High‐Resolution Multi‐Contrast MRI of the Carotid Artery Wall for Evaluation of Atherosclerotic Plaques

Clinical physiology: the crucial role of MRI in evaluation of peripheral artery disease

Peripheral artery disease (PAD) is a common vascular disease that primarily affects the lower limbs and is defined by the constriction or blockage of peripheral arteries and may involve microvascular dysfunction and tissue injury. Patients with diabetes have more prominent disease of microcirculation and develop peripheral neuropathy, autonomic dysfunction, and medial vascular calcification. Early and accurate diagnosis of PAD and disease characterization are essential for personalized management and therapy planning. Magnetic resonance imaging (MRI) provides excellent soft tissue contrast and multiplanar imaging capabilities and is useful as a noninvasive imaging tool in the comprehensive physiological assessment of PAD. This review provides an overview of the current state of the art of MRI in the evaluation and characterization of PAD, including an analysis of the many applicable MR imaging techniques, describing the advantages and disadvantages of each approach. We also present recent developments, future clinical applications, and future MRI directions in assessing PAD. The development of new MR imaging technologies and applications in preclinical models with translation to clinical research holds considerable potential for improving the understanding of the pathophysiology of PAD and clinical applications for improving diagnostic precision, risk stratification, and treatment outcomes in patients with PAD.

Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images

Accurate and automatic carotid artery segmentation for magnetic resonance (MR) images is eagerly expected, which can greatly assist a comprehensive study of atherosclerosis and accelerate the translation. Although many efforts have been made, identification of the inner lumen and outer wall in diseased vessels is still a challenging task due to complex vascular deformation, blurred wall boundary, and confusing componential expression. In this paper, we introduce a novel fully automatic 3D framework for simultaneously segmenting the carotid artery from high-resolution multi-contrast MR sequences based on deep learning. First, an optimal channel fitting structure is designed for identity mapping, and a novel 3D residual U-net is used as a basic network. Second, high-resolution MR images are trained using both patch-level and global-level strategies, and the two pre-segmentation results are optimized based on structural characteristics. Third, the optimized pre-segmentation results are cascaded with the patch-cropped MR volume data and trained to segment the carotid lumen and wall. Extensive experiments demonstrate the proposed method outperforms the state-of-the-art 3D Unet-based segmentation models.

Loss of fine structure and edge sharpness in fast-spin-echo carotid wall imaging: measurements and comparison with multiple-spin-echo in normal and atherosclerotic subjects

PURPOSE

To test whether the k-space acquisition strategy used by fast-spin-echo (FSE) is a major source of blurring in carotid wall and plaque imaging, and investigate an alternative acquisition approach.

MATERIALS AND METHODS

The effect of echo train length (ETL) and T(2) on the amount of blurring was studied in FSE simulations of vessel images. Edge sharpness was measured in black-blood T(1) W and proton density-weighted (PDW) carotid images acquired from 5 normal volunteers and 19 asymptomatic patients using both FSE and multiple-spin-echo (Multi-SE) sequences at 3 Tesla (T). Plaque images were classified and divided in group α (tissues' average T(2) ∼ 40-70 ms) and group β (plaque components with shorter T(2) ).

RESULTS

Simulations predicted 26.9% reduction of vessel edge sharpness from Multi-SE to FSE images (ETL = 9, T(2) = 60 ms). This agreed with in vivo measurements in normal volunteers (27.4%) and in patient group α (26.2%), while in group β the loss was higher (31.6%).

CONCLUSION

FSE significantly reduced vessel edge sharpness along the phase-encoding direction in T(1) W and PDW images. Blurring was stronger in the presence of plaque components with short T(2) times. This study shows a limitation of FSE and the potential of Multi-SE to improve the quality of carotid imaging.