Development of a real-time fetal brain tracking method to be applied to BOLD MRI and diffusion MRI aimed at monitoring cerebral development in-utero

Lead Research Organisation: King's College London
Department Name: Imaging & Biomedical Engineering

Abstract

Fetal brain MRI offers unique insights into life before birth. Fetal BOLD and diffusion MRI measurements are correlated with fetal health and growth, thus offering an opportunity to probe processes involved in human development noninvasively. One of the main challenges of fetal MR is unpredictable fetal motion. Typically multi-slice 2D acquisitions are chosen. Thereby, the rapid single-shot acquisitions allow freezing of the motion during the readout of each slice/volume. Post-processing techniques (SVR) allow reconstruction of a continuous 3D volume out of these slices. However, techniques that rely on the acquisition of multiple repetitions all combined in the following analysis, such as BOLD MRI and dMRI, are significantly affected by inter-volume motion and usually require post-processing to improve spatiotemporal analysis. Therefore, these MR techniques would highly benefit from brain tracking for motion robustness. Such methodology has been implemented in adults- 3D-EPI navigators are acquired and processed for scan-to-scan prospective motion correction. Its extension to fetal imaging is, however, challenging due to the surrounding uterine environment and maternal tissue. While examples of fetal 3D-EPI navigators have been shown, they are prone to motion artifacts and their processing involved manual intervention and slow conventional registration. We are therefore developing an intrinsically motion-robust fetal MRI method that uses a 3D U-Net for fetal head position and translational motion estimation. Our method thereby uses the acquisition volumes directly as navigators. This is possible due to the relatively high speed of such acquisitions (TR<5 s). A fast real-time feedback system, via Gadgetron, will provide the scanner with the brain position estimation to update the VOI position before each dynamic and maintain it irrespective of fetal motion. We aim for this method to monitor brain development in-utero and to be adopted in routine clinical evaluation.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R513064/1 01/10/2018 30/09/2023
2444304 Studentship EP/R513064/1 01/10/2020 30/06/2024 Sara Neves Silva
EP/T517963/1 01/10/2020 30/09/2025
2444304 Studentship EP/T517963/1 01/10/2020 30/06/2024 Sara Neves Silva
 
Description Fetal MRI provides an ideal tool for characterizing brain development and growth. It is, however, extremely susceptible to unpredictable, rapid, large fetal motion due to being a relatively slow imaging technique. Fetal motion leads to corruption of the images, and hence images either need to be re-acquired (extending the duration of the scan) or post-processing needs to be applied offline. This is specifically a challenge in functional MRI experiments which require the acquisition of images in time-series format for spatiotemporal analysis, e.g., assessment of brain oxygenation or brain tissue microstructure. In order to The method I have developed uses a deep learning model for localizing the fetal brain within the uterus and tracking the region of interest in real-time during the scan. The tracking method has been inserted into a pipeline of the scanner and allows the update of the field of view (FOV) of the images according to the motion of the fetal head - essentially tracking the translational displacements of the head in real-time during the scan. The method has been implemented in two clinical scans: low field scanner (0.55T) and higher field strength (3T) and has been tested on 20 fetal subjects, with very positive results of motion tracking and FOV adjustment even for fetuses of young gestational ages. The localization code and data are open sources for all researchers.
Exploitation Route The next steps include deploying the method to other modalities such as fetal diffusion MRI (for analysis of brain tissue microstructure) and to cohorts of pregnant participants diagnosed with pregnancy complications such as pre-eclampsia and congenital heart disease.
Sectors Healthcare

 
Description The method has already been applied to research fetal scans, and in some cases, maternal/fetal pathologies were involved, e.g., pre-eclampsia, premature rupture of the membranes (amniotic sac) before labor begins.
First Year Of Impact 2023
Sector Healthcare
 
Description Coding Workshop for Young Women 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Schools
Results and Impact This is part of the School's community engagement programme with local communities and the centre we engaged with particularly works with women and girls from underserved communities in the local area. The coding workshop involved a series of short games to explore different coding concepts, by solving a computing mystery and finding a 'hacker'. The target audience (around 15 pupils) ranged between 6-12 years of age. There were a range of different activities from code breaking and puzzle solving to 'programming' a dance to find clues about the hacker, all illustrating an element of coding knowledge. The activity additionally included simple programming using Scratch language. The intent was to spark interest of young girls in coding/programming.
Year(s) Of Engagement Activity 2023