New methods for carotid MRI

Magnetic resonance imaging (MRI) is a promising modality for assessment and analysis of arterial plaque because of its inherent 3D nature, excellent soft tissue discrimination, and lack of ionizing radiation. Recently, clinicians have used non-invasive MRI measurements of carotid artery plaque for diagnosis and management of carotid atherosclerosis. However, the development of vessel wall imaging (VWI) in MRI is currently restricted due to low signal-to-noise ratio (SNR), limited resolution and motion artifacts due to long scan times. In addition the standard VWI technique used in MRI relies on suppression of signal from flowing blood in the artery lumen to provide necessary contrast between the plaque and surrounding tissue/blood. In this thesis, I present new methods for imaging of the carotid arteries to improve on some of the current limitations in carotid MRI.;First, I introduce a new technique for improving blood suppression in carotid MRI. In standard carotid MRI approaches a preparatory sequence is used for suppression of flowing blood in order to improve plaque contrast with respect to the lumen. However, the complicated and cyclic flow patterns at the carotid bifurcation result in slow and stagnant blood flow. MR carotid artery images frequently suffer from plaque mimicking artifacts, which may result in incorrect diagnosis. In addition these artifacts are exacerbated in 3D imaging, which might alleviate some SNR limitations. The proposed method utilizes a hybrid preparation for blood suppression that is more robust to stagnant or re-circulant flow with 3D imaging.;Second, I present a new anisotropic 3D cones sampling trajectory for accelerating data acquisition. In carotid MRI a highly anisotropic field-of-view (FOV) is sufficient to cover the anatomy of interest (carotid bifurcation). The proposed trajectory is an extension of Gurney's design to anisotropic FOVs. The resulting FOV is shaped like a flat cylinder while spatial resolution remains isotropic. 3D carotid imaging with a 73.2% reduction in scan time compared to isotropic FOV cones is demonstrated.;Finally, I present a new method for image reconstruction from undersampled data using compressed sensing (CS) theory. CS is a relatively new theory that allows for acceleration and de-noising, and is independent of the traditional MR acceleration techniques. The proposed method utilizes a variant of CS known as model-based CS which allows for higher and more robust acceleration. Preliminary studies have verified the feasibility of CS for achieving modest acceleration rates (<3) in carotid imaging. The proposed method improves on this rate by exploiting correlations and dependencies by imposing a data-driven statistical model. The signal model is trained on an application/anatomy specific training database. A modified recovery algorithm is used to encourage sparse solutions that comply with the learnt model while maintaining robustness of recovery. 3D carotid imaging with rate 4.5 fold acceleration was successfully demonstrated in patients without compromising clinically relevant quantitative endpoints or image quality.