Cerebrovascular Atlas From MRA Imaging of 1336 Subjects

Wang Xinyu; Jialu Liu; Xiaoping Lou

doi:10.5566/ias.3442

Authors

Xinyu Wang Key Laboratory of The Ministry of Education for Optoelectronic MeasurementTechnology and Instrument, Beijing Information Science and Technology University, Beijing 100192, China
Jialu Liu Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China c School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
Xiaoping Lou Key Laboratory of The Ministry of Education for Optoelectronic MeasurementTechnology and Instrument

DOI:

https://doi.org/10.5566/ias.3442

Keywords:

Angiography, Cerebral vessels, Magnetic resonance imaging, statistical atlas

Abstract

This study aimed to create a comprehensive statistical atlas of cerebral arteries to accurately capture variations among individuals and across different age groups. We utilized 1,336 publicly available multicenter magnetic resonance angiography (MRA) and T1-weighted MRI datasets, employing an automated blood vessel segmentation method, FFCM-MRF, to segment all blood vessels and measure their radii. Subsequently, the binary segmentation and vascular radius images were nonlinearly registered to the Montreal Neurological Institute (MNI) brain template using the T1-weighted MRI dataset. This process resulted in the creation of atlases that illustrate the probability of arterial occurrence, the average arterial radius, and the standard deviation of the arterial radius. The constructed vascular statistical atlas effectively showcases the major arteries and, when integrated with the probability atlas and the average vessel radius atlas, indicates a significantly higher probability of larger arteries, which decreases as the vessel radius diminishes. This observation aligns with previous research findings, and the similarity between the probability atlas and individual vascular images reached as high as 0.9659. In conclusion, this atlas effectively covers arterial radius information across nearly the entire age range, enabling the identification of variations between individual arterial voxel radii and the population using this atlas, thereby providing an important reference for cerebral vascular research.

References

Avants BB, Tustison N, Song G (2009). Advanced normalization tools (ANTS). Insight J 2(365):1–35.

Cool D, Chillet D, Kim J, Guyon JP, Foskey M, Aylward S (2003). Tissue-based affine registration of brain images to form a vascular density atlas. Med Image Comput Comput Assist Interv 6:9-15.

Cui Y, Huang H, Liu J, Zhao M, Li C, Han X, et al. (2024). FFCM-MRF: An accurate and generalizable cerebrovascular segmentation pipeline for humans and rhesus monkeys based on TOF-MRA. Comput Biol Med 170:107996.

Danielsson PE (1980). Euclidean distance mapping. Comput Graph Image Process 14:227–48.

Dunås, T., Wåhlin, A., Ambarki K, Zarrinkoob L, Malm J, Eklund A (2017). A stereotactic probabilistic atlas for the major cerebral arteries. Neuroinformatics 15:101–110.

Dufour A, Tankyevych O, Talbot H, Ronse C (2011). A statistical arteriovenous cerebral atlas. MICCAI Workshop Comput Vis (Intra)Vasc Imaging 73–80.

Forkert ND, Fiehler J, Suniaga S, Wersching H, Knecht S, Kemmling A (2013). A statistical cerebroarterial atlas derived from 700 MRA datasets. Methods Inf Med 52:467–474.

Iadecola C, Gottesman RF (2018). Cerebrovascular alterations in Alzheimer disease: Incidental or pathogenic? Circ Res 123:406–8.

Lee TC, Kashyap RL, Chu CN (1994). Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP Graph Model Image Process 56:462–78.

Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Mazoyer B (2001). A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 356:1293–1322.

Mouches P, Forkert ND (2019). A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects. Sci Data 6:29.

Özütemiz C (2024). Cerebrovascular imaging at 7T: A new high. Semin Roentgenol 59:148–56.

Passat N, Ronse C, Baruthio J, Armspach JP, Maillot C (2005). Cerebral vascular atlas generation for anatomical knowledge modeling and segmentation purpose. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) 2:331–7.

Perlmutter D, Rhoton A (1976). Microsurgical anatomy of the anterior cerebral-anterior communicating-recurrent artery complex. J Neurosurg 45:259–72.

Saeki N, Rhoton A (1977). Microsurgical anatomy of the upper basilar artery and the posterior circle of Willis. J Neurosurg 46:563–78.

Smith SM (2002). Fast robust automated brain extraction. Hum Brain Mapp 17:143–55.

Smoker WR, Price MJ, Keyes WD, Corbett JJ and Gentry LR (1986). High-resolution computed tomography of the basilar artery: 1. Normal size and position. AJNR Am J Neuroradiol 7:55-60.

Sun Z, et al. (2022). Age-related tortuosity of carotid and vertebral arteries: Quantitative evaluation with MR angiography. Front Neurol 13:858805.

Zeal AA, Rhoton AL (1978). Microsurgical anatomy of the posterior cerebral artery. J Neurosurg 48:534–59.

Zhang B, Wu Z, Liu S, Zhou S, Li N, Zhao G (2019). A device-independent novel statistical modeling for cerebral TOF-MRA data segmentation. In: Workshop on Clinical Image-Based Procedures. Lect Notes Comput Sci 11840:172–81. Springer.