Rationale and Objectives
To assess the impact of an alternative preprocessing workflow on noncontrast- and contrast-enhanced abdominal four-dimensional flow magnetic resonance imaging (4D flow MRI) data consistency.
Materials and Methods
Twenty patients with cirrhosis and portal hypertension (5 women; 53 ± 10 years old) underwent 4D flow MRI at 3.0T before and after administration of 0.03 mmol/kg of gadofosveset trisodium with velocity sensitivities of 100 and 50 cm/s for arterial and venous flow quantifications, respectively. 4D flow MRI data were preprocessed using the conventional workflow (workflow 1), applying noise filters prior to eddy current correction, and an alternative workflow (workflow 2), first correcting for eddy currents and using noise filtering only if needed for anti-aliasing. Vessel segmentation quality was ranked by independent reviewers and compared via Wilcoxon signed-rank tests. Flow quantification and conservation of mass at two portal and one arterial branch points were compared via paired t tests.
Results
Segmentation quality was significantly higher for workflow 2 ( P < 0.05) with excellent interobserver agreement (κ = 0.92). Workflow 2 resulted in larger flow values ( P < 0.05) with improved conservation of mass (7.3 ± 6.1% vs. 27.7 ± 25.0%, P < 0.001 [portal]; 10.7 ± 9.0% vs. 21.7 ± 21.6%, P = 0.02 [arterial]). Peak velocities and abdominal aortic flow were similar ( P > 0.05). Noncontrast acquisitions yielded significantly smaller portal flow values ( P < 0.05) with improved conservation of mass (5.8 ± 4.7% vs. 8.7 ± 6.9%, P = 0.05 [portal]; 6.2 ± 4.5% vs. 13.7 ± 10.2%, P = 0.03 [arterial]).
Conclusions
Superior abdominal 4D flow MRI data consistency was obtained by applying eddy current correction before any other data manipulation, using noise masking and velocity anti-aliasing cautiously, and using noncontrast acquisitions.
Introduction
Abdominal time-resolved three-dimensional flow magnetic resonance imaging (4D flow MRI) is an emerging technology for noninvasively quantifying complex hemodynamics. Potential clinical applications include better characterization of cirrhosis-associated thrombocytopenia, quantification of portosystemic shunting prior to liver transplantation to maximize graft survival, transjugular intrahepatic portosystemic shunt function, differential hepatic segment blood flow for planning locoregional oncological therapies, diagnosing mesenteric angina, and renal artery stenosis in patients who cannot receive gadolinium .
However, fully assessing abdominal hemodynamics can be challenging due to the range of velocities, flow rates, and vessel diameters encountered. This is especially true in the setting of cirrhosis and portal hypertension where portosystemic shunts create even wider ranges of velocities and flow patterns, complex vascular anatomy, and imaging difficulties such as artifacts, irregular breathing, and ascites. These features can complicate vessel segmentation, leading to loss of vascular anatomy and flow data in abdominal 4D flow MRI studies of patients with large portosystemic shunts , limiting the transition of this promising technology into clinical workflows. To be used clinically, such as thoracic 4D flow MRI at our institution, abdominal 4D flow MRI must achieve a high degree of consistency to justify its relatively long acquisition and postprocessing times.
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Materials and Methods
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Patient Cohort
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Table 1
Patient Demographics
Age (years) 53 ± 10 Gender (M/F) 15/5 Heart rate (bpm) 71 ± 12 BSA (m 2 ) 2.1 ± 0.3 Cirrhosis etiology Hepatitis C Infection 8/20(40%) \* Alcoholic steatohepatitis 5/20(25%) \* Primary sclerosing cholangitis 3/20(15%) \* Nonalcohol steatohepatitis 2/20(10%) Hepatitis B Infection 2/20(10%) Autoimmune hepatitis 1/20(5%) \* Cryptogenic 1/20(5%)
All values reported as mean ± standard deviation unless indicated otherwise.
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Magnetic Resonance Imaging
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Magnetic Resonance Data Processing
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Semi-qualitative Vessel Ranking
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Statistical Analysis
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Results
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Table 2
Summary of Velocity, Flow, and Segmentation Quality Comparisons
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venc, velocity encoding sensitivities.
Significant P values ≤ 0.05 are highlighted. Low venc (50 cm/s) datasets were used for venous flow quantification. High venc (100 cm/s) datasets were used for arterial flow quantification. Low venc postcontrast datasets were used for segmentation quality.
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Table 3
Preprocessing Workflow Comparison Summary
Parameter Workflow 1 Workflow 2P Value Preprocessing steps random noise correction → eddy current correction → anti-aliasing (for all acquisitions) eddy current correction → random noise correction and anti-aliasing (only for high venc acquisitions) — Hepatic veins segmentation quality 1.3 ± 1.3 2.6 ± 0.9 <0.001 Portal system segmentation quality 2.2 ± 1.1 2.9 ± 0.4 <0.001 Arterial segmentation quality 2.7 ± 0.7 3.0 ± 0.0 <0.001 Relative error in portal flow 27.7 ± 25.0% 7.3 ± 6.1% <0.001 Relative error in arterial flow 21.7 ± 21.6% 10.7 ± 9.0% 0.02
venc, velocity encoding sensitivities.
Segmentation quality rankings: completely absent (0); small vessel remnant captured (1); vessel captured with rough borders and/or no branches (2); and vessel captured with clear borders and/or branches (3).
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Discussion
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Acknowledgments
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