A detailed spatio-temporal atlas of the white matter tracts for the fetal brain.

This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.

Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study.

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.

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. We reconstructed fetal brain DTI using a motion-tracked slice-to-volume registration framework. Images were segmented into 14 regions of interest (ROIs) through label propagation using a fetal DTI atlas, with expert refinement. Statistical analysis involved assessing changes in fractional anisotropy (FA) and mean diffusivity (MD) throughout gestation using mixed-effects models, and identifying points of change in trajectory for ROIs with nonlinear trends. Results showed significant GA-related changes in FA and MD in all ROIs except in the thalamus' FA and corpus callosum's MD. Hemispheric asymmetries were found in the FA of the periventricular white matter (pvWM), intermediate zone, and subplate and in the MD of the ganglionic eminence and pvWM. This study provides valuable insight into the normal patterns of development of MD and FA in the fetal brain. These changes are closely linked with cytoarchitectonic changes and display indications of early functional specialization.

Fetal Brain Growth in the Early Second Trimester.

BACKGROUND AND PURPOSE: Recent advances in fetal MR imaging technology have enabled acquisition of diagnostic images in the early second trimester. Interpretation of these examinations is limited by a lack of familiarity with the developmental changes that occur during these early stages of growth. This study aimed to characterize normal fetal brain growth between the 12th and 20th weeks of gestational age. MATERIALS AND METHODS: This study was conducted as an observational retrospective analysis. Data were obtained from a tertiary care center's PACS database. All fetuses included had late fetal MR imaging (>20 weeks) or postnatal MR imaging, which confirmed normality. Each MR image was manually segmented, with ROIs placed to calculate the volume of the supratentorial parenchyma, brainstem, cerebellum, ventricular CSF, and extra-axial CSF. A linear regression analysis was used to evaluate gestational age as a predictor of the volume of each structure. RESULTS: Thirty-one subjects with a mean gestational age of 17.23 weeks (range, 12-19 weeks) were studied. There was a positive, significant association between gestational age and intracranial, supratentorial parenchyma; brainstem cerebellum; intraventricular CSF; and extra-axial CSF volumes (P < .001). Growth was fastest in the supratentorial parenchyma and extra-axial CSF. Fetal sex was not associated with the volume in any of the ROIs. CONCLUSIONS: This study demonstrates distinct trajectories for the major compartments of the fetal brain in the early second trimester. The fastest growth rates were observed in the supratentorial brain and extra-axial CSF.

Population Atlas Analysis of Emerging Brain Structural Connections in the Human Fetus.

BACKGROUND: A lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences. PURPOSE: To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI). STUDY TYPE: Retrospective. POPULATION: Publicly available diffusion atlases, based on 60 healthy women (age 18-45 years) with normal prenatal care, from 23 and 35 weeks of gestation. FIELD STRENGTH/SEQUENCE: 3.0 Tesla/DTI acquired with diffusion-weighted echo planar imaging (EPI). ASSESSMENT: We performed whole-brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small-worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality. STATISTICAL TESTS: Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. The t-tests were used to assess SW. P-values were corrected using Holm-Bonferroni method. A corrected P-value <0.05 was considered statistically significant. RESULTS: Network analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32-7.37), LE (5.43%/week, 95% CI 3.63-7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes. DATA CONCLUSION: Extensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Characterizing normal perinatal development of the human brain structural connectivity.

Early brain development is characterized by the formation of a highly organized structural connectome. The interconnected nature of this connectome underlies the brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development and imaging difficulties. Combined with high inter-subject variability, these factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational framework, based on spatio-temporal averaging, for determining such baselines. We used this framework to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in perinatal stage. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. We observed increases in global and local efficiency, a decrease in characteristic path length, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. The new computational method and results are useful for assessing normal and abnormal development of the structural connectome early in life.

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. Many artifacts that are barely appreciable at 1.5 T are amplified at 3 T. A systematic approach to imaging at 3 T that incorporates appropriate patient positioning, a thoughtful protocol design, and sequence optimization minimizes the impact of these artifacts and allows radiologists to reap the benefits of the increased SNR. The sequences used are the same at both field strengths and include single-shot T2-weighted, balanced steady-state free-precession, three-dimensional T1-weighted spoiled gradient-echo, and echo-planar imaging. Synergistic use of these acquisitions to sample various tissue contrasts and in various planes provides valuable information about fetal anatomy and pathologic conditions. In the authors' experience, fetal imaging at 3 T outperforms imaging at 1.5 T for most indications when performed under optimal circumstances. The authors condense the cumulative experience of fetal imaging specialists and MRI technologists who practice at a large referral center into a guideline covering all major aspects of fetal MRI at 3 T, from patient preparation to image interpretation. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

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. We labeled 14 regions of interest (ROIs): cortical plate (CP), subplate (SP), Intermediate zone-subventricular zone-ventricular zone (IZ/SVZ/VZ), Ganglionic Eminence (GE), anterior and posterior limbs of the internal capsule (ALIC, PLIC), genu (GCC), body (BCC), and splenium (SCC) of the corpus callosum (CC), hippocampus, lentiform Nucleus, thalamus, brainstem, and cerebellum. A series of linear regressions were used to assess GA as a predictor of FA and MD for each ROI. The combination of MD and FA allowed the identification of all ROIs. Increasing GA was significantly associated with decreasing FA in the CP, SP, IZ/SVZ/IZ, GE, ALIC, hippocampus, and BCC (p < .03, for all), and with increasing FA in the PLIC and SCC (p < .002, for both). Increasing GA was significantly associated with increasing MD in the CP, SP, IZ/SVZ/IZ, GE, ALIC, and CC (p < .03, for all). We developed a set of expert-annotated labels for a DTI spatiotemporal atlas of the fetal brain and presented a pilot analysis of developmental changes in cerebral microstructure between 23 and 30 weeks of GA.

Nephrogenic Systemic Fibrosis in Patients with Chronic Kidney Disease after the Use of Gadolinium-Based Contrast Agents: A Review for the Cardiovascular Imager.

Gadolinium-enhanced cardiac magnetic resonance has revolutionized cardiac imaging in the last two decades and has emerged as an essential and powerful tool for the characterization and treatment guidance of a wide range of cardiovascular diseases. However, due to the high prevalence of chronic renal dysfunction in patients with cardiovascular conditions, the risk of nephrogenic systemic fibrosis (NSF) after gadolinium exposure has been a permanent concern. Even though the newer macrocyclic agents have proven to be much safer in patients with chronic kidney disease and end-stage renal failure, clinicians must fully understand the clinical characteristics and risk factors of this devastating pathology and maintain a high degree of suspicion to prevent and recognize it. This review aimed to summarize the existing evidence regarding the physiopathology, clinical manifestations, diagnosis, and prevention of NSF related to the use of gadolinium-based contrast agents.

Multiple Coronary Fistulas After Several Penetrating Chest Wounds: A Rare Case of Refractory Heart Failure.

Coronary artery fistulas are rare coronary abnormalities. Most of these fistulas have a congenital origin, and only a few are acquired. We report the case of a patient with late-acquired multiple coronary fistulas secondary to a stab wound, diagnosed in the setting of ischemic heart failure secondary to coronary steal syndrome. (Level of Difficulty: Intermediate.).

Impact of a pharmacist-based multidimensional intervention aimed at decreasing the risk of hyperkalemia in heart failure patients: A Latin-American experience.

AIMS: Hyperkalemia is a potentially life-threatening condition associated with the use of heart failure (HF) medications, which can lead to increased morbidity and mortality. Novel approaches for hyperkalemia prevention are needed, especially in limited-resource settings. Despite multiple studies showing the beneficial impact of pharmaceutical-counseling in several outcomes, there is a knowledge-gap regarding its impact on hyperkalemia prevention. METHODS: A case-control study was performed in patients from the Adult Heart Failure Clinic Registry in our institution. Cases were selected using a definition of serum potassium K+ ≥5.5 mmol/L. To study the association between hyperkalemia and relevant risk factors, we performed a multivariate logistic regression analysis using the Least Absolute Shrinkage and Selection Operator (LASSO) method for variable selection. We also fitted a Classification and Regression Tree (CART) to establish complex interactions and effect modifiers between the selected variables. RESULTS: We matched 483 controls (eligible HF patients without hyperkalemia) to 132 cases (eligible HF patients with hyperkalemia based on age and calendar, yielding a total sample size of 615 patients (270 females) for this study. Cases had statistically significant lower odds of receiving a pharmacist-based multidimensional intervention (PBMI) (OR 0.57; 95% CI, 0.43-0.80) or having HF with reduced ejection fraction (OR 0.56; 95% CI, 0.18-0.72). On the other hand, patients who presented hyperkalemia had statistically significant higher odds of having a history of chronic kidney disease stage 4 (OR 4.97; 95% CI, 2.24-11.01) or 5 (OR 6.73; 95% CI, 1.69-26.84) and being on enalapril at doses =40 mg/day (OR, 9.90; 95% CI 5.81-16.87). CONCLUSIONS: PBMI is a practical approach to prevent hyperkalemia in HF patients in a limited-resource setting. However, clinical trials are needed to assess its effectiveness.