Free breathing contrast-enhanced time-resolved magnetic resonance angiography in pediatric and adult congenital heart disease

Contrast enhanced magnetic resonance angiography (MRA) is generally performed during a long breath-hold (BH), limiting its utility in infants and small children. This study proposes a free-breathing (FB) time resolved MRA (TRA) technique for use in pediatric and adult congenital heart disease (CHD)

Perturbed spiral real-time phase-contrast MR with compressive sensing reconstruction for assessment of flow in children

PURPOSE: we implemented a golden‐angle spiral phase contrast sequence. A commonly used uniform density spiral and a new ‘perturbed’ spiral that produces more incoherent aliases were assessed. The aim was to ascertain whether greater incoherence enabled more accurate Compressive Sensing reconstruction and superior measurement of flow and velocity. METHODS: A range of ‘perturbed’ spiral trajectories based on a uniform spiral trajectory were formulated. The trajectory that produced the most noise‐like aliases was selected for further testing. For in‐silico and in‐vivo experiments, data was reconstructed using total Variation L1 regularisation in the spatial and temporal domains. In‐silico, the reconstruction accuracy of the ‘perturbed’ golden spiral was compared to uniform density golden‐angle spiral. For the in‐vivo experiment, stroke volume and peak mean velocity were measured in 20 children using ‘perturbed’ and uniform density golden‐angle spiral sequences. These were compared to a reference standard gated Cartesian sequence. RESULTS: In‐silico, the perturbed spiral acquisition produced more accurate reconstructions with less temporal blurring (NRMSE ranging from 0.03 to 0.05) than the uniform density acquisition (NRMSE ranging from 0.06 to 0.12). This translated in more accurate results in‐vivo with no significant bias in the peak mean velocity (bias: −0.1, limits: −4.4 to 4.1 cm/s; P = 0.98) or stroke volume (bias: −1.8, limits: −9.4 to 5.8 ml, P = 0.19). CONCLUSION: We showed that a ‘perturbed’ golden‐angle spiral approach is better suited to Compressive Sensing reconstruction due to more incoherent aliases. This enabled accurate real‐time measurement of flow and peak velocity in children

Reducing Contrast Agent Dose in Cardiovascular MR Angiography with Deep Learning

BACKGROUND: Contrast-enhanced magnetic resonance angiography (MRA) is used to assess various cardiovascular conditions. However, gadolinium-based contrast agents (GBCAs) carry a risk of dose-related adverse effects. PURPOSE: To develop a deep learning method to reduce GBCA dose by 80%. STUDY TYPE: Retrospective and prospective. POPULATION: A total of 1157 retrospective and 40 prospective congenital heart disease patients for training/validation and testing, respectively. FIELD STRENGTH/SEQUENCE: A 1.5 T, T1-weighted three-dimensional (3D) gradient echo. ASSESSMENT: A neural network was trained to enhance low-dose (LD) 3D MRA using retrospective synthetic data and tested with prospective LD data. Image quality for LD (LD-MRA), enhanced LD (ELD-MRA), and high-dose (HD-MRA) was assessed in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and a quantitative measure of edge sharpness and scored for perceptual sharpness and contrast on a 1-5 scale. Diagnostic confidence was assessed on a 1-3 scale. LD- and ELD-MRA were assessed against HD-MRA for sensitivity/specificity and agreement of vessel diameter measurements (aorta and pulmonary arteries). STATISTICAL TESTS: SNR, CNR, edge sharpness, and vessel diameters were compared between LD-, ELD-, and HD-MRA using one-way repeated measures analysis of variance with post-hoc t-tests. Perceptual quality and diagnostic confidence were compared using Friedman's test with post-hoc Wilcoxon signed-rank tests. Sensitivity/specificity was compared using McNemar's test. Agreement of vessel diameters was assessed using Bland-Altman analysis. RESULTS: SNR, CNR, edge sharpness, perceptual sharpness, and perceptual contrast were lower (P < 0.05) for LD-MRA compared to ELD-MRA and HD-MRA. SNR, CNR, edge sharpness, and perceptual contrast were comparable between ELD and HD-MRA, but perceptual sharpness was significantly lower. Sensitivity/specificity was 0.824/0.921 for LD-MRA and 0.882/0.960 for ELD-MRA. Diagnostic confidence was 2.72, 2.85, and 2.92 for LD, ELD, and HD-MRA, respectively (PLD-ELD , PLD-HD < 0.05). Vessel diameter measurements were comparable, with biases of 0.238 (LD-MRA) and 0.278 mm (ELD-MRA). DATA CONCLUSION: Deep learning can improve contrast in LD cardiovascular MRA. LEVEL OF EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2

Machine learning in Magnetic Resonance Imaging: Image reconstruction.

Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction. A wide range of approaches have been proposed, which can be applied in k-space and/or image-space. Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation. In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends

Adiposity is associated with blunted cardiovascular, neuroendocrine and cognitive responses to acute mental stress

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited - Copyright @ 2012 Jones et al.Obesity and mental stress are potent risk factors for cardiovascular disease but their relationship with each other is unclear. Resilience to stress may differ according to adiposity. Early studies that addressed this are difficult to interpret due to conflicting findings and limited methods. Recent advances in assessment of cardiovascular stress responses and of fat distribution allow accurate assessment of associations between adiposity and stress responsiveness. We measured responses to the Montreal Imaging Stress Task in healthy men (N=43) and women (N=45) with a wide range of BMIs. Heart rate (HR) and blood pressure (BP) measures were used with novel magnetic resonance measures of stroke volume (SV), cardiac output (CO), total peripheral resistance (TPR) and arterial compliance to assess cardiovascular responses. Salivary cortisol and the number and speed of answers to mathematics problems in the task were used to assess neuroendocrine and cognitive responses, respectively. Visceral and subcutaneous fat was measured using T2*-IDEAL. Greater BMI was associated with generalised blunting of cardiovascular (HR:β=−0.50 bpm.unit−1, P=0.009; SV:β=−0.33 mL.unit−1, P=0.01; CO:β=−61 mL.min−1.unit−1, P=0.002; systolic BP:β=−0.41 mmHg.unit−1, P=0.01; TPR:β=0.11 WU.unit−1, P=0.02), cognitive (correct answers: r=−0.28, P=0.01; time to answer: r=0.26, P=0.02) and endocrine responses (cortisol: r=−0.25, P=0.04) to stress. These associations were largely determined by visceral adiposity except for those related to cognitive performance, which were determined by both visceral and subcutaneous adiposity. Our findings suggest that adiposity is associated with centrally reduced stress responsiveness. Although this may mitigate some long-term health risks of stress responsiveness, reduced performance under stress may be a more immediate negative consequence.This work is funded by the UK National Institute of Health Research (NIHR), Siemens Medical Systems, British Heart Foundation (BHF), NIHR Senior Research Fellowship & The Fondation Leducq, BHF Intermediate Fellowship

Velocity unwrap for high resolution slice-selective Fourier Velocity Encoding using spiral SENSE

Quantification of peak velocity is important in the assessment of stenotic flow jets. It is possible to measure peak velocity accurately using Fourier Velocity Encoding (FVE). In this study, a fast, high-resolution slice-selective FVE sequence was developed with the use of spiral trajectories, parallel-imaging, partial-Fourier in the velocity-dimension and a novel velocity-unwrap technique. The resulting sequence was acquired within a short breath-hold. Peak velocities were compared from Doppler ultrasound (US), phase-contrast MR (PCMR) and FVE. Experiments carried out in-vitro and in-vivo showed that PCMR tended to underestimate peak velocity compared to Doppler US, whereas FVE agreed well with Doppler US