitative comparisons were done between DC-CDPM and an exactly similar CDPM model except not conditioned on the optimization output. Qualitatively, the DC-CDPM could reconstruct fully sampled images compared with CDPM. Furthermore, the image profile along a tumor indicated better performance of DC-CDPM. Quantitatively, the DC-CDPM outperformed CDPM in four out of six quantitative metrics and had a consistent performance throughout the different under-sampling levels. Our method could allow us to perform brain imaging with substantially lower acquisition time while achieving similar image quality of fully sampled MRI images with a long acquisition time.