Work

Intraoperative neuroimage compensation using data-driven computational models

Year: 2003

Type: conference-paper

Abstract: Loss of coregistration preoperative imaging studies and teh surgical field due to brain deformation during surgery is an important problem that has recently received considerable attention. Methods for compensating or correcting for the loss of congruence between image and surgical views are taking a variety of forms and involve a spectrum of data acquisition techniques and/or image processing schemes. This paper describes an emerging approach to intraoperative image compensation which combines pre- and intraoperative data acquisition with computational biomechanical modeling to estimate full volume deformation distributions that result from neurosurgical interventions. The strategy updates preoperative scans by projectin gthis displacement estimation from neurosurgical interventions. The strategy updates preoperative scans by projecting this displacement estimation onto the coregistered imaging study to deform it into a new image volume which reflects the geometrical chagnes in the surgical field which have occurred during surgery. Discussion and summary of developments associated with this idea which have appeared in the recent literature are presented.

Source: Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

DOI: 10.1117/12.487764

Author: Keith D. Paulsen

Institution: Dartmouth College

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: Dartmouth College, National Institutes of Health