PMID- 17224978
OWN - NLM
STAT- MEDLINE
DCOM- 20070322
LR  - 20170410
IS  - 0026-1270 (Print)
IS  - 0026-1270 (Linking)
VI  - 46
IP  - 1
DP  - 2007
TI  - AAM-based segmentation for imaging cardiac electrophysiology.
PG  - 36-42
AB  - OBJECTIVES: Activation time (AT) imaging from electrocardiographic (ECG) mapping 
      data has been developing for several years. By coupling 4-dimensional volume data 
      (3D + time) the electrical sequence can be computed non-invasively. In this paper 
      an approach for extracting the ventricular and atrial blood masses for 
      structurally normal hearts by using cine-gated short-axis data obtained via 
      magnetic resonance imaging (MRI) is introduced. METHODS: The blood masses are 
      extracted by employing Active Appearance Models (AAMs). The ventricular blood 
      masses are segmented, applying the AAMs after providing apex cordis and base of 
      the heart in the volume data, whereas the more complex geometry of the atria 
      requires a more specific attempt. On account of this the atrium was divided into 
      three divisions of appearance, where the images of the volume data in the related 
      divisions have a maximum affinity. The first division reaches from the base of 
      the heart to initial visibility of the upper and left lower pulmonary vein. The 
      second division up from there to the last occurrence and the third division from 
      there to the end of the visibility of the right upper and lower pulmonary vein. 
      After extracting the cardiac blood masses the result gets triangulated and 
      remeshed for activation time imaging. RESULTS: With this method the cardiac 
      models of eight patients were extracted and the AT imaging approach was applied 
      to single-beat ECG data of atrial and ventricular depolarization. CONCLUSION: The 
      advantage of the proposed AAM approach is that only a few initial parameters have 
      to be set. Therefore, the approach can be integrated into a processing pipeline 
      that works semi-automatically. The extracted models can be used for further 
      investigations.
FAU - Pfeifer, B
AU  - Pfeifer B
AD  - Institute for Biomedical Signal Processing and Imaging, University for Health 
      Sciences, Medical Informatics and Technology (UMIT), Hall i.T., Austria. 
      bernhard.pfeifer@umit.at
FAU - Seger, M
AU  - Seger M
FAU - Hintermuller, C
AU  - Hintermuller C
FAU - Fischer, G
AU  - Fischer G
FAU - Muhlthaler, H
AU  - Muhlthaler H
FAU - Modre-Osprian, R
AU  - Modre-Osprian R
FAU - Tilg, B
AU  - Tilg B
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - Germany
TA  - Methods Inf Med
JT  - Methods of information in medicine
JID - 0210453
SB  - IM
EIN - Methods Inf Med. 2007;46(1):III
MH  - Atrial Function/*physiology
MH  - Electrocardiography
MH  - Electrophysiology/*methods
MH  - Humans
MH  - Image Processing, Computer-Assisted/*methods
MH  - Magnetic Resonance Imaging, Cine/*methods
MH  - Models, Theoretical
MH  - Time Factors
MH  - *Ventricular Function
EDAT- 2007/01/17 09:00
MHDA- 2007/03/23 09:00
CRDT- 2007/01/17 09:00
PHST- 2007/01/17 09:00 [pubmed]
PHST- 2007/03/23 09:00 [medline]
PHST- 2007/01/17 09:00 [entrez]
AID - 07010036 [pii]
PST - ppublish
SO  - Methods Inf Med. 2007;46(1):36-42.