Literature DB >> 10416804

Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences.

J P Thirion1, G Calmon.   

Abstract

Evaluating precisely the temporal variations of lesion volumes is very important for at least three types of practical applications: pharmaceutical trials, decision making for drug treatment or surgery, and patient follow-up. In this paper we present a volumetric analysis technique, combining precise rigid registration of three-dimensional (3-D) (volumetric) medical images, nonrigid deformation computation, and flow-field analysis. Our analysis technique has two outcomes: the detection of evolving lesions and the quantitative measurement of volume variations. The originality of our approach is that no precise segmentation of the lesion is needed but the approximative designation of a region of interest (ROI) which can be automated. We distinguish between tissue transformation (image intensity changes without deformation) and expansion or contraction effects reflecting a change of mass within the tissue. A real lesion is generally the combination of both effects. The method is tested with synthesized volumetric image sequences and applied, in a first attempt to quantify in vivo a mass effect, to the analysis of a real patient case with multiple sclerosis (MS).

Entities:  

Mesh:

Year:  1999        PMID: 10416804     DOI: 10.1109/42.774170

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  24 in total

1.  Segmentation of image ensembles via latent atlases.

Authors:  Tammy Riklin-Raviv; Koen Van Leemput; Bjoern H Menze; William M Wells; Polina Golland
Journal:  Med Image Anal       Date:  2010-06-04       Impact factor: 8.545

Review 2.  A review of the automated detection of change in serial imaging studies of the brain.

Authors:  Julia Patriarche; Bradley Erickson
Journal:  J Digit Imaging       Date:  2004-06-29       Impact factor: 4.056

3.  Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients.

Authors:  Julia Willamena Patriarche; Bradley James Erickson
Journal:  J Digit Imaging       Date:  2007-09       Impact factor: 4.056

4.  Comparing pairwise and simultaneous joint registrations of decorrelating interval exams using entropic graphs.

Authors:  B Ma; R Narayanan; H Park; A O Hero; P H Bland; C R Meyer
Journal:  Inf Process Med Imaging       Date:  2007

Review 5.  Noncalcified lung nodules: volumetric assessment with thoracic CT.

Authors:  Marios A Gavrielides; Lisa M Kinnard; Kyle J Myers; Nicholas Petrick
Journal:  Radiology       Date:  2009-04       Impact factor: 11.105

6.  Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge.

Authors:  Marthony Robins; Jayashree Kalpathy-Cramer; Nancy A Obuchowski; Andrew Buckler; Maria Athelogou; Rudresh Jarecha; Nicholas Petrick; Aria Pezeshk; Berkman Sahiner; Ehsan Samei
Journal:  Acad Radiol       Date:  2018-09-12       Impact factor: 3.173

7.  The role of image registration in brain mapping.

Authors:  A W Toga; P M Thompson
Journal:  Image Vis Comput       Date:  2001-01-01       Impact factor: 2.818

8.  Brain Shape Characterization from Deformation.

Authors:  Lawrence H Staib; Marcel Jackowski; Xenophon Papademetris
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2006

9.  Quantitative imaging to assess tumor response to therapy: common themes of measurement, truth data, and error sources.

Authors:  Charles R Meyer; Samuel G Armato; Charles P Fenimore; Geoffrey McLennan; Luc M Bidaut; Daniel P Barboriak; Marios A Gavrielides; Edward F Jackson; Michael F McNitt-Gray; Paul E Kinahan; Nicholas Petrick; Binsheng Zhao
Journal:  Transl Oncol       Date:  2009-12       Impact factor: 4.243

10.  Change detection & characterization: a new tool for imaging informatics and cancer research.

Authors:  Julia W Patriarche; Bradley J Erickson
Journal:  Cancer Inform       Date:  2007-05-12
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