Standardized Evaluation of Cerebral Arteriovenous Malformations Using Flow Distribution Network Graphs and Dual-venc 4D Flow MRI

Abstract

Background: Cerebral arteriovenous malformations (AVMs) are pathological connections between arteries and veins. Dual-venc 4D flow MRI, an extended 4D flow MRI method with improved velocity dynamic range, provides time-resolved 3D cerebral hemodynamics.

Purpose: To optimize dual-venc 4D flow imaging parameters for AVM; to assess the relationship between spatial resolution, acceleration, and flow quantification accuracy; and to introduce and apply the flow distribution network graph (FDNG) paradigm for storing and analyzing complex neurovascular 4D flow data.

Study type: Retrospective cohort study.

Subjects/phantom: Scans were performed in a specialized flow phantom: 26 healthy subjects (age 41 ± 17 years) and five AVM patients (age 27-68 years).

Field strength/sequence: Dual-venc 4D flow with varying spatial resolution and acceleration factors were performed at 3T field strength.

Assessment: Quantification accuracy was assessed in vitro by direct comparison to measured flow. FDNGs were used to quantify and compare flow, peak velocity (PV), and pulsatility index (PI) between healthy controls with various Circle of Willis (CoW) anatomy and AVM patients.

Statistical tests: In vitro measurements were compared to ground truth with Student's t-test. In vivo groups were compared with Wilcoxon rank-sum test and Kruskal-Wallis test.

Results: Flow was overestimated in all in vitro experiments, by an average 7.1 ± 1.4% for all measurement conditions. Error in flow measurement was significantly correlated with number of voxels across the channel (P = 3.11 × 10-28 ) but not with acceleration factor (P = 0.74). For the venous-arterial PV and PI ratios, a significant difference was found between AVM nidal and extranidal circulation (P = 0.008 and 0.05, respectively), and between AVM nidal and healthy control circulation (P = 0.005 and 0.003, respectively).

Data conclusion: Dual-venc 4D flow MRI and standardized FDNG analysis might be feasible in clinical applications. Venous-arterial ratios of PV and PI are proposed as network-based biomarkers characterizing AVM nidal hemodynamics.

Level of evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1718-1730.

Keywords: 4D flow; arteriovenous malformation; intracranial; quantitative imaging biomarker; vascular.

© 2019 International Society for Magnetic Resonance in Medicine.

Figures

Figure 1.

3D schematic of flow phantom consisting of a cube with 3 flow channels, each channel (4, 6, and 8mm) contains flow in 3 consecutive, orthogonal segments of identical length between inlet and outlet. To emulate branched flow networks, inlets to all channels are fed by branches of one flexible tube (pump source) outside imaged volume; channel outlets are similarly linked (pump return).

Figure 2.

Workflow for (A) in vitro flow phantom experiment and (B) in vivo healthy control and (C) in vivo AVM patient data: Preprocessing (noise and phase offset correction, generation of pseudo-complex difference PC-MRA), automated quantification (segmentation, centerpoint and centerline extraction, analysis plane placement (every 1 mm in each flow segment) with velocity profile, quantification). Only large arteries and veins, as well as those feeding or draining the AVM, were included in quantification and represented in a flow distribution network diagram (FDNG). The FDNG is a mechanism for internal representation of vessel connectivity that allows for straightforward computation of flow conservation across any vessel junction or the whole brain.

Figure 3.

Standardized FDNG with full COW, illustrating metrics including A. Total cerebral Blood flow or Mean Incoming Blood Velocity (equations 6-7), B. Arterial flow or PV (equations 8-9) and C. Venous-Arterial Flow, PV or PI ratio (equations 10-11). The general COW network is represented with vessels in light grey and vessels involved in equations 6-11 were colored either dark grey (if they are in the numerator of the equation) or black (if they are in the denominator). Arrowheads show direction of flow.

Figure 4.

In vitro phantom experiment. (A) Flow measurements for each channel plotted relative to number of voxels across it. For each channel, expected flow varies with pump flow rate. Dashed lines show ground truth (blue) and 10% error (orange). Red squares indicate mean flow measurement error significantly higher than 10%. (B) Sum of parallel flows in phantom, relative to number of imaged voxels across smallest (4 mm) channel. (C) Repeatability coefficient for net flow, expressed as percent of the mean flow rate. (D) Repeatability coefficient for peak velocity, expressed as percent of the mean value.

Figure 5.

(A) For each of the most common COW architectures in the sample, velocity pathlines at systole are shown for a representative subject. For each of these subjects a network graph (blue) is superimposed on the PCMRA (yellow). Within-group standardized FDNG colors show group mean vessel flow as percentage of total inflow over group. (B) For each AVM case, the FDNG shows median flow [mL/cycle] in each vessel.

Figure 6.

(A) Velocity pathlines at approximate systole (determined on an individual basis, at typically approx. 240 ms after recorded R wave) for 5 AVM cases; (B) For each AVM case, the FDNG shows median flow [mL/cycle] in each vessel.

Figure 7.

In-vivo blood flow derived parameter comparison between controls and AVM patients: (A) TCBF [mL/cycle] (B) Arterial flow [% TCBF] (C) Venous-arterial flow ratio (D) Arterial PV [m/s] (E) Venous-arterial PV ratio (F) Venous-arterial PV ratio. Dashed lines denote a ratio of 1 for cerebral arterial flow relative to TCBF or venous relative to arterial flow. Significant differences are denoted by asterisks between measurements connected by brackets.