Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization

Abstract

Purpose: The goal of this work is to propose a motion robust reconstruction method for diffusion-weighted MRI that resolves shot-to-shot phase mismatches without using phase estimation.

Methods: Assuming that shot-to-shot phase variations are slowly varying, spatial-shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low-rank constraint on the spatial-shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method.

Results: The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase-estimation-based methods to achieve even higher image quality.

Conclusion: We introduced the shot-locally low-rank method, a reconstruction technique for multishot diffusion-weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.

Keywords: locally low-rank; motion-induced phase; multishot diffusion-weighted imaging; virtual conjugate shot.

Fig. 1.

4-shot and 8-shot diffusion-weighted images (resolution = 0.85 × 0.85 × 3 mm³, b-value = 1000 s/mm², nex = 4) reconstructed by different methods, and the difference between shot-LLR images without and with using VCS (3× difference). For 8-shot results (second row), the arrowheads highlight aliasing artifacts in POCS-MUSE and POCS-ICE, while shading artifacts exist in shot-LLR with/without VCS.

Fig. 2.

8-shot brain diffusion-weighted images (resolution = 0.85 × 0.85 × 3 mm³, b-value = 1000 s/mm², nex = 1) reconstructed by POCS-ICE (a), shot-LLR (b) and POCS-ICE with shot-LLR as initialization (c). Figure 2a, 2b and 2c have the same window level. The yellow arrow shows aliasing artifacts in POCS-ICE. The curve (d) shows values of the cost function used in POCS-ICE and the horizontal axis is the iteration axis. Using shot-LLR as initialization makes the algorithm converge much faster and to a better local minimum.

Fig. 3.

3 out of 30 directions 8-shot diffusion-weighted images (columns i-iii, resolution = 0.85 × 0.85 × 3 mm³, b-value = 1000 s/mm², nex = 2), and corresponding fractional anisotropy (FA) maps color-encoded by the principal diffusion tensor eigenvector (V1) (column iv) of different methods. Inaccurate phase estimation leads to erroneously estimated DTI V1 (Fig. 3ab, column iv, blue regions highlighted by arrows) and a loss of structural details (Fig. 3ab, column iv white boxes). Shot-LLR initialized POCS-ICE reduces artifacts in POCS-ICE, and also removes shading from shot-LLR.

Fig. 4.

4-shot images reconstructed by different methods with severe (rows a-c) respiratory motion. Columns i-vi show images from different acquisitions and column vii shows the averaged images of columns i-vi. Shot-LLR significantly reduced the aliasing artifacts compared with POCS-MUSE and POCS-ICE in the case of severe motion.

Fig. 5.

Breast images of a volunteer (1st and 2nd columns) and a patient (3rd column) (slice thickness = 4 mm, b-value = 600 s/mm², nex = 2) reconstructed by different methods under different numbers of shots and different in-plane resolutions. The last row shows the results using the conventional method, in which single-shot and parallel imaging were used, and the reduction factor was 4. Yellow arrows highlight aliasing artifacts. In the 3rd column, an enlarged view of the tumor is provided in the white boxes. Note the improved depiction of the lesion detail in shot-LLR vs conventional single-shot DWI, with reduced artifacts compared to POCS-ICE and POCS-MUSE.