Introduction to the Technical Aspects of Computed Diffusion-weighted Imaging for Radiologists

Abstract

Diffusion-weighted (DW) imaging is a magnetic resonance (MR) imaging method. It is an indispensable sequence for the diagnosis of acute cerebral infarction and is recognized as a standard tool in oncologic imaging. Computed DW imaging refers to the synthesizing of arbitrary b-value DW images from a set of measured b-value images by voxelwise fitting. Computed DW imaging is advantageous because it generates DW images with a higher diffusion effect than that achievable by using the MR imaging units in use today. Additionally, computed DW imaging can reduce imaging time while producing images characterized by a higher signal-to-noise ratio than what the acquired DW images would display at the corresponding b values. By fitting input images acquired at a lower b value and correspondingly a shorter echo time, the signal intensity of the resulting computed DW image is closer to the ideal case. Computed DW images are generated by employing mathematical models that use mono-, bi-, or triexponential equations. To generate accurate computed DW images, the appropriate model must be selected, and the image parameters for the input data must be chosen accordingly. In addition, to reduce artifacts on computed DW images, the misalignment of input data must be corrected with the aid of image registration techniques. ^©RSNA, 2018.