Highly accelerated vessel-selective arterial spin labelling angiography using sparsity and smoothness constraints

Purpose: To demonstrate that vessel-selectivity in dynamic arterial spin labeling angiography can be achieved without any scan time penalty or noticeable loss of image quality compared to conventional arterial spin labeling angiography. Methods: Simulations on a numerical phantom were used to assess whether the increased sparsity of vessel-encoded angiograms compared to non-vessel-encoded angiograms alone can improve reconstruction results in a compressed sensing framework. Further simulations were performed to study whether the difference in relative sparsity between non-selective and vessel-selective dynamic angiograms were sufficient to achieve similar image quality at matched scan times in the presence of noise. Finally, data were acquired from 5 healthy volunteers to validate the technique in vivo. All data, both simulated and in vivo, were sampled in 2D using a golden angle radial trajectory and reconstructed by enforcing image domain sparsity and temporal smoothness on the angiograms in a parallel imaging and compressed sensing framework. Results: Relative sparsity was established as a primary factor governing the reconstruction fidelity. Using the proposed reconstruction scheme, differences between vessel-selective and non-selective angiography were negligible compared to the dominant factor of total scan time in both simulations and in vivo experiments at acceleration factors up to R = 34. The reconstruction quality was not heavily dependent on hand-tuning the parameters of the reconstruction. Conclusion: The increase in relative sparsity of vessel-selective angiograms compared to nonselective angiograms can be leveraged to achieve higher acceleration without loss of image quality, resulting in the acquisition of vessel-selective information at no scan time cost

Association of midlife cardiovascular risk profiles with cerebral perfusion at older ages

© 2019 AMA. All rights reserved. Importance: Poor cardiovascular health is an established risk factor for dementia, but little is known about its association with brain physiology in older adults. Objective: To examine the association of cardiovascular risk factors, measured repeatedly during a 20-year period, with cerebral perfusion at older ages. Design, Setting, and Participants: In this longitudinal cohort study, individuals were selected from the Whitehall II Imaging Substudy. Participants were included if they had no clinical diagnosis of dementia, had no gross brain structural abnormalities on magnetic resonance imaging scans, and had received pseudocontinuous arterial spin labeling magnetic resonance imaging. Cardiovascular risk was measured at 5-year intervals across 5 phases from September 1991 to October 2013. Arterial spin labeling scans were acquired between April 2014 and December 2014. Data analysis was performed from June 2016 to September 2018. Exposures: Framingham Risk Score (FRS) for cardiovascular disease, comprising age, sex, high-density lipoprotein cholesterol level, total cholesterol level, systolic blood pressure, use of antihypertensive medications, cigarette smoking, and diabetes, was assessed at 5 visits. Main Outcomes and Measures: Cerebral blood flow (CBF; in milliliters per 100 g of tissue per minute) was quantified with pseudocontinuous arterial spin labeling magnetic resonance imaging. Results: Of 116 adult participants, 99 (85.3%) were men. At the first examination, mean (SD) age was 47.1 (5.0) years; at the last examination, mean (SD) age was 67.4 (4.9) years. Mean (SD) age at MRI scan was 69.3 (5.0) years. Log-FRS increased with time (B = 0.058; 95% CI, 0.044 to 0.072;

Hot Spot or Not: A Comparison of Spatial Statistical Methods to Predict Prospective Malaria Infections.

Within affected communities, Plasmodium falciparum infections may be skewed in distribution such that single or small clusters of households consistently harbour a disproportionate number of infected individuals throughout the year. Identifying these hotspots of malaria transmission would permit targeting of interventions and a more rapid reduction in malaria burden across the whole community. This study set out to compare different statistical methods of hotspot detection (SaTScan, kernel smoothing, weighted local prevalence) using different indicators (PCR positivity, AMA-1 and MSP-1 antibodies) for prediction of infection the following year. Two full surveys of four villages in Mwanza, Tanzania were completed over consecutive years, 2010-2011. In both surveys, infection was assessed using nested polymerase chain reaction (nPCR). In addition in 2010, serologic markers (AMA-1 and MSP-119 antibodies) of exposure were assessed. Baseline clustering of infection and serological markers were assessed using three geospatial methods: spatial scan statistics, kernel analysis and weighted local prevalence analysis. Methods were compared in their ability to predict infection in the second year of the study using random effects logistic regression models, and comparisons of the area under the receiver operating curve (AUC) for each model. Sensitivity analysis was conducted to explore the effect of varying radius size for the kernel and weighted local prevalence methods and maximum population size for the spatial scan statistic. Guided by AUC values, the kernel method and spatial scan statistics appeared to be more predictive of infection in the following year. Hotspots of PCR-detected infection and seropositivity to AMA-1 were predictive of subsequent infection. For the kernel method, a 1 km window was optimal. Similarly, allowing hotspots to contain up to 50% of the population was a better predictor of infection in the second year using spatial scan statistics than smaller maximum population sizes. Clusters of AMA-1 seroprevalence or parasite prevalence that are predictive of infection a year later can be identified using geospatial models. Kernel smoothing using a 1 km window and spatial scan statistics both provided accurate prediction of future infection

Hitting Hotspots: Spatial Targeting of Malaria for Control and Elimination

Teun Bousema and colleagues argue that targeting malaria “hotspots” is a highly efficient way to reduce malaria transmission at all levels of transmission intensity

Combined angiography and perfusion using radial imaging and arterial spin labeling

Purpose: To demonstrate the feasibility of a novel non-invasive MRI technique for the comprehensive evaluation of blood flow to the brain: combined angiography and perfusion using radial imaging and arterial spin labeling (CAPRIA). Methods: In the CAPRIA pulse sequence, blood labeled with a pseudocontinuous arterial spin labeling (PCASL) pulse train is continuously imaged as it flows through the arterial tree and into the brain tissue using a golden ratio radial readout. From a single raw data set, this flexible imaging approach allows the reconstruction of both high spatial/temporal resolution angiographic images with a high undersampling factor and low spatial/temporal resolution perfusion images with a low undersampling factor. The sparse and high SNR nature of angiographic images ensures that radial undersampling artifacts are relatively benign, even when using a simple regridding image reconstruction. Pulse sequence parameters were optimized through sampling efficiency calculations and the numerical evaluation of modified PCASL signal models. Comparison was made against conventional PCASL angiographic and perfusion acquisitions. Results: 2D CAPRIA data in healthy volunteers demonstrated the feasibility of this approach, with good vessel visualization in the angiographic images and clear tissue perfusion signal when reconstructed at 108 ms and 252 ms temporal resolution, respectively. Images were qualitatively similar to those from conventional acquisitions, but CAPRIA had significantly higher SNR-efficiency (48% improvement on average, p = 0.02). Conclusion: The CAPRIA technique shows potential for the efficient evaluation of both macrovascular blood flow and tissue perfusion within a single scan, with potential applications in a range of cerebrovascular diseases

Highly accelerated vessel-selective arterial spin labelling angiography using sparsity and smoothness constraints

Purpose: To demonstrate that vessel-selectivity in dynamic arterial spin labeling angiography can be achieved without any scan time penalty or noticeable loss of image quality compared to conventional arterial spin labeling angiography. Methods: Simulations on a numerical phantom were used to assess whether the increased sparsity of vessel-encoded angiograms compared to non-vessel-encoded angiograms alone can improve reconstruction results in a compressed sensing framework. Further simulations were performed to study whether the difference in relative sparsity between non-selective and vessel-selective dynamic angiograms were sufficient to achieve similar image quality at matched scan times in the presence of noise. Finally, data were acquired from 5 healthy volunteers to validate the technique in vivo. All data, both simulated and in vivo, were sampled in 2D using a golden angle radial trajectory and reconstructed by enforcing image domain sparsity and temporal smoothness on the angiograms in a parallel imaging and compressed sensing framework. Results: Relative sparsity was established as a primary factor governing the reconstruction fidelity. Using the proposed reconstruction scheme, differences between vessel-selective and non-selective angiography were negligible compared to the dominant factor of total scan time in both simulations and in vivo experiments at acceleration factors up to R = 34. The reconstruction quality was not heavily dependent on hand-tuning the parameters of the reconstruction. Conclusion: The increase in relative sparsity of vessel-selective angiograms compared to nonselective angiograms can be leveraged to achieve higher acceleration without loss of image quality, resulting in the acquisition of vessel-selective information at no scan time cost