Correcting B0 inhomogeneity-induced distortions in whole-body diffusion MRI of bone

Abstract

Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p < 10–20). On average (± SD), participants’ largest error reduction was 11.8 mm (± 3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI 5.0–7.2); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p < 10–12). These findings support the use of distortion correction techniques to improve localization of bone on DWI.

Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets

“…The method employed a non-rigid registration algorithm between the joint probability density distributions of a newly acquired image and a reference MRI dataset. However, we sought to develop a fast, WBDWI-only approach, as mis-registrations between diffusion-weighted imaging and other contrasts (caused by magnetic field inhomogeneities and the use of echo-planar imaging in WBDWI [17]) prohibit the use of joint histograms. Blackledge et al [18] proposed an algorithm that might improve the standardisation and interpretation of WBDWI, which synthesises new contrast by combining voxel-wise ADC with voxel-wise estimates of the ADC uncertainty (thus removing the need for high-b-value images).…”

A method for measuring B0 field inhomogeneity using quantitative double‐echo in steady‐state

“…For example, correction of field-induced distortions has shown to improve localization of bone in diffusion-weighted imaging. 7 Furthermore, B 0 mapping can be used to check if the right condition exists for effective fat saturation, chemical shift-based imaging methodologies, 8,9 or simultaneous fat-water imaging. 10 B 0 inhomogeneity corrections usually require ancillary scans for B 0 mapping using the Water Saturation Shift Referencing 11 (WASSR) method or the two Gradient-Echo (2-GRE) method.…”

“…More generally, obtaining information about static magnetic field ($$$ {B}_0 $$$) inhomogeneities is beneficial for a variety of musculoskeletal (MSK) MRI methodologies. For example, correction of field‐induced distortions has shown to improve localization of bone in diffusion‐weighted imaging 7 . Furthermore, $$$ {B}_0 $$$ mapping can be used to check if the right condition exists for effective fat saturation, chemical shift‐based imaging methodologies, 8,9 or simultaneous fat‐water imaging 10 .…”