Automated detection of white matter signal abnormality using T2 relaxometry: application to brain segmentation on term MRI in very preterm infants

Duration of mechanical ventilation is more critical for brain growth than postnatal hydrocortisone in extremely preterm infants

Hydrocortisone is used in preterm infants. However, early disruption of growth velocities was observed in infants exposed to hydrocortisone. This retrospective study aimed to explore the postnatal brain growth of extremely preterm infants requiring hydrocortisone treatment as well as its association with perinatal factors. Extremely preterm infants exposed to hydrocortisone from 2011 to 2016 who survived up to 12 months were included. Each of them was matched with two infants not treated with hydrocortisone exhibiting similar gestational ages and nearly similar birth head circumferences. The outcome variables were brain tissue areas on MRIs performed at term-equivalent age and postnatal head circumference growth up to a corrected age of 12 months. Univariate and multiple regression analyses were performed. Infants treated with hydrocortisone (n=20) were matched with 40 infants not exposed to hydrocortisone. The infants exposed to hydrocortisone exhibited a lower birth weight (p=0.04) and a longer duration of mechanical ventilation (p<0.0001). Infants treated with hydrocortisone exhibited a smaller basal ganglia/thalamus area (p=0.04) at term-equivalent age and a smaller head circumference at a corrected age of 12 months (p=0.003). However, the basal ganglia/thalamus area and the postnatal brain growth were independently associated with the duration of mechanical ventilation and not with hydrocortisone. Interestingly, a significant interaction between hydrocortisone and sex was observed (p=0.04).Conclusion: This study supports previous data that indicated no obvious impact of hydrocortisone on brain growth and highlights the relationship between the severity of the neonatal course and postnatal brain growth in extremely preterm infants. What is Known: • Postnatal hydrocortisone disrupts transiently growth velocities including the head circumference growth. • Postnatal hydrocortisone has less impact on neurodevelopment than dexamethasone. What is New: • Hydrocortisone prescribed for infants in the most severe conditions did not show independent effect on brain growth up to the corrected age of 12 months. However, a different effect of hydrocortisone according to sex can't be excluded and needs further explorations. • Perinatal factors as birth weight and duration of mechanical ventilation were determinant for the subsequent brain growth.

Perinatal Risk and Protective Factors in the Development of Diffuse White Matter Abnormality on Term-Equivalent Age Magnetic Resonance Imaging in Infants Born Very Preterm

OBJECTIVE

To identify perinatal clinical diseases and treatments that are associated with the development of objectively diagnosed diffuse white matter abnormality (DWMA) on structural magnetic resonance imaging (MRI) at term-equivalent age in infants born very preterm.

STUDY DESIGN

A prospective cohort of 392 infants born very preterm (≤32 weeks of gestational age) was enrolled from 5 level III/IV neonatal intensive care units between September 2016 and November 2019. MRIs of the brain were collected at 39 to 45 weeks of postmenstrual age to evaluate DWMA volume. A predefined list of pertinent maternal characteristics, pregnancy/delivery data, and neonatal intensive care unit data were collected for enrolled patients to identify antecedents of objectively diagnosed DWMA.

RESULTS

Of the 392 infants in the cohort, 377 (96%) had high-quality MRI data. Their mean (SD) gestational age was 29.3 (2.5) weeks. In multivariable linear regression analyses, pneumothorax (P = .027), severe bronchopulmonary dysplasia (BPD) (P = .009), severe retinopathy of prematurity (P < .001), and male sex (P = .041) were associated with increasing volume of DWMA. The following factors were associated with decreased risk of DWMA: postnatal dexamethasone therapy for severe BPD (P = .004), duration of caffeine therapy for severe BPD (P = .009), and exclusive maternal milk diet at neonatal intensive care unit discharge (P = .049).

CONCLUSIONS

Severe retinopathy of prematurity and BPD exhibited the strongest adverse association with development of DWMA. We also identified treatments and nutritional factors that appear protective against the development of DWMA that also have implications for the clinical care of infants born very preterm.

Antecedents of Objectively Diagnosed Diffuse White Matter Abnormality in Very Preterm Infants

BACKGROUND

Diffuse white matter abnormality (diffuse excessive high signal intensity) is the most common finding on structural brain magnetic resonance imaging (MRI) at term-equivalent age in very preterm infants. Yet, there remains a large gap in our understanding of the etiology of diffuse white matter abnormality. Our objective was to evaluate perinatal and neonatal inflammation-associated antecedents of diffuse white matter abnormality on MRI.

METHODS

We prospectively enrolled 110 very preterm infants born at ≤31 weeks gestational age and collected data on multiple perinatal/neonatal exposures, especially inflammation initiating-illnesses. We performed structural MRI at term-equivalent age and quantified the volume of diffuse white matter abnormality objectively. Multivariable regression was used to identify clinical antecedents of diffuse white matter abnormality.

RESULTS

The mean (S.D.) birth gestational age of the final study sample of 98 very preterm infants was 28.3 (2.5) weeks. Multiple inflammation initiating-illnesses were associated with diffuse white matter abnormality in univariate analyses. In multivariable linear regression analyses controlling for gestational age, severe retinopathy of prematurity (P < 0.001) and bronchopulmonary dysplasia (P = 0.006) were independent risk factors, whereas maternal treatment with 17-hydroxyprogesterone (P < 0.001) was protective of later development of objectively quantified diffuse white matter abnormality.

CONCLUSIONS

We identified several perinatal and neonatal antecedent clinical factors associated with diffuse white matter abnormality. Although we found some support for inflammation as a common underlying mechanism, larger studies are needed to validate inflammation as a potential common pathway to the development of diffuse white matter abnormality in very preterm infants.

Objectively Diagnosed Diffuse White Matter Abnormality at Term Is an Independent Predictor of Cognitive and Language Outcomes in Infants Born Very Preterm

OBJECTIVE

To externally validate the independent value of objectively diagnosed diffuse white matter abnormality (DWMA; also known as diffuse excessive high signal intensity) volume to predict neurodevelopmental outcomes in very preterm infants (≤31 weeks of gestational age).

STUDY DESIGN

A prospective, multicenter, regional population-based cohort study in 98 very preterm infants without severe brain injury on magnetic resonance imaging (MRI). DWMA volume was diagnosed objectively on structural MRI at term-equivalent age using our published algorithm. Multivariable linear regression was used to assess the value of DWMA volume to predict cognitive and language scores on the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) at 2 years corrected age.

RESULTS

Of the infants who returned for follow-up (n = 74), the mean (SD) gestational age was 28.2 (2.4) weeks, and 42 (56.8%) were boys. In bivariable analyses, DWMA volume was a significant predictor of Bayley-III cognitive and language scores. In multivariable analyses, controlling for known predictors of Bayley-III scores (ie, socioeconomic status, gestational age, sex, and global brain abnormality score), DWMA volume remained a significant predictor of cognitive (P < .001) and language (P = .04) scores at 2 years. When dichotomized, objectively diagnosed severe DWMA was a significant predictor of cognitive and language impairments, whereas visual qualitative diagnosis of DWMA was a poor predictor.

CONCLUSIONS

In this multicenter, prospective cohort study, we externally validated our previous findings that objectively diagnosed DWMA is an independent predictor of cognitive and language development in very preterm infants. We also demonstrated again that visually-diagnosed DWMA is not predictive of neurodevelopmental outcomes.

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain development. However, computing such segmentations is very challenging, especially for 6-month infant brain, due to the poor image quality, among other difficulties inherent to infant brain MRI, e.g., the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes. This study investigates the problem with an ensemble of semi-dense fully convolutional neural networks (CNNs), which employs T1-weighted and T2-weighted MR images as input. We demonstrate that the ensemble agreement is highly correlated with the segmentation errors. Therefore, our method provides measures that can guide local user corrections. To the best of our knowledge, this work is the first ensemble of 3D CNNs for suggesting annotations within images. Our quasi-dense architecture allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net (Çiçek, et al.). We also investigated the impact that early or late fusions of multiple image modalities might have on the performances of deep architectures. We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.

Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks

Diffuse white matter abnormality (DWMA), or diffuse excessive high signal intensity is observed in 50-80% of very preterm infants at term-equivalent age. It is subjectively defined as higher than normal signal intensity in periventricular and subcortical white matter in comparison to normal unmyelinated white matter on T2-weighted MRI images. Despite the well-documented presence of DWMA, it remains debatable whether DWMA represents pathological tissue injury or a transient developmental phenomenon. Manual tracing of DWMA exhibits poor reliability and reproducibility and unduly increases image processing time. Thus, objective and ideally automatic assessment is critical to accurately elucidate the biologic nature of DWMA. We propose a deep learning approach to automatically identify DWMA regions on T2-weighted MRI images. Specifically, we formulated DWMA detection as an image voxel classification task; that is, the voxels on T2-weighted images are treated as samples and exclusively assigned as DWMA or normal white matter voxel classes. To utilize the spatial information of individual voxels, small image patches centered on the given voxels are retrieved. A deep convolutional neural networks (CNN) model was developed to differentiate DWMA and normal voxels. We tested our deep CNN in multiple validation experiments. First, we examined DWMA detection accuracy of our CNN model using computer simulations. This was followed by in vivo assessments in a cohort of very preterm infants (N = 95) using cross-validation and holdout validation. Finally, we tested our approach on an independent preterm cohort (N = 28) to externally validate our model. Our deep CNN model achieved Dice similarity index values ranging from 0.85 to 0.99 for DWMA detection in the aforementioned validation experiments. Our proposed deep CNN model exhibited significantly better performance than other popular machine learning models. We present an objective and automated approach for accurately identifying DWMA that may facilitate the clinical diagnosis of DWMA in very preterm infants.

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

OBJECTIVE

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.

STUDY DESIGN

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.

RESULTS

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.

CONCLUSIONS

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.

Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI

BACKGROUND

Sequence optimization in neonates might improve detection sensitivity of abnormalities for a variety of conditions. However this has been historically challenging because tissue properties such as the longitudinal relaxation time and proton density differ significantly between neonates and adults.

OBJECTIVE

To optimize the magnetization-prepared rapid gradient echo (MP-RAGE) sequence to enhance both signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) efficiencies.

MATERIALS AND METHODS

We optimized neonatal MP-RAGE sequence through (1) reducing receive bandwidth to decrease noise, (2) shortening acquisition train length (acquisition number per repetition time or total number of read-out radiofrequency rephrasing pulses) using slice partial Fourier acquisition and (3) simulating the solution of Bloch's equation under optimal receive bandwidth and acquisition train length. Using the optimized sequence parameters, we scanned 12 healthy full-term infants within 2 weeks of birth and four preterm infants at 40 weeks' corrected age.

RESULTS

Compared with a previously published neonatal protocol, we were able to reduce the total scan time by reduce the total scan time by 60% and increase the average SNR efficiency by 160% (P<0.001) and the average CNR efficiency by 26% (P=0.029).

CONCLUSION

Our in vivo neonatal brain imaging experiments confirmed that both SNR and CNR efficiencies significantly increased with our proposed protocol. Our proposed optimization methodology could be readily extended to other populations (e.g., older children, adults), as well as different organ systems, field strengths and MR sequences.

T2 Relaxometry MRI Predicts Cerebral Palsy in Preterm Infants

BACKGROUND AND PURPOSE

T2-relaxometry brain MR imaging enables objective measurement of brain maturation based on the water-macromolecule ratio in white matter, but the outcome correlation is not established in preterm infants. Our study aimed to predict neurodevelopment with T2-relaxation values of brain MR imaging among preterm infants.

MATERIALS AND METHODS

From January 1, 2012, to May 31, 2015, preterm infants who underwent both T2-relaxometry brain MR imaging and neurodevelopmental follow-up were retrospectively reviewed. T2-relaxation values were measured over the periventricular white matter, including sections through the frontal horns, midbody of the lateral ventricles, and centrum semiovale. Periventricular T2 relaxometry in relation to corrected age was analyzed with restricted cubic spline regression. Prediction of cerebral palsy was examined with the receiver operating characteristic curve.

RESULTS

Thirty-eight preterm infants were enrolled for analysis. Twenty patients (52.6%) had neurodevelopmental abnormalities, including 8 (21%) with developmental delay without cerebral palsy and 12 (31.6%) with cerebral palsy. The periventricular T2-relaxation values in relation to age were curvilinear in preterm infants with normal development, linear in those with developmental delay without cerebral palsy, and flat in those with cerebral palsy. When MR imaging was performed at >1 month corrected age, cerebral palsy could be predicted with T2 relaxometry of the periventricular white matter on sections through the midbody of the lateral ventricles (area under the receiver operating characteristic curve = 0.738; cutoff value of >217.4 with 63.6% sensitivity and 100.0% specificity).

CONCLUSIONS

T2-relaxometry brain MR imaging could provide prognostic prediction of neurodevelopmental outcomes in premature infants. Age-dependent and area-selective interpretation in preterm brains should be emphasized.

Neuropathology Associated With Diffuse Excessive High Signal Intensity Abnormalities on Magnetic Resonance Imaging in Very Preterm Infants

BACKGROUND

Diffuse excessive high signal intensity abnormality is the most common finding on term-equivalent age magnetic resonance imaging in extremely preterm infants. Yet its clinical significance remains a matter of debate, in part because of a lack of prior imaging-pathology correlational studies.

PATIENT PRESENTATIONS

We present two 24-week-gestation infants with complicated clinical courses who died at 33 and 46 weeks postmenstrual age with magnetic resonance imaging evidence of diffuse excessive high signal intensity. Two patients with periventricular leukomalacia and two without injury were examined for comparison. Immunohistochemistry characterized the presence of reactive astrocytes, microglia, myelin, and axons. Infants with periventricular leukomalacia demonstrated the typical microscopic necrosis with spheroids, gliosis/microgliosis with reduction in stainable myelin and axons. Infants with diffuse excessive high signal intensity showed vacuolated regions with increased reactive astrocytes and microglia and fewer oligodendroglial cell bodies/processes and dramatic reduction in axon number.

CONCLUSION

These two individuals with diffuse excessive high signal intensity exhibited pathologic characteristics that were overlapping but distinct from those of periventricular leukomalacia.

Deep and Structured Robust Information Theoretic Learning for Image Analysis

This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e., missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general paradigm, we, respectively, discuss three types of the RIT implementations with linear subspace embedding, deep transformation, and structured sparse learning. In practice, the RIT and deep RIT are exploited to solve the image categorization task whose performances will be verified on various benchmark data sets. The structured sparse RIT is further applied to a medical image analysis task for brain magnetic resonance image segmentation that allows group-level feature selections on the brain tissues.

Differentiating T2 hyperintensity in neonatal white matter by two-compartment model of diffusional kurtosis imaging

In conventional neonatal MRI, the T2 hyperintensity (T2h) in cerebral white matter (WM) at term-equivalent age due to immaturity or impairment is still difficult to identify. To clarify such issue, this study used the metrics derived from a two-compartment WM model of diffusional kurtosis imaging (WM-DKI), including intra-axonal, extra-axonal axial and radial diffusivities (D_a, D_e,// and D_e,⊥), to compare WM differences between the simple T2h and normal control for both preterm and full-term neonates, and between simple T2h and complex T2h with hypoxic-ischemic encephalopathy (HIE). Results indicated that compared with control, the simple T2h showed significantly increased D_e,// and D_e,⊥, but no significant change in D_a in multiple premyelination regions, indicative of expanding extra-axonal diffusion microenvironment; while myelinated regions showed no changes. However, compared with simple T2h, the complex T2h with HIE had decreased D_a, increased D_e,⊥ in both premyelination and myelinated regions, indicative of both intra- and extra-axonal diffusion alterations. While diffusion tensor imaging (DTI) failed to distinguish simple T2h from complex T2h with HIE. In conclusion, superior to DTI-metrics, WM-DKI metrics showed more specificity for WM microstructural changes to distinguish simple T2h from complex T2h with HIE.

Longitudinal development in the preterm thalamus and posterior white matter: MRI correlations between diffusion weighted imaging and T2 relaxometry

Infants born prematurely are at increased risk of adverse neurodevelopmental outcome. The measurement of white matter tissue composition and structure can help predict functional performance. Specifically, measurements of myelination and indicators of myelination status in the preterm brain could be predictive of later neurological outcome. Quantitative imaging of myelin could thus serve to develop biomarkers for prognosis or therapeutic intervention; however, accurate estimation of myelin content is difficult. This work combines diffusion MRI and multi-component T2 relaxation measurements in a group of 37 infants born very preterm and scanned between 27 and 58 weeks equivalent gestational age. Seven infants have longitudinal data at two time points that we analyze in detail. Our aim is to show that measurement of the myelin water fraction is achievable using widely available pulse sequences and state-of-the-art algorithmic modeling of the MR imaging procedure and that a multi-component fitting routine to multi-shell diffusion weighted data can show differences in neurite density and local spatial arrangement in grey and white matter. Inference on the myelin water fraction allows us to demonstrate that the change in diffusion properties of the preterm thalamus is not solely due to myelination (that increase in myelin content accounts for about a third of the observed changes) whilst the decrease in the posterior white matter T2 has no significant component that is due to myelin water content. This work applies multi-modal advanced quantitative neuroimaging to investigate changing tissue properties in the longitudinal setting. Hum Brain Mapp 37:2479-2492, 2016. © 2016 Wiley Periodicals, Inc.

Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI

Multivariate analysis (MVA) is a class of statistical and pattern recognition techniques that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neurological medical imaging related challenges including the evaluation of healthy brain development, the automated analysis of brain tissues and structures through image segmentation, evaluating the effects of genetic and environmental factors on brain development, evaluating sensory stimulation's relationship with functional brain activity and much more. Compared to adult imaging, pediatric, neonatal and fetal imaging have attracted less attention from MVA researchers, however, recent years have seen remarkable MVA research growth in pre-adult populations. This paper presents the results of a systematic review of the literature focusing on MVA applied to healthy subjects in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in brain MRI, the field is still young and significant research growth will continue into the future.

Aberrant Executive and Frontoparietal Functional Connectivity in Very Preterm Infants With Diffuse White Matter Abnormalities

BACKGROUND

Diffuse white matter abnormalities are identified in up to 80% of very preterm infants on magnetic resonance imaging at 40 weeks' postmenstrual age. Several studies have observed an association between diffuse white matter abnormalities and cognitive deficits. We hypothesized that very preterm infants (gestational age ≤32 weeks) with diffuse white matter abnormalities will exhibit reduced executive control and frontoparietal functional connectivity compared with infants without diffuse white matter abnormalities measured using resting state functional magnetic resonance imaging at term-equivalent age.

METHODS

We quantified diffuse white matter abnormality volume objectively using an automated segmentation approach and defined diffuse white matter abnormality severity as no-mild (volume ≤50th percentile; N = 13) and moderate-severe (N = 14). Resting state networks of interests were identified using probabilistic independent component analysis. Within network functional connectivity was calculated between the different pair of nodes in a given network using partial correlation coefficients.

RESULTS

We studied 27 very preterm infants born at a mean (standard deviation) gestational age of 26.9 (2.0) weeks and imaged at 39.6 (1.4) weeks' postmenstrual age. Within-network connectivity was significantly reduced in the moderate-severe diffuse white matter abnormalities group than in the no-mild diffuse white matter abnormalities group for the executive control (P < 0.001) and frontoparietal (P = 0.02) networks. As expected, connectivity in three control resting state networks was similar: visual (P = 0.17), motor (P = 0.89), and somatosensory (P = 0.69) networks.

CONCLUSIONS

Very preterm infants with moderate or severe diffuse white matter abnormalities exhibited reduced functional connectivity in important cognitive and attention networks. This aberrant connectivity may be the early life antecedent to the cognitive deficits reported at 2 years of age or later in such infants.

Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation

Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination processes. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6-8months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter.

Atlas-guided quantification of white matter signal abnormalities on term-equivalent age MRI in very preterm infants: findings predict language and cognitive development at two years of age

The developmental significance of the frequently encountered white matter signal abnormality (WMSA) findings on MRI around term-equivalent age (TEA) in very preterm infants, remains in question. The use of conventional qualitative analysis methods is subjective, lacks sufficient reliability for producing accurate and reproducible WMSA diagnosis, and possibly contributes to suboptimal neurodevelopmental outcome prediction. The advantages of quantitative over qualitative diagnostic approaches have been widely acknowledged and demonstrated. The purpose of this study is to objectively and accurately quantify WMSA on TEA T2-weighted MRI in very preterm infants and to assess whether such quantifications predict 2-year language and cognitive developmental outcomes. To this end, we constructed a probabilistic brain atlas, exclusively for very preterm infants to embed tissue distributions (i.e. to encode shapes, locations and geometrical proportion of anatomical structures). Guided with this atlas, we then developed a fully automated method for WMSA detection and quantification using T2-weighted images. Computer simulations and experiments using in vivo very preterm data showed very high detection accuracy. WMSA volume, particularly in the centrum semiovale, on TEA MRI was a significant predictor of standardized language and cognitive scores at 2 years of age. Independent validation of our automated WMSA detection algorithm and school age follow-up are important next steps.

Automatically quantified diffuse excessive high signal intensity on MRI predicts cognitive development in preterm infants

BACKGROUND

Cognitive and language impairments constitute the majority of disabilities observed in preterm infants. It remains unclear if diffuse excessive high signal intensity on magnetic resonance imaging at term represents delayed white matter maturation or pathology.

METHODS

We hypothesized that diffusion tensor imaging-based objectively quantified diffuse excessive high signal intensity measures at term will be strong predictors of cognitive and language development at 2 years in a cohort of 41 extremely low birth weight (≤1000 g) infants. Using an automated probabilistic atlas, mean diffusivity maps were used to objectively segment and quantify diffuse excessive high signal intensity volume and mean, axial, and radial diffusivity measures. Standardized neurodevelopment was assessed at 2 years of age using the Bayley Scales of Infant Development, third edition.

RESULTS

Thirty-six of the 41 infants (88%) had complete developmental data at follow-up. Objectively quantified diffuse excessive high signal intensity volume correlated significantly with cognitive and language scores at 2 years (P < 0.001 for both). The sum values of the three diffusivity measures in detected diffuse excessive high signal intensity regions also correlated significantly with the Bayley scores (r(2) 34.7%; P < 0.001 for each). Infants in the highest quartile for diffuse excessive high signal intensity volumes had scores between 19 and 24 points lower than infants in the lowest quartile (P < 0.01). When diagnosed subjectively by neuroradiologists however, Bayley scores were not significantly lower in infants with extensive diffuse excessive high signal intensity.

CONCLUSIONS

These findings lend further evidence that diffuse excessive high signal intensity is pathologic and that objectively quantified diffusion-based diffuse excessive high signal intensity volume at term is associated with cognitive and language impairments. Our approach could be used for risk stratification and early intervention for such high-risk extremely preterm infants.