Maria Deprez

TL;DR: To investigate the potential of continuous radiofrequency (RF) shifting (SWEEP) as a technique for creating densely sampled data while maintaining a stable signal state for dynamic imaging.

TL;DR: In this article, a multi-label 3D UNet with a GAN discriminator was proposed to localize both fetal brain and trunk in fetal MRI stacks. But the proposed method is robust for datasets with both full and partial coverage of the fetal body.

TL;DR: This paper shows that the novel regularization method outperforms widely used and recent DWI denoising algorithms and can be successfully applied to the super-resolution reconstruction of high-resolution volume from thick-slice data.

TL;DR: In this article, the authors evaluated how accurately 2D ultrasound-derived measurements of the fetal thymus reflect the 3D volume of the gland derived from motion corrected MRI images.

TL;DR: This work aims to predict tissue segmentation maps on an unseen dataset, which has both different acquisition parameters and population bias when compared to the authors' training data by investigating two unsupervised domain adaptation (UDA) techniques and comparing the two methods with a baseline fully-supervised segmentation network.