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Title: Registration of lung nodules using a semi-rigid model: Method and preliminary results

Abstract

The tracking of lung nodules across computed tomography (CT) scans acquired at different times for the same patient is helpful for the determination of malignancy. We are developing a nodule registration system to facilitate this process. We propose to use a semi-rigid method that considers principal structures surrounding the nodule and allows relative movements among the structures. The proposed similarity metric, which evaluates both the image correlation and the degree of elastic deformation amongst the structures, is maximized by a two-layered optimization method, employing a simulated annealing framework. We tested our method by simulating five cases that represent physiological deformation as well as different nodule shape/size changes with time. Each case is made up of a source and target scan, where the source scan consists of a nodule-free patient CT volume into which we inserted ten simulated lung nodules, and the target scan is the result of applying a known, physiologically based nonrigid transformation to the nodule-free source scan, into which we inserted modified versions of the corresponding nodules at the same, known locations. Five different modification strategies were used, one for each of the five cases: (1) nodules maintain size and shape, (2) nodules disappear, (3) nodules shrink uniformlymore » by a factor of 2, (4) nodules grow uniformly by a factor of 2, and (5) nodules grow nonuniformly. We also matched 97 real nodules in pairs of scans (acquired at different times) from 12 patients and compared our registration to a radiologist's visual determination. In the simulation experiments, the mean absolute registration errors were 1.0{+-}0.8 mm (s.d.), 1.1{+-}0.7 mm (s.d.), 1.0{+-}0.7 mm (s.d.), 1.0{+-}0.6 mm (s.d.), and 1.1{+-}0.9 mm (s.d.) for the five cases, respectively. For the 97 nodule pairs in 12 patient scans, the mean absolute registration error was 1.4{+-}0.8 mm (s.d.)« less

Authors:
; ; ; ; ;  [1]
  1. Department of Electrical Engineering and Department of Radiology, Stanford University, Stanford, California 94305 (United States)
Publication Date:
OSTI Identifier:
20951054
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 34; Journal Issue: 2; Other Information: DOI: 10.1118/1.2432073; (c) 2007 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; ANNEALING; CARCINOMAS; COMPUTERIZED TOMOGRAPHY; DEFORMATION; ELASTICITY; ERRORS; IMAGE PROCESSING; IMAGES; LUNGS; MODIFICATIONS; OPTIMIZATION; PATIENTS; SIMULATION

Citation Formats

Sun Shaohua, Rubin, Geoffrey D., Paik, David, Steiner, Robert M., Zhuge Feng, and Napel, Sandy. Registration of lung nodules using a semi-rigid model: Method and preliminary results. United States: N. p., 2007. Web. doi:10.1118/1.2432073.
Sun Shaohua, Rubin, Geoffrey D., Paik, David, Steiner, Robert M., Zhuge Feng, & Napel, Sandy. Registration of lung nodules using a semi-rigid model: Method and preliminary results. United States. doi:10.1118/1.2432073.
Sun Shaohua, Rubin, Geoffrey D., Paik, David, Steiner, Robert M., Zhuge Feng, and Napel, Sandy. Thu . "Registration of lung nodules using a semi-rigid model: Method and preliminary results". United States. doi:10.1118/1.2432073.
@article{osti_20951054,
title = {Registration of lung nodules using a semi-rigid model: Method and preliminary results},
author = {Sun Shaohua and Rubin, Geoffrey D. and Paik, David and Steiner, Robert M. and Zhuge Feng and Napel, Sandy},
abstractNote = {The tracking of lung nodules across computed tomography (CT) scans acquired at different times for the same patient is helpful for the determination of malignancy. We are developing a nodule registration system to facilitate this process. We propose to use a semi-rigid method that considers principal structures surrounding the nodule and allows relative movements among the structures. The proposed similarity metric, which evaluates both the image correlation and the degree of elastic deformation amongst the structures, is maximized by a two-layered optimization method, employing a simulated annealing framework. We tested our method by simulating five cases that represent physiological deformation as well as different nodule shape/size changes with time. Each case is made up of a source and target scan, where the source scan consists of a nodule-free patient CT volume into which we inserted ten simulated lung nodules, and the target scan is the result of applying a known, physiologically based nonrigid transformation to the nodule-free source scan, into which we inserted modified versions of the corresponding nodules at the same, known locations. Five different modification strategies were used, one for each of the five cases: (1) nodules maintain size and shape, (2) nodules disappear, (3) nodules shrink uniformly by a factor of 2, (4) nodules grow uniformly by a factor of 2, and (5) nodules grow nonuniformly. We also matched 97 real nodules in pairs of scans (acquired at different times) from 12 patients and compared our registration to a radiologist's visual determination. In the simulation experiments, the mean absolute registration errors were 1.0{+-}0.8 mm (s.d.), 1.1{+-}0.7 mm (s.d.), 1.0{+-}0.7 mm (s.d.), 1.0{+-}0.6 mm (s.d.), and 1.1{+-}0.9 mm (s.d.) for the five cases, respectively. For the 97 nodule pairs in 12 patient scans, the mean absolute registration error was 1.4{+-}0.8 mm (s.d.)},
doi = {10.1118/1.2432073},
journal = {Medical Physics},
number = 2,
volume = 34,
place = {United States},
year = {Thu Feb 15 00:00:00 EST 2007},
month = {Thu Feb 15 00:00:00 EST 2007}
}