4D Flow MRI Pressure Estimation Using Velocity Measurement-Error-Based Weighted Least-Squares

Zhang, Jiacheng; Brindise, Melissa C.; Rothenberger, Sean; Schnell, Susanne; Markl, Michael; Saloner, David; Rayz, Vitaliy L.; Vlachos, Pavlos P.

doi:10.1109/tmi.2019.2954697

Cited by 27 publications

(29 citation statements)

References 35 publications

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“…When it comes to alternative methods based on full‐field data, Rivera‐Rivera et al 4 extracted relative pressures directly from the Navier‐Stokes equations using 4D Flow MRI data, reporting much milder pressure changes, however, doing so in the dural sinus where significantly lower flow velocities prevail. Similarly, Zhang et al 35 reported lower relative pressure magnitudes in aneurysmal geometries using a error‐weighted PPE approach, however, acknowledged that the extension of their approach into narrower segments is limited due to the need for central difference gradient estimators. Still, assessment similar to that performed in this study (mapping spatiotemporal dependence and noise sensitivity in a cerebrovascular setting) remains to be performed for these alternative full‐field techniques.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

Marlevi

Schollenberger

Aristova

et al. 2021

Magnetic Resonance in Med

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Purpose Hemodynamic alterations are indicative of cerebrovascular disease. However, the narrow and tortuous cerebrovasculature complicates image‐based assessment, especially when quantifying relative pressure. Here, we present a systematic evaluation of image‐based cerebrovascular relative pressure mapping, investigating the accuracy of the routinely used reduced Bernoulli (RB), the extended unsteady Bernoulli (UB), and the full‐field virtual work‐energy relative pressure (νWERP) method. Methods Patient‐specific in silico models were used to generate synthetic cerebrovascular 4D Flow MRI, with RB, UB, and νWERP performance quantified as a function of spatiotemporal sampling and image noise. Cerebrovascular relative pressures were also derived in 4D Flow MRI from healthy volunteers (n=8), acquired at two spatial resolutions (dx = 1.1 and 0.8 mm). Results The in silico analysis indicate that accurate relative pressure estimations are inherently coupled to spatial sampling: at dx = 1.0 mm high errors are reported for all methods; at dx = 0.5 mm νWERP recovers relative pressures at a mean error of 0.02 ± 0.25 mm Hg, while errors remain higher for RB and UB (mean error of −2.18 ± 1.91 and −2.18 ± 1.87 mm Hg, respectively). The dependence on spatial sampling is also indicated in vivo, albeit with higher correlative dependence between resolutions using νWERP (k = 0.64, R2 = 0.81 for dx = 1.1 vs. 0.8 mm) than with RB or UB (k = 0.04, R2 = 0.03, and k = 0.07, R2 = 0.07, respectively). Conclusion Image‐based full‐field methods such as νWERP enable cerebrovascular relative pressure mapping; however, accuracy is directly dependent on utilized spatial resolution.

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Section: Discussionmentioning

confidence: 99%

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

Marlevi

Schollenberger

Aristova

et al. 2021

Magnetic Resonance in Med

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“…Though this equation is commonly used, some investigators note that the modified Bernoulli equation does not account for downstream pressure recovery and may overestimate the pressure gradient [26,27]. More complex pressure calculations from the Navier-Stokes equations have been investigated [28] but have not yet been applied clinically.…”

Section: Arterial and Venous Stenosismentioning

confidence: 99%

Hemodynamic Assessment of Structural Heart Disease Using 4D Flow MRI: How We Do It

Jacobs

Hahn

Horowitz

et al. 2021

American Journal of Roentgenology

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The publication of this Accepted Manuscript is provided to give early visibility to the contents of the article, which will undergo additional copyediting, typesetting, and review before it is published in its final form. During the production process, errors may be discovered that could affect the content of the Accepted Manuscript. All legal disclaimers that apply to the journal pertain. The reader is cautioned to consult the definitive version of record before relying on the contents of this document.

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“…The 2D pressure field, P , inside the ventricles is computed using the pressure Poisson equation, Neumann boundary conditions account for temporal variation of pressure. The solver reconstructs P using a least squares formulation, through an in-house algorithm 23 . This analysis returns quantities for peak early and late AV valve velocities (E,A), EL, VS, and peak suction and reversal AV valve-to-apex pressure differences (Δ P ).…”

Section: Speckle Tracking Algorithmmentioning

confidence: 99%

Fetal and Neonatal Echocardiographic Analysis of Biomechanical Alterations for the Hypoplastic Left Heart

Meyers¹,

Brindise²,

Payne

et al. 2020

Preprint

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Objectives To show simultaneous quantification of flow and mechanics of cardiac function from fetal and neonatal echocardiograms using an integrated set of automated, physics-based, echocardiography analysis methods. Background Quantifying ventricular biomechanics from fetal and neonatal echocardiograms presents unique and significant challenges. Existing analysis tools are designed for adults and cannot accurately assess fetal subjects. Methods We used in-house developed analysis algorithms to quantify ventricular biomechanics from four-chamber B-mode and color Doppler routine examinations recordings for three hypoplastic left heart (HLHS) patients at 33-weeks' gestation and first week post-birth along with age-matched controls. Chamber morphology, tissue motion, atrioventricular valve inflow, global longitudinal strain, and hemodynamic flow parameters were measured. Results Prenatal cardiac output differed between control (LV:157±139 mL/min, RV:257±218 mL/min) and HLHS subjects (410±128 mL/min). This difference persisted for control (LV:233±74 mL/min, RV:242±140 mL/min) and HLHS subjects (637±298 mL/min) after birth. Peak global longitudinal strain measurements did not differ in utero between control (LV:12.2±4.1%, RV:12.1±4.9%) and HLHS subjects (RV:12.7±4.2%). After birth, myocardial contraction increased for the control (LV:15.4±2.8%, RV:22.9±6.9%) and HLHS subjects (14.4±6.2%). Postnatal early filling mitral flow velocity for the control subjects (LV:58.8±17.6 cm/s) and early-filling tricuspid flow of the HLHS subjects (64.8±23.7cm/s) were similar, while the late filling velocity decreased for the control subject LV (33.5±8.1 cm/s) compared to the HLHS subjects (66.9±23.0 cm/s). Importantly, flow energy loss in the fetal HLHS hearts was increased (0.35±0.19 m3/s2) compared to the control subjects (LV:0.09±0.07 m3/s2, RV:0.17±0.12 m3/s2), and further increased postnatally for the HLHS subjects (0.55±0.24 m3/s2) compared to the control subjects (LV:0.23±0.20 m3/s2, RV:0.09±0.06 m3/s2). Conclusions We demonstrate the feasibility of integrated quantitative measurements of fetal and neonatal ventricular hemodynamics and biomechanics using only four-chamber B-mode and color Doppler recordings.

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4D Flow MRI Pressure Estimation Using Velocity Measurement-Error-Based Weighted Least-Squares

Cited by 27 publications

References 35 publications

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI

Hemodynamic Assessment of Structural Heart Disease Using 4D Flow MRI: How We Do It

Fetal and Neonatal Echocardiographic Analysis of Biomechanical Alterations for the Hypoplastic Left Heart

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