Literature DB >> 12111312

Image registration and subtraction to detect active T(2) lesions in MS: an interobserver study.

I Leng Tan1, Ronald A van Schijndel, Franz Fazekas, Massimo Filippi, Peter Freitag, David H Miller, Tarek A Yousry, Petra J W Pouwels, Herman J Adèr, Frederik Barkhof.   

Abstract

Serial MRI studies are used to analyse change in multiple sclerosis (MS) lesion volume in clinical trials. As such an evaluation is very time consuming and subject to quantification errors, one might assess only the change in number or size of lesions using subtracted images. The advantage of subtracted images is that both new and/or enlarging and resolving and/or shrinking lesions can be evaluated, resulting in a more precise volume change than a net volume change. We studied the interobserver agreement in the detection of active MS lesions using paired dual-echo T(2)-weighted spin-echo studies (3-mm slices) of 30 MS patients with a range of MS disease activity on MRI from treatment trials. Using an automatic matching algorithm based on mutual information, the follow-up scan was registered to baseline, after which subtracted images were obtained. After a training session with formulation of guidelines, six observers identified new, enlarging, resolving and shrinking lesions on subtracted images. Weighted kappa (kappa) values were calculated to assess interobserver agreement. Good agreement was found for new lesions (kappa 0.69 +/- 0.08), while moderate agreement was found for enlarging lesions (kappa 0.52 +/- 0.06). When new and enlarging lesions were combined, good agreement was found for "positive" activity (kappa 0.71 +/-0.06). The interobserver agreement was poor for resolving lesions (kappa 0.31 +/- 0.07), and moderate for shrinking lesions (kappa 0.53 +/- 0.08). In conclusion, the use of subtracted images in the visual detection of new T(2) lesions resulted in a good level of interobserver agreement for "positive" disease activity. Subtraction of registered images is a reliable, time efficient method to assess disease progression in MS.

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Year:  2002        PMID: 12111312     DOI: 10.1007/s00415-002-0712-6

Source DB:  PubMed          Journal:  J Neurol        ISSN: 0340-5354            Impact factor:   4.849


  18 in total

1.  Effect of automated image registration on radiologist interpretation.

Authors:  Bradley J Erickson; Jayawant Mandrekar; Liqin Wang; Julia W Patriarche; Brian J Bartholmai; Christropher P Wood; E Paul Lindell; Anne-Marie Sykes; Gordon F Harms; Rebecca M Lindell; Katherine Andirole
Journal:  J Digit Imaging       Date:  2007-06       Impact factor: 4.056

2.  Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method.

Authors:  A Galletto Pregliasco; A Collin; A Guéguen; M A Metten; J Aboab; R Deschamps; O Gout; L Duron; J C Sadik; J Savatovsky; A Lecler
Journal:  AJNR Am J Neuroradiol       Date:  2018-06-07       Impact factor: 3.825

3.  Synchronized navigation of current and prior studies using image registration improves radiologist's efficiency.

Authors:  Daniel Forsberg; Amit Gupta; Christopher Mills; Brett MacAdam; Beverly Rosipko; Barbara A Bangert; Michael D Coffey; Christos Kosmas; Jeffrey L Sunshine
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-11-26       Impact factor: 2.924

Review 4.  Current and Emerging Therapies in Multiple Sclerosis: Implications for the Radiologist, Part 2-Surveillance for Treatment Complications and Disease Progression.

Authors:  C McNamara; G Sugrue; B Murray; P J MacMahon
Journal:  AJNR Am J Neuroradiol       Date:  2017-04-20       Impact factor: 3.825

5.  A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies.

Authors:  Onur Ganiler; Arnau Oliver; Yago Diez; Jordi Freixenet; Joan C Vilanova; Brigitte Beltran; Lluís Ramió-Torrentà; Alex Rovira; Xavier Lladó
Journal:  Neuroradiology       Date:  2014-03-04       Impact factor: 2.804

6.  Optimal presentation modes for detecting brain tumor progression.

Authors:  B J Erickson; C P Wood; T J Kaufmann; J W Patriarche; J Mandrekar
Journal:  AJNR Am J Neuroradiol       Date:  2011-08-18       Impact factor: 3.825

7.  Multiple sclerosis: identification of temporal changes in brain lesions with computer-assisted detection software.

Authors:  M Bilello; M Arkuszewski; P Nucifora; I Nasrallah; E R Melhem; L Cirillo; J Krejza
Journal:  Neuroradiol J       Date:  2013-05-10

8.  Segmentation of subtraction images for the measurement of lesion change in multiple sclerosis.

Authors:  Y Duan; P G Hildenbrand; M P Sampat; D F Tate; I Csapo; B Moraal; R Bakshi; F Barkhof; D S Meier; C R G Guttmann
Journal:  AJNR Am J Neuroradiol       Date:  2008-02       Impact factor: 3.825

9.  Subtraction MR images in a multiple sclerosis multicenter clinical trial setting.

Authors:  Bastiaan Moraal; Dominik S Meier; Peter A Poppe; Jeroen J G Geurts; Hugo Vrenken; William M A Jonker; Dirk L Knol; Ronald A van Schijndel; Petra J W Pouwels; Christoph Pohl; Lars Bauer; Rupert Sandbrink; Charles R G Guttmann; Frederik Barkhof
Journal:  Radiology       Date:  2008-11-26       Impact factor: 11.105

10.  Slowly eroding lesions in multiple sclerosis.

Authors:  Varun Sethi; Govind Nair; Martina Absinta; Pascal Sati; Arun Venkataraman; Joan Ohayon; Tianxia Wu; Kelly Yang; Colin Shea; Blake E Dewey; Irene Cm Cortese; Daniel S Reich
Journal:  Mult Scler       Date:  2016-07-11       Impact factor: 6.312

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