Original contributionQuantification of morphometry and intensity features of intracranial arteries from 3D TOF MRA using the intracranial artery feature extraction (iCafe): A reproducibility study
Section snippets
Background
Atherosclerosis is a major cause of cardio- and cerebrovascular disease mortality and morbidity globally [[1], [2], [3]]. Plaque progression leads to arterial stenosis and/or occlusion, which may cause downstream ischemic events due to atherothrombosis or hypoperfusion [4]. As a subset of atherosclerosis, intracranial atherosclerosis accounts for about 30–50% and 10% of cerebrovascular ischemic events in Asian and Caucasian populations, respectively [5].
In current clinical practice, luminal
Patient studies
After institutional review board review and approval, 24 subjects with documented intracranial arterial stenosis were enrolled in the study after obtaining informed consent. Subjects were scanned during two different scan sessions on a 3T Philips Ingenia Scanner (Philips Healthcare, Best, the Netherlands) using a standard head coil. Imaging parameters for TOF MRA were as follows: TR/TE = 14.7/3.5 ms, flip angle = 18°, acquired in-plane resolution = 0.6 mm × 0.6 mm, interpolated in-plane
Results
Sixteen males and eight females were recruited for this study. Subjects included in this study had an age range from 45 to 81, and a mean age of 61.0 ± 9.6.
Intracranial artery structures of each subject were generated using iCafe with manual corrections applied after automated vessel segment, from which the vascular features were extracted. The processing time needed for each case was between 20 min to 1 h. An example of generated artery tracing and extracted features is shown in Fig. 2.
In the
Discussion
In this study assessing a novel, semi-automated method (iCafe) that provides quantitative measurements of intracranial vasculature on TOF-MRA, we found: 1) both stenosis detection and quantification of artery features agree well for inter-scan, intra-operator and inter-operator reproducibility analyses; 2) reproducibility for the lengths of proximal and distal arteries was similar; and 3) reproducibility of the average normalized intensity for the large artery subgroup was similar to the
Conclusion
iCafe is a highly reproducible image analysis tool that can reliably detect locations of stenosis and extract intracranial arterial morphometry and intensity features from TOF MRA to provide both clinical diagnostic assistance and facilitate future investigative quantitative analyses.
List of abbreviations
- TOF
time-of-flight
- CoW
circle of Willis
- MRA
magnetic resonance angiography
- iCafe
intracranial artery feature extraction
- ICC
intra-class correlation coefficient
- CV
coefficient of variation
- MCA
middle cerebral artery
- ACA
anterior cerebral artery
- PCA
posterior cerebral artery
- ICA
internal carotid artery
- BA
basilar artery
- VA
vertebral artery
- Pcomm
posterior communicating artery
- Acomm
anterior communicating artery
- OA
ophthalmic artery
- PPV
positive predictive values
Ethics approval and consent to participate
This study was approved by institutional review board, and all subjects gave written informed consent prior to enrollment in the study.
Consent for publication
Written informed consent was obtained from all participants for inclusion of their data in publications.
Availability of software
iCafe is available for other groups for non-commercial research under an academic license. Please contact Dr. Chun Yuan ([email protected]) to request an executable file.
Competing interests
The authors declare that they have no competing interests.
Funding
This research was supported by grants from the National Institutes of Health (R01-NS083503, R01-NS092207 and R01-HL103609) and Philips Healthcare.
Authors' contributions
LC developed the iCafe software and wrote the manuscript. LC and QY processed data. MM identified stenosis positions of 8 subjects for comparison. LC, JS, DH did statistical analysis. NB, KP and TH collected image data. MM, JH, CY conceived, directed and advised on the project. All authors read and approved the final manuscript.
Acknowledgements
We are grateful to the support of NVIDIA Corporation with donation of the Titan Xp GPU.
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