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.

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.

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.

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. Control subjects consisted of normally developing fetuses between 19 and 40 weeks recruited for a separate prospective study. All images were acquired at 3 Tesla and were processed with retrospective motion correction and slice-to-volume reconstruction to generate super-resolution 3-dimensional volumes. These volumes were registered to a common atlas space and segmented in 29 anatomic parcellations. RESULTS: A total of 174 fetal MRIs in 149 fetuses were analyzed (99 controls [mean GA: 29.2 ± 5.2 weeks], 34 fetuses left-sided CDH [mean GA: 28.4 ± 5.3 weeks], and 16 fetuses right-sided CDH [mean GA: 27 ± 5.4 weeks]). In fetuses with left-sided CDH, brain parenchymal volume was -8.0% (95% confidence interval [CI] [-13.1, -2.5]; p = .005) lower than normal controls. Differences ranged from -11.4% (95% CI [-18, -4.3]; p < .001) in the corpus callosum to -4.6% (95% CI [-8.9, -0.1]; p = .044) in the hippocampus. In fetuses with right-sided CDH, brain parenchymal volume was -10.1% (95% CI [-16.8, -2.7]; p = .008) lower than controls. Differences ranged from -14.1% (95% CI [-21, -6.5]; p < .001) in the ventricular zone to -5.6% (95% CI [-9.3, -1.8]; p = .025) in the brainstem. CONCLUSION: Left and right CDH are associated with lower fetal brain volumes.

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. This manuscript also incorporates input from our pediatric-trained MRI technologists, in an attempt to compile a practical guideline covering all major aspects of neonatal MRI, from its execution to its interpretation.

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.

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 18.8 ± 3.9 years) with a diagnosis of relapsing and remitting POMS (mean age at disease onset 13.8 ± 2.9 years, mean duration 5.1 ± 1.9 years) and healthy controls (8 males, mean age 26.4 ± 6.5 years) were recruited and imaged at 3 T. A 90-gradient set Cube and Sphere acquisition and a novel DCI model known as DIstribution of Anisotropic MicrOstructural eNvironments with Diffusion-weighted imaging (DIAMOND) were used to calculate a single anisotropic compartment, an isotropic compartment, and a free diffusion compartment. Lesions and contralateral normal-appearing white matter (NAWM) in patients and whole brain WM for controls were labeled. RESULTS: Eleven patients and 11 controls were recruited. When comparing lesions and contralateral NAWM in patients using DCI, compartmental axial diffusivity, radial diffusivity (cRD), and mean diffusivity (cMD) were higher in lesions. Conversely, compartmental fractional anisotropy (cFA) and heterogeneity index were lower in lesions. An analysis of DTI equivalents showed the same trends. In whole-brain NAWM of patients compared to controls, cRD and cMD were higher and cFA was lower in patients. CONCLUSION: Lesions in POMS can be accurately characterized by a DCI model. Incipient changes in NAWM seen in DCI may not be readily observable by DTI.

Characterization of Pediatric Imaging Trends and Likelihood of Exam Cancellation in the COVID-19 Pandemic.

RATIONALE AND OBJECTIVE: The COVID-19 pandemic has caused unprecedented changes in radiology practice worldwide. There is a need for a framework of pediatric radiology resource allocation for future acute resource-limited settings.The aim of this study is to quantify and analyze changes in pediatric radiology practice during the COVID-19 pandemic considering demographic and clinical characteristics. MATERIALS AND METHODS: We retrospectively searched our institution's electronic health record for pediatric imaging exams from 09/15/19 to 05/01/20, with 03/15/20 as the dividing date between baseline and pandemic periods. Age, modality, exam indication, need for anesthesia/sedation, and exam completion or cancellation were recorded. All exams were compared between baseline and pandemic periods using a chi-square test and a logistic regression multivariate analysis. RESULTS: 15,424 exams were included for analysis [13,715 baseline period (mean age 10±5 years; 7440 males); 1047 COVID-19 period (mean age 9±5 years; 565 males)]. A statistically significantly lower proportion of adolescent exams (45.5% vs 53.3%), radiography modality (62.4% vs 70.4%) and non-traumatic pain indication (39.1% vs 46.3%) was observed during the COVID-19 period. Conversely, we found a higher proportion of neonatal (5.8% vs 3.8%), infant (5.6% vs 4.1%) and early childhood patients (12.9% vs 9.8%), CT (7.4% vs 5.9%) and ultrasound modalities (18.3% vs 13.5%), oncologic (8.8% vs 6.5%) and congenital/development disorder indications (6% vs 3.9%), and studies performed under anesthesia (2.7% vs 1.3%). Regarding exam completion rates, the neonatal age group (OR 1.960 [95% CI 0.353 - 0.591]; p <0.020) and MRI modality (OR 1.502 [95% CI: 0.214 - 0.318]; p <0.049) had higher odds of completion during the COVID-19 pandemic, while fluoroscopy modality was associated with lower odds of completion (OR 0.524 [95% CI: 0.328 - 0.839]; p = 0.011). CONCLUSION: The composition and completion of pediatric radiology exams changed substantially during the COVID-19 pandemic. A sub-set of exams resilient to cancellation was identified.

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 3.0-T half-Fourier acquired single-shot turbo spin-echo sequence MRI was performed in healthy fetuses from prospectively recruited pregnant volunteers from March 2013 to May 2019. A previously validated section-to-volume reconstruction algorithm was used to generate intensity-normalized superresolution three-dimensional volumes that were registered to a fetal brain MRI atlas with 28 anatomic regions of interest. Atlas-based segmentation was performed and manually refined. Labels included the bilateral hippocampus, amygdala, caudate nucleus, lentiform nucleus, thalamus, lateral ventricle, cerebellum, cortical plate, hemispheric white matter, internal capsule, ganglionic eminence, ventricular zone, corpus callosum, brainstem, hippocampal commissure, and extra-axial cerebrospinal fluid. For fetuses younger than 31 weeks of GA, the subplate and intermediate zones were delineated. A linear regression analysis was used to determine weekly age-related change adjusted for sex and laterality. Results The final analytic sample consisted of 122 MRI scans in 98 fetuses (mean GA, 29 weeks ± 5 [range, 20-38 weeks]). All structures had significant volume growth with increasing GA (P < .001). Weekly age-related change for individual structures in the brain parenchyma ranged from 2.0% (95% CI: 0.9, 3.1; P < .001) in the hippocampal commissure to 19.4% (95% CI: 18.7, 20.1; P < .001) in the cerebellum. The largest sex-related differences were 22.1% higher volume in male fetuses for the lateral ventricles (95% CI: 10.9, 34.4; P < .001). There was rightward volumetric asymmetry of 15.6% for the hippocampus (95% CI: 14.2, 17.2; P < .001) and leftward volumetric asymmetry of 8.1% for the lateral ventricles (95% CI: 3.7, 12.2; P < .001). Conclusion With use of a spatiotemporal MRI atlas, volumetric growth of the fetal brain showed complex trajectories dependent on structure, gestational age, sex, and laterality. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Rollins in this issue.

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. Acquisition included structural images processed with a super-resolution algorithm and DTI images processed with a motion-tracked slice-to-volume registration algorithm. The DTI from individual subjects were used to generate 16 templates, each specific to a week of GA; this was accomplished by means of a tensor-to-tensor diffeomorphic deformable registration method integrated with kernel regression in age. Deterministic tractography was performed to outline the forceps major, forceps minor, bilateral corticospinal tracts (CST), bilateral inferior fronto-occipital fasciculus (IFOF), bilateral inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculus (UF). The mean fractional anisotropy (FA) and mean diffusivity (MD) was recorded for all tracts. For a subset of tracts (forceps major, CST, and IFOF) we manually divided the tractograms into anatomy conforming segments to evaluate within-tract changes. We found tract-specific, nonlinear, age related changes in FA and MD. Early in gestation, these trends appear to be dominated by cytoarchitectonic changes in the transient white matter fetal zones while later in gestation, trends conforming to the progression of myelination were observed. We also observed significant (local) heterogeneity in within-tract developmental trajectories for the CST, IFOF, and forceps major.

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. However, none of those prior works has addressed the fetal brain because obtaining reliable fetal training data is challenging. To address this problem, in this work we propose a novel methodology that utilizes fetal scans as well as scans from prematurely-born infants. High-quality newborn scans are used to estimate accurate maps of the parameter of interest. These parameter maps are then used to generate DW-MRI data that match the measurement scheme and noise distribution that are characteristic of fetal data. In order to demonstrate the effectiveness and reliability of the proposed data generation pipeline, we used the generated data to train a convolutional neural network (CNN) to estimate color fractional anisotropy (CFA). We evaluated the trained CNN on independent sets of fetal data in terms of reconstruction accuracy, precision, and expert assessment of reconstruction quality. Results showed significantly lower reconstruction error (n=100,p<0.001) and higher reconstruction precision (n=20,p<0.001) for the proposed machine learning pipeline compared with standard estimation methods. Expert assessments on 20 fetal test scans showed significantly better overall reconstruction quality (p<0.001) and more accurate reconstruction of 11 regions of interest (p<0.001) with the proposed method.

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. Our proposed method is based on a multilayer perceptron that learns to map the diffusion-weighted measurements, interpolated onto a fixed spherical grid in the q space, to the target fODF. Importantly, we also propose methods for synthesizing reliable simulated training data. We show that the model can be effectively trained with simulated or real training data. Our phantom experiments show that the proposed method results in more accurate fODF estimation and tractography than several competing methods including the multi-tensor model, Bayesian estimation, spherical deconvolution, and two other machine learning techniques. On real data, we compare our method with other techniques in terms of accuracy of estimating the ground-truth fODF. The results show that our method is more accurate than other methods, and that it performs better than the competing methods when applied to under-sampled diffusion measurements. We also compare our method with the Sparse Fascicle Model in terms of expert ratings of the accuracy of reconstruction of several commissural, projection, association, and cerebellar tracts. The results show that the tracts reconstructed with the proposed method are rated significantly higher by three independent experts. Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.

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. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.

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

BACKGROUND. 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. OBJECTIVE. 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. METHODS. We retrospectively reviewed data from our institutional nonsedated MRI program. Inclusion criteria were outpatient nonsedated MRI referral, age 1-7 years old, and neuroradiologic indication. Exclusion criteria were MRI examinations for ventricular checks and contrast material use. Success was determined by reviewing the clinical MRI report. We recorded patient age and sex, type of MRI examination (brain, spine, craniospinal, head and neck, and brain with MRA), protocol length, presence of child life specialist, video goggle use, and MRI appointment time (routine daytime appointment or evening appointment). We used descriptive statistics to summarize patient demographics and clinical data and logistic regression models to evaluate predictors of success in the entire sample. Subset analyses were performed for children from 1 to < 3 years old and 3 to 7 years old. RESULTS. We analyzed 217 patients who underwent nonsedated MRI examinations (median age, 5.1 years). Overall success rate was 82.0% (n = 178). The success rates were 81.4% (n = 127) for brain, 90.3% (n = 28) for spine, 71.4% (n = 10) for craniospinal, 66.7% (n = 6) for head and neck, and 100% (n = 7) for brain with MRA. Age was significantly associated with success (odds ratio [OR], 1.33; p = .009). In children 1 to < 3 years old, none of the factors analyzed were significant predictors of success (all, p > .48). In children 3-7 years old, protocol duration (OR, 0.96; 95% CI, 0.93-0.99; p = .02) and video goggle use (OR, 6.38; 95% CI, 2.16-18.84; p = .001) were significantly associated with success. CONCLUSION. A multidisciplinary approach with age-appropriate resources enables a high success rate for nonsedated neuroradiologic MRI in children 1-7 years old. CLINICAL IMPACT. Using age as the primary criterion to determine the need for MAC may lead to overuse of these services. Dissemination of information regarding nonsedated MRI practice could reduce the rate of sedated MRI in young children.

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. MATERIALS AND METHODS: We included 33 children and adolescents (12 female, 21 male; mean age 10±5 years) with TSC and renal cyst or angiomyolipoma on 3-tesla (T) MRI from 2017 to 2019. All studies included both free-breathing standard echoplanar DWI and simultaneous multi-slice DWI sequences. Subjective and quantitative image quality was evaluated using a predefined 5-point scale. ADC values were obtained for all renal cysts and angiomyolipomas ≥5 mm. All statistical analysis was performed using Stata/SE v15.1. RESULTS: Simultaneous multi-slice DWI ADC values were slightly lower compared to standard echoplanar DWI for both renal cysts and angiomyolipomas (mean difference 0.05×10-3 mm2/s, 95% confidence interval [CI] 0.40-0.50 and 0.024×10-3 mm2/s, 95% CI 0.17-0.21, respectively, with P>0.1). Our results showed that renal lesions with ADC values >1.69×10-3 mm2/s were all cysts, whereas lesions with values <1.16×10-3 mm2/s were all angiomyolipomas. However, ADC values could not discriminate between lipid-rich and lipid-poor angiomyolipomas (P>0.1, for both sequences). CONCLUSION: A 55% reduction in scan time was achieved using simultaneous multi-slice DWI for abdominal imaging in children with TSC, with near identical image quality as standard DWI. These results suggest that multi-slice techniques should be considered more broadly as an MRI acceleration technique in children.

Predictors of Anesthetic Exposure in Pediatric MRI.

BACKGROUND. 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. OBJECTIVE. 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. METHODS. Electronic health records were retrospectively searched to identify MRI examinations performed from 2016 to 2019 in patients 0-18 years old who received propofol anesthetic. Brain; brain and spine; brain and abdomen; and brain, head, and neck MRI examinations were included. Demographic, clinical, and imaging data were extracted for each examination, including anesthesia maintenance phase time, MRI scan time, and normalized propofol dose. MRI scan time and propofol dose were compared between groups using a t test. A multiple linear regression with backward selection (threshold, p < .05) was used to evaluate MRI scan time as a predictor of total propofol dose, adjusting for sex, age, time between scan and study end, body part, American Society of Anesthesiologists (ASA) classification, diagnosis, magnet strength, and IV contrast medium administration as covariates. RESULTS. A total of 501 examinations performed in 426 patients (172 girls, 254 boys; mean age, 6.55 ± 4.59 [SD] years) were included. Single body part examinations were shorter than multiple body part examinations (mean, 52.7 ± 18.4 vs 89.3 ± 26.4 minutes) and required less propofol (mean, 17.7 ± 5.7 vs 26.1 ± 7.7 mg/kg; all p < .001). Among single body part examinations, a higher ASA classification, oncologic diagnosis, 1.5-T magnet, and IV contrast medium administration were associated with longer MRI scan times (all p ≤ .009) and higher propofol exposure (all p ≤ .005). In multivariable analysis, greater propofol exposure was predicted by MRI scan time (mean dose per minute of examination, 0.178 mg/kg; 95% CI, 0.155-0.200; p < .001), multiple body part examination (p = .04), and IV contrast medium administration (p = .048); lower exposure was predicted by 3-T magnet (p = .04). CONCLUSION. Anesthetic exposure during pediatric MRI can be quantified and predicted based on imaging and clinical variables. CLINICAL IMPACT. This study serves as a valuable baseline for future efforts to reduce anesthetic doses and scan times in pediatric MRI.

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). Imaging optimization is a challenge because a change to improve one scan parameter often leads to worsening of another. Moreover, the recent introduction of fetal MRI on 3-tesla (T) scanners to achieve better SNR can amplify some technical issues. Motion, banding artifacts and aliasing artifacts impact the quality of fetal acquisitions at any field strength. High specific absorption rate (SAR) and artifacts from inhomogeneities in the radiofrequency field are important limitations of high-field-strength imaging. We discuss technical barriers that impact image quality and are important limitations to prenatal MRI diagnosis, and propose solutions to improve image quality and reduce artifacts.

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. With an approved protocol, fetal MRI exams were performed on healthy pregnant women at 3 Tesla and included multiple (2-8) diffusion scans of the fetal head (1-2 b = 0 s/mm2 images and 12 diffusion-sensitized images at b = 500 s/mm2 ). Diffusion data from 32 fetuses (13 females) with median gestational age (GA) of 33 weeks 4 days were processed with MT-SVR and deterministic tractography seeded by regions of interest corresponding to 12 major fiber tracts. Multivariable regression analysis was used to evaluate the association of GA with volume, FA, and ADC for each tract. For all tracts, the volume and FA increased, and the ADC decreased with GA. Associations reached statistical significance for: FA and ADC of the forceps major; volume and ADC for the forceps minor; FA, ADC, and volume for the cingulum; ADC, FA, and volume for the uncinate fasciculi; ADC of the inferior fronto-occipital fasciculi, ADC of the inferior longitudinal fasciculi; and FA and ADC for the corticospinal tracts. These quantitative results demonstrate the complex pattern and rates of tract-specific, GA-related microstructural changes of the developing white matter in human fetal brain.