Image Guided Therapy

Virtually every device that supports minimally invasive procedures relies on processed images. These images provide preoperative data and guide surgery. While already useful for brain, spine and musculoskeletal surgery, current systems have limitations: the integration is awkward, they slow down the procedures they are intended to facilitate, and data preparation in clinical setting is too time consuming. To overcome such limitations Image Guided Therapy Enabling Technology is developing robust and flexible algorithms that incorporate knowledge about anatomy and pathology and provide intuitive user interfaces.

The team is using several clinical applications to drive the development of the novel segmentation technologies. During the passed year, the team has increased its focus on coronary artery diseases and has started to develop methods for computerized assessment of plaque. Plaque may be differentiated between calcified, fibrous and lipid plaque by mean of lesion density. To monitor progression of coronary plaque in follow-up examinations, intelligent tools are needed that can store and reload the position of lesions in the coronary tree, e.g. based on coronary position landmarks, for re-evaluation of corresponding lesions. The technologies developed within this project are important enabling components towards the realization of such tools.

Recently the team focused on optimizing the developed centerline method for pig CTA data. This information is used for planning of and guidance during surgical intervention in the LapUS project (PI Dr. Vosburgh).

	Figure 1: Models of bones and vessels (left) segmented from CT of a pig. Symbolic description of vessels using centerlines (right).

Figure 2: Overview of the 3D slicer platform. The left panel is used to select modules and parameters. The right panel is for 3D visualization (top) and axial, sagittal, and coronal image displays (bottom).

Figure 3: Screen shots from the adaptive filtering module. This module is used for shape-based interpolation and filtering of data, a preprocessing step for segmentation.

Figure 4: Screen shots from the flux diffusion module. This module is used as a preprocessing step for segmentation to reduce the impact of noise in the image data.

Figure 5: Screen shots from the levelset segmentation module. Results from this module, see above Figure 1.

Selected Publications

Krissian K, Westin CF. Fast sub-voxel re-initialization of the distance map for level set methods. Pattern Recognition Letters 2005.

Krissian K, Vosburgh K, Kikinis R, Westin CF. Speckle-contrained anisotropic diffusion for ultrasound images. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). San Diego CA, USA, 2005.

Kozinska D, Holland C, Krissian K, Westin CF, Guttmann CRG. A method for the analysis of the geometrical relationship between white matter pathology and the vascular architecture of the brain. Neuroimage 2004;22:1671-1678.

Watanabe M, Kikinis R, Westin CF. Level set-based integration of segmentation and computational fluid dynamics for flow correction in phase contrast angiography. Academic Radiology 2003;10(12):1416-23.

Lorigo LM, Faugeras OD, Grimson WEL, Keriven R, Kikinis R, Nabavi A, Westin CF. Curves: Curve evolution for vessel segmentation. Medical Image Analysis 2001;5:195-206.

LMI CIMIT Publications

A list of all publication related to CIMIT projects of the PI (C-F Westin) and his laboratory (Laboratory of Mathematics in Imaging, LMI) can be found here.