Gadolinium dose reduction for brain MRI using conditional deep learning
- URL: http://arxiv.org/abs/2403.03539v1
- Date: Wed, 6 Mar 2024 08:35:29 GMT
- Title: Gadolinium dose reduction for brain MRI using conditional deep learning
- Authors: Thomas Pinetz, Erich Kobler, Robert Haase, Julian A. Luetkens, Mathias
Meetschen, Johannes Haubold, Cornelius Deuschl, Alexander Radbruch, Katerina
Deike, Alexander Effland
- Abstract summary: Two main challenges for these approaches are the accurate prediction of contrast enhancement and the synthesis of realistic images.
We address both challenges by utilizing the contrast signal encoded in the subtraction images of pre-contrast and post-contrast image pairs.
We demonstrate the effectiveness of our approach on synthetic and real datasets using various scanners, field strengths, and contrast agents.
- Score: 66.99830668082234
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, deep learning (DL)-based methods have been proposed for the
computational reduction of gadolinium-based contrast agents (GBCAs) to mitigate
adverse side effects while preserving diagnostic value. Currently, the two main
challenges for these approaches are the accurate prediction of contrast
enhancement and the synthesis of realistic images. In this work, we address
both challenges by utilizing the contrast signal encoded in the subtraction
images of pre-contrast and post-contrast image pairs. To avoid the synthesis of
any noise or artifacts and solely focus on contrast signal extraction and
enhancement from low-dose subtraction images, we train our DL model using
noise-free standard-dose subtraction images as targets. As a result, our model
predicts the contrast enhancement signal only; thereby enabling synthesization
of images beyond the standard dose. Furthermore, we adapt the embedding idea of
recent diffusion-based models to condition our model on physical parameters
affecting the contrast enhancement behavior. We demonstrate the effectiveness
of our approach on synthetic and real datasets using various scanners, field
strengths, and contrast agents.
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