Image processing of liver computed tomography angiographic (CTA) images for laser induced thermotherapy (LITT) planning

Yue Li; Xiang Gao; Qingyu Tang; Shangkai Gao

doi:10.1117/12.911144

17 February 2012 Image processing of liver computed tomography angiographic (CTA) images for laser induced thermotherapy (LITT) planning

Yue Li, Xiang Gao, Qingyu Tang, Shangkai Gao

Author Affiliations +

Proceedings Volume 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling; 831638 (2012) https://doi.org/10.1117/12.911144
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Analysis of patient images is highly desired for simulating and planning the laser-induced thermotherapy (LITT) to study the cooling effect of big vessels around tumors during the procedure. In this paper, we present an image processing solution for simulating and planning LITT on liver cancer using computed tomography angiography (CTA) images. This includes first performing a 3D anisotropic filtering on the data to remove noise. The liver region is then segmented with a level sets based contour tracking method. A 3D level sets based surface evolution driven by boundary statistics is then used to segment the surfaces of vessels and tumors. Then the medial lines of vessels were extracted by a thinning algorithm. Finally the vessel tree is found on the thinning result, by first constructing a shortest path spanning tree by Dijkstra algorithm and then pruning the unnecessary branches. From the segmentation and vessel skeletonization results, important geometric parameters of the vessels and tumors are calculated for simulation and surgery planning. The proposed methods was applied to a patient's image and the result is shown.

Citation Download Citation

Yue Li, Xiang Gao, Qingyu Tang, and Shangkai Gao "Image processing of liver computed tomography angiographic (CTA) images for laser induced thermotherapy (LITT) planning", Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 831638 (17 February 2012); https://doi.org/10.1117/12.911144

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available