SUMMARY: Identification of carotid artery atherosclerosis is conventionally based on measurements of luminal stenosis and surface irregularities using in vivo imaging techniques including sonography, CT and MR angiography, and digital subtraction angiography. However, histopathologic studies demonstrate considerable differences between plaques with identical degrees of stenosis and indicate that certain plaque features are associated with increased risk for ischemic events. The ability to look beyond the lumen using highly developed vessel wall imaging methods to identify plaque vulnerable to disruption has prompted an active debate as to whether a paradigm shift is needed to move away from relying on measurements of luminal stenosis for gauging the risk of ischemic injury. Further evaluation in randomized clinical trials will help to better define the exact role of plaque imaging in clinical decision-making. However, current carotid vessel wall imaging techniques can be informative. The goal of this article is to present the perspective of the ASNR Vessel Wall Imaging Study Group as it relates to the current status of arterial wall imaging in carotid artery disease.

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer—Current Efforts; Collaborative Opportunities; and Moving Forward—Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

Purpose: To develop a cardiac and respiratory self‐gated four‐dimensional (4D) coronary MRA technique for simultaneous cardiac anatomy and function visualization.Methods: A contrast‐enhanced, ungated spoiled gradient echo sequence with self‐gating (SG) and 3DPR trajectory was used for image acquisition. Data were retrospectively binned into different cardiac and respiratory phases based on information extracted from SG projections using principal component analysis. Each cardiac phase was reconstructed using a respiratory motion‐corrected self‐calibrating SENSE framework, and those belong to the quiescent period were retrospectively combined for coronary visualization. Healthy volunteer studies were conducted to evaluate the efficacy of the SG method, the accuracy of the left ventricle (LV) function parameters and the quality of coronary artery visualization.Results: SG performed reliably for all subjects including one with poor electrocardiogram (ECG). The LV function parameters showed excellent agreement with those from a conventional cine protocol. For coronary imaging, the proposed method yielded comparable apparent signal to noise ratio and coronary sharpness and lower apparent contrast to noise ratio on three subjects compared with an ECG and navigator‐gated Cartesian protocol and an ECG‐gated, respiratory motion‐corrected 3DPR protocol.Conclusion: A fully self‐gated 4D whole‐heart imaging technique was developed, potentially allowing cardiac anatomy and function assessment from a single measurement. Magn Reson Med 72:1208–1217, 2014.

Purpose: The aim of this study was to implement and optimize a novel application of diffusion‐prepared balanced steady‐state free precession (bSSFP) to perform in vivo cardiac diffusion‐weighted MRI.Theory and Methods: Diffusion‐prepared sequences have the flexibility to diffusion encode with a multi‐shot image readout. The diffusion preparation was optimized to reduce sensitivity to cardiac bulk motion with second order motion compensation (M1M2). The image readout consisted of a three‐dimensional (3D) centric‐encoded segmented bSSFP acquisition that incorporated a prospective navigator. Ten healthy subjects were scanned twice using the proposed technique diffusion preparation with and without M1M2 using three orthogonal directions under varying off‐resonance conditions. Trace apparent diffusion coefficient (trADC) maps and the left ventricular (LV) trADC were calculated.Results: M1M2 diffusion‐prepared scans resulted in LV trADC values of 1.5 ± 0.4 × 10⁻³ mm²/s that were reproducible yielding no statistical differences (P = 0.54). M1M2 diffusion‐prepared images showed no ghosting artifacts and/or signal fallout. The non–motion‐compensated diffusion‐prepared scans yielded LV trADC values of 6.6 ± 0.9 × 10⁻³ mm²/s and diffusion‐prepared images with severe bulk motion‐induced artifacts.Conclusion: We developed a novel free‐breathing bulk motion compensated diffusion‐prepared 3D segmented bSSFP technique able to perform in vivo cardiac diffusion‐weighted MRI on a conventional clinical MR scanner. Magn Reson Med 72:1257–1267, 2014.

Purpose: To develop a three‐dimensional retrospective image‐based motion correction technique for whole‐heart coronary MRA with self‐navigation that eliminates both the need to setup a diaphragm navigator and gate the acquisition.Methods: The proposed technique uses one‐dimensional self‐navigation to track the superior–inferior translation of the heart, with which the acquired three‐dimensional radial k‐space data is segmented into different respiratory bins. Respiratory motion is then estimated in image space using an affine transform model and subsequently this information is used to perform efficient motion correction in k‐space. The performance of the proposed technique on healthy volunteers is compared with the conventional navigator gating approach as well as data binning using diaphragm navigator.Results: The proposed method is able to reduce the imaging time to 7.1±0.5 min from 13.9±2.6 min with conventional navigator gating. The scan setup time is reduced as well due to the elimination of the navigator. The proposed method yields excellent image quality comparable with either conventional navigator gating or the navigator binning approach.Conclusion: We have developed a new respiratory motion correction technique for coronary MRA that enables 1 mm³ isotropic resolution and whole‐heart coverage with 7 min of scan time. Further tests on patient population are needed to determine its clinical usage. Magn Reson Med 71:67–74, 2014.