A hybrid hierarchical strategy for registration of 7T TOF-MRI to 7T PC-MRI intracranial vessel data.
Phase-contrast MRI
Time-of-flight MRI
Vascular co-registration
Vessel centerline
Journal
International journal of computer assisted radiology and surgery
ISSN: 1861-6429
Titre abrégé: Int J Comput Assist Radiol Surg
Pays: Germany
ID NLM: 101499225
Informations de publication
Date de publication:
May 2023
May 2023
Historique:
received:
17
05
2022
accepted:
09
01
2023
medline:
20
4
2023
pubmed:
21
1
2023
entrez:
20
1
2023
Statut:
ppublish
Résumé
7T time-of-flight (TOF) MRI provides high resolution for the evaluation of cerebrovascular vessels and pathologies. In combination with 4D flow fields acquired with phase-contrast (PC) MRI, hemodynamic information can be extracted to enhance the analysis by providing direct measurements in the larger arteries or patient-specific boundary conditions. Hence, a registration between both modalities is required. To combine TOF and PC-MRI data, we developed a hybrid registration approach. Vessels and their centerlines are segmented from the TOF data. The centerline is fit to the intensity ridges of the lower resolved PC-MRI data, which provides temporal information. We used a metric that utilizes a scaled sum of weighted intensities and gradients on the normal plane. The registration is then guided by decoupled local affine transformations. It is applied hierarchically following the branching order of the vessel tree. A landmark validation over Monte Carlo simulations yielded an average mean squared error of 184.73 mm and an average Hausdorff distance of 15.20 mm. The hierarchical traversal that transforms child vessels with their parents registers even small vessels not detectable in the PC-MRI. The presented work combines high-resolution tomographic information from 7T TOF-MRI and measured flow data from 4D 7T PC-MRI scan for the arteries of the brain. This enables usage of patient-specific flow parameters for realistic simulations, thus supporting research in areas such as cerebral small vessel disease. Automatization and free deformations can help address the limiting error measures in the future.
Identifiants
pubmed: 36662415
doi: 10.1007/s11548-023-02836-y
pii: 10.1007/s11548-023-02836-y
pmc: PMC10113302
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
837-844Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : SA 3461/3-1
Organisme : Bundesministerium für Bildung und Forschung
ID : 13GW0473A
Informations de copyright
© 2023. The Author(s).
Références
Int J Comput Assist Radiol Surg. 2018 Nov;13(11):1781-1793
pubmed: 30159832
J Magn Reson Imaging. 2012 Nov;36(5):1015-36
pubmed: 23090914
Clin Neuroradiol. 2021 Sep;31(3):643-651
pubmed: 32974727
Comput Biol Med. 2019 Dec;115:103507
pubmed: 31698232
Med Biol Eng Comput. 2008 Nov;46(11):1097-112
pubmed: 19002516
J Cardiovasc Magn Reson. 2011 Mar 09;13:19
pubmed: 21388544
Med Image Anal. 2001 Jun;5(2):143-56
pubmed: 11516708
Med Image Anal. 2017 Jan;35:1-17
pubmed: 27294558
J Biomech Eng. 2014 Apr;136(4):
pubmed: 24292415
IEEE Trans Med Imaging. 2012 Aug;31(8):1557-72
pubmed: 22531755
J Cereb Blood Flow Metab. 2020 Jan;40(1):85-99
pubmed: 30295558
Int J Comput Assist Radiol Surg. 2019 Oct;14(10):1805-1813
pubmed: 31363984
Cardiovasc Diagn Ther. 2014 Apr;4(2):173-92
pubmed: 24834414
Lancet Neurol. 2019 Jul;18(7):684-696
pubmed: 31097385
IEEE Pulse. 2011 Nov;2(6):60-70
pubmed: 22147070