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This study aims to enhance the resolution of Magnetic Resonance Imaging (MRI) using a cutting-edge diffusion probabilistic Deep Learning (DL) technique, addressing the challenges posed by long image acquisition times and limited scanning dimensions. In this research, we propose a novel approach utilizing a probabilistic DL model to synthesize High-Resolution MRI (HR-MRI) images from Low-Resolution (LR) inputs. The proposed model consists of two main steps. In the forward process, Gaussian noise is systematically introduced to LR images through a Markov chain. In the reverse process, a U-Net model is trained using a loss function based on Kullback-Leibler divergence, which maximizes the likelihood of producing ground truth images. We assess the effectiveness of our method on T2-FLAIR images from 120 brain patients in the public BraTS2020 T2-FLAIR database. To gauge performance, we compare our approach with a clinical bicubic model (referred to as Bicubic) and Conditional Generative Adversarial Networks (CGAN). On the BraTS2020 dataset, our framework enhances the Peak Signal-to-Noise Ratio (PSNR) of LR images by 7%, whereas CGAN results in a 3% reduction. The corresponding Multi-scale Structural similarity (MSSIM) values for the proposed method and CGAN are 0.972±0.017 and 0.966±0.024. In this study, we have examined the potential of a diffusion probabilistic DL framework to elevate MRI image resolution. Our proposed method demonstrates the capability to generate high-quality HR images while avoiding issues such as mode collapse or learning multimodal distributions, which are commonly observed in CGAN-based approaches. This framework has the potential to significantly reduce MRI acquisition times for HR imaging, thereby mitigating the risk of motion artifacts and crosstalk.
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