Extraction of liver vessel centerlines under guidance of patient-specific models.
Fast extraction of blood vessels of abdominal organs is still a challenging task especially in intra-procedural treatments due to large tissue deformation. In this study, we propose a novel joint vessel extraction and registration framework. This vessel extraction technique is under the guidance of prior knowledge patient specific models. The proposed technique automatically provides correspondence between extracted vessels and pre-procedural vessels, which is important for image guidance such as labeled vessels from pre-procedural models, improves the quality of disease diagnosis using multiple images and follow-up, and provides important information for nonrigid image registration. Another key component in our framework is to dynamically update mapped pre-procedural models by rapidly registering the patient model to the current image based on strain energy, point marks and 3D extracted vessels currently available. We have demonstrated the effectiveness of our technique in extraction of vessels from liver MR images. Validation shows a extraction error of 3.99 mm. This technique has the potential to significantly improve the quality of intra-procedural image guidance, diagnosis of disease and treatment planning.
Department of Medical Imaging, University of Toronto and CIGITI, Hospital for Sick Children, Toronto, Canada. Edward.Huang@sickkids.ca
Xishi
X
Huang
Sameer
S
Zaheer
Anwar
A
Abdalbari
Thomas
T
Looi
Jing
J
Ren
James
J
Drake
D003198:Computer Simulation; D006499:Hepatic Artery / Q000033:anatomy & histology*; D006503:Hepatic Veins / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity