Motion correction of multi-b-value diffusion-weighted imaging in the liver

Abstract

Rationale and objectives: Motion artifacts are a significant source of error in the acquisition and quantification of parameters from multi-b-value diffusion-weighted imaging (DWI). The objective of this article is to present a reliable method to reduce motion-related artifacts during free-breathing at higher b-values when signal levels are low.

Materials and methods: Twelve patients referred for magnetic resonance imaging of the liver underwent a clinical magnetic resonance imaging examination of the abdominal region that included DWI. Conventional single-shot spin-echo echo planar imaging acquisitions of the liver during free breathing were repeated in a "time-resolved" manner during a single acquisition to obtain data for multi-b-value analysis, alternating between low and high b-values. Image registration using a normalized mutual information similarity measure was used to correct for spatial misalignment of diffusion-weighted volumes caused by motion. Registration error was estimated indirectly by comparing the normalized root-mean-square error (NRMSE) values of data fitted to the biexponential intra-voxel incoherent motion model before and after motion correction. Regions of interest (ROIs) were selected in the liver close to the surface of the liver and close to internal structures such as large bile ducts and blood vessels.

Results: For the 12 patient datasets, the mean NRMSE value for the motion-corrected ROIs (0.38 ± 0.16) was significantly lower than the mean NRMSE values for the non-motion-corrected ROIs (0.41 ± 0.13) (P < .05). In cases where there was substantial respiratory motion during the acquisition, visual inspection verified that the algorithm markedly improved alignment of the liver contours between frames.

Conclusions: The proposed method addresses motion-related artifacts to increase robustness in multi-b-value acquisitions.

Figure 1

Schematic diagram of the motion correction of multi-b-value diffusion-weighted imaging technique. (a) The data are acquired during two phases. Phase 1: Image frames are acquired without diffusion-weighted gradients (b = 0) to minimize T₁-weighting because of incomplete recovery of longitudinal magnetization at shorter TRs. Phase 2: Image frames are acquired with non-zero b-values. High (H₁, H₂, H₃, …) and low (L₁, L₂, L₃, …) b-values are interleaved. This is to allow spatial transformation parameters for low-signal high-b–value images to be approximated using the spatial transformation parameters from adjacent low b-value frames. (b) Three b = 0 frames are acquired (Phase 1) followed by four blocks of b-values (4 × 9, total of 36) (Phase 2). In total, 39 frames are acquired.

Figure 2

Images obtained using motion correction of multi-b-value diffusion-weighted imaging. (a) Images obtained with diffusion sensitivity parameters 200, 400, and 800 s/mm². (b) Scatter plots corresponding (from left to right) to the images shown in (a). By comparing the plots, we can detect if the mutual information or pixel distance can be used as a similarity measure. Reduced signal can limit the accuracy of motion correction. We used the criteria sum(abs(HX-HY))<0.5, where HX and HY are the normalized standard entropy of the source and target images. For the plots, the sum(abs(HX-HY))values corresponding to b-values 200, 400 and 800 s/mm² were 0.34, 0.45, and 0.74, respectively, suggesting that b-values >400 s/mm² result in images with low signal and contrast.

Figure 3

Plot of three-dimensional affine transformation parameters for the 39 frames acquired during an abdominal exam. (a) Translational parameters (translate X, translate Y, and translate Z), (b) Shear parameters (shear XY, shear XZ, and shear YX) and (c) (shear YZ, shear ZX, and shear ZY). Motion parameters were estimated during acquisition of high-signal low b-value images (colored markers represent target frames), which can then be interpolated to the adjacent high b-value images that lack sufficient signal for reliable motion estimation (black markers represent interpolated frames).

Figure 4

Regions of interest (ROIs) placed on a diffusion-weighted image (b = 0) of the liver (a). Plots of mean signal intensity and the biexponential fit for the ROIs pre- (b) and postregistration (c). The biexponential parameters D*, D, and f were extracted. The preregistration fitted parameters were D* = 0.04 mm²/s, D = 1.3 × 10⁻³ mm²/s, and f = 0.17 for blue ROI, and D* = 0.014 mm²/s, D = 1.2 × 10⁻³ mm²/s, and f = 0.27 for green ROI. The postregistration fitted parameters were D* = 0.04 mm²/s, D = 1.0 × 10⁻³ mm²/s, and f = 0.25 for blue ROI, and D* = 0.063 mm²/s, D = 0.9 × 10⁻³ mm²/s, and f = 0.44 for green ROI.

Figure 5

Registration of target image to the reference image. (a) Reference image (frame 1, b = 0 s/mm²). For comparison, the contour of the liver (blue) from frame 1 is copied in its original location in each subsequent frame. (b) Pre- (left) and postregistration images of the same slice from frame 29 (b = 70 s/mm²). In the preregistered image, the liver and the contour do not match. Postregistration, there is an improvement in the alignment of the liver edge and the contour copied from frame 1. (c) Similarly, for frame 36 (b = 150 s/mm²), the liver edge in the preregistered image (left) does not match the contour. The alignment is improved in the postregistration image (right).