Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation. However, their success heavily relies on tedious pre-processing and calibration procedures that typically induce a significant computational cost, thus hampering their clinical applicability. In this work we put forth a machine learning framework that enables the seamless synthesis of non-invasive in-vivo measurement techniques and computational flow dynamics models derived from first physical principles. We illustrate this new paradigm by showing how one-dimensional models of pulsatile flow can be used to constrain the output of deep neural networks such that their predictions satisfy the conservation of mass and momentum principles. Once trained on noisy and scattered clinical data of flow and wall displacement, these networks can return physically consistent predictions for velocity, pressure and wall displacement pulse wave propagation, all without the need to employ conventional simulators. A simple post-processing of these outputs can also provide a cheap and effective way for estimating Windkessel model parameters that are required for the calibration of traditional computational models. The effectiveness of the proposed techniques is demonstrated through a series of prototype benchmarks, as well as a realistic clinical case involving in-vivo measurements near the aorta/carotid bifurcation of a healthy human subject.

Restoration of coronary blood flow after a heart attack can cause reperfusion injury potentially leading to impaired cardiac function, adverse tissue remodeling and heart failure. Iron is an essential biometal that may have a pathologic role in this process. There is a clinical need for a precise noninvasive method to detect iron for risk stratification of patients and therapy evaluation. Here, we report that magnetic susceptibility imaging in a large animal model shows an infarct paramagnetic shift associated with duration of coronary artery occlusion and the presence of iron. Iron validation techniques used include histology, immunohistochemistry, spectrometry and spectroscopy. Further mRNA analysis shows upregulation of ferritin and heme oxygenase. While conventional imaging corroborates the findings of iron deposition, magnetic susceptibility imaging has improved sensitivity to iron and mitigates confounding factors such as edema and fibrosis. Myocardial infarction patients receiving reperfusion therapy show magnetic susceptibility changes associated with hypokinetic myocardial wall motion and microvascular obstruction, demonstrating potential for clinical translation.

BackgroundEndogenous contrast T1ρ cardiovascular magnetic resonance (CMR) can detect scar or infiltrative fibrosis in patients with ischemic or non-ischemic cardiomyopathy. Existing 2D T1ρ techniques have limited spatial coverage or require multiple breath-holds. The purpose of this project was to develop an accelerated, free-breathing 3D T1ρ mapping sequence with whole left ventricle coverage using a multicoil, compressed sensing (CS) reconstruction technique for rapid reconstruction of undersampled k-space data.MethodsWe developed a cardiac- and respiratory-gated, free-breathing 3D T1ρ sequence and acquired data using a variable-density k-space sampling pattern (A = 3). The effect of the transient magnetization trajectory, incomplete recovery of magnetization between T1ρ-preparations (heart rate dependence), and k-space sampling pattern on T1ρ relaxation time error and edge blurring was analyzed using Bloch simulations for normal and chronically infarcted myocardium. Sequence accuracy and repeatability was evaluated using MnCl2 phantoms with different T1ρ relaxation times and compared to 2D measurements. We further assessed accuracy and repeatability in healthy subjects and compared these results to 2D breath-held measurements.ResultsThe error in T1ρ due to incomplete recovery of magnetization between T1ρ-preparations was T1ρhealthy = 6.1% and T1ρinfarct = 10.8% at 60 bpm and T1ρhealthy = 13.2% and T1ρinfarct = 19.6% at 90 bpm. At a heart rate of 60 bpm, error from the combined effects of readout-dependent magnetization transients, k-space undersampling and reordering was T1ρhealthy = 12.6% and T1ρinfarct = 5.8%. CS reconstructions had improved edge sharpness (blur metric = 0.15) compared to inverse Fourier transform reconstructions (blur metric = 0.48). There was strong agreement between the mean T1ρ estimated from the 2D and accelerated 3D data (R2 = 0.99; P < 0.05) acquired on the MnCl2 phantoms. The mean R1ρ estimated from the accelerated 3D sequence was highly correlated with MnCl2 concentration (R2 = 0.99; P < 0.05). 3D T1ρ acquisitions were successful in all human subjects. There was no significant bias between undersampled 3D T1ρ and breath-held 2D T1ρ (mean bias = 0.87) and the measurements had good repeatability (COV2D = 6.4% and COV3D = 7.1%).ConclusionsThis is the first report of an accelerated, free-breathing 3D T1ρ mapping of the left ventricle. This technique may improve non-contrast myocardial tissue characterization in patients with heart disease in a scan time appropriate for patients.Electronic supplementary materialThe online version of this article (10.1186/s12968-018-0507-2) contains supplementary material, which is available to authorized users.

Background: Uterine artery (UtA) hemodynamics might be used to predict risk of hypertensive pregnancy disorders, including preeclampsia and intrauterine growth restriction. Purpose or Hypothesis: To determine the feasibility of 4D flow MRI in pregnant subjects by characterizing UtA anatomy, computing UtA flow, and comparing UtA velocity, and pulsatility and resistivity indices (PI, RI) with transabdominal Doppler ultrasound (US). Study Type: Prospective cross-sectional study from June 6, 2016, to May 2, 2018. Population or Subjects or Phantom or Specimen or Animal Model: Forty-one singleton pregnant subjects (age [range] = 27.0 ± 5.9 [18–41] years) in their second or third trimester. We additionally scanned three subjects who had prepregnancy diabetes or chronic hypertension. Field Strength/Sequence: The subjects underwent UtA and placenta MRI using noncontrast angiography and 4D flow at 1.5T. Assessment: UtA anatomy was described based on 4D flow-derived noncontrast angiography, while UtA flow properties were characterized by net flow, systolic/mean/diastolic velocity, PI and RI through examination of 4D flow data. PI and RI are standard hemodynamic parameters routinely reported on Doppler US. Statistical Tests: Spearman’s rank correlation, Wilcoxon signed rank tests, and Bland-Altman plots were used to preliminarily investigate the relationships between flow parameters, gestational age, and Doppler US. Results: 4D flow MRI and UtA flow quantification was feasible in all subjects. There was considerable heterogeneity in UtA geometry in each subject between left and right UtAs and between subjects. Mean 4D flow-based parameters were: mean bilateral flow rate = 605.6 ± 220.5 mL/min, PI = 0.72 ± 0.2, and RI = 0.47 ± 0.1. Bilateral flow did not change with gestational age. We found that MRI differed from US in terms of lower PI (mean difference −0.1) and RI (mean difference < −0.1) with Wilcoxon signed rank test P = 0.05 and P = 0.13, respectively. Data Conclusion: 4D flow MRI is a feasible approach for describing UtA anatomy and flow in pregnant subjects. Level of Evidence: Technical Efficacy: Stage 1

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