Literature DB >> 17889270

Estimation of error in maximal intensity projection-based internal target volume of lung tumors: a simulation and comparison study using dynamic magnetic resonance imaging.

Jing Cai1, Paul W Read, Joseph M Baisden, James M Larner, Stanley H Benedict, Ke Sheng.   

Abstract

PURPOSE: To evaluate the error in four-dimensional computed tomography (4D-CT) maximal intensity projection (MIP)-based lung tumor internal target volume determination using a simulation method based on dynamic magnetic resonance imaging (dMRI). METHODS AND MATERIALS: Eight healthy volunteers and six lung tumor patients underwent a 5-min MRI scan in the sagittal plane to acquire dynamic images of lung motion. A MATLAB program was written to generate re-sorted dMRI using 4D-CT acquisition methods (RedCAM) by segmenting and rebinning the MRI scans. The maximal intensity projection images were generated from RedCAM and dMRI, and the errors in the MIP-based internal target area (ITA) from RedCAM (epsilon), compared with those from dMRI, were determined and correlated with the subjects' respiratory variability (nu).
RESULTS: Maximal intensity projection-based ITAs from RedCAM were comparatively smaller than those from dMRI in both phantom studies (epsilon = -21.64% +/- 8.23%) and lung tumor patient studies (epsilon = -20.31% +/- 11.36%). The errors in MIP-based ITA from RedCAM correlated linearly (epsilon = -5.13nu - 6.71, r(2) = 0.76) with the subjects' respiratory variability.
CONCLUSIONS: Because of the low temporal resolution and retrospective re-sorting, 4D-CT might not accurately depict the excursion of a moving tumor. Using a 4D-CT MIP image to define the internal target volume might therefore cause underdosing and an increased risk of subsequent treatment failure. Patient-specific respiratory variability might also be a useful predictor of the 4D-CT-induced error in MIP-based internal target volume determination.

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Year:  2007        PMID: 17889270     DOI: 10.1016/j.ijrobp.2007.07.2322

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  16 in total

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2.  Lung dynamic MRI deblurring using low-rank decomposition and dictionary learning.

Authors:  Shuiping Gou; Yueyue Wang; Jiaolong Wu; Percy Lee; Ke Sheng
Journal:  Med Phys       Date:  2015-04       Impact factor: 4.071

3.  Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network.

Authors:  W Z Sun; M Y Jiang; L Ren; J Dang; T You; F-F Yin
Journal:  Phys Med Biol       Date:  2017-08-03       Impact factor: 3.609

4.  On correlated sources of uncertainty in four dimensional computed tomography data sets.

Authors:  Eric D Ehler; Wolfgang A Tome
Journal:  Technol Cancer Res Treat       Date:  2010-06

5.  Feasibility of automated pancreas segmentation based on dynamic MRI.

Authors:  S Gou; J Wu; F Liu; P Lee; S Rapacchi; P Hu; K Sheng
Journal:  Br J Radiol       Date:  2014-10-01       Impact factor: 3.039

6.  Initial clinical observations of intra- and interfractional motion variation in MR-guided lung SBRT.

Authors:  David H Thomas; Anand Santhanam; Amar U Kishan; Minsong Cao; James Lamb; Yugang Min; Dylan O'Connell; Yingli Yang; Nzhde Agazaryan; Percy Lee; Daniel Low
Journal:  Br J Radiol       Date:  2018-01-22       Impact factor: 3.039

7.  Development and prospective in-patient proof-of-concept validation of a surface photogrammetry + CT-based volumetric motion model for lung radiotherapy.

Authors:  M Ranjbar; P Sabouri; S Mossahebi; D Leiser; M Foote; J Zhang; G Lasio; S Joshi; A Sawant
Journal:  Med Phys       Date:  2019-10-25       Impact factor: 4.071

8.  Determination of patient-specific internal gross tumor volumes for lung cancer using four-dimensional computed tomography.

Authors:  Muthuveni Ezhil; Sastry Vedam; Peter Balter; Bum Choi; Dragan Mirkovic; George Starkschall; Joe Y Chang
Journal:  Radiat Oncol       Date:  2009-01-27       Impact factor: 3.481

9.  Adaptive respiratory signal prediction using dual multi-layer perceptron neural networks.

Authors:  Wenzheng Sun; Qichun Wei; Lei Ren; Jun Dang; Fang-Fang Yin
Journal:  Phys Med Biol       Date:  2020-09-14       Impact factor: 3.609

10.  Contour propagation using non-uniform cubic B-splines for lung tumor delineation in 4D-CT.

Authors:  Yongchuan Liu; Renchao Jin; Mi Chen; Enmin Song; Xiangyang Xu; Sheng Zhang; Chih-Cheng Hung
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-07-16       Impact factor: 2.924

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