Background and Purpose-Although the ability of MRI to investigate carotid plaque composition is well established, the mechanism and the significance of plaque gadolinium (Gd) enhancement remain unknown. We evaluated clinical and histological significance of Gd enhancement of carotid plaque in patients undergoing endarterectomy for carotid stenosis. Methods-Sixty-nine patients scheduled for a carotid endarterectomy prospectively underwent a 3-T MRI. Carotid plaque enhancement was assessed on T1-weighted images performed before and 5 minutes after Gd injection. Enhancement was recorded according to its localization. Histological analysis was performed of the entire plaque and of the area with matched contrast enhancement on MR images. Results-Gd enhancement was observed in 59% patients. Three types of carotid plaques were identified depending on enhancement location (shoulder region, shoulder and fibrous cap, and central in the plaque). Fibrous cap rupture, intraplaque hemorrhage, and plaque Gd enhancement was significantly more frequent in symptomatic than in asymptomatic patients (P=0.043, P<0.0001, and P=0.034, respectively). After histological analysis, Gd enhancement was significantly associated with vulnerable plaque (American Heart Association VI, P=0.006), neovascularization (P<0.0001), macrophages (P=0.030), and loose fibrosis (P<0.0001). Prevalence of neovessels, macrophages, and loose fibrosis in the area of Gd enhancement was 97%, 87%, and 80%, respectively, and was different depending on the enhancement location in the plaque. Fibrous cap status and composition were different depending on the type of plaque. Conclusions-Gd enhancement of carotid plaque is associated with vulnerable plaque phenotypes and related to an inflammatory process. (Stroke. 2012;43:3023-3028.)
Over the last decade, dual-energy CT scanners have gone from prototypes to clinically available machines, and spectral photon counting CT scanners are following. They require a specific reconstruction process, consisting of two steps: material decomposition and tomographic reconstruction. Image-based methods perform reconstruction, then decomposition, while projection-based methods perform decomposition first, and then reconstruction. As an alternative, "one-step inversion" methods have been proposed, which perform decomposition and reconstruction simultaneously. Unfortunately, one-step methods are typically slower than their two-step counterparts, and in most CT applications, reconstruction time is critical. This paper therefore proposes to compare the convergence speeds of five one-step algorithms. We adapted all these algorithms to solve the same problem: spectral photoncounting CT reconstruction from five energy bins, using a three materials decomposition basis and spatial regularization. The paper compares a Bayesian method which uses non-linear conjugate gradient for minimization [5], three methods based on quadratic surrogates [19, 37, 22], and a primal-dual method based on MOCCA, a modified Chambolle-Pock algorithm [3]. Some of these methods have been accelerated by using µ-preconditioning, i.e. by performing all internal computations not with the actual materials the object is made of, but with carefully chosen linear combinations of those. In this paper, we also evaluated the impact of three different µ-preconditioners on convergence speed. Our experiments on simulated data revealed vast differences in the number of iterations required to reach a common image quality objective: Mechlem2018 [22] needed 10 iterations, Cai2013, Long2014 and Weidinger2016 [5, 19, 37] several hundreds, and Barber2016 [3] several thousands. We also sum up other practical aspects, like memory footprint and the need to tune extra parameters.
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