Work

Diffusion‐PEPTIDE: Distortion‐ and blurring‐free diffusion imaging with self‐navigated motion‐correction and relaxometry capabilities

Year: 2020

Type: article

Abstract: Purpose To implement the time‐resolved relaxometry PEPTIDE technique into a diffusion acquisition to provide self‐navigated, distortion‐ and blurring‐free diffusion imaging that is robust to motion, while simultaneously providing T2 and mapping. Theory and Methods The PEPTIDE readout was implemented into a spin‐echo diffusion acquisition, enabling reconstruction of a time‐series of T2‐ and ‐weighted images, free from conventional echo planar imaging (EPI) distortion and blurring, for each diffusion‐encoding. Robustness of PEPTIDE to motion and shot‐to‐shot phase variation was examined through a deliberate motion‐corrupted diffusion experiment. Two diffusion‐relaxometry in vivo brain protocols were also examined: (1)1 × 1 × 3 mm3 across 32 diffusion directions in 20 min, (2)1.5 × 1.5 × 3.0 mm3 across 6 diffusion‐weighted images in 3.4 min. T2, , and diffusion parameter maps were calculated from these data. As initial exploration of the rich diffusion‐relaxometry data content for use in multi‐compartment modeling, PEPTIDE data were acquired of a gadolinium‐doped asparagus phantom. These datasets contained two compartments with different relaxation parameters and different diffusion orientation properties, and T2 relaxation variations across these diffusion directions were explored. Results Diffusion‐PEPTIDE showed the capability to provide high quality diffusion images and T2 and maps from both protocols. The reconstructions were distortion‐free, avoided potential resolution losses exceeding 100% in equivalent EPI acquisitions, and showed tolerance to nearly 30° of rotational motion. Expected variation in T2 values as a function of diffusion direction was observed in the two‐compartment asparagus phantom (P < .01), demonstrating potential to explore diffusion‐PEPTIDE data for multi‐compartment modeling. Conclusions Diffusion‐PEPTIDE provides highly robust diffusion and relaxometry data and offers potential for future applications in diffusion‐relaxometry multi‐compartment modeling.

Source: Magnetic Resonance in Medicine

DOI: 10.1002/mrm.28579

Authors: Merlin J. Fair, Congyu Liao, Mary Kate Manhard, Kawin Setsompop

Institutions: Harvard University, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology

Language: English

Topic: Advanced Neuroimaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: bronze