Automatic analysis of the anatomy of arteriovenous malformations using 3D and 4D MRA image sequences.
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Automatic analysis of the anatomy of arteriovenous malformations using 3D and 4D MRA image sequences. / Forkert, Nils; Säring, Dennis; Handels, Heinz.
In: Stud Health Technol Inform, Vol. 160, No. 2, 2, 2010, p. 1268-1272.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Automatic analysis of the anatomy of arteriovenous malformations using 3D and 4D MRA image sequences.
AU - Forkert, Nils
AU - Säring, Dennis
AU - Handels, Heinz
PY - 2010
Y1 - 2010
N2 - The cerebral arteriovenous malformation (AVM) is an abnormal connection between arteries and veins without capillaries in between, leading to increased blood pressure which might result in a rupture and acute bleeding. Exact knowledge about the patient's individual anatomy of the AVM is needed for improved therapy planning. This paper describes a method for automatic extraction of the AVM and automatic recognition of its feeders and draining veins and en passage vessels based on 3D and 4D MRA image sequences. After registration of the MRA datasets the AVM is segmented using a support vector machine based on blood velocity information, a vesselness measure and the bolus arrival time. The extracted hemodynamic information is then used to detect feeders and draining veins of the AVM. The segmentation of the AVM was validated based on manual segmentations for five patient datasets, whereas a mean Dice value of 0.74 was achieved. The presented hemodynamic characterization was able to detect feeders and draining veins with an accuracy of 100%. In summary the presented approach can improve presurgical planning of AVM surgeries.
AB - The cerebral arteriovenous malformation (AVM) is an abnormal connection between arteries and veins without capillaries in between, leading to increased blood pressure which might result in a rupture and acute bleeding. Exact knowledge about the patient's individual anatomy of the AVM is needed for improved therapy planning. This paper describes a method for automatic extraction of the AVM and automatic recognition of its feeders and draining veins and en passage vessels based on 3D and 4D MRA image sequences. After registration of the MRA datasets the AVM is segmented using a support vector machine based on blood velocity information, a vesselness measure and the bolus arrival time. The extracted hemodynamic information is then used to detect feeders and draining veins of the AVM. The segmentation of the AVM was validated based on manual segmentations for five patient datasets, whereas a mean Dice value of 0.74 was achieved. The presented hemodynamic characterization was able to detect feeders and draining veins with an accuracy of 100%. In summary the presented approach can improve presurgical planning of AVM surgeries.
M3 - SCORING: Zeitschriftenaufsatz
VL - 160
SP - 1268
EP - 1272
IS - 2
M1 - 2
ER -
