Acquisition guidelines and quality assessment tools for analyzing neonatal diffusion tensor MRI data

Brain Development From Newborn to Adolescence: Evaluation by Neurite Orientation Dispersion and Density Imaging

Neurite orientation dispersion and density imaging (NODDI) is a diffusion model specifically designed for brain magnetic resonance imaging. Despite recent studies suggesting that NODDI modeling might be more sensitive to brain development than diffusion tensor imaging (DTI), these studies were limited to a relatively small age range and mainly based on the manually operated region of interest analysis. Therefore, this study applied NODDI to investigate brain development in a large sample size of 214 subjects ranging in ages from 0 to 14. The whole brain was automatically segmented into 122 regions. The maturation trajectory of each region was characterized by the time course of diffusion metrics and further quantified using nonlinear regression. The NODDI-derived metrics, neurite density index (NDI) and orientation dispersion index (ODI), increased with age. And these two metrics were superior to the DTI-derived metrics in SVM regression models of age. The NDI in white matter exhibited a more rapid growth than that in gray matter (including the cortex and deep nucleus). These diffusion indicators experienced conspicuous increases during early childhood and the growth speed slowed down in adolescence. Region-specific maturation patterns were described throughout the brain, including white matter, cortical and deep gray matter. These development patterns were evaluated and discussed on the basis of NODDI’s model assumptions. To summarize, this study verified the high sensitivity of NODDI to age over a crucial developmental period from newborn to adolescence. Moreover, the existing knowledge of brain development has been complemented, suggesting that NODDI has a potential capability in the investigation of brain development.

Early prediction of unilateral cerebral palsy in infants at risk: MRI versus the hand assessment for infants

BACKGROUND

Neonates with unilateral perinatal brain injury (UPBI) are at risk for developing unilateral spastic cerebral palsy (USCP). This study compares several predictors for USCP later in life.

METHODS

Twenty-one preterm and 24 term born infants with UPBI were included, with an MRI scan including diffusion tensor imaging (DTI) performed at term equivalent age or around 3 months after birth, respectively. T2-weighted images and DTI-based tractography were used to measure the surface area, diameter, and fractional anisotropy (FA) of both corticospinal tracts (CSTs). The hand assessment for infants (HAI) was performed before 5, between 5 and 8 and between 8 and 12 months of (corrected) age. Asymmetry indices were derived from all techniques and related to USCP at ≥2 years of age.

RESULTS

MRI measures and HAI scores were significantly lower for the affected compared to the unaffected side. Before 5 months of age, FA asymmetry on DTI yielded the highest area under the curve compared to conventional MRI and HAI.

CONCLUSIONS

Prediction of USCP after UPBI is reliable using asymmetry of the CST on MRI, as well as clinical hand assessment. Before 5 months of age, DTI tractography provides strongest predictive information, while HAI specifically aids to prognosis of USCP at later age points.

White matter extension of the Melbourne Children's Regional Infant Brain atlas: M-CRIB-WM

Brain atlases providing standardised identification of neonatal brain regions are key in investigating neurological disorders of early childhood. Our previously developed Melbourne Children's Regional Infant Brain (M-CRIB) and M-CRIB 2.0 neonatal brain atlases provide standardised parcellation of 100 brain regions including cortical, subcortical, and cerebellar regions. The aim of this study was to extend M-CRIB atlas coverage to include 54 white matter (WM) regions. Participants were 10 healthy term-born neonates that were used to create the initial M-CRIB atlas. WM regions were manually segmented based on T₂ images and co-registered diffusion tensor imaging-based, direction-encoded colour maps. Our labelled regions imitate the Johns Hopkins University neonatal atlas, with minor anatomical modifications. All segmentations were reviewed and approved by a paediatric radiologist and a neurosurgery research fellow for anatomical accuracy. The resulting neonatal WM atlas comprises 54 WM regions: 24 paired regions, and six unpaired regions comprising five corpus callosum subdivisions, and one pontine crossing tract. Detailed protocols for manual WM parcellations are provided, and the M-CRIB-WM atlas is presented together with the existing M-CRIB cortical, subcortical, and cerebellar parcellations in 10 individual neonatal MRI data sets. The novel M-CRIB-WM atlas, along with the M-CRIB cortical and subcortical atlases, provide neonatal whole brain MRI coverage in the first multi-subject manually parcellated neonatal atlas compatible with atlases commonly used at older time points. The M-CRIB-WM atlas is publicly available, providing a valuable tool that will help facilitate neuroimaging research into neonatal brain development in both healthy and diseased states.

Inter-site harmonization based on dual generative adversarial networks for diffusion tensor imaging: application to neonatal white matter development

Background

Site-specific variations are challenges for pooling analyses in multi-center studies. This work aims to propose an inter-site harmonization method based on dual generative adversarial networks (GANs) for diffusion tensor imaging (DTI) derived metrics on neonatal brains.

Results

DTI-derived metrics (fractional anisotropy, FA; mean diffusivity, MD) are obtained on age-matched neonates without magnetic resonance imaging (MRI) abnormalities: 42 neonates from site 1 and 42 neonates from site 2. Significant inter-site differences of FA can be observed. The proposed harmonization approach and three conventional methods (the global-wise scaling, the voxel-wise scaling, and the ComBat) are performed on DTI-derived metrics from two sites. During the tract-based spatial statistics, inter-site differences can be removed by the proposed dual GANs method, the voxel-wise scaling, and the ComBat. Among these methods, the proposed method holds the lowest median values in absolute errors and root mean square errors. During the pooling analysis of two sites, Pearson correlation coefficients between FA and the postmenstrual age after harmonization are larger than those before harmonization. The effect sizes (Cohen’s d between males and females) are also maintained by the harmonization procedure.

Conclusions

The proposed dual GANs-based harmonization method is effective to harmonize neonatal DTI-derived metrics from different sites. Results in this study further suggest that the GANs-based harmonization is a feasible pre-processing method for pooling analyses in multi-center studies.

Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data

The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.

Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age.

•

A comprehensive and automated pipeline to consistently analyse neonatal dMRI data.

•

Optimised motion and distortions correction to address newborn specific challenges.

•

The automated QC framework allows to detect issues and to quantify data quality.

•

Automated white matter segmentation allows to extract tract-specific masks.

•

Preliminary data analysis of 140 infants imaged at 38–44 weeks post-menstrual age.

Cerebellar Growth Impairment Characterizes School-Aged Children Born Preterm without Perinatal Brain Lesions

BACKGROUND AND PURPOSE

Infants born preterm are commonly diagnosed with structural brain lesions known to affect long-term neurodevelopment negatively. Yet, the effects of preterm birth on brain development in the absence of intracranial lesions remain to be studied in detail. In this study, we aim to quantify long term consequences of preterm birth on brain development in this specific group.

MATERIALS AND METHODS

Neonatal cranial sonography and follow-up T1-weighted MR imaging and DTI were performed to evaluate whether the anatomic characteristics of the cerebrum and cerebellum in a cohort of school-aged children (6-12 years of age) were related to gestational age at birth in children free of brain lesions in the perinatal period.

RESULTS

In the cohort consisting of 36 preterm (28-37 weeks' gestational age) and 66 term-born infants, T1-weighted MR imaging and DTI at 6-12 years revealed a reduction of cerebellar white matter volume (β = 0.387, P < .001), altered fractional anisotropy of cerebellar white matter (β = -0.236, P = .02), and a reduction of cerebellar gray and white matter surface area (β = 0.337, P < .001; β = 0.375, P < .001, respectively) in relation to birth age. Such relations were not observed for the cerebral cortex or white matter volume, surface area, or diffusion quantities.

CONCLUSIONS

The results of our study show that perinatal influences that are not primarily neurologic are still able to disturb long-term neurodevelopment, particularly of the developing cerebellum. Including the cerebellum in future neuroprotective strategies seems therefore essential.

Auditory structural connectivity in preterm and healthy term infants during the first postnatal year

Assessing language development in the first postnatal year is difficult, as receptive and expressive skills are rudimentary. Although outward manifestations of change are limited, the auditory language system is thought to undergo critical development at this age, as the foundations are laid for the rapid onset of spoken language in the second and third years. We recruited 11 infants, 7 healthy controls (gestational age = 40.69 ± 0.56; range from 40 to 41.43) and preterm babies (gestational age = 28.04 ± 0.95; range from 27.43 to 29.43) who underwent a Magnetic Resonance Imaging study during the first postnatal year (age at scan = 194.18 ± 97.98). We assessed white matter tracts using diffusion-weighted magnetic resonance imaging with probabilistic tractography. Fractional anisotropy was found to be largely mature even at one month, although there was a little further increase during the first postnatal year in both the acoustic radiation and the direct brainstem-Heschl's pathway.

Moderate and late preterm infants exhibit widespread brain white matter microstructure alterations at term-equivalent age relative to term-born controls

Despite the many studies documenting cerebral white matter microstructural alterations associated with very preterm birth (<32 weeks' gestation), there is a dearth of similar research in moderate and late preterm infants (born 32-36 weeks' gestation), who experience higher rates of neurodevelopmental delays than infants born at term (≥ 37 weeks' gestation). We therefore aimed to determine whether whole brain white matter microstructure differs between moderate and late preterm infants and term-born controls at term-equivalent age, as well as to identify potential perinatal risk factors for white matter microstructural alterations in moderate and late preterm infants. Whole brain white matter microstructure was studied in 193 moderate and late preterm infants and 83 controls at term-equivalent age by performing Tract-Based Spatial Statistics analysis of diffusion tensor imaging data. Moderate and late preterm infants had lower fractional anisotropy and higher mean, axial and radial diffusivities compared with controls in nearly 70% of the brain's major white matter fiber tracts. In the moderate and late preterm group, being born small for gestational age and male sex were associated with lower fractional anisotropy, largely within the optic radiation, corpus callosum and corona radiata. In conclusion, moderate and late preterm infants exhibit widespread brain white matter microstructural alterations compared with controls at term-equivalent age, in patterns consistent with delayed or disrupted white matter microstructural development. These findings may underpin some of the neurodevelopmental delays observed in moderate and late preterm children.

A new neonatal cortical and subcortical brain atlas: the Melbourne Children's Regional Infant Brain (M-CRIB) atlas

Investigating neonatal brain structure and function can offer valuable insights into behaviour and cognition in healthy and clinical populations; both at term age, and longitudinally in comparison with later time points. Parcellated brain atlases for adult populations are readily available, however warping infant data to adult template space is not ideal due to morphological and tissue differences between these groups. Several parcellated neonatal atlases have been developed, although there remains strong demand for manually parcellated ground truth data with detailed cortical definition. Additionally, compatibility with existing adult atlases is favourable for use in longitudinal investigations. We aimed to address these needs by replicating the widely-used Desikan-Killiany (2006) adult cortical atlas in neonates. We also aimed to extend brain coverage by complementing this cortical scheme with basal ganglia, thalamus, cerebellum and other subcortical segmentations. Thus, we have manually parcellated these areas volumetrically using high-resolution neonatal T₂-weighted MRI scans, and initial automated and manually edited tissue classification, providing 100 regions in all. Linear and nonlinear T₂-weighted structural templates were also generated. In this paper we provide manual parcellation protocols, and present the parcellated probability maps and structural templates together as the Melbourne Children's Regional Infant Brain (M-CRIB) atlas.

Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T₂-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T₂-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T₂-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T₂-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray matter for coronal images acquired at 30 weeks. This demonstrates that MANTiS' performance is competitive with existing techniques. For the WUNDeR dataset, mean Dice scores comparing MANTiS with manually edited segmentations demonstrated good agreement, where all scores were above 0.75, except for the hippocampus and amygdala. The results show that MANTiS is able to segment neonatal brain tissues well, even in images that have brain abnormalities common in preterm infants. MANTiS is available for download as an SPM toolbox from http://developmentalimagingmcri.github.io/mantis.

White matter alterations of the corticospinal tract in adults born very preterm and/or with very low birth weight

White matter (WM) injury, either visible on conventional magnetic resonance images (MRI) or measurable by diffusion tensor imaging (DTI), is frequent in preterm born individuals and often affects the corticospinal tract (CST). The relation between visible and invisible white mater alterations in the reconstructed CST of preterm subjects has so far been studied in infants, children and up to adolescence. Therefore, we probabilistically tracked the CST in 53 term-born and 56 very preterm and/or low birth weight (VP/VLBW, < 32 weeks of gestation and/or birth weight < 1,500 g) adults (mean age 26 years) and compared their DTI parameters (axial, radial, mean diffusivity--AD, RD, MD, fractional anisotropy--FA) in the whole CST and slice-wise along the CST. Additionally, we used the automatic, tract-based-spatial-statistics (TBSS) as an alternative to tractography. We compared control and VP/VLBW and subgroups with and without CST WM lesions visible on conventional MRI. Compared to controls, VP/VLBW subjects had significantly higher diffusivity (AD, RD, MD) in the whole CST, slice-wise along the CST, and in multiple regions along the TBSS skeleton. VP/VLBW subjects also had significantly lower (TBSS) and higher (tractography) FA in regions along the CST, but no different mean FA in the tracked CST as a whole. Diffusion changes were weaker, but remained significant for both, tractography and TBSS, when excluding subjects with visible CST lesions. Chronic CST injury persists in VP/VLBW adults even in the absence of visible WM lesions, indicating long-term structural WM changes induced by premature birth.

Recent advancements in diffusion MRI for investigating cortical development after preterm birth-potential and pitfalls

Preterm infants are born during a critical period of brain maturation, in which even subtle events can result in substantial behavioral, motor and cognitive deficits, as well as psychiatric diseases. Recent evidence shows that the main source for these devastating disabilities is not necessarily white matter (WM) damage but could also be disruptions of cortical microstructure. Animal studies showed how moderate hypoxic-ischemic conditions did not result in significant neuronal loss in the developing brain, but did cause significantly impaired dendritic growth and synapse formation alongside a disturbed development of neuronal connectivity as measured using diffusion magnetic resonance imaging (dMRI). When using more advanced acquisition settings such as high-angular resolution diffusion imaging (HARDI), more advanced reconstruction methods can be applied to investigate the cortical microstructure with higher levels of detail. Recent advances in dMRI acquisition and analysis have great potential to contribute to a better understanding of neuronal connectivity impairment in preterm birth. We will review the current understanding of abnormal preterm cortical development, novel approaches in dMRI, and the pitfalls in scanning vulnerable preterm infants.

Identification of neonatal white matter on DTI: influence of more inclusive thresholds for atlas segmentation

Purpose

Semi-automated diffusion tensor imaging (DTI) analysis of white matter (WM) microstructure offers a clinically feasible technique to assess neonatal brain development and provide early prognosis, but is limited by variable methods and insufficient evidence regarding optimal parameters. The purpose of this research was to investigate the influence of threshold values on semi-automated, atlas-based brain segmentation in very-low-birth-weight (VLBW) preterm infants at near-term age.

Materials and Methods

DTI scans were analyzed from 45 VLBW preterm neonates at near-term-age with no brain abnormalities evident on MRI. Brain regions were selected with a neonatal brain atlas and threshold values: trace <0.006 mm²/s, fractional anisotropy (FA)>0.15, FA>0.20, and FA>0.25. Relative regional volumes, FA, axial diffusivity (AD), and radial diffusivity (RD) were compared for twelve WM regions.

Results

Near-term brain regions demonstrated differential effects from segmentation with the three FA thresholds. Regional DTI values and volumes selected in the PLIC, CereP, and RLC varied the least with the application of different FA thresholds. Overall, application of higher FA thresholds significantly reduced brain region volume selected, increased variability, and resulted in higher FA and lower RD values. The lower threshold FA>0.15 selected 78±21% of original volumes segmented by the atlas, compared to 38±12% using threshold FA>0.25.

Conclusion

Results indicate substantial and differential effects of atlas-based DTI threshold parameters on regional volume and diffusion scalars. A lower, more inclusive FA threshold than typically applied for adults is suggested for consistent analysis of WM regions in neonates.

Differential vulnerability of white matter structures to experimental infantile hydrocephalus detected by diffusion tensor imaging

PURPOSE

The differential vulnerability of white matter (WM) to acute and chronic infantile hydrocephalus and the related effects of early and late reservoir treatment are unknown, but diffusion tensor imaging (DTI) could provide this information. Thus, we characterized WM integrity using DTI in a clinically relevant model.

METHODS

Obstructive hydrocephalus was induced in 2-week-old felines by intracisternal kaolin injection. Ventricular reservoirs were placed 1 (early) or 2 (late) weeks post-kaolin and tapped frequently based solely on neurological deficit. Hydrocephalic and age-matched control animals were sacrificed 12 weeks postreservoir. WM integrity was evaluated in the optic system, corpus callosum, and internal capsule prereservoir and every 3 weeks using DTI. Analyses were grouped as acute (<6 weeks) or chronic (≥6 weeks).

RESULTS

In the corpus callosum during acute stages, fractional anisotropy (FA) decreased significantly with early and late reservoir placement (p = 0.0008 and 0.0008, respectively), and diffusivity increased significantly in early (axial, radial, and mean diffusivity, p = 0.0026, 0.0012, and 0.0002, respectively) and late (radial and mean diffusivity, p = 0.01 and 0.0038, respectively) groups. Chronically, the corpus callosum was thinned and not detectable by DTI. FA was significantly lower in the optic chiasm and tracts (p = 0.0496 and 0.0052, respectively) with late but not early reservoir placement. In the internal capsule, FA in both reservoir groups increased significantly with age (p < 0.05) but diffusivity remained unchanged.

CONCLUSIONS

All hydrocephalic animals treated with intermittent ventricular reservoir tapping demonstrated progressive ventriculomegaly. Both reservoir groups demonstrated WM integrity loss, with the CC the most vulnerable and the optic system the most resilient.

A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging

PURPOSE

The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI).

MATERIALS AND METHODS

The proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients).

RESULTS

The relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05).

CONCLUSION

The proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements.

Choice of diffusion tensor estimation approach affects fiber tractography of the fornix in preterm brain

BACKGROUND AND PURPOSE

Neonatal DTI enables quantitative assessment of microstructural brain properties. Although its use is increasing, it is not widely known that vast differences in tractography results can occur, depending on the diffusion tensor estimation methodology used. Current clinical work appears to be insufficiently focused on data quality and processing of neonatal DTI. To raise awareness about this important processing step, we investigated tractography reconstructions of the fornix with the use of several estimation techniques. We hypothesized that the method of tensor estimation significantly affects DTI tractography results.

MATERIALS AND METHODS

Twenty-eight DTI scans of infants born <29 weeks of gestation, acquired at 30-week postmenstrual age and without intracranial injury observed, were prospectively collected. Four diffusion tensor estimation methods were applied: 1) linear least squares; 2) weighted linear least squares; 3) nonlinear least squares, and 4) robust estimation of tensors by outlier rejection. Quality of DTI data and tractography results were evaluated for each method.

RESULTS

With nonlinear least squares and robust estimation of tensors by outlier rejection, significantly lower mean fractional anisotropy values were obtained than with linear least squares and weighted linear least squares. Visualized quality of tract reconstruction was significantly higher by use of robust estimation of tensors by outlier rejection and correlated with quality of DTI data.

CONCLUSIONS

Quality assessment and choice of processing methodology have considerable impact on neonatal DTI analysis. Dedicated acquisition, quality assessment, and advanced processing of neonatal DTI data must be ensured before performing clinical analyses, such as associating microstructural brain properties with patient outcome.