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Data assimilation in brain deformation modeling.

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Call No.	Ph.D. 2004
Author	Lunn, Karen E.
Title	Data assimilation in brain deformation modeling.
Date	2003
Abstract	One of the important challenges facing image-guided neurosurgery is the fact that the brain deforms significantly over the course of an operation during open-cranial procedures. This results in a loss of registration between the brain and the corresponding preoperative brain images. As a result, the navigational power of the image guidance is diminished. Biomechanical models of brain deformation are increasingly being used to compensate for brain shift by computing updated images during surgery. These model estimates of deformation can potentially be improved by incorporating sparse displacement data available in the operating room. Two data assimilation inverse methods have been developed to estimate the unknown boundary and volumetric forces necessary to achieve a least squares fit between the model and the data. The weighted basis solutions (WBS) method synthesizes a discrete set of predefined modes of deformation to achieve a best fit to the data. An improved but more complex strategy called the adjoint equations method (AEM) estimates the full set of forcing conditions. These inversion schemes have been applied to porcine models as well as clinical procedures. They are compared to the best prior estimate (BPE), or the model prediction that is unconstrained by sparse data. The WBS scheme improved the BPE model estimates by 18%, while the AEM strategy increased the accuracy by 31%. In general, studies collected in this project indicate that the adjoint equations method (AEM) shows the best potential for improving model estimates of deformation. In porcine studies, the AEM reconstructed the brain shift to within an average of 1.3 mm of the measured values, a 33% improvement over the best prior estimate (BPE). In clinical cases, AEM captured deformation to with 2.3 mm, on average, or a 30% improvement over the BPE. Based on these studies, it is argued that while both methods successfully integrate intraoperative data to enhance model-based estimates of brain deformation, the adjoint equations method exhibits more potential as a launching pad for future improvements to the model.
Description	xiv, 190 leaves : ill. (some col.) 28 cm.
Format	application/pdf
Advisor	Keith D. Paulsen.
Subject	Diagnostic imaging -- Digital techniques. Diagnostic imaging -- Mathematical models. Image processing -- Mathematics. Intraoperative monitoring. Brain -- Magnetic resonance imaging. Ultrasonic encephalography.
College	Thayer School of Engineering.
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D.)--Dartmouth College.
Copyright	All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means without permission in writing from the author.
DOI	http://dx.doi.org/10.1349/ddlp.56

Title	Page 1
Format	application/pdf
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D.)--Dartmouth College.
Full Text	Data Assimilation in Brain Deformation Modeling A Thesis Submitted to the Faculty in partial ful¯llment of the requirements for the degree of Doctor of Philosophy by Karen E. Lunn Thayer School of Engineering Dartmouth College Hanover, New Hampshire December 2003 Examining Committee: Keith D. Paulsen, Ph.D., Chair Francis Kennedy, Ph.D., Member David W. Roberts, M.D., Member Derek L.G. Hill, Ph.D. Member Dean of Graduate Studies (Signature of Author) °c 2003 Trustees of Dartmouth College

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Data assimilation in brain deformation modeling.

Data assimilation in brain deformation modeling.