Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.

Journal: Journal of applied clinical medical physics

PMID: 39716853

Abstract

PURPOSE: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.

Authors

Kenya Murase

Department of Future Diagnostic Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
Atsushi Nakamoto

From the Department of Radiology, Osaka University Graduate School of Medicine.
Noriyuki Tomiyama

Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.

Keywords

Algorithms Brain Neoplasms Computer Simulation Contrast Media Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Monte Carlo Method Neural Networks, Computer Radiotherapy Planning, Computer-Assisted Recurrent Neural Networks

External Resources

View on PubMed Access via DOI PubMed (39716853)

Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.

Abstract

Authors

Keywords

External Resources

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