Characterizing microstructural development in the fetal brain using diffusion MRI from 23 to 36 weeks of gestation.

We utilized motion-corrected diffusion tensor imaging (DTI) to evaluate microstructural changes in healthy fetal brains during the late second and third trimesters. Data were derived from fetal magnetic resonance imaging scans conducted as part of a prospective study spanning from 2013 March to 2019 May. The study included 44 fetuses between the gestational ages (GAs) of 23 and 36 weeks.

Fetal MRI at 3 T: Principles to Optimize Success.

Fetal MRI has emerged as a cornerstone of prenatal imaging, helping to establish the correct diagnosis in pregnancies affected by congenital anomalies. In the past decade, 3 T imaging was introduced as an alternative to increase the signal-to-noise ratio (SNR) of the pulse sequences and improve anatomic detail. However, imaging at a higher field strength is not without challenges.

Brain growth in fetuses with congenital diaphragmatic hernia.

Background And Purpose: To perform a volumetric evaluation of the brain in fetuses with right or left congenital diaphragmatic hernia (CDH), and to compare brain growth trajectories to normal fetuses.

Methods: We identified fetal MRIs performed between 2015 and 2020 in fetuses with a diagnosis of CDH. Gestational age (GA) range was 19-40 weeks.

Magnetic resonance imaging in neonates: a practical approach to optimize image quality and increase diagnostic yield.

Magnetic resonance imaging has emerged as a preferred modality in pediatric imaging because of its high soft-tissue contrast and the lack of ionizing radiation. It is important to recognize that despite its many advantages, several challenges to performing neonatal MRI arise from the lack of patient compliance and the small size of the anatomy. This manuscript presents the approach to patient preparation used at the authors' institution, summarizes general principles of image optimization and hardware selection, and reviews common indications across various organ systems.

Detailed anatomic segmentations of a fetal brain diffusion tensor imaging atlas between 23 and 30 weeks of gestation.

This work presents detailed anatomic labels for a spatiotemporal atlas of fetal brain Diffusion Tensor Imaging (DTI) between 23 and 30 weeks of post-conceptional age. Additionally, we examined developmental trajectories in fractional anisotropy (FA) and mean diffusivity (MD) across gestational ages (GA). We performed manual segmentations on a fetal brain DTI atlas.

Evaluation of white matter microstructure in pediatric onset multiple sclerosis with diffusion compartment imaging.

Background And Purpose: Pediatric-onset multiple sclerosis (POMS) shows earlier axonal involvement and greater axonal loss than in adults. We aim to characterize the white matter (WM) microstructural changes in POMS using a diffusion compartment imaging (DCI) model and compare it to standard diffusion tensor imaging (DTI).

Methods: Eleven patients (2 males, mean age 18.

Normal Growth, Sexual Dimorphism, and Lateral Asymmetries at Fetal Brain MRI.

Background Tools in image reconstruction, motion correction, and segmentation have enabled the accurate volumetric characterization of fetal brain growth at MRI. Purpose To evaluate the volumetric growth of intracranial structures in healthy fetuses, accounting for gestational age (GA), sex, and laterality with use of a spatiotemporal MRI atlas of fetal brain development. Materials and Methods T2-weighted 3.

Spatiotemporal changes in diffusivity and anisotropy in fetal brain tractography.

Population averaged diffusion atlases can be utilized to characterize complex microstructural changes with less bias than data from individual subjects. In this study, a fetal diffusion tensor imaging (DTI) atlas was used to investigate tract-based changes in anisotropy and diffusivity in vivo from 23 to 38 weeks of gestational age (GA). Healthy pregnant volunteers with typically developing fetuses were imaged at 3 T.

Deep learning-based parameter estimation in fetal diffusion-weighted MRI.

Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a result, accurate and robust parameter estimation in fetal DW-MRI remains an open problem. Recently, deep learning techniques have been successfully used for DW-MRI parameter estimation in non-fetal subjects.

A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging.

Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel.

Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI.

Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the diffusion signal or mathematical simplifications, which can impact the estimation accuracy. In this paper, we propose a data-driven method that avoids some of these pitfalls.

Image-quality optimization and artifact reduction in fetal magnetic resonance imaging.

Fetal MRI allows for earlier and better detection of complex congenital anomalies. However, its diagnostic utility is often limited by technical barriers that introduce artifacts and reduce image quality. The main determinants of fetal MR image quality are speed of acquisition, spatial resolution and signal-to-noise ratio (SNR).

Performance of simultaneous multi-slice accelerated diffusion-weighted imaging for assessing focal renal lesions in pediatric patients with tuberous sclerosis complex.

Background: Diffusion-weighted imaging (DWI) is a useful MRI technique to characterize abdominal lesions in children, but long acquisition times can lead to image degradation. Simultaneous multi-slice accelerated DWI is a promising technique to shorten DWI scan times.

Objective: To test the feasibility of simultaneous multi-slice DWI of the kidneys in pediatric patients with tuberous sclerosis complex (TSC) and to evaluate the accelerated protocol regarding image quality and quantitative apparent diffusion coefficient (ADC) values compared to standard echoplanar DWI sequence.

Success of Nonsedated Neuroradiologic MRI in Children 1-7 Years Old.

MRI use and the need for monitored anesthesia care (MAC) in children have increased. However, MAC is associated with examination delays, increased cost, and safety concerns. The purpose of this study was to evaluate the success rate of nonsedated neuroradiologic MRI studies in children 1-7 years old and to investigate factors associated with success.

Predictors of Anesthetic Exposure in Pediatric MRI.

Anesthetic exposure in children may impact long-term neurocognitive outcomes. Therefore, minimizing pediatric MRI scan time in children under anesthesia and the associated anesthetic exposure is necessary. The purpose of this study was to evaluate pediatric MRI scan time as a predictor of total propofol dose, considering imaging and clinical characteristics as covariates.

In vivo characterization of emerging white matter microstructure in the fetal brain in the third trimester.

The third trimester of pregnancy is a period of rapid development of fiber bundles in the fetal white matter. Using a recently developed motion-tracked slice-to-volume registration (MT-SVR) method, we aimed to quantify tract-specific developmental changes in apparent diffusion coefficient (ADC), fractional anisotropy (FA), and volume in third trimester healthy fetuses. To this end, we reconstructed diffusion tensor images from motion corrected fetal diffusion magnetic resonance imaging data.