Advancements in 7T magnetic resonance diffusion imaging: Technological innovations and applications in neuroimaging

Abstract

Abstract The development of 7‐Tesla (7T) magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths, especially in neuroscience research. This review investigates whether 7T diffusion imaging offers significant benefits over lower field strengths by addressing the following: Technical challenges and corresponding strategies: Challenges include achieving shorter transverse relaxation/effective transverse relaxation times and greater B0 and B1 inhomogeneities. Advanced techniques including high‐performance gradient systems, parallel imaging, multi‐shot acquisition, and parallel transmission can mitigate these issues. Comparison of 3‐Tesla and 7T diffusion imaging: Technologies such as multiplexed sensitivity encoding and deep learning reconstruction (DLR) have been developed to mitigate artifacts and improve image quality. This comparative analysis demonstrates significant improvements in the signal‐to‐noise ratio and spatial resolution at 7T with a powerful gradient system, facilitating enhanced visualization of microstructural changes. Despite greater geometric distortions and signal inhomogeneity at 7T, the system shows clear advantages in high b‐value imaging and high‐resolution diffusion tensor imaging. Additionally, multiplexed sensitivity encoding significantly reduces image blurring and distortion, and DLR substantially improves the signal‐to‐noise ratio and image sharpness. 7T diffusion applications in structural analysis and disease characterization: This review discusses the potential applications of 7T diffusion imaging in structural analysis and disease characterization.

Keywords

• 7T MRI
• B0 inhomogeneity
• B1 inhomogeneity
• deep learning reconstruction
• diffusion imaging
• disease characterization