Three‐dimensional inversion recovery manganese‐enhanced MRI of mouse brain using super‐resolution reconstruction to visualize nuclei involved in higher brain function

Abstract

The visualization of activity in mouse brain using inversion recovery spin echo (IR-SE) manganese-enhanced MRI (MEMRI) provides unique contrast, but suffers from poor resolution in the slice-encoding direction. Super-resolution reconstruction (SRR) is a resolution-enhancing post-processing technique in which multiple low-resolution slice stacks are combined into a single volume of high isotropic resolution using computational methods. In this study, we investigated, first, whether SRR can improve the three-dimensional resolution of IR-SE MEMRI in the slice selection direction, whilst maintaining or improving the contrast-to-noise ratio of the two-dimensional slice stacks. Second, the contrast-to-noise ratio of SRR IR-SE MEMRI was compared with a conventional three-dimensional gradient echo (GE) acquisition. Quantitative experiments were performed on a phantom containing compartments of various manganese concentrations. The results showed that, with comparable scan times, the signal-to-noise ratio of three-dimensional GE acquisition is higher than that of SRR IR-SE MEMRI. However, the contrast-to-noise ratio between different compartments can be superior with SRR IR-SE MEMRI, depending on the chosen inversion time. In vivo experiments were performed in mice receiving manganese using an implanted osmotic pump. The results showed that SRR works well as a resolution-enhancing technique in IR-SE MEMRI experiments. In addition, the SRR image also shows a number of brain structures that are more clearly discernible from the surrounding tissues than in three-dimensional GE acquisition, including a number of nuclei with specific higher brain functions, such as memory, stress, anxiety and reward behavior.

Interactive Local Super-Resolution Reconstruction of Whole-Body MRI Mouse Data: A Pilot Study with Applications to Bone and Kidney Metastases

“…In our case, that would mean using 7 low-resolution images. Using more than this number of low-resolution images will not have a significant impact on the resolution, but will increase the SNR slightly (for an in-depth study of these trade-offs, we refer the reader to [3] , [4] ).…”

“…This slice thickness results in a large partial volume effect, making precise detection and localization of tumors difficult, especially for early stage tumors and micro tumors [2] . Recently, a resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has been demonstrated to improve visualization and localization of micro-structures in molecular MRI [3] , [4] . SRR computes a high-resolution image by combining a number of low-resolution images with varying fields-of-view (FOV).…”