STEP: Self-supporting tailored k-space estimation for parallel imaging reconstruction

Characterization of non-stenotic plaques in intracranial arteries with multi-contrast, multi-planar vessel wall image analysis

OBJECTIVES

Non-stenotic plaques have been observed in intracranial arteries but are less understood compared to those in coronary and carotid arteries. We sought to compare plaque distribution and morphology between stenotic and non-stenotic intracranial plaques with MR vessel wall imaging (VWI) and quantitative image analysis.

MATERIALS AND METHODS

Twenty-four patients with intracranial arterial stenosis or luminal irregularity on clinical imaging were scanned with a multi-contrast VWI protocol. Plaques were detected as focal wall thickening on co-registered multiplanar reformats of multi-contrast VWI, with assessment of the location and morphology. TOF-MRA was independently reviewed for any appreciable stenosis using the WAISD criteria.

RESULTS

Across 504 arterial segments, a total of 80 plaques were detected, including 23 (29%) with stenosis on TOF-MRA, 56 (70%) without, and 1 (1%) not covered by TOF-MRA. Plaques involving the ICA were more likely to be non-stenotic than those involving other segments (80% versus 55%, p = 0.030) whereas the basilar artery (40%) and PCA (33%) had the lowest proportions of non-stenotic plaques. Maximum wall thickness, indicative of plaque burden, correlated poorly with degree of stenosis (p = 0.10) and overlapped substantially between stenotic and non-stenotic plaques (1.9 [1.5, 2.4] versus 2.0 [1.5, 2.2] mm, p = 0.074).

CONCLUSIONS

Intracranial plaques without appreciable stenosis on TOF-MRA represent a large proportion of lesions throughout arterial segments but disproportionately affect the ICA. Morphological characterization of plaques with and without stenosis shows that luminal stenosis is a poor indicator of the underlying burden of intracranial atherosclerosis.

Accelerated multi-contrast high isotropic resolution 3D intracranial vessel wall MRI using a tailored k-space undersampling and partially parallel reconstruction strategy

OBJECTIVE

To develop a 3D multi-contrast IVW protocol with 0.5-mm isotropic resolution and a scan time of 5 min per sequence.

MATERIALS AND METHODS

Pre-contrast T1w VISTA, DANTE prepared PDw VISTA, SNAP, and post-contrast T1w VISTA were accelerated using cartesian undersampling with target ordering method (CUSTOM) and self-supporting tailored k-space estimation for parallel imaging reconstruction (STEP). CUSTOM + STEP IVW was compared to full-sample IVW, SENSE-accelerated IVW, and CUSTOM + zero-filled Fourier reconstruction in normal volunteers and subjects with intracranial atherosclerotic disease (ICAD). Image quality, vessel delineation, CSF suppression, and blood suppression were compared.

RESULTS

CUSTOM + STEP vessel wall delineation was comparable to full-sample IVW and better than SENSE IVW for vessel wall delineation on T1w VISTA and luminal contrast on SNAP. Average image quality and wall depiction were significantly improved using STEP reconstruction compared with zero-filled Fourier reconstruction, with no significant difference in CSF or blood suppression.

CONCLUSIONS

CUSTOM + STEP allowed multi-contrast 3D 0.5-mm isotropic IVW within 30 min. Although some quantitative and qualitative scores for CUSTOM - STEP were lower than fully sampled IVW, CUSTOM + STEP provided comparable vessel wall delineation as full-sample IVW and was superior to SENSE. CUSTOM + STEP IVW was well tolerated by patients and showed good delineation of ICAD plaque.

Learning Joint-Sparse Codes for Calibration-Free Parallel MR Imaging

The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an - - minimization objective with an norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to state-of-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements.