Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results

Lipeng Ning; Elisenda Bonet-Carne; Francesco Grussu; Farshid Sepehrband; Enrico Kaden; Jelle Veraart; Stefano B. Blumberg; Can Son Khoo; Marco Palombo; Jaume Coll-Font; Benoit Scherrer; Simon K.  Warfield; Suheyla Cetin Karayumak; Yogesh Rathi; Simon Koppers; Leon Weninger; Julia Ebert; Dorit Merhof; Daniel Moyer; Maximilian Pietsch; Daan Christiaens; Rui Teixeira; Jacques-Donald Tournier; Andrey Zhylka; Josien Pluim; Greg Parker; Umesh Rudrapatna; John Evans; Cyril Charron; Derek K. Jones; Chantal M.W. Tax

doi:10.1007/978-3-030-05831-9_18

Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results

Lipeng Ning, Elisenda Bonet-Carne, Francesco Grussu, Farshid Sepehrband, Enrico Kaden, Jelle Veraart, Stefano B. Blumberg, Can Son Khoo, Marco Palombo, Jaume Coll-Font, Benoit Scherrer, Simon K. Warfield, Suheyla Cetin Karayumak, Yogesh Rathi, Simon Koppers, Leon Weninger, Julia Ebert, Dorit Merhof, Daniel Moyer, Maximilian PietschDaan Christiaens, Rui Teixeira, Jacques-Donald Tournier, Andrey Zhylka, Josien Pluim, Greg Parker, Umesh Rudrapatna, John Evans, Cyril Charron, Derek K. Jones, Chantal M.W. Tax

Medical Image Analysis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic

12 Citations (Scopus)

296 Downloads (Pure)

Abstract

We present a summary of competition results in the multi-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC). MUSHAC is an open competition intended to stimulate the development of computational methods that reduce scanner- and protocol-related variabilities in multi-shell diffusion MRI data across multi-site studies. Twelve different methods from seven research groups have been tested in this challenge. The results show that cross-vendor harmonization and enhancement can be performed by using suitable computational algorithms such as deep convolutional neural networks. Moreover, parametric models for multi-shell diffusion MRI signals also provide reliable performances.

Original language	English
Title of host publication	Computational Diffusion MRI
Subtitle of host publication	International MICCAI Workshop, Granada, Spain, September 2018
Editors	Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M.W. Tax
Place of Publication	Cham
Publisher	Springer
Chapter	18
Pages	217-224
Number of pages	8
ISBN (Electronic)	978-3-030-05831-9
ISBN (Print)	978-3-030-05830-2
DOIs	https://doi.org/10.1007/978-3-030-05831-9_18
Publication status	Published - 2019
Event	9th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2018) : Atrial Segmentation and LV Quantification Challenges - Granada, Spain Duration: 16 Sept 2018 → 16 Sept 2018 Conference number: 9

Publication series

Name	Mathematics and Visualization
ISSN (Print)	1612-3786
ISSN (Electronic)	2197-666X

Conference

Conference	9th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2018)
Abbreviated title	STACOM 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 16/09/18
Other	held in conjunction with the 21st Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018

Keywords

Deep learning
Diffusion MRI
Harmonisation
Parametric model
Spherical harmonics

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10.1007/978-3-030-05831-9_18

Final author versionFinal author version , 587 KB

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Ning, L., Bonet-Carne, E., Grussu, F., Sepehrband, F., Kaden, E., Veraart, J., Blumberg, S. B., Khoo, C. S., Palombo, M., Coll-Font, J., Scherrer, B., Warfield, S. K., Karayumak, S. C., Rathi, Y., Koppers, S., Weninger, L., Ebert, J., Merhof, D., Moyer, D., ... Tax, C. M. W. (2019). Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results. In E. Bonet-Carne, F. Grussu, L. Ning, F. Sepehrband, & C. M. W. Tax (Eds.), Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018 (pp. 217-224). (Mathematics and Visualization). Springer. https://doi.org/10.1007/978-3-030-05831-9_18

Ning, Lipeng ; Bonet-Carne, Elisenda ; Grussu, Francesco et al. / Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results. Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018. editor / Elisenda Bonet-Carne ; Francesco Grussu ; Lipeng Ning ; Farshid Sepehrband ; Chantal M.W. Tax. Cham : Springer, 2019. pp. 217-224 (Mathematics and Visualization).

@inproceedings{20d6fc43b38d47dca091d0b4b01f61b2,

title = "Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results",

abstract = "We present a summary of competition results in the multi-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC). MUSHAC is an open competition intended to stimulate the development of computational methods that reduce scanner- and protocol-related variabilities in multi-shell diffusion MRI data across multi-site studies. Twelve different methods from seven research groups have been tested in this challenge. The results show that cross-vendor harmonization and enhancement can be performed by using suitable computational algorithms such as deep convolutional neural networks. Moreover, parametric models for multi-shell diffusion MRI signals also provide reliable performances.",

keywords = "Deep learning, Diffusion MRI, Harmonisation, Parametric model, Spherical harmonics",

author = "Lipeng Ning and Elisenda Bonet-Carne and Francesco Grussu and Farshid Sepehrband and Enrico Kaden and Jelle Veraart and Blumberg, {Stefano B.} and Khoo, {Can Son} and Marco Palombo and Jaume Coll-Font and Benoit Scherrer and Warfield, {Simon K.} and Karayumak, {Suheyla Cetin} and Yogesh Rathi and Simon Koppers and Leon Weninger and Julia Ebert and Dorit Merhof and Daniel Moyer and Maximilian Pietsch and Daan Christiaens and Rui Teixeira and Jacques-Donald Tournier and Andrey Zhylka and Josien Pluim and Greg Parker and Umesh Rudrapatna and John Evans and Cyril Charron and Jones, {Derek K.} and Tax, {Chantal M.W.}",

year = "2019",

doi = "10.1007/978-3-030-05831-9_18",

language = "English",

isbn = "978-3-030-05830-2",

series = "Mathematics and Visualization",

publisher = "Springer",

pages = "217--224",

editor = "Elisenda Bonet-Carne and Francesco Grussu and Lipeng Ning and Farshid Sepehrband and Tax, {Chantal M.W.}",

booktitle = "Computational Diffusion MRI",

address = "Germany",

note = "9th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2018) : Atrial Segmentation and LV Quantification Challenges, STACOM 2018 ; Conference date: 16-09-2018 Through 16-09-2018",

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Ning, L, Bonet-Carne, E, Grussu, F, Sepehrband, F, Kaden, E, Veraart, J, Blumberg, SB, Khoo, CS, Palombo, M, Coll-Font, J, Scherrer, B, Warfield, SK, Karayumak, SC, Rathi, Y, Koppers, S, Weninger, L, Ebert, J, Merhof, D, Moyer, D, Pietsch, M, Christiaens, D, Teixeira, R, Tournier, J-D, Zhylka, A , Pluim, J, Parker, G, Rudrapatna, U, Evans, J, Charron, C, Jones, DK & Tax, CMW 2019, Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results. in E Bonet-Carne, F Grussu, L Ning, F Sepehrband & CMW Tax (eds), Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018. Mathematics and Visualization, Springer, Cham, pp. 217-224, 9th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2018) , Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-05831-9_18

Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results. / Ning, Lipeng; Bonet-Carne, Elisenda; Grussu, Francesco et al.
Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018. ed. / Elisenda Bonet-Carne; Francesco Grussu; Lipeng Ning; Farshid Sepehrband; Chantal M.W. Tax. Cham: Springer, 2019. p. 217-224 (Mathematics and Visualization).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic

TY - GEN

T1 - Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results

AU - Ning, Lipeng

AU - Bonet-Carne, Elisenda

AU - Grussu, Francesco

AU - Sepehrband, Farshid

AU - Kaden, Enrico

AU - Veraart, Jelle

AU - Blumberg, Stefano B.

AU - Khoo, Can Son

AU - Palombo, Marco

AU - Coll-Font, Jaume

AU - Scherrer, Benoit

AU - Warfield, Simon K.

AU - Karayumak, Suheyla Cetin

AU - Rathi, Yogesh

AU - Koppers, Simon

AU - Weninger, Leon

AU - Ebert, Julia

AU - Merhof, Dorit

AU - Moyer, Daniel

AU - Pietsch, Maximilian

AU - Christiaens, Daan

AU - Teixeira, Rui

AU - Tournier, Jacques-Donald

AU - Zhylka, Andrey

AU - Pluim, Josien

AU - Parker, Greg

AU - Rudrapatna, Umesh

AU - Evans, John

AU - Charron, Cyril

AU - Jones, Derek K.

AU - Tax, Chantal M.W.

N1 - Conference code: 9

PY - 2019

Y1 - 2019

N2 - We present a summary of competition results in the multi-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC). MUSHAC is an open competition intended to stimulate the development of computational methods that reduce scanner- and protocol-related variabilities in multi-shell diffusion MRI data across multi-site studies. Twelve different methods from seven research groups have been tested in this challenge. The results show that cross-vendor harmonization and enhancement can be performed by using suitable computational algorithms such as deep convolutional neural networks. Moreover, parametric models for multi-shell diffusion MRI signals also provide reliable performances.

AB - We present a summary of competition results in the multi-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC). MUSHAC is an open competition intended to stimulate the development of computational methods that reduce scanner- and protocol-related variabilities in multi-shell diffusion MRI data across multi-site studies. Twelve different methods from seven research groups have been tested in this challenge. The results show that cross-vendor harmonization and enhancement can be performed by using suitable computational algorithms such as deep convolutional neural networks. Moreover, parametric models for multi-shell diffusion MRI signals also provide reliable performances.

KW - Deep learning

KW - Diffusion MRI

KW - Harmonisation

KW - Parametric model

KW - Spherical harmonics

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DO - 10.1007/978-3-030-05831-9_18

M3 - Conference contribution

SN - 978-3-030-05830-2

T3 - Mathematics and Visualization

SP - 217

EP - 224

BT - Computational Diffusion MRI

A2 - Bonet-Carne, Elisenda

A2 - Grussu, Francesco

A2 - Ning, Lipeng

A2 - Sepehrband, Farshid

A2 - Tax, Chantal M.W.

PB - Springer

CY - Cham

T2 - 9th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2018)

Y2 - 16 September 2018 through 16 September 2018

ER -

Ning L, Bonet-Carne E, Grussu F, Sepehrband F, Kaden E, Veraart J et al. Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results. In Bonet-Carne E, Grussu F, Ning L, Sepehrband F, Tax CMW, editors, Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain, September 2018. Cham: Springer. 2019. p. 217-224. (Mathematics and Visualization). doi: 10.1007/978-3-030-05831-9_18

Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): progress and results

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Keywords

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