Keywords: Fetal, Motion Correction

Motivation: Maternal breathing and fetal bulk motion frequently limit the utility of fetal 4D flow MRI. 

Goal(s): To demonstrate the effects of maternal respiratory and fetal bulk motion correction on 4D flow MRI 

Approach: Prospective undersampled fetal 4D flow data were acquired in two subjects, followed by compressed sensing reconstruction that included maternal respiratory gating and bulk motion correction. Standard SENSE-accelerated 4D flow acquisitions without motion correction (N=22) provided reference for the ability to quantify flow. 

Results: Comparisons of the motion corrected data to normative performance illustrate the technique’s potential for mitigating motion in fetal 4D flow, with equivalence to standard SENSE accelerated scans. 

Impact: The proposed sequence and flexible reconstruction workflow provide motion robustness for fetal 4D flow MRI. Further exploration of motion correction techniques has potential to enhance spatial and temporal resolution and to mitigate motion-related errors over extended scanning durations.

Keywords: Fetal, Phantoms, Fetus, Biometrics, Tissue mimicking, Cross-modality imaging

Motivation: Fetal MRI necessitates extra care due to inherent safety concerns, complicating the optimization of MRI protocols and calibration with ultrasound assessments.

Goal(s): To mitigate the aforementioned safety problems, we aim to create a cross-modality, tissue-mimicking, anatomically correct fetal phantom.

Approach: We designed a fetus phantom encapsulated within a natural rubber balloon filled with mimicking amniotic fluid, creating an amniotic sac analogue. This design utilized varying compositions of agarose, gadolinium, and gelatin to replicate different fetal organs with distinct T1 and T2 values.

Results: The developed phantom comprises various fetal organs and relaxation times, enabling precise fetal biometric comparisons between MRI and ultrasound modalities.

Impact: With the help of the developped cross-modality, tissue-mimicking and anatomical mimicking fetus phantom, the aforementioned constraints on fetal MR protocol optimization and calibration between different MR and ultrasound should be relaxed.

Keywords: Data Acquisition, Multimodal, Quantitative imaging, Mesoscopic

Motivation: White matter injuries are common in very premature babies and carry a significant risk of lifelong motor/cognitive disabilities.
 

Goal(s): Create mesoscopic resolution anatomical imaging, relaxometries, and diffusion MRI data to collect full 3D coverage of multiparametric information on tissue composition and connectivity, and compare to co-registered histology.

Approach: Each brain is imaged in situ at 3T shortly after death for registration purposes. After sample preparation, brains are imaged at 7T, then at 11.7T.

Results: We develop a unique multimodal mesoscopic (~100µm isotropic) post-mortem MRI atlas of brain development during the prenatal period (from 20 to 41 gestational weeks -GW) using 11,7T MRI.

Impact: The premature Human Connectome Project (p-HCP) provides the first mesoscopic multimodal quantitative MRI data at 11.7T of the anatomy, connectivity, cytoarchitecture, and microstructure of normal prenatal neurodevelopment during the second and third trimester of pregnancy.

Keywords: Fetal, Fetus

Motivation: The complex and rapid changes during human brain development call for a matched analysis of both structure and function. T2* relaxometry delivers non-invasive insights and pairs well with low field MRI. However, regional assessment is currently lacking.

Goal(s): Investigate the ability of low field MRI to quantify regional fetal brain T2*.

Approach: We acquired dynamic multi-echo gradient-echo sequences at 0.55T and developed automatic high-resolution reconstruction and segmentation to obtain the mean T2* values of 7 individual brain tissues.

Results: Fetal brain tissues vary both in absolute T2* value and in progression have differing T2* values and growth curves throughout gestation.

Impact: Regional fetal brain T2* values, obtained with an automatic pipeline, match the complexity, speed of change and growth during early human brain development and thus carry the potential to play a significant role in future research studies and clinical monitoring.

Keywords: Fetal, Fetus

Motivation: Low field MRI is a promising direction for fetal imaging. Yet, there are no reported models of fetal development specific to 0.55T.

Goal(s): We aim to formalise normal fetal growth models for structural 0.55T MRI. 

Approach: We use registration-based approach for generation of spatio-temporal templates from 3D D/SVR reconstructed images of the fetal brain and body and apply deep learning segmentation to parcellate organs for volumetry from >100 control subjects.

Results: This work introduces the first T2w fetal atlases and volumetry growth charts for 0.55T brain and body MRI depicting normal development across 22-38 weeks gestation. All models are publicly available online.

Impact: This work is the first step toward formalisation of analysis protocols for normal fetal brain and body development and optimisation of segmentation methods for low field strength fetal MRI. 

Keywords: Fetal, Fetus

Motivation: Preterm infants’ nutritional management should aim to replicate the intrauterine body composition. However, intrauterine body composition reference charts are lacking.

Goal(s): We aimed to construct MRI-based intrauterine body mass (BM), fat mass (FM), percent FM (%FM), fat-free mass (FFM), and percent FFM (%FFM) body composition reference charts.

Approach: Fetal body composition was computed from T₂-weighted and fat-water images. Body and subcutaneous fat volumes were automatically segmented using neural networks, and BM, FM, %FM, FFM, and %FFM were calculated.   

Results: Data of 176 participants with apparently normal singleton fetuses were included. All parameters significantly changed throughout gestation, and differences between sexes were seen.

Impact: MRI-based intrauterine BM, FM and FFM body composition reference charts may be used as reference for appropriate prenatal growth and may assist in nutritional management of preterm infants.  

Keywords: Fetal, Fetus

Motivation: Conventional anatomical imaging of the fetal brain is limited by low resolution due to an inherently large field-of-view and a restricted matrix size.

Goal(s): To achieve high-resolution fetal brain imaging and evaluate the image quality compared to conventional fetal brain imaging.

Approach: Fetal brain anatomical imaging was performed using optimized outer volume suppression for higher resolution. Image quality was scored by neuroradiologists, and image sharpness was calculated using gradient norms.

Results: High-resolution anatomical images acquired using our approach demonstrated improved image quality both quantitatively and qualitatively, without an increased scan time.

Impact: High-resolution fetal brain anatomical imaging with a reduced field-of-view achieved by optimized outer volume suppression demonstrates improved image quality compared to conventional imaging methods. This approach may help increase diagnostic accuracy in identifying brain abnormalities in utero.

Keywords: Fetal, Brain, Multi-Echo Analysis

Motivation: fMRI of the fetus in the womb must overcome the challenges of fetal motion and heterogeneous tissue boundaries. Multi-Echo fMRI reduces signal dropout and thermal noise, improving contrast-to-noise ratio of the BOLD signal. This can produce higher quality fetal MRI and allow adequate functional assessment of fetal brain development.

Goal(s): This study aims to denoise ME-fMRI of the fetal brain with TE-dependent analysis (tedana) in a subset of 10 cases (gestational age >35weeks).

Approach: Multi-echo gradient echo EPI scans were acquired in 80 fetuses. The fetal brain is segmented and passed to the analysis.

Results: Credible BOLD components are successfully identified using tedana.

Impact: TE-dependent analysis denoises ME-fMRI data of fetuses which undergo motion during scanning and contain heterogeneous tissue boundaries. This study assesses capabilities of ME-fMRI analysis to determine BOLD components in the fetal brain,paving the way for future research and clinical usage.

Keywords: Fetal, Fetus, model-based recontruction; T2* mapping; QSM

Motivation: Quantitative $$$T_{2}^{*}$$$ and susceptibility mapping is of great interest for fetal MRI. While conventional EPI-based approaches are efficient, they usually suffer from motion and field distortion artifacts.

Goal(s): To develop a distortion-free and motion-robust quantitative $$$T_{2}^{*}$$$ and susceptibility mapping approach for fetal brain.

Approach: A stack-of-star multi-echo FLASH sequence and model-based reconstruction were developed for quantitative mapping of $$$T_{2}^{*}$$$ and $$$B_{0}$$$ of fetal brain. Motion estimation and correction is incorporated into the reconstruction to reduce motion artifacts.

Results: Initial findings indicate accurate $$$T_{2}^{*}$$$ measurements. Motion-corrected image reconstruction effectively minimized motion artifacts. Fetal $$$T_{2}^{*}$$$ and $$$B_{0}$$$ maps are obtained with reasonable quantitative $$$T_{2}^{*}$$$ values.

Impact: Our technique enables distortion-free and motion-robust quantitative $$$T_{2}^{*}$$$ and $$$B_{0}$$$ mapping for the fetal brain, utilizing a stack-of-star multi-echo acquisition and model-based reconstruction. It has the potential to address motion and field distortion artifacts typically encountered in EPI-based methods.

Keywords: Fetal, Fetus

Motivation: In utero neurodevelopment is complex and not well understood, particularly in fetuses with Congenital Heart Disease.

Goal(s): Normatively model microstructural maturation in transient fetal compartments

Approach: Diffusion MRI was acquired in a healthy control cohort of 235 fetuses (22–37 weeks gestation). White matter bundles were estimated and divided into cross-sections. Gaussian Process Regression models were fit to diffusion metrics in each cross-section, and Z-scores calculated along the tract for 26 fetuses with CHD.

Results: We observe gradients of change, highlighting abnormal regions along the white matter unique to each subject. We did not find consistent patterns or associations with a specific diagnosis.

Impact: We establish normative trajectories in diffusion MR signal at the level of individual fetal brain compartments that reflect developing microstructure, improving understanding of dynamic fetal brain development and allowing us to predict deviations from the norm.

Keywords: Fetal, Fetus

Motivation: The subcortical nuclei have important brain connectivity, which are rarely studied in fetal brain development.

Goal(s): The aim of this study is to analyze the developmental characteristics of fetal subcortical nuclei and their relationship with gestational age and cortical development.

Approach: The subcortical nuclei and cortical volumes were manually segmented from the three-dimensional (3D) volume parameters of tomosynthesis to volume reconstruction (SVR) images

Results: We found a good linear relationship between subcortical nuclei and gestational age, with agreement on the left and right sides. In addition, we found good coupling between the subcortical nuclei and right cerebral cortex development.

Impact:  3D Volumetric MR to assess the developmental characteristics of fetal subcortical nuclei and their relationship with gestational age. The normative values of fetal intracranial structures across a range of gestations could e used as a reference tool in prenatal counseling.

Keywords: Fetal, Machine Learning/Artificial Intelligence

Motivation: To evaluate the intracranial structures and distinct components (grew matter [GM] and white matter [WM]) adjacent to the occipital horn of the lateral ventricle T2WI radiomics features in healthy fetuses and fetuses with ventriculomegaly (FVs),

Goal(s): and to predict postnatal changes in the size of the enlarged lateral ventricle in FVs.

Approach: Utilizing WM-radiomics on the affected sides of FVs, the SVM algorithm effectively predicted the changes in ventricle size, 

Results: as evidenced by the highest area under the curve (AUC) values of 0.771 and 0.738 in both the training and validation sets based on DeLong’s test (all P < 0.05).

Impact: An MRI-based occipital WM-radiomics model holds the potential to predict trends in changing ventriculomegaly.The image-based predictive model exhibits applicability in prenatal care. Leveraging image analysis and machine learning techniques may provide further insight into the pathophysiologic features of ventriculomegaly. 

Keywords: Prenatal, Prenatal

Motivation: Understanding placental dysfunction's role in small-for-gestational-age (SGA) fetal neurodevelopment is crucial; this study seeks to fill that gap, enhancing prenatal care.

Goal(s): The primary goal is to assess whether MRI can effectively quantify the relationship between placental function and fetal brain development in SGA pregnancies.

Approach: The study used IVIM, and T2* mapping to evaluate placental and fetal brain development, applying Pearson correlation and t tests for comparative analysis.

Results: Significant differences in placental perfusion and cortical properties between control and SGA groups were reported, demonstrating the feasibility of using MRI for in-utero assessment.

Impact: This study’s MRI approach could change prenatal care, allowing earlier detection of small-for-gestational age–related brain development issues, prompting interventions, and guiding research into neurodevelopmental support for affected neonates, with potential long-term cognitive benefits.

Keywords: Fetal, Fetus

Motivation: Susceptibility weighted imaging is of great interest in fetal MRI. Conventional single-shot EPI often suffers from field distortion and motion artifacts due to long readout.

Goal(s): To develop a susceptibility-weighted isotropic fetal brain imaging technique with reduced artifacts.

Approach: ZOOMit EPI was used for data acquisition. A slice-to-volume reconstruction was further employed to correct the motion between slices and reconstruct an isotropic high-resolution volume of the fetal brain.

Results: Zoomit EPI produces images with reduced artifacts due to reduced acquisition time. The slice-to-volume reconstruction further corrects the motion between slices and reconstruct an isotropic high-resolution SWI-weighted volume of the fetal brain.

Impact: Zoomit EPI offers faster scan times and produces images with reduced artifacts. With Zoomit EPI, high-resolution SWI-weighted isotropic fetal brain imaging has been achieved using slice-to-volume reconstruction.

Keywords: Fetal, Fetus, fetal lung, diffusion, distortion correction

Motivation: Accurately assessing fetal lung maturity and development with quantitative diffusion parameters could guide critical care for at-risk fetuses, but has been severely limited by artifacts.

Goal(s): Develop an MRI technique to enable reliable diffusion imaging of fetal lungs.

Approach: Investigated a multi-echo spin echo sequence to provide motion/distortion correction.

Results: The technique improved lung analysis, in particular, resulting in more consistent ADC fitting. The method also increased the geometric fidelity of the diffusion image with structural images.

Impact: This work demonstrates a novel MRI technique to enable reliable diffusion imaging of fetal lungs, overcoming current barriers of motion and artifacts. Improving lung maturity assessment during pregnancy has the potential to transform care for at-risk fetuses through earlier interventions.

Keywords: Neonatal, Neonatal, Neuro; Pediatrics; High-Field-MRI

Motivation: Extremely preterm neonates often show myelination delay in the brain, which is associated with long-term neurodevelopmental outcome impairments. Traditional imaging lacks myelin specificity, therefore, we implemented inhomogeneous magnetization transfer (ihMT).  

Goal(s): This study explores the use of ihMT at 7 Tesla for myelin assessment in extremely preterm neonates.

Approach: The ihMT data was acquired from a phantom, demonstrating its specificity for myelin content. Six neonates and five adults were scanned with ihMT, and an ROI-based analysis was performed. 

Results: Phantom and human data confirm ihMT's potential for myelin evaluation. As expected, neonates exhibit lower ihMTR values in key brain regions compared to adults. 

Impact: This study is an important first step in discovering myelin development in the extremely preterm neonatal brain. Differences in myelin development across the extremely preterm population could be used to predict long-term neurodevelopmental outcome in the future. 

Keywords: Normal Development, Screening, Myelination, Infant Brain, Early Development, Structural MRI

Motivation: While myelination is known to progress rapidly in the infant brain, the specific myelination progression in finer cortical regions and white matter (WM) tracts remain elusive. The precise effects of environmental impacts on infant brain myelination maturation require further investigation.

Goal(s): Our goal was to delineate differential myelination maturation trajectories across cortical and WM regions during infancy and explore their associations with environmental factors.

Approach: T1-weighted and T2-weighted images were used to map myelin content and analyzed with generalized additive models.

Results: Differential myelination processes were found across cortical and white matter regions, with significant correlations to socioeconomic status and parental stress.

Impact: This research advances understanding of complicated yet organized patterns in the early developing brain, informing pediatric care strategies. It enables targeted interventions for at-risk groups based on environmental impacts, potentially improving long-term cognitive outcomes. Further studies could investigate intervention efficacy.

Keywords: Normal Development, Normal development, large animals-nonhuman primates, ultra-high resolution, diffusion MRI, common coordinate framework

Motivation: Macaque brain structures change dramatically from birth to adulthood. However, there is no neonate macaque brain common coordinate framework (CCF) serving as neuroanatomical reference for mapping genetic, cellular, and molecular information.

Goal(s): To establish an ultra-high-resolution CCF for macaque brain at birth. 

Approach: We acquired ultra-high resolution diffusion MRI (dMRI) of neonate macaque brain and annotated fine neuroanatomical structures and investigated white matter tract development from neonate to adult macaque through dMRI-based tractography.

Results: The established neonate macaque brain CCF is featured with 0.2mm isotropic ultra-high diffusion imaging resolution, comprehensive gray and white matter labels, and a coordinate framework. 

Impact: This first neonate macaque brain CCF with 0.2mm isotropic ultra-high diffusion imaging resolution serves as neuroanatomical reference, enables mapping genetic, cellular, and molecular information, and provides image templates, laying the foundation for the brain development and evolution discoveries.

Keywords: Neuro, Brain Connectivity, Neurodevelopment, Paediatrics, HIV exposure, resting state fMRI

Motivation: Children who are HIV-exposed uninfected present with an increased risk of adverse motor developmental outcomes. Little is known about the pathophysiological mechanisms governing these outcomes.

Goal(s): This study aimed to investigate functional connectivity within the motor resting-state network in infants who are HIV-exposed uninfected using resting-state functional MRI.

Approach: We used Group Independent Component Analysis to identify the motor resting-state network and multivariate linear regression was used to compare its functional connectivity between groups.

Results: Infants who are HIV-exposed uninfected showed significant connectivity alterations in 26 connections within the motor resting-state network when compared to infants who are unexposed.

Impact: Functional connectivity alterations observed in the motor resting-state network could be linked to adverse motor developmental outcomes in children who are HIV-exposed uninfected. Future research will look at associations between functional connectivity of motor resting state network and motor development.

Keywords: Neuro, Quantitative Susceptibility mapping, congenital heart disease

Motivation: QSM has potential in monitoring altered cerebral oxygenation and quantifying cerebral microhemorrhages (CMH) in neonates with congenital heart disease (CHD).

Goal(s): To use 1) QSM to detect post-surgical changes in cerebral oxygenation and 2) a deep-learning algorithm to quantify CMH.

Approach: Cerebral susceptibility (χ) normalized to the ventricle was compared pre- vs. post-surgery and between CHD types. Deep-learning based quantification of CMH burden was compared pre- vs. post-surgery and assessed for relationship to cardiac bypass duration.

Results: Normalized post-surgery χ trended lower in neonates with transposition of great arteries compared to single ventricle physiology. Post-operative CMH burden was associated with cardiac bypass duration.

Impact: QSM-derived cerebral susceptibility post-surgery varies based on lesion type in congenital heart disease (CHD), supporting a potential role for QSM in detecting cerebral oxygenation changes. Cerebral microhemorrhages are common in neonates with CHD and are associated with surgical parameters.