Next-Generation Diagnostics: Deep Learning based Approaches for Medical Image Analysis

Abstract

High-resolution medical imaging plays a pivotal role in accurate diagnostics and effective patient care. However, the extended acquisition times required for detailed imaging often lead to patient discomfort, motion artifacts, and increased scan failures. To address these challenges, advanced deep learning approaches are emerging as transformative tools in medical imaging. In this study, we propose a conditional denoising diffusion model-based framework designed to enhance the resolution and reconstruction quality of medical images, including Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopic Imaging (MRSI). The framework incorporates a data fidelity term into the reverse sampling process to ensure consistency with physical acquisition models while improving reconstruction accuracy. Furthermore, it leverages a Self-Attention UNet architecture to upsample low-resolution MRSI data, preserving fine-grained details and critical structural information essential for clinical diagnostics. The proposed model demonstrates adaptability across varying undersampling rates and spatial resolutions, as a network trained on acceleration factor 8 generalizes effectively to other acceleration factors. Evaluations on publicly available fastMRI datasets and MRSI data highlight significant improvements over state-of-the-art methods, achieving superior metrics in SSIM, PSNR, and LPIPS while maintaining diagnostic relevance. Notably, the diffusion model excels in preserving intricate structural details, detecting small tumors, and maintaining texture integrity, particularly in glioma imaging for mapping tumor metabolism associated with IDH1 and IDH2 mutations. These findings underscore the potential of deep learning-based diffusion models to revolutionize medical imaging, enabling faster, more accurate scans and improving diagnostic workflows across clinical and research applications.