Gadolinium dose reduction for brain MRI using conditional deep learning
Thomas Pinetz Erich Kobler Robert Haase Julian A. Luetkens Mathias Meetschen Johannes Haubold Cornelius Deuschl Alexander Radbruch Katerina Deike Alexander Effland
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.