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Title: Targeted 2D/3D registration using ray normalization and a hybrid optimizer

Abstract

X-ray images are often used to guide minimally invasive procedures in interventional radiology. The use of a preoperatively obtained 3D volume can enhance the visualization needed for guiding catheters and other surgical devices. However, for intraoperative usefulness, the 3D dataset needs to be registered to the 2D x-ray images of the patient. We investigated the effect of targeting subvolumes of interest in the 3D datasets and registering the projections with C-arm x-ray images. We developed an intensity-based 2D/3D rigid-body registration using a Monte Carlo-based hybrid algorithm as the optimizer, using a single view for registration. Pattern intensity (PI) and mutual information (MI) were two metrics tested. We used normalization of the rays to address the problems due to truncation in 3D necessary for targeting. We tested the algorithm on a C-arm x-ray image of a pig's head and a 3D dataset reconstructed from multiple views of the C-arm. PI and MI were comparable in performance. For two subvolumes starting with a set of initial poses from +/-15 mm in x, from +/-3 mm (random), in y and z and +/-4 deg in the three angles, the robustness was 94% for PI and 91% for MI, with accuracy of 2.4 mmmore » (PI) and 2.6 mm (MI), using the hybrid algorithm. The hybrid optimizer, when compared with a standard Powell's direction set method, increased the robustness from 59% (Powell) to 94% (hybrid). Another set of 50 random initial conditions from [+/-20] mm in x,y,z and [+/-10] deg in the three angles, yielded robustness of 84% (hybrid) versus 38% (Powell) using PI as metric, with accuracies 2.1 mm (hybrid) versus 2.0 mm (Powell)« less

Authors:
;  [1];  [2]
  1. Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts 01655 (United States)
  2. (United States)
Publication Date:
OSTI Identifier:
20853839
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 33; Journal Issue: 12; Other Information: DOI: 10.1118/1.2388156; (c) 2006 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ACCURACY; ALGORITHMS; BIOMEDICAL RADIOGRAPHY; CALIBRATION STANDARDS; EQUIPMENT; HEAD; IMAGE PROCESSING; IMAGES; MONTE CARLO METHOD; SURGERY; SWINE; X RADIATION

Citation Formats

Dey, Joyoni, Napel, Sandy, and Department of Radiology, Stanford University, Palo Alto, California. Targeted 2D/3D registration using ray normalization and a hybrid optimizer. United States: N. p., 2006. Web. doi:10.1118/1.2388156.
Dey, Joyoni, Napel, Sandy, & Department of Radiology, Stanford University, Palo Alto, California. Targeted 2D/3D registration using ray normalization and a hybrid optimizer. United States. doi:10.1118/1.2388156.
Dey, Joyoni, Napel, Sandy, and Department of Radiology, Stanford University, Palo Alto, California. Fri . "Targeted 2D/3D registration using ray normalization and a hybrid optimizer". United States. doi:10.1118/1.2388156.
@article{osti_20853839,
title = {Targeted 2D/3D registration using ray normalization and a hybrid optimizer},
author = {Dey, Joyoni and Napel, Sandy and Department of Radiology, Stanford University, Palo Alto, California},
abstractNote = {X-ray images are often used to guide minimally invasive procedures in interventional radiology. The use of a preoperatively obtained 3D volume can enhance the visualization needed for guiding catheters and other surgical devices. However, for intraoperative usefulness, the 3D dataset needs to be registered to the 2D x-ray images of the patient. We investigated the effect of targeting subvolumes of interest in the 3D datasets and registering the projections with C-arm x-ray images. We developed an intensity-based 2D/3D rigid-body registration using a Monte Carlo-based hybrid algorithm as the optimizer, using a single view for registration. Pattern intensity (PI) and mutual information (MI) were two metrics tested. We used normalization of the rays to address the problems due to truncation in 3D necessary for targeting. We tested the algorithm on a C-arm x-ray image of a pig's head and a 3D dataset reconstructed from multiple views of the C-arm. PI and MI were comparable in performance. For two subvolumes starting with a set of initial poses from +/-15 mm in x, from +/-3 mm (random), in y and z and +/-4 deg in the three angles, the robustness was 94% for PI and 91% for MI, with accuracy of 2.4 mm (PI) and 2.6 mm (MI), using the hybrid algorithm. The hybrid optimizer, when compared with a standard Powell's direction set method, increased the robustness from 59% (Powell) to 94% (hybrid). Another set of 50 random initial conditions from [+/-20] mm in x,y,z and [+/-10] deg in the three angles, yielded robustness of 84% (hybrid) versus 38% (Powell) using PI as metric, with accuracies 2.1 mm (hybrid) versus 2.0 mm (Powell)},
doi = {10.1118/1.2388156},
journal = {Medical Physics},
number = 12,
volume = 33,
place = {United States},
year = {Fri Dec 15 00:00:00 EST 2006},
month = {Fri Dec 15 00:00:00 EST 2006}
}