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Image and Structure Registration

Paper List

See also: The IPAG Dissertation Archive.

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Papers

o Entropy--based, multiple--portal--to--3D CT registration for prostate radiotherapy using iteratively estimated segmentation

R. Bansal, L. Staib, Z. Chen, A. Rangarajan, J. Knisely, R. Nath, and J.S. Duncan.
Medical Image Computing and Computer--Assisted Intervention (MICCAI) 1999.

Abstract

In external beam radiotherapy (EBRT), patient setup verification over the entire course of fractionated treatment is necessary for accurate delivery of specified dose to the tumor. We develop an information theoretic minimax entropy registration framework for patient setup verification using portal images and the treatment planning 3D CT data set. Within this framework we propose to simultaneously and iteratively segment the portal images and register them to the 3D CT data set to achieve robust and accurate estimation of the pose parameters. Appropriate entropies are evaluated, in an iterative fashion, to segment the portal images and to find the registration parameters. Earlier, we reported our work using a single portal image to estimate the transformation parameters. In this work, we extend the algorithm to utilize dual portal images. In addition, we show the performance of the algorithm on real patient data, analyze the performance of the algorithm under different initializations and noise conditions, and note the wide range of parameters that can be estimated. We also present a coordinate descent interpretation of the proposed algorithm to further clarify the formulation.

BibTeX Entry

@Article{Bansal99,
author = "R. Bansal and L. Staib and Z. Chen and A. Rangarajan and
	    J. Knisely and R. Nath and J.S. Duncan",
title =  "Entropy--Based, Multiple--Portal--to--3DCT Registration
	    for Prostate Radiotherapy Using Iteratively Estimated
	    Segmentation",
journal ="Medical Image Computing and Computer--Assisted
	    Intervention (MICCAI'99)",
year =   1999,
volume = "LNCS--1679",
pages =  "567--578",
month =  "19--22 September"}

o Integrated approaches to non-rigid registration in medical images

Y. Wang and L. H. Staib
In Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision, Princeton, NJ, October 1998.

Abstract

This paper describes two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second to maintain smoothness and continuity. A Bayesian formulation, based on each physical model, on an intensity similarity measure, and on statistical shape information embedded in corresponding boundary points, is employed to derive more accurate and robust approaches to non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approaches. It is shown that statistical boundary shape information significantly augments and improves physical model based non-rigid registration and the two methods we present each have advantages under different conditions.

BibTeX Entry

@InProceedings{WangWacv98,
author =   "Y. Wang and  L. H. Staib",
title =    "Integrated approaches to non-rigid registration in medical images",
pages =    "102-108",
booktitle ="Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision",
year =     "October 1998",
address =  "Princeton, NJ"}

o Elastic model based non-rigid registration incorporating statistical shape information

Y. Wang and L. H. Staib
In Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention, Cambridge, MA, October 1998.

Abstract

This paper describes a new method of non-rigid registration using the combined power of elastic and statistical shape models. The transformations are constrained to be consistent with a physical model of elasticity to maintain smoothness and continuity. A Bayesian formulation, based on this model, on an intensity similarity measure, and on statistical shape information embedded in corresponding boundary points, is employed to find a more accurate and robust non-rigid registration.

A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach. It is shown that statistical boundary shape information significantly augments and improves elastic model based non-rigid registration.

BibTeX Entry

@InProceedings{WangMiccai98,
author =   "Y. Wang and  L. H. Staib",
title =    "Elastic model based non-rigid registration incorporating 
            statistical shape information",
pages =    "1162-1173",
booktitle ="Proceedings of the First International Conference on Medical 
            Image Computing and Computer-Assisted Intervention",
year =     "October 1998",
address =  "Cambridge, MA"}

o A novel approach for the registration of 2D portal and 3D CT images for treatment setup verification in radiotherapy

R. Bansal, L. Staib, Z. Chen, A. Rangarajan, J. Knisely, R. Nath, and J.S. Duncan.
Medical Image Computing and Computer--Assisted Intervention (MICCAI) 1998.

Abstract

In this paper we present a framework to simultaneously segment portal images and register them to 3D treatment planning CT data sets for the purpose of radiotherapy setup verification. Due to the low resolution and low contrast of the portal image, taken with a high energy treatment photon beam, registration to the 3D CT data is a difficult problem. However, if some structure can be segmented in the portal image, it can be used to help registration, and if there is an estimate of the registration parameters, it can help improve the segmention of the portal image. The minimax entropy algorithm proposed in this paper evaluates appropriate entropies in order to segment the portal image and to find the registration parameters iteratively. The proposed algorithm can be used, in general, for registering a high resolution image to a low resolution image. Finally, we show the proposed algorithm's relation to the mutual information metric proposed in the literature for multi­modality image registration.

BibTeX Entry

@Article{Bansal98,
author = "R. Bansal and L. Staib and Z. Chen and A. Rangarajan and
	    J. Knisely and R. Nath and J.S. Duncan",
title =  "A Novel Approach for the Registration of 2{D} Portal and 3{D}
{CT} Images for Treatment Setup Verification in
Radiotherapy",
journal ="Medical Image Computing and Computer--Assisted
	    Intervention (MICCAI'98)",
year =   1998,
volume = "LNCS--1496",
pages =  "1075--1086",
month =  "10--12 October"}

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