Duerden, E. G. | bioRxiv Science

bioRxiv2022

A radiofrequency coil for infants and toddlers

BackgroundInfants and toddlers are a challenging population on which to perform MRI of the brain, both in research and clinical settings. Due to the large range in head size during the early years of development, paediatric neuro-MRI requires a radiofrequency (RF) coil, or set of coils, that is tailored to head size to provide the highest image quality. Mitigating techniques must also be employed to reduce and correct for subject motion. ObjectiveTo develop an RF coil with a tailored mechanical-electrical design that can adapt to the head size of three-month-old infants to three-year-old toddlers. Materials and methodsAn RF coil was designed with tight-fitting coil elements to improve SNR in comparison to commercially available adult head coils, while simultaneously aiding in immobilization. The coil was designed without visual obstruction to facilitate an unimpeded view of the childs face and the potential application of camera or motion-tracking systems. ResultsDespite the lack of elements over the face, the paediatric coil produced higher SNR over most of the brain compared to adult coils, including more than 2-fold in the periphery. Acceleration rates of 4-fold in each Cartesian direction could be achieved. Higher SNR allowed for shorter acquisition times through accelerated imaging protocols, reducing the probability of motion during a scan. ConclusionModification to the acquisition protocol, with immobilization of the head through the adjustable coil geometry, and subsequently combined with a motion tracking system provides a compelling platform for scanning paediatric populations without sedation and with improved image quality.

biophysics↗

bioRxiv2022

An automated BIDS-App for brain segmentation of human fetal functional MRI data

Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics [≥]0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.

neuroscience↗