Tora Dunås1, Anders Wåhlin2,3, Khalid Ambarki2,4, Laleh Zarrinkoob5, Richard Birgander2, Jan Malm5, Anders Eklund2,3,4. 1. Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden. tora.dunas@umu.se. 2. Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden. 3. Umeå Center for Functional Brain Imaging, Umeå University, S-901 87, Umeå, Sweden. 4. Centre for Biomedical Engineering and Physics, Umeå University, S-901 87, Umeå, Sweden. 5. Department of Clinical Neuroscience, Umeå University, S-901 87, Umeå, Sweden.
Abstract
OBJECTIVES: In order to introduce 4D flow magnetic resonance imaging (MRI) as a standard clinical instrument for studying the cerebrovascular system, new and faster postprocessing tools are necessary. The objective of this study was to construct and evaluate a method for automatic identification of individual cerebral arteries in a 4D flow MRI angiogram. MATERIALS AND METHODS: Forty-six elderly individuals were investigated with 4D flow MRI. Fourteen main cerebral arteries were manually labeled and used to create a probabilistic atlas. An automatic atlas-based artery identification method (AAIM) was developed based on vascular-branch extraction and the atlas was used for identification. The method was evaluated by comparing automatic with manual identification in 4D flow MRI angiograms from 67 additional elderly individuals. RESULTS: Overall accuracy was 93%, and internal carotid artery and middle cerebral artery labeling was 100% accurate. Smaller and more distal arteries had lower accuracy; for posterior communicating arteries and vertebral arteries, accuracy was 70 and 89%, respectively. CONCLUSION: The AAIM enabled fast and fully automatic labeling of the main cerebral arteries. AAIM functionality provides the basis for creating an automatic and powerful method to analyze arterial cerebral blood flow in clinical routine.
OBJECTIVES: In order to introduce 4D flow magnetic resonance imaging (MRI) as a standard clinical instrument for studying the cerebrovascular system, new and faster postprocessing tools are necessary. The objective of this study was to construct and evaluate a method for automatic identification of individual cerebral arteries in a 4D flow MRI angiogram. MATERIALS AND METHODS: Forty-six elderly individuals were investigated with 4D flow MRI. Fourteen main cerebral arteries were manually labeled and used to create a probabilistic atlas. An automatic atlas-based artery identification method (AAIM) was developed based on vascular-branch extraction and the atlas was used for identification. The method was evaluated by comparing automatic with manual identification in 4D flow MRI angiograms from 67 additional elderly individuals. RESULTS: Overall accuracy was 93%, and internal carotid artery and middle cerebral artery labeling was 100% accurate. Smaller and more distal arteries had lower accuracy; for posterior communicating arteries and vertebral arteries, accuracy was 70 and 89%, respectively. CONCLUSION: The AAIM enabled fast and fully automatic labeling of the main cerebral arteries. AAIM functionality provides the basis for creating an automatic and powerful method to analyze arterial cerebral blood flow in clinical routine.
Entities:
Keywords:
Atlases as topic; Automatic data processing; Cerebral angiography; Circle of Willis; Magnetic resonance angiography
Authors: Kristen Devault; Pierre A Gremaud; Vera Novak; Mette S Olufsen; Guillaume Vernières; Peng Zhao Journal: Multiscale Model Simul Date: 2008-01-27 Impact factor: 1.930
Authors: Sepideh Amin-Hanjani; Xinjian Du; Dilip K Pandey; Keith R Thulborn; Fady T Charbel Journal: J Cereb Blood Flow Metab Date: 2014-11-12 Impact factor: 6.200
Authors: Arthur W Toga; Kristi A Clark; Paul M Thompson; David W Shattuck; John Darrell Van Horn Journal: Neurosurgery Date: 2012-07 Impact factor: 4.654
Authors: Susanne G Mueller; Michael W Weiner; Leon J Thal; Ronald C Petersen; Clifford R Jack; William Jagust; John Q Trojanowski; Arthur W Toga; Laurel Beckett Journal: Alzheimers Dement Date: 2005-07 Impact factor: 21.566
Authors: Mark Jenkinson; Christian F Beckmann; Timothy E J Behrens; Mark W Woolrich; Stephen M Smith Journal: Neuroimage Date: 2011-09-16 Impact factor: 6.556