Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation

Pedro A. Gómez; Jonathan I. Sperl; Tim Sprenger; Claudia Metzler-Baddeley; Derek K. Jones; Philipp Saemann; Michael Czisch; Marion I. Menzel; Bjoern H. Menze

doi:10.1007/978-3-662-46224-9_28

Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation

Pedro A. Gómez, Jonathan I. Sperl, Tim Sprenger, Claudia Metzler-Baddeley, Derek K. Jones, Philipp Saemann, Michael Czisch, Marion I. Menzel, Bjoern H. Menze

Informatics 16 - Chair of Computer Aided Medical Procedures

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

A joint reconstruction framework for multi-contrast MR images is presented and evaluated. The evaluation takes place in function of quality criteria based on reconstruction results and performance in the automatic segmentation of Multiple Sclerosis (MS) lesions. We show that joint reconstruction can effectively recover artificially corrupted images and is robust to noise.

Original language	English
Title of host publication	Bildverarbeitung fur die Medizin 2015
Subtitle of host publication	Algorithmen - Systeme - Anwendungen, Proceedings des Workshops, 2015
Editors	Thomas Martin Deserno, Thomas Tolxdorff, Heinz Handels, Hans-Peter Meinzer
Publisher	Kluwer Academic Publishers
Pages	155-160
Number of pages	6
ISBN (Print)	9783662462232
DOIs	https://doi.org/10.1007/978-3-662-46224-9_28
State	Published - 2015
Event	Workshops on Image Processing for Medicine,2015:Algorthim-Systems-Applications - Lubeck, Germany Duration: 15 Mar 2015 → 17 Mar 2015

Publication series

Name	Informatik aktuell
ISSN (Print)	1431-472X

Conference

Conference	Workshops on Image Processing for Medicine,2015:Algorthim-Systems-Applications
Country/Territory	Germany
City	Lubeck
Period	15/03/15 → 17/03/15

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10.1007/978-3-662-46224-9_28

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Gómez, P. A., Sperl, J. I., Sprenger, T., Metzler-Baddeley, C., Jones, D. K., Saemann, P., Czisch, M., Menzel, M. I., & Menze, B. H. (2015). Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation. In T. M. Deserno, T. Tolxdorff, H. Handels, & H.-P. Meinzer (Eds.), Bildverarbeitung fur die Medizin 2015: Algorithmen - Systeme - Anwendungen, Proceedings des Workshops, 2015 (pp. 155-160). (Informatik aktuell). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-662-46224-9_28

Gómez, Pedro A. ; Sperl, Jonathan I. ; Sprenger, Tim et al. / Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation. Bildverarbeitung fur die Medizin 2015: Algorithmen - Systeme - Anwendungen, Proceedings des Workshops, 2015. editor / Thomas Martin Deserno ; Thomas Tolxdorff ; Heinz Handels ; Hans-Peter Meinzer. Kluwer Academic Publishers, 2015. pp. 155-160 (Informatik aktuell).

@inproceedings{52c74d996ec44706b989041deeaaa116,

title = "Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation",

abstract = "A joint reconstruction framework for multi-contrast MR images is presented and evaluated. The evaluation takes place in function of quality criteria based on reconstruction results and performance in the automatic segmentation of Multiple Sclerosis (MS) lesions. We show that joint reconstruction can effectively recover artificially corrupted images and is robust to noise.",

author = "G{\'o}mez, {Pedro A.} and Sperl, {Jonathan I.} and Tim Sprenger and Claudia Metzler-Baddeley and Jones, {Derek K.} and Philipp Saemann and Michael Czisch and Menzel, {Marion I.} and Menze, {Bjoern H.}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; Workshops on Image Processing for Medicine,2015:Algorthim-Systems-Applications ; Conference date: 15-03-2015 Through 17-03-2015",

year = "2015",

doi = "10.1007/978-3-662-46224-9_28",

language = "English",

isbn = "9783662462232",

series = "Informatik aktuell",

publisher = "Kluwer Academic Publishers",

pages = "155--160",

editor = "Deserno, {Thomas Martin} and Thomas Tolxdorff and Heinz Handels and Hans-Peter Meinzer",

booktitle = "Bildverarbeitung fur die Medizin 2015",

}

Gómez, PA, Sperl, JI, Sprenger, T, Metzler-Baddeley, C, Jones, DK, Saemann, P, Czisch, M, Menzel, MI & Menze, BH 2015, Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation. in TM Deserno, T Tolxdorff, H Handels & H-P Meinzer (eds), Bildverarbeitung fur die Medizin 2015: Algorithmen - Systeme - Anwendungen, Proceedings des Workshops, 2015. Informatik aktuell, Kluwer Academic Publishers, pp. 155-160, Workshops on Image Processing for Medicine,2015:Algorthim-Systems-Applications, Lubeck, Germany, 15/03/15. https://doi.org/10.1007/978-3-662-46224-9_28

Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation. / Gómez, Pedro A.; Sperl, Jonathan I.; Sprenger, Tim et al.
Bildverarbeitung fur die Medizin 2015: Algorithmen - Systeme - Anwendungen, Proceedings des Workshops, 2015. ed. / Thomas Martin Deserno; Thomas Tolxdorff; Heinz Handels; Hans-Peter Meinzer. Kluwer Academic Publishers, 2015. p. 155-160 (Informatik aktuell).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation

AU - Gómez, Pedro A.

AU - Sperl, Jonathan I.

AU - Sprenger, Tim

AU - Metzler-Baddeley, Claudia

AU - Jones, Derek K.

AU - Saemann, Philipp

AU - Czisch, Michael

AU - Menzel, Marion I.

AU - Menze, Bjoern H.

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2015.

PY - 2015

Y1 - 2015

N2 - A joint reconstruction framework for multi-contrast MR images is presented and evaluated. The evaluation takes place in function of quality criteria based on reconstruction results and performance in the automatic segmentation of Multiple Sclerosis (MS) lesions. We show that joint reconstruction can effectively recover artificially corrupted images and is robust to noise.

AB - A joint reconstruction framework for multi-contrast MR images is presented and evaluated. The evaluation takes place in function of quality criteria based on reconstruction results and performance in the automatic segmentation of Multiple Sclerosis (MS) lesions. We show that joint reconstruction can effectively recover artificially corrupted images and is robust to noise.

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U2 - 10.1007/978-3-662-46224-9_28

DO - 10.1007/978-3-662-46224-9_28

M3 - Conference contribution

AN - SCOPUS:85012215425

SN - 9783662462232

T3 - Informatik aktuell

SP - 155

EP - 160

BT - Bildverarbeitung fur die Medizin 2015

A2 - Deserno, Thomas Martin

A2 - Tolxdorff, Thomas

A2 - Handels, Heinz

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PB - Kluwer Academic Publishers

T2 - Workshops on Image Processing for Medicine,2015:Algorthim-Systems-Applications

Y2 - 15 March 2015 through 17 March 2015

ER -

Gómez PA, Sperl JI, Sprenger T, Metzler-Baddeley C, Jones DK, Saemann P et al. Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation. In Deserno TM, Tolxdorff T, Handels H, Meinzer HP, editors, Bildverarbeitung fur die Medizin 2015: Algorithmen - Systeme - Anwendungen, Proceedings des Workshops, 2015. Kluwer Academic Publishers. 2015. p. 155-160. (Informatik aktuell). doi: 10.1007/978-3-662-46224-9_28

Joint reconstruction of multi-contrast MRI for multiple sclerosis lesion segmentation

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