Early-to-Late Prediction of DCE-MRI Contrast-Enhanced Images in Using Generative Adversarial Networks

We consider the problem of predicting early-to-late Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) in breast cancer sequences. This is approached with conditional generative adversarial networks that synthesize the late response image given the early response. We propose a novel loss...

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Main Authors: Fonnegra, Ruben D., Liliana Hernandez, Maria, Caicedo, Juan C., Diaz, Gloria M.
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Biological system modeling
Breast cancer
Breast DCE-MRI
Generative adversarial networks
Image synthesis
late response
Magnetic resonance imaging
Measurement
Protocols
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creator Fonnegra, Ruben D.
Liliana Hernandez, Maria
Caicedo, Juan C.
Diaz, Gloria M.
description We consider the problem of predicting early-to-late Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) in breast cancer sequences. This is approached with conditional generative adversarial networks that synthesize the late response image given the early response. We propose a novel loss function to improve the ability of GAN models to learn the relevant temporal tissue dynamics under this setting, as well as a clinically relevant metric to assess performance. Our experiments show that the proposed strategy predicts accurate responses and could serve as a solution to implement fast diagnostic protocols.
doi_str_mv 10.1109/ISBI53787.2023.10230509
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source IEEE Xplore All Conference Series
subjects Analytical models
Biological system modeling
Breast cancer
Breast DCE-MRI
Generative adversarial networks
Image synthesis
late response
Magnetic resonance imaging
Measurement
Protocols
title Early-to-Late Prediction of DCE-MRI Contrast-Enhanced Images in Using Generative Adversarial Networks
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