Magn Reson Med

To improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI. A 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets. Our method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age. Our technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied "offline" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease

Transfer learning for automatic aorta segmentation in 4D-Flow magnetic resonance imaging data

Treball de fi de grau en BiomèdicaTutors: Gonzalo Maso Talou, Oscar CamaraThe lack of standardized pipelines for image processing, together with the noisy nature of data, challenges the application of deep learning (DL) techniques for the segmentation of cardiovascular structures in 4D-flow magnetic resonance imaging (MRI) data. Furthermore, DL-based algorithms require large, well-curated and annotated datasets for training, which is not straightforward. We therefore present a transfer learning approach to automatically perform aortic segmentation with 4D-flow MRI contrast-free data coming from different clinical sites and acquisition machines. Three datasets were considered: VH1, VH2 (only used for testing) and CAMRI. Two convolutional neural networks, based on the nnU-net framework, were trained with manual segmentations: LD (trained on VH1) and SD (trained on CAMRI). Performance was assessed using Dice (DS) and Jaccard score, Haussdorf distance, and average symmetrical surface distance. Transfer learning was applied using LD network to predict CAMRI data. LD network segmentations of VH2 and CAMRI datasets showed a median DS of 0.944 and 0.700 respectively. TL, using only a small fraction of CAMRI data for training, improved LD network generalization capabilities, increasing DS to 0.868. TL performance was comparable to SD network trained on all CAMRI data (0.897 DS). We demonstrate the applicability of the nnU-net framework for fast and automated 3D aortic segmentation in 4D-flow MRI datasets from different clinical sites with a comparable state-of-the-art performance, even without the need of contrast. TL approach increased generalisation, thus suggesting that it can reduce the tedious and time-consuming human intervention in the segmentation process, facilitating the availability of large annotated databases