TY - GEN
T1 - Multi-modal synthesis of ASL-MRI features with KPLS regression on heterogeneous data
AU - Lassila, Toni
AU - Faria, Helena M.
AU - Sarrami-Foroushani, Ali
AU - Meneghello, Francesca
AU - Venneri, Annalena
AU - Frangi, Alejandro F.
N1 - Funding Information:
This work was funded by the FP7 project VPH-DARE@IT “Virtual Physiological Human: DementiA Research Enabled by IT” (FP7-ICT-2011-5.2-601055).
Funding Information:
Acknowledgements. This work was funded by the FP7 project VPH-DARE@IT
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Machine learning classifiers are frequently trained on heterogeneous multi-modal imaging data, where some patients have missing modalities. We address the problem of synthesising arterial spin labelling magnetic resonance imaging (ASL-MRI) - derived cerebral blood flow (CBF) - features in a heterogeneous data set. We synthesise ASL-MRI features using T1-weighted structural MRI (sMRI) and carotid ultrasound flow features. To deal with heterogeneous data, we extend the kernel partial least squares regression (kPLSR) - method to the case where both input and output data have partial coverage. The utility of the synthetic CBF features is tested on a binary classification problem of mild cognitive impairment patients vs. controls. Classifiers based on sMRI and synthetic ASL-MRI features are combined using a maximum probability rule, achieving a balanced accuracy of 92% (sensitivity 100 %, specificity 80 %) in a separate validation set. Comparison is made against support vector machine-classifiers from literature.
AB - Machine learning classifiers are frequently trained on heterogeneous multi-modal imaging data, where some patients have missing modalities. We address the problem of synthesising arterial spin labelling magnetic resonance imaging (ASL-MRI) - derived cerebral blood flow (CBF) - features in a heterogeneous data set. We synthesise ASL-MRI features using T1-weighted structural MRI (sMRI) and carotid ultrasound flow features. To deal with heterogeneous data, we extend the kernel partial least squares regression (kPLSR) - method to the case where both input and output data have partial coverage. The utility of the synthetic CBF features is tested on a binary classification problem of mild cognitive impairment patients vs. controls. Classifiers based on sMRI and synthetic ASL-MRI features are combined using a maximum probability rule, achieving a balanced accuracy of 92% (sensitivity 100 %, specificity 80 %) in a separate validation set. Comparison is made against support vector machine-classifiers from literature.
UR - http://www.scopus.com/inward/record.url?scp=85053910335&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00931-1_54
DO - 10.1007/978-3-030-00931-1_54
M3 - Conference contribution
AN - SCOPUS:85053910335
SN - 9783030009304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 473
EP - 481
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 Italia
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -