Fast 4D flow MRI intracranial segmentation and quantification in tortuous arteries

Abstract

Purpose 4D flow MRI enables blood flow measurement in all major cerebral arteries with one scan. Clinical translational hurdles are time demanding post-processing and user-dependence induced variability during analysis. To overcome these shortcomings, a centerline processing scheme (CPS) for semi-automated segmentation and quantification is described, validated, and implemented in carotid siphons of healthy subjects. Materials and Methods A CPS for 4D flow data was developed to automatically separate cerebral artery trees. Flow parameters were quantified at planes along the centerline oriented perpendicular to the vessel path. At 3T, validation against 2D phase-contrast (PC) MR and 4D flow manual processing was performed on an intracranial flow phantom for constant flow, while pulsatile flow validation was performed in the internal carotid artery (ICA) of ten healthy volunteers. The CPS and 4D manual processing times were measured and compared. Flow and area measurements were also demonstrated along the length of the ICA siphon. Results Phantom measurements for area and flow were highly correlated between the CPS and 2D measurements (area: R = 0.95, flow: R = 0.94), while in-vivo waveforms were highly correlated (R = 0.93). Processing time was reduced by a factor of 4.6 compared with manual processing. Whole ICA measurements revealed significantly decreased area in the most distal segment of the carotid siphon (p = 0.0017), with flow unchanged (p = 0.84). Conclusion This study exhibits fast semi-automated analysis of intracranial 4D flow MRI. Internal consistency was shown through flow conservation along the tortuous ICA siphon, which is typically a difficult to assess.

Accuracy of blood flow assessment in cerebral arteries with 4D flow MRI: Evaluation with three segmentation methods

“…The clustering methods, regardless of input data, were robust in terms of producing flow values with good precision, but with a systematic underestimation of 4D flow MRI compared with 2D PCMRI. Combining the CD in the clustering method with velocity data and the magnitude image improved the agreement, but the systematic difference, which was similar to previous k‐means clustering results in ICA, must still be considered too large to be accepted for clinical use. In addition, we included a larger set of arteries that revealed a flow dependency in the clustering methods, reducing its generalizability.…”

“…The centerline was divided into branches, connected by junction points, and each branch was assigned a unique identification number . This centerline approach has previously been implemented on in vivo 4D flow MRI data, both for flow assessment and vessel identification . In order to match the 4D and the 2D measurement locations, the centerline‐point closest to each of the 2D center‐points was identified and used as the seed points for the 4D flow segmentation methods.…”

“…For identifying an appropriate method for segmentation of 4D flow MRI data, with the purpose of separating the vessel lumen from the surrounding tissue, we investigated both k‐means clustering and two threshold algorithms. K‐means clustering offers different combinations of reconstructed input data, where CD together with velocity previously has been proposed in combination with the centerline processing . The threshold algorithms are divided into global or local thresholding, where the global is previously investigated but without the centerline processing, while the local thresholding was developed and presented in this study.…”

Comparison of 4D flow and 2D velocity-encoded phase contrast MRI sequences for the evaluation of aortic hemodynamics

“…Alternatively, 4D flow MRI (3D CINE PC-MRI with three-directional velocity encoding) enables post-hoc time-resolved three-dimensional visualization of blood flow and retrospective quantification at any location in a 3D volume [5]. The usefulness of the technique has been increasingly demonstrated for the assessment of blood flow hemodynamics in diverse cardiovascular territories such as the aorta [6], ventricles [7], atria [8], pulmonary arteries [9], intracranial arteries [10], portal [11] and splanchnic arteries [12]. …”

Using flow feature to extract pulsatile blood flow from 4D flow MRI images

“…1 Extracting accurate blood flows is a key step for post-processing tasks such as visualization and quantitative studies. Most existing approaches [2][3][4] extract peak systole blood velocities, when the flow velocity is the highest during one cardiac cycle. The movement of the elastic vessels over cardiac cycles is not considered which may lead to inaccurate flows.…”