Yang, Qi

The role of calcium in atherosclerosis is controversial and the relationship between vascular calcification and plaque vulnerability is not fully understood. Although calcifications are present in ≈50% to 60% of carotid plaques, their association with cerebrovascular ischemic events remains unclear. In this review, we summarize current understanding of carotid plaque calcification. We outline the role of calcium in atherosclerotic carotid disease by analyzing laboratory studies and histopathologic studies, as well as imaging findings to understand clinical implications of carotid artery calcifications. Differences in mechanism of calcium deposition express themselves into a wide range of calcification phenotypes in carotid plaques. Some patterns, such as rim calcification, are suggestive of plaques with inflammatory activity with leakage of the vasa vasourm and intraplaque hemorrhage. Other patterns such as dense, nodular calcifications may confer greater mechanical stability to the plaque and reduce the risk of embolization for a given degree of plaque size and luminal stenosis. Various distributions and patterns of carotid plaque calcification, often influenced by the underlying systemic pathological condition, have a different role in affecting plaque stability. Modern imaging techniques afford multiple approaches to assess geometry, pattern of distribution, size, and composition of carotid artery calcifications. Future investigations with these novel technologies will further improve our understanding of carotid artery calcification and will play an important role in understanding and minimizing stroke risk in patients with carotid plaques.

Purpose Diffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but variability due to session and scanner biases is a challenge. Methods To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm 2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de‐identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi‐compartment neurite orientation dispersion and density model, (3) white‐matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region‐wise fractional anisotropy, mean diffusivity, and principal eigenvector; region‐wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle‐wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. Results We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. Conclusions This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.

Background: Preclinical studies and pilot patient studies have shown that chronic infarctions can be detected and characterized from cardiac magnetic resonance without gadolinium-based contrast agents using native-T1 maps at 3T. We aimed to investigate the diagnostic capacity of this approach for characterizing chronic myocardial infarctions (MIs) in a multi-center setting. Methods: Patients with a prior MI (n=105) were recruited at 3 different medical centers and were imaged with native-T1 mapping and late gadolinium enhancement (LGE) at 3T. Infarct location, size, and transmurality were determined from native-T1 maps and LGE. Sensitivity, specificity, receiver-operating characteristic metrics, and inter- and intraobserver variabilities were assessed relative to LGE. Results: Across all subjects, T1 of MI territory was 1621±110 ms, and remote territory was 1225±75 ms. Sensitivity, specificity, and area under curve for detecting MI location based on native-T1 mapping relative to LGE were 88%, 92%, and 0.93, respectively. Native-T1 maps were not different for measuring infarct size (native-T1 maps: 12.1±7.5%; LGE: 11.8±7.2%, P =0.82) and were in agreement with LGE ( R 2 =0.92, bias, 0.09±2.6%). Corresponding inter- and intraobserver assessments were also highly correlated (interobserver: R 2 =0.90, bias, 0.18±2.4%; and intraobserver: R 2 =0.91, bias, 0.28±2.1%). Native T1 maps were not different for measuring MI transmurality (native-T1 maps: 49.1±15.8%; LGE: 47.2±19.0%, P =0.56) and showed agreement ( R 2 =0.71; bias, 1.32±10.2%). Corresponding inter- and intraobserver assessments were also in agreement (interobserver: R 2 =0.81, bias, 0.1±9.4%; and intraobserver: R 2 =0.91, bias, 0.28±2.1%, respectively). While the overall accuracy for detecting MI with native-T1 maps at 3T was high, logistic regression analysis showed that MI location was a prominent confounder. Conclusions: Native-T1 mapping can be used to image chronic MI with high degree of accuracy, and as such, it is a viable alternative for scar imaging in patients with chronic MI who are contraindicated for LGE. Technical advancements may be needed to overcome the imaging confounders that currently limit native-T1 mapping from reaching equivalent detection levels as LGE.