Diffusion imaging with prospective motion correction and reacquisition

Abstract

A major source of artifacts in diffusion-weighted imaging is subject motion. Slow bulk subject motion causes misalignment of data when more than one average or diffusion gradient direction is acquired. Fast bulk subject motion can cause signal dropout artifacts in diffusion-weighted images and results in erroneous derived maps, e.g., fractional anisotropy maps. To address both types of artifacts, a fully automatic method is presented that combines prospective motion correction with a reacquisition scheme. Motion correction is based on the prospective acquisition correction method modified to work with diffusion-weighted data. The images to reacquire are determined automatically during the acquisition from the imaging data, i.e., no extra reference scan, navigators, or external devices are necessary. The number of reacquired images, i.e., the additional scan duration can be adjusted freely. Diffusion-weighted prospective acquisition correction corrects slow bulk motion well and reduces misalignment artifacts like image blurring. Mean absolute residual values for translation and rotation were <0.6 mm and 0.5°. Reacquisition of images affected by signal dropout artifacts results in diffusion maps and fiber tracking free of artifacts. The presented method allows the reduction of two types of common motion related artifacts at the cost of slightly increased acquisition time.

Figure 1a

Translational (left column) and rotational (right column) motion estimated by DW-PACE and AFNI in experiment 1 (with TR delay, top box) and experiment 2 (without TR delay, bottom box) for subject 1 when instructed to move the head about the z-axis (paradigm ‘R’). Largest rotational motion values are found for rotations about the z-axis as instructed with a maximum near 5 degrees. Top row in each box (scenario c – motion & no correction) shows motion without DW-PACE correction. Motion values estimated by DW-PACE and AFNI are comparable. Bottom row in each box (scenario d – motion & correction) shows motion with DW-PACE correction. Residual motion values estimated by AFNI are small. This also applies to smaller concurrent translational motion. See Tables 2 and 3 for mean differences. Note: scenarios (a – no motion & no correction) and (b – no motion & correction) without motion are not shown. See Table 1 for description of experiments and scenarios.

Figure 1b

Translational (left column) and rotational (right column) motion estimated by DW-PACE and AFNI in experiment 1 (with TR delay, top box) and experiment 2 (without TR delay, bottom box) for subject 2 when instructed to move the head along the z-axis (paradigm ‘T’). Largest translational motion values are found for translations along the z-axis as instructed with a maximum near 8 mm. Top row in each box (scenario c – motion & no correction) shows motion without DW-PACE correction. Motion values estimated by DW-PACE and AFNI are comparable. Bottom row in each box (scenario d – motion & correction) shows motion with DW-PACE correction. Residual motion values estimated by AFNI are small. This also applies to smaller concurrent rotational motion. See Tables 2 and 3 for mean differences. Note: scenarios (a – no motion & no correction) and (b – no motion & correction) without motion are not shown. See Table 1 for description of experiments and scenarios.

Figure 2

Trace DWI (top row in each box) and color-coded FA maps (bottom row in each box) of experiment 1 (with TR delay, top box) and experiment 2 (without TR delay, bottom box). First column: scenario (a – no motion & no correction) i.e. no motion & no correction. Second column: scenario (b – no motion & correction) i.e. no motion & DW-PACE correction. Third columns: scenario (c – motion & no correction) i.e. motion & no correction. Forth column: scenario (d – motion & correction) i.e. motion & DW-PACE correction. Use of DW-PACE does not have a negative impact on image quality even when no motion is present (second column). In case of motion but without motion correction, image blurring, edge artifacts and color changes can be seen in trace DWI and color-coded FA maps (third column). Motion is corrected when using DW-PACE and resulting maps (fourth column) are comparable to maps without motion (first and second column). See Table 1 for description of experiments and scenarios.

Figure 3

Mean squared error (MSE) of the trace of the diffusion-weighted data on a slice-by-slice basis between scenario (a – no motion & no correction) and the other scenarios for experiment 1 (with TR delay, left) and experiment 2 (without TR delay, right). The data shown is from paradigm ‘T’ of subject 2 only. Results for other paradigms and subjects look comparable. For both experiments, scenario (c – motion & no correction) shows large increase in MSE compared to scenario (b – no motion & correction). Scenario (d – motion & correction) shows lower MSE compared to scenario (c – motion & no correction). Reacquisition always leads to lower mean MSE values in experiment 2. See also Table 4 for results on a whole volume basis. See Table 1 for description of experiments and scenarios.

Figure 4

Diffusion-weighted magnitude (top row in each box) and phase (bottom row in each box) images from experiment 2 (without TR delay). Original data (top box) and reacquired data (bottom box). Scores for slices 17, 29, 30, and 45 (columns left to right) were as follows: 0.319, 1.300, 1.136, and 0.280 i.e. motion was detected in the magnitude data for slices 29 and 30 (two middle columns) and in the phase data for slices 17 and 45 (left-most and right-most column).

Figure 5

Color-coded FA maps from experiment 2 (without TR delay) scenarios (a – no motion & no correction) (top row) and (d – motion & correction) using data without (middle row) and with (bottom row) reacquisition. Color code is as follows: red: right/left, green: anterior/posterior, blue: superior/inferior. Artifacts due to motion are clearly visible when data was corrupted by motion (middle row). After reacquisition of the corrupted images, the color-coded FA maps (bottom row) are comparable to those without motion (top row).

Figure 6

Diffusion-weighted magnitude images (left column), phase images (middle column) and color-coded FA maps (right column) from three tumor subjects. Original scan data (top row in each box) was replaced by reacquired data (bottom row in each box). Subject motion causes signal attenuation in the diffusion-weighted magnitude image as well as a large number of 2π phase transitions in the phase image (top box). Effect of reacquisition is visible in color-coded FA maps in which the erroneous blue tint disappears. Middle box shows an example of concentric phase pattern, possibly caused by CSF pulsation. Phase image of reacquired data does not show the concentric phase pattern. Color-coded FA maps do not show any obvious differences due to the very subtle effect on the diffusion-weighted magnitude images and the large number of diffusion gradient directions being used. A slice affected by spikes is shown in the bottom box. The reacquired images do not show any spike-related artifacts. Color-coded FA maps show only subtle differences due to the large number of diffusion gradient directions being used.