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 W.M. 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 W.M. Tax

^*Corresponding author for this work

Research output: Contribution to journal › Conference paper › peer-review

11 Citations (Scopus)

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
Pages (from-to)	217-224
Number of pages	8
Journal	Mathematics and Visualization
Issue number	226249
DOIs	https://doi.org/10.1007/978-3-030-05831-9_18
Publication status	E-pub ahead of print - 3 May 2019
Event	International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 20 Sept 2018 → 20 Sept 2018

Keywords

Deep learning
Diffusion MRI
Harmonisation
Parametric model
Spherical harmonics

Access to Document

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

Cite this

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. W. M. (2019). Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC): Progress and Results. Mathematics and Visualization, (226249), 217-224. Advance online publication. https://doi.org/10.1007/978-3-030-05831-9_18

@article{2a2b0b45c8da4ee2bf35e7f9dc903af0,

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 Tournier, {Jacques Donald} 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 W.M.}",

year = "2019",

month = may,

day = "3",

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

language = "English",

pages = "217--224",

journal = "Mathematics and Visualization",

issn = "1612-3786",

publisher = "Springer Science and Business Media Deutschland GmbH",

number = "226249",

note = "International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 20-09-2018 Through 20-09-2018",

}

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, JD, Zhylka, A, Pluim, J, Parker, G, Rudrapatna, U, Evans, J, Charron, C, Jones, DK & Tax, CWM 2019, 'Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC): Progress and Results', Mathematics and Visualization, no. 226249, pp. 217-224. https://doi.org/10.1007/978-3-030-05831-9_18

TY - JOUR

T1 - Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC)

T2 - International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018

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 W.M.

PY - 2019/5/3

Y1 - 2019/5/3

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

UR - http://www.scopus.com/inward/record.url?scp=85066914373&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-05831-9_18

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

M3 - Conference paper

AN - SCOPUS:85066914373

SN - 1612-3786

SP - 217

EP - 224

JO - Mathematics and Visualization

JF - Mathematics and Visualization

IS - 226249

Y2 - 20 September 2018 through 20 September 2018

ER -

Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC): Progress and Results

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this