Classification-based summation of cerebral digital subtraction angiography series for image post-processing algorithms

Beitrag in einer Fachzeitschrift

Details zur Publikation

Autorinnen und Autoren: Schuldhaus D, Spiegel M, Redel T, Polyanskaya M, Struffert T, Hornegger J, Dörfler A
Zeitschrift: Physics in Medicine and Biology
Verlag: Institute of Physics: Hybrid Open Access
Jahr der Veröffentlichung: 2011
Band: 56
Heftnummer: 6
Seitenbereich: 1791-1802
ISSN: 0031-9155


X-ray-based 2D digital subtraction angiography (DSA) plays a major role in the diagnosis, treatment planning and assessment of cerebrovascular disease, i.e. aneurysms, arteriovenous malformations and intracranial stenosis. DSA information is increasingly used for secondary image post-processing such as vessel segmentation, registration and comparison to hemodynamic calculation using computational fluid dynamics. Depending on the amount of injected contrast agent and the duration of injection, these DSA series may not exhibit one single DSA image showing the entire vessel tree. The interesting information for these algorithms, however, is usually depicted within a few images. If these images would be combined into one image the complexity of segmentation or registration methods using DSA series would drastically decrease. In this paper, we propose a novel method automatically splitting a DSA series into three parts, i.e. mask, arterial and parenchymal phase, to provide one final image showing all important vessels with less noise and moving artifacts. This final image covers all arterial phase images, either by image summation or by taking the minimum intensities. The phase classification is done by a two-step approach. The mask/arterial phase border is determined by a Perceptron-based method trained from a set of DSA series. The arterial/parenchymal phase border is specified by a threshold-based method. The evaluation of the proposed method is two-sided: (1) comparison between automatic and medical expert-based phase selection and (2) the quality of the final image is measured by gradient magnitudes inside the vessels and signal-to-noise (SNR) outside. Experimental results show a match between expert and automatic phase separation of 93%/50% and an average SNR increase of up to 182% compared to summing up the entire series. © 2011 Institute of Physics and Engineering in Medicine.

FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Dörfler, Arnd Prof. Dr.
Neuroradiologische Abteilung im Radiologischen Institut
Hornegger, Joachim Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Polyanskaya, Maria
Lehrstuhl für Informatik 5 (Mustererkennung)
Schuldhaus, Dominik
Lehrstuhl für Informatik 5 (Mustererkennung)
Spiegel, Martin Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)

Einrichtungen weiterer Autorinnen und Autoren

Siemens AG, Healthcare Sector


Schuldhaus, D., Spiegel, M., Redel, T., Polyanskaya, M., Struffert, T., Hornegger, J., & Dörfler, A. (2011). Classification-based summation of cerebral digital subtraction angiography series for image post-processing algorithms. Physics in Medicine and Biology, 56(6), 1791-1802.

Schuldhaus, Dominik, et al. "Classification-based summation of cerebral digital subtraction angiography series for image post-processing algorithms." Physics in Medicine and Biology 56.6 (2011): 1791-1802.


Zuletzt aktualisiert 2019-12-04 um 14:50