Renal Blood Flow Measurements with Use of Phase-Contrast Magnetic Resonance Imaging: Normal Values and Reproducibility

Bax, Leon; Bakker, Chris J.G.; Klein, Willemijn M.; Blanken, Niels; Beutler, Jaap J.; Mali, Willem

doi:10.1097/01.rvi.0000161144.98350.28

Cited by 46 publications

(54 citation statements)

References 26 publications

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“…The volumetric flow measured in each pair of patient models is also closely matched, indicating that flow to the renal arteries is unaffected by the partial blockage of the renal artery ostia. We also note that at no point during the cardiac cycle do the renal arteries experience retrograde flow, which is consistent with what we know about blood flow to the kidneys [17].…”

Section: Discussionsupporting

confidence: 68%

Computational Modeling of Shear-Based Hemolysis Caused by Renal Obstruction

Segalova

Rao²,

Zarins

et al. 2012

Journal of Biomechanical Engineering

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As endovascular treatment of abdominal aortic aneurysms (AAAs) gains popularity, it is becoming possible to treat certain challenging aneurysmal anatomies with endografts relying on suprarenal fixation. In such anatomies, the bare struts of the device may be placed across the renal artery ostia, causing partial obstruction to renal artery blood flow. Computational fluid dynamics (CFD) was used to simulate blood flow from the aorta to the renal arteries, utilizing patient-specific boundary conditions, in three patient models and calculate the degree of shear-based blood damage (hemolysis). We used contrast-enhanced computed tomography angiography (CTA) data from three AAA patients who were treated with a novel endograft to build patient-specific models. For each of the three patients, we constructed a baseline model and endoframe model. The baseline model was a direct representation of the patient's 30-day post-operative CTA data. This model was then altered to create the endoframe model, which included a ring of metallic struts across the renal artery ostia. CFD was used to simulate blood flow, utilizing patient-specific boundary conditions. Pressures, flows, shear stresses, and the normalized index of hemolysis (NIH) were quantified for all patients. The overall differences between the baseline and endoframe models for all three patients were minimal, as measured though pressure, volumetric flow, velocity, and shear stress. The average NIH across the three baseline and endoframe models was 0.002 and 0.004, respectively. Results of CFD modeling show that the overall disturbance to flow caused by the presence of the endoframe struts is minimal. The magnitude of the NIH in all models was well below the accepted design and safety threshold for implantable medical devices that interact with blood flow.

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Section: Discussionsupporting

confidence: 68%

Computational Modeling of Shear-Based Hemolysis Caused by Renal Obstruction

Segalova

Rao²,

Zarins

et al. 2012

Journal of Biomechanical Engineering

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“…The pressure and flow waveforms are physiologic in shape and magnitude. Specifically, the renal flow is always antegrade (35), but the abdominal aortic flow is retrograde during part of the cardiac cycle in resting condition. The flow split ratios between the aortic and renal branches are 54:46 (predicted by the simulation) and 53:47 (measured by MR) for the resting condition, and 74:26 (predicted) and 72:28 (measured) for the light exercise condition.…”

Section: Resultsmentioning

confidence: 99%

“…The experiment mimicked two different physiological conditions: a resting condition, and a light exercise condition. We computed the resting, supra-renal level aortic flow waveform by summing the infra-renal blood flow velocity previously acquired by PCMRI in the same AAA patient, and published data of renal flow (35). For the light exercise condition, we increased the average flow to two times that of resting, increased the heart rate from 60 bpm to 80 bpm, and decreased the downstream resistance of the aortic outlet Rd1 (by turning on a valve to allow flow through Rd1-2 which is in parallel with Rd1-1) to mimic vasodilation of the lower extremity vessels.…”

Section: Methodsmentioning

confidence: 99%

In Vitro Validation of Finite-Element Model of AAA Hemodynamics Incorporating Realistic Outlet Boundary Conditions

Kung

Les

Medina

et al. 2011

Journal of Biomechanical Engineering

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Purpose To validate numerical simulations of flow and pressure in an abdominal aortic aneurysm (AAA) using phase-contrast MRI (PCMRI), and an in-vitro phantom under physiological flow and pressure conditions. Materials and Methods We constructed a 2-outlet physical flow phantom based on patient imaging data of an AAA, and developed a physical Windkessel model to use as outlet boundary conditions. We then acquired PCMRI data in the phantom while it operated under conditions mimicking a resting and a light exercise physiological state. Next, we performed in-silico numerical simulations, and compared experimentally measured velocities, flows, and pressures in the in-vitro phantom to those computed in the in-silico simulations. Results There was a high degree of agreement in all of the pressure and flow waveform shapes and magnitudes between the experimental measurements and simulated results. The average pressures and flow split difference between experiment and simulation were all within 2%. Velocity patterns showed good agreement between experimental measurements and simulated results, especially in the case of whole-cycle averaged comparisons. Conclusion We demonstrated methods to perform in-vitro phantom experiments with physiological flows and pressures, showing good agreement between numerically simulated and experimentally measured velocity fields and pressure waveforms in a complex, patient-specific AAA geometry.

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“…The analysis of the optimal number of clusters [10] revealed that usually two local minima (apart from K=2) in the plot of the DB indexes could be observed. This occurred for a small number of clusters (3)(4)(5) and a higher one (8-10) depending on the subject and kidney. By selecting such number of clusters, the cluster representing the renal artery was divided in two or more sub-clusters (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Blood flow quantification from PC-MRI acquisition is usually performed by manual or semi-automated delineations of the vessel area [3,4,5,6]. This is time consuming and subject to operator dependent variability.…”

Section: State Of the Art And New Contributionmentioning

confidence: 99%

Flow Quantification from 2D Phase Contrast MRI in Renal Arteries Using Clustering

et al.

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Abstract. We present an approach based on clustering to segment renal arteries from 2D PC Cine MR images to measure blood velocity and flow. Such information are important in grading renal artery stenosis and support the decision on surgical interventions like percutan transluminal angioplasty. Results show that the renal arteries could be extracted automatically and the corresponding velocity profiles could be calculated. Furthermore, the clustering could detect possible phase wrap effects automatically as well as differences in the blood flow patterns within the vessel.

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Renal Blood Flow Measurements with Use of Phase-Contrast Magnetic Resonance Imaging: Normal Values and Reproducibility

Cited by 46 publications

References 26 publications

Computational Modeling of Shear-Based Hemolysis Caused by Renal Obstruction

Computational Modeling of Shear-Based Hemolysis Caused by Renal Obstruction

In Vitro Validation of Finite-Element Model of AAA Hemodynamics Incorporating Realistic Outlet Boundary Conditions

Flow Quantification from 2D Phase Contrast MRI in Renal Arteries Using Clustering

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