Long-Term Stability of Gradient Characteristics Warrants Model-Based Correction of Diffusion Weighting Bias

Abstract

The study aims to test the long-term stability of gradient characteristics for model-based correction of diffusion weighting (DW) bias in an apparent diffusion coefficient (ADC) for multisite imaging trials. Single spin echo (SSE) DWI of a long-tube ice-water phantom was acquired quarterly on six MR scanners over two years for individual diffusion gradient channels, along with B0 mapping, as a function of right-left (RL) and superior-inferior (SI) offsets from the isocenter. Additional double spin-echo (DSE) DWI was performed on two systems. The offset dependences of derived ADC were fit to 4th-order polynomials. Chronic shim gradients were measured from spatial derivatives of B0 maps along the tube direction. Gradient nonlinearity (GNL) was modeled using vendor-provided gradient field descriptions. Deviations were quantified by root-mean-square differences (RMSD), normalized to reference ice-water ADC, between the model and reference (RMSDREF), measurement and model (RMSDEXP), and temporal measurement variations (RMSDTMP). Average RMSDREF was 4.9 ± 3.2 (%RL) and –14.8 ± 3.8 (%SI), and threefold larger than RMSDEXP. RMSDTMP was close to measurement errors (~3%). GNL-induced bias across gradient systems varied up to 20%, while deviation from the model accounted at most for 6.5%, and temporal variation for less than 3% of ADC reproducibility error. Higher SSE RMSDEXP = 7.5–11% was reduced to 2.5–4.8% by DSE, consistent with the eddy current origin. Measured chronic shim gradients below 0.1 mT/m had a minor contribution to ADC bias. The demonstrated long-term stability of spatial ADC profiles and consistency with system GNL models justifies retrospective and prospective DW bias correction based on system gradient design models. Residual errors due to eddy currents and shim gradients should be corrected independent of GNL.

Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom

“…Multiple studies of bias/repeatability/reproducibility of ADC on clinical MRIs using DWI phantoms are available [ 22 , 23 , 24 , 25 , 26 ]. Spatial nonuniformity of ADC as a function of x–y offset, as well as z-location on clinical scanners, has been studied using specialized phantoms and procedures [ 43 , 44 , 45 , 46 ] within FOVs much larger than typical for preclinical scanners. In our study, we limited ADC nonuniformity measurements to small offsets (±25 mm) relevant for mouse DWI along the bore axis (z-direction), since this typically is the most nonuniform direction on clinical scanners, as predicted by horizontal bore gradient coil design specifications [ 42 , 43 , 44 , 45 ].…”

On the origin ofR2orientation dependence angle offsets in white matter

“…62 Together with our observations with b =1000 and 2000 (s/mm 2 ), it could be speculated that some interactions (or cross-terms) between imaging and diffusion sensitizing gradients might have played an important role in modulating mean diffusivity. 63 Nonetheless, more research is warranted to further clarify this unresolved finding.…”

Diffusion‐Weighted Imaging of the Abdomen: Correction for Gradient Nonlinearity Bias in Apparent Diffusion Coefficient

“…Similar findings have been reported in previous studies . 14,17,30 Therefore, this bias should be considered and corrected prior to the analysis of clinical DWI. Our findings also confirmed that GNL affected the ADC measurements for the right liver lobe, kidneys, spleen, and paraspinal muscles.…”

“…[5][6][7][8][9][10][11] The gradient systems are designed to be as linear as possible but have in practice an unavoidable nonlinear component, 12 which causes spatial-dependent nonuniformity bias in the ADC measurements. [13][14][15][16][17][18][19][20][21] Several studies have used ambient temperature DWI phantom idea with isocenter ADC normalization to characterize spatial ADC bias caused by gradient nonlinearity (GNL). 13,22,23 Therefore, correction algorithms have been introduced in the new generation MRI systems to calibrate the images and overcome the GNLrelated image distortions.…”

“…The gradient systems are designed to be as linear as possible but have in practice an unavoidable nonlinear component, 12 which causes spatial‐dependent nonuniformity bias in the ADC measurements 13–21 . Several studies have used ambient temperature DWI phantom idea with isocenter ADC normalization to characterize spatial ADC bias caused by gradient nonlinearity (GNL) 13,22,23 .…”