Affiliation:
1. Department of Radiology Stanford University Stanford California USA
2. Department of Bioengineering Stanford University Stanford California USA
3. Department of Electrical Engineering Stanford University Stanford California USA
Abstract
AbstractPurposeDiffusion‐weighted imaging (DWI) suffers from geometric distortion and chemical shift artifacts due to the commonly used Echo Planar Imaging (EPI) trajectory. Even with fat suppression in DWI, severe B0 and B1
variations can result in residual fat, which becomes both a source of image artifacts and a confounding factor in diffusion‐weighted contrast in distinguishing benign and malignant tissues. This work presents a method for acquiring distortion‐free diffusion‐weighted images using spatiotemporal acquisition and joint reconstruction. Water‐fat separation is performed by chemical‐shift encoding.MethodsSpatiotemporal acquisition is employed to obtain distortion‐free images at a series of echo times. Chemical‐shift encoding is used for water‐fat separation. Reconstruction and separation are performed jointly in the spat‐spectral domain. To address the shot‐to‐shot motion‐induced phase in DWI, an Fast Spin Echo (FSE)‐based phase navigator is incorporated into the sequence to obtain distortion‐free phase information. The proposed method was validated in phantoms and in vivo for the brain, head and neck, and breast.ResultsThe proposed method enables the acquisition of distortion‐free diffusion‐weighted images in the presence of B0 field inhomogenieties commonly observed in the body. Water and fat components are separated with no obvious spectral leakage artifacts. The estimated Apparent Diffusion Coefficient (ADC) is comparable to that of multishot DW‐EPI.ConclusionDistortion‐free, water‐fat separated diffusion‐weighted images in body can be obtained through the utilization of spatiotemporal acquisition and joint reconstruction methods.
Funder
National Institute of Biomedical Imaging and Bioengineering
Center for Biomedical Informatics and Information Technology
GE Healthcare