TY - GEN
T1 - Deep learning with synthetic diffusion MRI data for free-water elimination in glioblastoma cases
AU - Molina-Romero, Miguel
AU - Wiestler, Benedikt
AU - Gómez, Pedro A.
AU - Menzel, Marion I.
AU - Menze, Bjoern H.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Glioblastoma is the most common and aggressive brain tumor. In clinical practice, diffusion MRI (dMRI) enables tumor infiltration assessment, tumor recurrence prognosis, and identification of white-matter tracks close to the resection volume. However, the vasogenic edema (free-water) surrounding the tumor causes partial volume contamination, which induces a bias in the estimates of the diffusion properties and limits the clinical utility of dMRI. We introduce a voxel-based deep learning method to map and correct free-water partial volume contamination in dMRI. Our model learns from synthetically generated data a non-parametric forward model that maps free-water partial volume contamination to volume fractions. This is independent of the diffusion protocol and can be used retrospectively. We show its benefits in glioblastoma cases: first, a gain of statistical power; second, quantification of free-water and tissue volume fractions; and third, correction of free-water contaminated diffusion metrics. Free-water elimination yields more relevant information from the available data.
AB - Glioblastoma is the most common and aggressive brain tumor. In clinical practice, diffusion MRI (dMRI) enables tumor infiltration assessment, tumor recurrence prognosis, and identification of white-matter tracks close to the resection volume. However, the vasogenic edema (free-water) surrounding the tumor causes partial volume contamination, which induces a bias in the estimates of the diffusion properties and limits the clinical utility of dMRI. We introduce a voxel-based deep learning method to map and correct free-water partial volume contamination in dMRI. Our model learns from synthetically generated data a non-parametric forward model that maps free-water partial volume contamination to volume fractions. This is independent of the diffusion protocol and can be used retrospectively. We show its benefits in glioblastoma cases: first, a gain of statistical power; second, quantification of free-water and tissue volume fractions; and third, correction of free-water contaminated diffusion metrics. Free-water elimination yields more relevant information from the available data.
KW - Brain tumor
KW - DTI
KW - Data harmonization
KW - Deep learning
KW - Fractional anisotropy
KW - Free-water elimination
KW - Glioblastoma
UR - http://www.scopus.com/inward/record.url?scp=85053903149&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00931-1_12
DO - 10.1007/978-3-030-00931-1_12
M3 - Conference contribution
AN - SCOPUS:85053903149
SN - 9783030009304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 98
EP - 106
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Davatzikos, Christos
A2 - Fichtinger, Gabor
A2 - Alberola-López, Carlos
A2 - Schnabel, Julia A.
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -