Predicting 15O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias

Jia Guo, Enhao Gong, Audrey P. Fan, Maged Goubran, Mohammad M. Khalighi, Greg Zaharchuk

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neural network (dCNN) was trained to combine single- and multi-delay arterial spin labeling (ASL) and structural images to predict gold-standard 15O-water PET CBF images obtained on a simultaneous PET/MRI scanner. The dCNN was trained and tested on 64 scans in 16 healthy controls (HC) and 16 cerebrovascular disease patients (PT) with 4-fold cross-validation. Fidelity to the PET CBF images and the effects of bias due to training on different cohorts were examined. The dCNN significantly improved CBF image quality compared with ASL alone (mean ± standard deviation): structural similarity index (0.854 ± 0.036 vs. 0.743 ± 0.045 [single-delay] and 0.732 ± 0.041 [multi-delay], P < 0.0001); normalized root mean squared error (0.209 ± 0.039 vs. 0.326 ± 0.050 [single-delay] and 0.344 ± 0.055 [multi-delay], P < 0.0001). The dCNN also yielded mean CBF with reduced estimation error in both HC and PT (P < 0.001), and demonstrated better correlation with PET. The dCNN trained with the mixed HC and PT cohort performed the best. The results also suggested that models should be trained on cases representative of the target population.

Original languageEnglish (US)
JournalJournal of Cerebral Blood Flow and Metabolism
DOIs
StateAccepted/In press - 2019
Externally publishedYes

Keywords

  • arterial spin labeling
  • Cerebral blood flow
  • deep convolutional neural network
  • magnetic resonance imaging
  • positron emission tomography

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine

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