Neonatal Magnetic Resonance Imaging Research Articles

Multi-atlas based neonatal brain extraction using atlas library clustering and local label fusion

Brain extraction is one of the most important preprocessing steps in cerebral magnetic resonance (MR) image analysis. Brain extraction from neonatal MR images is particularly challenging due to significant differences in head size and shape between neonates and rapid changes in neonatal brain structure in the weeks and months after birth. In this work, a multi-atlas-based neonatal brain extraction method using atlas library clustering and local label fusion (NOBELL) is presented. In NOBELL, an affinity propagation (AP) approach is first applied to cluster images of an atlas library into clusters represented by exemplars, which are used to select best matching clusters for target images. A local weighted voting strategy based on Jacobian determinant ranking is then employed to extract brain from target images using training images in best matching clusters. The performance of NOBELL was evaluated on T2- and T1-weighted scans of 40 neonates aged between 37 and 44 weeks. NOBELL outperformed two popular brain extraction tools, FSL’s Brain Extraction Tool (BET) and BrainSuite’s Brain Surface Extractor (BSE), and achieved higher accuracy with brain masks very close to manually extracted ones. NOBELL showed an average Jaccard coefficient of 0.974 (0.942) on T2 (T1)-weighted images in comparison with 0.908 (0.602) and 0.845 (0.762) achieved by BSE, and BET, respectively. NOBELL allows for accurate and efficient brain extraction, a crucial step in brain MRI applications such as accurate brain tissue segmentation and volume estimation as well as accurate cortical surface delineation in neonates.

Early alterations in cortical and cerebellar regional brain growth in Down Syndrome: An in vivo fetal and neonatal MRI assessment.

Down Syndrome (DS) is the most frequent genetic cause of intellectual disability with a wide spectrum of neurodevelopmental outcomes. At present, the relationship between structural brain morphology and the spectrum of cognitive phenotypes in DS, is not well understood. This study aimed to quantify the development of the fetal and neonatal brain in DS participants, with and without a congenital cardiac defect compared with a control population using dedicated, optimised and motion-corrected in vivo magnetic resonance imaging (MRI). We detected deviations in development and altered regional brain growth in the fetus with DS from 21 weeks' gestation, when compared to age-matched controls. Reduced cerebellar volume was apparent in the second trimester with significant alteration in cortical growth becoming evident during the third trimester. Developmental abnormalities in the cortex and cerebellum are likely substrates for later neurocognitive impairment, and ongoing studies will allow us to confirm the role of antenatal MRI as an early biomarker for subsequent cognitive ability in DS. In the era of rapidly developing technologies, we believe that the results of this study will assist counselling for prospective parents.

Invited Review: Factors associated with atypical brain development in preterm infants: insights from magnetic resonance imaging.

Preterm birth (PTB) is a leading cause of neurodevelopmental and neurocognitive impairment in childhood and is closely associated with psychiatric disease. The biological and environmental factors that confer risk and resilience for healthy brain development and long‐term outcome after PTB are uncertain, which presents challenges for risk stratification and for the discovery and evaluation of neuroprotective strategies. Neonatal magnetic resonance imaging reveals a signature of PTB that includes dysconnectivity of neural networks and atypical development of cortical and deep grey matter structures. Here we provide a brief review of perinatal factors that are associated with the MRI signature of PTB. We consider maternal and foetal factors including chorioamnionitis, foetal growth restriction, socioeconomic deprivation and prenatal alcohol, drug and stress exposures; and neonatal factors including co‐morbidities of PTB, nutrition, pain and medication during neonatal intensive care and variation conferred by the genome/epigenome. Association studies offer the first insights into pathways to adversity and resilience after PTB. Future challenges are to analyse quantitative brain MRI data with collateral biological and environmental data in study designs that support causal inference, and ultimately to use the output of such analyses to stratify infants for clinical trials of therapies designed to improve outcome.

Clinical analysis of eight cases of fetal intracranial hemorrhage in pregnancy

Objective To explore the clinical characteristics, treatment and prognosis of fetal intracranial hemorrhage in pregnancy and to improve the level of diagnosis and treatment. Methods We retrospectively analyzed the clinical data of eight cases of fetal intracranial hemorrhage in our hospital from 2014 to 2017, including the clinical manifestations, etiology, imaging features, treatment and prognosis. Results All the cases were diagnosed by prenatal color ultrasound or magnetic resonance imaging (MRI); one of the cases had decreased fetal movements and abnormal fetal heart rate monitoring, and the remaining seven cases had no special clinical symptoms. No clear cause was found in all the cases. Two patients with grade I fetal intracranial hemorrhage and 1 patient with grade II had a cesarean delivery, and no neurological sequelae were found in these neonates after 6 months of follow-up. There was one patient with grade III and four patients with grade IV fetal intracranial hemorrhage; one of the patients with grade IV was stillborn at the time of the discovery, and cesarean section was selected due to scarring of the uterus; intra-amniotic injection of ethacridine lactate was selected to induce labor in three cases, and vaginal delivery was selected; one of the patients with grade IV chose vaginal delivery, and the neonatal cranial brain magnetic resonance imaging after delivery showed no increase in intracranial lesions but showed incomplete development of the remaining nervous system. Conclusion Fetal intracranial hemorrhage can be diagnosed by prenatal color ultrasound and MRI, yet it is often impossible to determine the cause. The prognosis of fetal intracranial hemorrhage is related to grade, and the prognosis of cerebral hemorrhage in patients with grades III–IV is poor.

Neonatal hypoxic encephalopathy: Correlation between post-cooling brain MRI findings and 2 years neurodevelopmental outcome.

Objective:This study aims to evaluate the magnetic resonance imaging (MRI) brain patterns among hypoxic-ischemic encephalopathy (HIE) babies who underwent post-cooling MRI brain as well as to correlate the post-cooling brain scoring with patient's neurodevelopmental outcome at 2 years.Subjects and Methods:It was a retrospective cross sectional study carried out at a tertiary university hospital. Record of patients diagnosed with neonatal HIE from 2007 until 2016 who completed 72 h of cooling therapy and had MRI brain within 2 weeks of life were included in this study. A new scoring system by Trivedi et al. that emphasizes on subcortical deep gray matter and posterior limb internal capsule injury were utilized upon MRI assessment, using TW, T2W, and diffusion-weighted imaging (DWI) sequences. Cumulative MRI brain score was obtained and graded as none, mild, moderate, and severe brain injury. The MRI brain scoring was then correlated with patient's 2 years neurodevelopmental outcome using Fisher's Exact Test.Results:A total of 23 patients were eligible of which 19 term neonates were included. 13% of these neonates (n = 3) had mild MRI brain injury grading with 52.2% (n = 12) moderate and 34.8% (n = 8) severe. There was no significant correlation seen between MRI brain grading and developmental outcome at 2 years old (P > 0.05).Conclusion:There was no significant correlation between neonatal MRI brain injury grading and 2 years neurodevelopmental outcome. Nevertheless, the new MRI brain scoring by Trivedi et al. is reproducible and comprehensive as it involves various important brain structures, assessed from different MRI sequences.

A Novel Approach for Manual Segmentation of the Amygdala and Hippocampus in Neonate MRI.

The gross anatomy of the infant brain at term is fairly similar to that of the adult brain, but structures are immature, and the brain undergoes rapid growth during the first 2 years of life. Neonate magnetic resonance (MR) images have different contrasts compared to adult images, and automated segmentation of brain magnetic resonance imaging (MRI) can thus be considered challenging as less software options are available. Despite this, most anatomical regions are identifiable and thus amenable to manual segmentation. In the current study, we developed a protocol for segmenting the amygdala and hippocampus in T2-weighted neonatal MR images. The participants were 31 healthy infants between 2 and 5 weeks of age. Intra-rater reliability was measured in 12 randomly selected MR images, where 6 MR images were segmented at 1-month intervals between the delineations, and another 6 MR images at 6-month intervals. The protocol was also tested by two independent raters in 20 randomly selected T2-weighted images, and finally with T1 images. Intraclass correlation coefficient (ICC) and Dice similarity coefficient (DSC) for intra-rater, inter-rater, and T1 vs. T2 comparisons were computed. Moreover, manual segmentations were compared to automated segmentations performed by iBEAT toolbox in 10 T2-weighted MR images. The intra-rater reliability was high ICC ≥ 0.91, DSC ≥ 0.89, the inter-rater reliabilities were satisfactory ICC ≥ 0.90, DSC ≥ 0.75 for hippocampus and DSC ≥ 0.52 for amygdalae. Segmentations for T1 vs. T2-weighted images showed high consistency ICC ≥ 0.90, DSC ≥ 0.74. The manual and iBEAT segmentations showed no agreement, DSC ≥ 0.39. In conclusion, there is a clear need to improve and develop the procedures for automated segmentation of infant brain MR images.

Specific cognitive deficits in preschool age correlated with qualitative and quantitative MRI parameters in prematurely born children

Cognitive deficits after perinatal brain lesion in preterm infants are among the most common neurodevelopmental disturbances. The relationship between structural changes on at term magnetic resonance imaging (MRI) and cognitive deficits in the preschool age should be a special focus due to timely intervention. The aim of this study was to correlate qualitative and quantitative MRI parameters of perinatal brain lesion in preterm children, on early neonatal MRI and follow up MRI, with general and specific cognitive functions in the preschool age. Twenty-one preterm infants with verified perinatal lesions based on clinical and ultrasound data underwent a brain MRI at term-equivalent age and a second MRI between 3 and 5 years of age. Qualitative and quantitative MRI analyses were done. All subjects underwent cognitive assessment (3-5 years) using Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III) and Developmental Neuropsychological Assessment (NEPSY-II). Results show that many structural changes on at term MRI and on follow up MRI in preterm born children moderately correlate with specific cognitive deficits in preschool age. At term equivalent MRI, white matter changes and cortical thickness correlate to general and specific cognitive functions in infants born preterm. By analyzing follow up MRI at preschool age, structural changes of different white matter segments, corpus callosum, cortical thickness and lobe volume correlate to some specific cognitive functions. Besides general cognitive delay, specific cognitive deficits in preterm children should be targeted in research and intervention, optimally combined with MRI scanning, providing timely and early intervention of cognitive deficits after perinatal brain lesion. Our results, as well as previously published results, suggest the importance of detailed preschool neuropsychological assessment, prior to enrolment in the school system. Although preliminary, our results expand our understanding of the relationship between early brain developmental lesions and cognitive outcome following premature birth.

Multi-atlas based neonatal brain extraction using a two-level patch-based label fusion strategy

Compared to adults, brain extraction from magnetic resonance (MR) images of newborn infants is particularly challenging due to their smaller brain size, which causes lower spatial resolution, lower tissue contrast and ambiguous tissue intensity distribution. In this work, a multi-atlas patch-based label fusion method is presented for automatic brain extraction from neonatal head MR images. In this method, a number of atlases are first selected uniformly among a set of training images. After nonlinear alignment of the selected atlas images to the target image, a probabilistic gray level-coded brain mask is created and used to assign brain/non-brain labels to voxels with different degree of uncertainty using a modified non-local patch-based label fusion method based on the integration of low-level and in-depth search patch selection strategies. Experiments with 40 neonates aged between 37 and 44 weeks showed an average Dice, Jaccard and Conformity coefficients of 0.993, 0.986 and 0.986, respectively. We compared the performance of the proposed method with two multi atlas-based methods, i.e. NLPB and MASS, and two popular non-learning-based methods, i.e. BSE and BET. Compared to these methods, our method achieved higher accuracy with brain masks very close to manually extracted ones and produced lower false negative and false positive rates. Our proposed method allows for accurate and efficient brain extraction, a crucial step in brain MRI applications such as brain tissue segmentation and volume estimation in neonates.

Reduced structural brain asymmetry during neonatal life is potentially related to autism spectrum disorders in children born extremely preterm.

Disruption of the normal patterns of structural brain asymmetry, and in language-related areas, has been reported in individuals with autism spectrum disorder (ASD). We tested the hypothesis that 16 children born extremely preterm (EPT), and diagnosed with ASD at 6.5 years of age (EPT-ASD), would have different patterns of brain structural asymmetry, particularly in language-related areas, to 21 EPT children without ASD and 15 term-born children. They all underwent neonatal magnetic resonance imaging scans at 40 weeks of gestation. ASD diagnoses and the Weschler Intelligence Scale for Children-Fourth Edition, were performed in the EPT children, but not in the term group. Asymmetry indices (AIs) were assessed at three levels: global (hemispheres), lobar (brain lobes), and modular (primary sensorimotor, unimodal, and higher-order association areas). AIs were also assessed in language-related regions and correlational analyses were performed between these AIs and verbal scores. The EPT-ASD group showed reduced structural asymmetry at the modular level, mainly involving the higher-order association cortices and the language-related areas. Predominant positive correlations between language functioning and leftward AIs in the inferior frontal gyrus (opercular) and supplementary cortices, and rightward asymmetry in the angular and supramarginal gyri, were identified in the EPT-ASD group. The overall results suggest that reduced brain structural asymmetry identified during the neonatal period would be a risk factor for the development of ASD in EPT infants. This finding could identify EPT children at risk at an early stage, so that tailored interventions could be used to optimize their functions and quality of life. Autism Res 2019, 12: 1334-1343. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Babies born before the expected date (preterm) are more likely to develop autism, due to abnormal brain development. Compared with children without autism, preterm children with autism did not display the important physical differences between the two sides of their brain that are needed for normal functioning. As this alteration was found just after birth, this information could be used to identify children who are likely to develop autism, so that they can get medical support at an earlier age.

Utilization of neurosonography for evaluation of the corpus callosum malformations in the era of fetal magnetic resonance imaging.

We evaluated the ability of fetal neurosonography and magnetic resonance imaging (MRI) to asses callosal anomalies (CA) and associated cranial malformations. We also aimed to determine the long-term prognosis of the cases. Thirty-six cases of CA diagnosed combined with neurosonography and MRI between January 2012 and October 2017 were retrospectively reviewed. Seventeen of 36 fetuses were diagnosed complete agenesis of corpus callosum (CACC) (47.2%), 9 had partial agenesis of corpus callosum (PACC) (25%) and 10 was dysgenesis of the corpus callosum (DCC) (27.2%) at ultrasonography (US) examination. Fetal MRI reported 16 of cases as CACC (44.4%), 11 PACC (30.5%) and nine (25%) DCC. The overall consistency between neurosonography and MRI in the definition of CA were 91% of cases. Sulcation anomalies were present in 9 cases in the US (25%) and 11 cases in MRI (30.4%). Seven of cases showed posterior fossa abnormalities in the US (19.4%) and eight cases in MRI (22.1%). Neonatal MRI added new findings to fetal MRI and neurosonography including grade-1 intraventricular hemorrhage and periventricular leukomalacia in two cases (12.5%). Eighteen cases were terminated (50%), 17 cases were followed up and mean follow up interval was 39 ± 5.1 months. The neurologic outcome was abnormal in seven (41.7%) patients. Presence of associated brain anomalies worsened the prognosis. Fetal neurosonography has a comparable performance with MRI in the diagnosis of CA and associated anomalies. It should be used in collaboration with MRI to achieve accurate diagnosis which is crucial for counseling.

Accuracy of prenatal ultrasound in the diagnosis of corpus callosum anomalies

Objectives: The main objective of this study was to evaluate the accuracy of prenatal ultrasound to diagnose corpus callosum alterations, compared to prenatal magnetic resonance imaging (MRI), postnatal image techniques (ultrasound and/or MRI), and post-mortem examination in terminated pregnancies.Methods: Retrospective review of 86 cases of prenatal ultrasound diagnosis of corpus callosum anomalies between January 2007 and December 2015 at a third level Maternal Fetal Medicine center. The study reviewed the findings of prenatal ultrasound and MRI, post-mortem examination in cases of termination of pregnancy (TOP) or stillbirths and postnatal ultrasound, and MRI in neonates. The anomalies of corpus callosum (CC) were classified as complete agenesis of the corpus callosum (ACC), partial ACC, or dysgenesis of CC.Results: Fifty-eight (67.4%) cases resulted in TOP, 26 (30.2%) cases opted to continue with the pregnancy and two (2.3%) cases were lost to follow up. Among the 26 cases that continued with the pregnancy, 24 (92.3%) were live births and two (7.7%) were stillborn. All cases in which a third trimester MRI was performed (n = 46) confirmed the prenatal ultrasound diagnosis of CC anomaly. In seven (15.2%) of them, the MRI found additional intracranial findings and in three cases (6.5%) the type of CC anomaly (complete, partial, or dysgenesis) was reclassified (Kappa index: 0.86, 95% CI: 0.71–1.00). CC anomalies were confirmed in 46 (95.8%) of the 48 cases in which a post-mortem examination was available, the type of anomaly being reclassified in three cases (6.3%) (Kappa index: 0.88, 95% CI: 0.75–1.00). Among the 10 cases in which a postnatal ultrasound was performed, the CC anomaly was confirmed in all and the type of anomaly was reclassified in 1 (10%) of them (Kappa index: 0.75, 95% CI: 0.32–1.00).Conclusion: Corpus callosum agenesis can be detected on the routine mid-trimester ultrasound scan. Prenatal ultrasound and MRI can accurately classify the type of CC abnormality. Moreover, third trimester MRI can detect additional intracranial anomalies in 15% of cases.

A Multi-Atlas Label Fusion Tool for Neonatal Brain MRI Parcellation and Quantification.

Structure-by-structure analysis, in which the brain magnetic resonance imaging (MRI) is parcellated based on its anatomical units, is widely used to investigate chronological changes in morphology or signal intensity during normal development, as well as to identify the alterations seen in various diseases or conditions. The multi-atlas label fusion (MALF) method is considered a highly accurate parcellation approach, and anticipated for clinical application to quantitatively evaluate early developmental processes. However, the current MALF methods, which are designed for neonatal brain segmentations, are not widely available. In this study, we developed a T1-weighted, neonatal, multi-atlas repository and integrated it into the MALF-based brain segmentation tools in the cloud-based platform, MRICloud. The cloud platform ensures users instant access to the advanced MALF tool for neonatal brains, with no software or installation requirements for the client. The Web platform by braingps.mricloud.org will eliminate the dependence on a particular operating system (eg, Windows, Macintosh, or Linux) and the requirement for high computational performance of the user's computers. The MALF-based, fully automated, image parcellation could achieve excellent agreement with manual parcellation, and the whole and regional brain volumes quantified through this method demonstrated developmental trajectories comparable to those from a previous publication. This solution will make the latest MALF tools readily available to users, with minimum barriers, and will expedite and accelerate advancements in developmental neuroscience research, neonatology, and pediatric neuroradiology.

White Matter Lesions Detected by Magnetic Resonance Imaging in Neonates and Children With Congenital Myotonic Dystrophy

BackgroundCongenital myotonic dystrophy (CDM1) is an autosomal dominant genetic disorder caused by abnormal cytosine-thymine-guanine trinucleotide repeat expansion that results in weakness and cognitive deficits. Studies detailing brain magnetic resonance imaging (MRI) findings in neonates and children with this condition are limited. ObjectiveWe evaluated the brain MRI findings in children, including neonates with CDM1, to assess the nature of central nervous system involvement and progression of MRI lesions over time. MethodsThe Cincinnati Children's Hospital neuromuscular disease database was used to identify 16 patients with CDM1 with genetically proven CDM1 who had undergone brain MRI. Hospital charts were reviewed to collect clinical information. ResultsNinety-four percent of patients had an abnormal MRI showing injury to the white matter. Nine patients underwent imaging before eight days of life, and eight of these patients showed signs of injury to the white matter. Three neonates had follow-up MRI scans, and all showed progression of injury. Seven patients had the first MRI between age 29 days and 22 years, and all had abnormalities involving the white matter. Two patients had additional congenital brain malformations, and one patient also harbored a mutation in CDKL5 with resultant epilepsy. ConclusionsWhite matter abnormalities are found in patients with CDM1, even in the neonatal period. Many patients present with hypoxia and receive a diagnosis of hypoxic-ischemic encephalopathy and may even undergo therapeutic hypothermia. If MRI findings of white matter injury do not correlate with hypotonia and weakness, further evaluation for CDM1 should be considered.

Neonatal brain abnormalities and brain volumes associated with goal setting outcomes in very preterm 13-year-olds.

Executive dysfunction including impaired goal setting (i.e., planning, organization skills, strategic reasoning) is documented in children born very preterm (VP; <30weeks/<1250g), however the neurological basis for this impairment is unknown. This study sought to examine the relationship between brain abnormalities and brain volumes on neonatal magnetic resonance imaging (MRI) and goal setting abilities of VP 13-year-olds. Participants were 159 children born VP in a prospective longitudinal study. Qualitative brain abnormality scores and quantitative brain volumes were derived from neonatal MRI brain scans (40weeks' gestational age ± 2weeks). Goal setting at 13years was assessed using the Delis-Kaplan Executive Function Systems Tower Test, the Rey Complex Figure, and the Behavioural Assessment of the Dysexecutive System for Children Zoo Map and Six Part Test. A composite score was generated denoting overall performance on these goal setting measures. Separate regression models examined the association of neonatal brain abnormality scores and brain volumes with goal setting performance. There was evidence that higher neonatal white matter, deep grey matter and cerebellum abnormality scores were associated with poorer goal setting scores at 13years. There was also evidence of positive associations between total brain volume, cerebellum, thalamic and cortical grey matter volumes and goal setting performance. Evidence for the associations largely persisted after controlling for potential confounders. Neonatal brain abnormality and brain volumes are associated with goal setting outcome in VP 13-year-olds. Used in conjunction with other clinical indicators, neonatal MRI may help to identify VP children at risk for later executive dysfunction.

An Efficient Tissue Segmentation of Neonatal Brain Magnetic Resonance Imaging

The growth of brain can be assessed by using MRI. The MRI images of the neonatal brain have a much lower Contrast-to-Noise Ratio (CNR), frequently have lower signal-to-noise ratio due to the small size of the neonatal brain and vary enormously in terms of brain shape and appearance as a result of rapid brain development during this period. In addition, the partial volume effect present due to the inverted signal intensity in White Matter (WM) presents an obstacle for tissue classi?cation. The Manual segmentation of the abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. This system provides a framework for accurate intensity based segmentation of newborn brain using region growing algorithm.

Fetal Cerebellar Vermis Assessment by MRI. What Does It Add?

Background: Posterior fossa anomalies (PFAs) are considered one of the commonest brain anomalies identified by the fetal imaging techniques. The current study aims to estimate the significance of cerebellar vermis assessment by fetal MRI in fetal brain malformations. Patients and Methods: The study was conducted at a tertiary University hospital in Upper Egypt. Data were gathered prospectively from sixty pregnant women with sonographically diagnosed fetal brain malformations referred for Magnetic Resonance Imaging (MRI) between April 2015 and May 2019 followed by postpartum neonatal MRI brain examination for non-terminated and live birth cases. Results: Of the sixty fetuses with brain malformations, additional cerebellar vermis findings were seen on MRI in 10 cases (16.6%). Fetal MRI sensitivity, specificity, positive and negative predictive values were 100% that were in correlation with those of prenatal ultrasound turned out to be significantly higher. Conclusion: This study recommends fetal MRI for cerebellar vermis assessment in fetal brain malformations to guide the clinical management.

Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets.

Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated identification tools. Clinical trials involving motion‐prone neonates typically pool data to obtain sufficient power, and automated quality control protocols are especially important to safeguard data quality. Current study tested an open source method to detect major artifacts among 2D neonatal MRI via supervised machine learning. A total of 1,020 two‐dimensional transverse T2‐weighted MRI images of preterm newborns were examined and classified as either QC Pass or QC Fail. Then 70 features across focus, texture, noise, and natural scene statistics categories were extracted from each image. Several different classifiers were trained and their performance was compared with subjective rating as the gold standard. We repeated the rating process again to examine the stability of the rating and classification. When tested via 10‐fold cross validation, the random undersampling and adaboost ensemble (RUSBoost) method achieved the best overall performance for QC Fail images with 85% positive predictive value along with 75% sensitivity. Similar classification performance was observed in the analyses of the repeated subjective rating. Current results served as a proof of concept for predicting images that fail quality control using no‐reference objective image features. We also highlighted the importance of evaluating results beyond mere accuracy as a performance measure for machine learning in imbalanced group settings due to larger proportion of QC Pass quality images.

Neonatal Pulmonary Magnetic Resonance Imaging of Bronchopulmonary Dysplasia Predicts Short-Term Clinical Outcomes.

Bronchopulmonary dysplasia (BPD) is a serious neonatal pulmonary condition associated with premature birth, but the underlying parenchymal disease and trajectory are poorly characterized. The current National Institute of Child Health and Human Development (NICHD)/NHLBI definition of BPD severity is based on degree of prematurity and extent of oxygen requirement. However, no clear link exists between initial diagnosis and clinical outcomes. We hypothesized that magnetic resonance imaging (MRI) of structural parenchymal abnormalities will correlate with NICHD-defined BPD disease severity and predict short-term respiratory outcomes. A total of 42 neonates (20 severe BPD, 6 moderate, 7 mild, 9 non-BPD control subjects; 40 ± 3-wk postmenstrual age) underwent quiet-breathing structural pulmonary MRI (ultrashort echo time and gradient echo) in a neonatal ICU-sited, neonatal-sized 1.5 T scanner, without sedation or respiratory support unless already clinically prescribed. Disease severity was scored independently by two radiologists. Mean scores were compared with clinical severity and short-term respiratory outcomes. Outcomes were predicted using univariate and multivariable models, including clinical data and scores. MRI scores significantly correlated with severities and predicted respiratory support at neonatal ICU discharge (P < 0.0001). In multivariable models, MRI scores were by far the strongest predictor of respiratory support duration over clinical data, including birth weight and gestational age. Notably, NICHD severity level was not predictive of discharge support. Quiet-breathing neonatal pulmonary MRI can independently assess structural abnormalities of BPD, describe disease severity, and predict short-term outcomes more accurately than any individual standard clinical measure. Importantly, this nonionizing technique can be implemented to phenotype disease, and has potential to serially assess efficacy of individualized therapies.

Thirteen-Year Outcomes in Very Preterm Children Associated with Diffuse Excessive High Signal Intensity on Neonatal Magnetic Resonance Imaging

To investigate the association between white matter diffuse excessive high signal intensity (DEHSI) on neonatal magnetic resonance imaging in very preterm infants and neurobehavioral outcomes at the age of 13 years. Magnetic resonance images of very preterm children (<30 weeks gestational age or <1250 g birth weight) were evaluated at term-equivalent age with DEHSI classified into 5 grades. Additionally, visibility of the posterior periventricular crossroads was assessed. General intelligence, memory, attention, executive function, motor abilities, and behavior were examined in 125 children at age 13 years and related to DEHSI grades using linear regression. DEHSI was detected in 93% of infants; 21% grade 1, 22% grade 2, 32% grade 3, and 18% grade 4. Neurobehavioral outcomes were similar for all DEHSI groups. There was weak evidence that higher DEHSI grades related to higher verbal IQ and attention and that lower DEHSI grades related to better planning ability. Adjustment for gestational age, birth weight standard score, and sex further weakened these effects. Only 12 children had invisible posterior crossroads and showed slightly poorer outcomes at 13 years of age. There was little evidence that neonatal DEHSI serves as a sensitive biomarker for later impairment. Further investigation on the importance of invisible posterior periventricular crossroads in larger samples is needed.

An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI

Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand. The method proposed in this article relies on such a multi-atlas strategy. More precisely, it uses two paradigms: first, a non-local model based on patches; second, an iterative optimization scheme. Coupling both concepts allows us to consider patches related not only to the image information, but also to the current segmentation. This strategy is compared to other multi-atlas methods proposed in the literature. Experiments on dHCP datasets show that the proposed approach provides robust cortex segmentation results.