Pediatric diffusion tensor imaging: Normal database and observation of the white matter maturation in early childhood

www.elsevier.com/locate/ynimg NeuroImage 29 (2006) 493 – 504

Pediatric diffusion tensor imaging: Normal database and observation of the white matter maturation in early childhood Laurent Hermoye,a Christine Saint-Martin,a Guy Cosnard,a Seung-Koo Lee,f Jinna Kim,f Marie-Cecile Nassogne,b Renaud Menten,a Philippe Clapuyt,a Pamela K. Donohue,e Kegang Hua,c Setsu Wakana,c,d Hangyi Jiang,c,d Peter C.M. van Zijl,c,d and Susumu Mori,c,d,* a

Diagnostic Radiology Unit, Saint-Luc University Hospital, Universite Catholique de Louvain, Brussels, Belgium Pediatric Neurology Unit, Saint-Luc University Hospital, Universite Catholique de Louvain, Brussels, Belgium c Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA d F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA e Department of Pediatrics, Johns Hopkins University School of Medicine and Department of Population and Family Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21202, USA f Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea b

Received 1 April 2005; revised 22 July 2005; accepted 1 August 2005 Available online 27 September 2005

Recent advances in diffusion tensor imaging (DTI) have made it possible to reveal white matter anatomy and to detect neurological abnormalities in children. However, the clinical use of this technique is hampered by the lack of a normal standard of reference. The goal of this study was to initiate the establishment of a database of DTI images in children, which can be used as a normal standard of reference for diagnosis of pediatric neurological abnormalities. Seven pediatric volunteers and 23 pediatric patients (age range: 0 – 54 months) referred for clinical MR examinations, but whose brains were shown to be normal, underwent anatomical and DTI acquisitions on a 1.5 T MR scanner. The white matter maturation, as observed on DTI color maps, was described and illustrated. Changes in diffusion fractional anisotropy (FA), average apparent diffusion constant (ADCave), and T2weighted (T2W) signal intensity were quantified in 12 locations to characterize the anatomical variability of the maturation process. Almost all prominent white matter tracts could be identified from birth, although their anisotropy was often low. The evolution of FA, shape, and size of the white matter tracts comprised generally three phases: rapid changes during the first 12 months; slow modifications during the second year; and relative stability after 24 months. The time courses of FA, ADCave, and T2W signal intensity confirmed our visual observations that maturation of the white matter and the normality of its architecture can be assessed with DTI in young children. The database is available online and is expected to foster the use of this promising technique in the diagnosis of pediatric pathologies. D 2005 Elsevier Inc. All rights reserved.

* Corresponding author. Department of Radiology, Johns Hopkins University School of Medicine, 217 Traylor Building, 720 Rutland Avenue, Baltimore, MD 21205, USA. Fax: +1 410 614 1948. E-mail address: [email protected] (S. Mori). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2005.08.017

Introduction MRI plays a crucial role in the radiological diagnosis of pediatric brain pathologies. However, anatomical evaluation of the brain in the early phases of development is challenging. In the first 24 months after birth, the brain undergoes considerable anatomical changes, which significantly influence MR relaxation parameters. In newborn brains, the T1 and T2 of the cell-dense gray matter are shorter than those of white matter, but, after myelination, the contrast inverts (Barkovich et al., 1988). During this contrast transition period, MRI may even fail to differentiate between gray and white matter. Recently, it has been shown that DTI, in which the contrast is based on structural alignment, provides more stable anatomical contrasts in pediatric brains (Baratti et al., 1999; Dubois et al., 2004; Huppi and Inder, 2001; Maas et al., 2004; McKinstry et al., 2002a; Mori et al., 2001; Mukherjee et al., 2002; Neil et al., 1998; Partridge et al., 2004; Schneider et al., 2004). DTI can differentiate not only gray and white matter but can also reveal the anatomy of the white matter tracts in newborn brains (Berman et al., 2004). DTI has already shown promising results in the study of various developmental brain diseases, such as periventricular leukomalacia (Hoon et al., 2002; Huppi et al., 2001), holoprosencephaly (Albayram et al., 2002), callosal dysgenesis (Lee et al., 2004b), focal cortical dysplasia (Lee et al., 2004a), perinatal brain injury (Huppi et al., 2001; McKinstry et al., 2002b), tumor (Gauvain et al., 2001), and developmental delay (Filippi et al., 2003). While the potential of DTI as a new diagnostic tool for pediatric brain imaging is undoubtedly high, it is also true that pediatric DTI studies have a unique shortcoming, namely, the lack of normal data.

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Fig. 1. Site-specific age distribution of the 7 healthy pediatric volunteers and the 23 pediatric patients.

Because of its high sensitivity to motion, DTI of children under 4 years of age requires sedation, which is not permitted in healthy volunteers in most countries. Whereas newborns (0 month) can be scanned while sleeping, this becomes increasingly difficult as children get older and more alert. As a result, normal DTI data for the pediatric brain are scarce, although such data are essential for neuroradiologists to differentiate normal from abnormal cases. The goal of this study is to provide the radiological, neurological, and pediatric communities with a standard of reference for the clinical interpretation of pediatric DTI images. This collection of normal data was made possible by a multi-institutional collaboration, in which a common DTI protocol was established, tested, and used at three participating sites. To increase the

Fig. 2. Representative axial color maps at 0, 3, 6, 9, 12, 24, 36, and 48 months. A color map at adult age and the corresponding T1-weighted anatomical images are represented in the last two columns. To focus on the anatomical changes, only minimum annotation is applied to the color maps. For more comprehensive anatomical assignment, readers may refer to Wakana et al. (2004), the human white matter atlas (Mori et al., 2005), or consult our multimedia DTI atlas (www.DTIatlas.org). Abbreviations are: alic: anterior limb of internal capsule, cbt: corticobulbar tract, cg: cingulum, cst: corticospinal tract, fx: fornix, icp: inferior cerebellar peduncle, ifo: inferior fronto-occipital fasciculus, ilf: inferior longitudinal fasciculus, mcp: middle cerebellar peduncle, ml: medial lemniscus, plic: posterior limb of internal capsule, slf: superior longitudinal fasciculus, unc: uncinate fasciculus.

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Fig. 2 (continued).

accessibility of this database, a web site has been created at which the DTI images can be visualized and from which they can be downloaded.

Subjects and methods Subjects Seven healthy pediatric volunteers and 23 pediatric patients referred for a clinical MR examination were included in the study (17 boys, 13 girls; mean age: 16 T 16 months; age range: 0 – 54 months; Fig. 1). The clinical indications were pathologies related to the internal ear (n = 6), the orbits (n = 3), the spine (n = 3), fit (n = 6), trauma (n = 1), infectious disease (n = 1), genetic disease (n = 1), and vascular/cisternal malformation (n = 2). There were 24 children classified as white and 6 children classified as Asian. The clinical history of each patient was carefully inspected by a

pediatric neurologist (M.N.) to rule out developmental abnormalities. All the subjects were full-term. In all the patients, the cerebral anatomy, as depicted by an anatomical T2 sequence, was normal. Five healthy adult patients (2 men, 3 women; age range: 22 – 29 years) were included to obtain an adult standard of reference. This study was approved by Institutional Review Board of all participating sites, and written informed consent was obtained from each subject’s parents. MR data Images were acquired using a SENSE head coil on 1.5 T wholebody MR scanners (Philips Medical Systems, Best, The Netherlands) equipped with explorer gradients (40 mT/m). For acquisition, an 8-element arrayed RF coil, converted to 6-channel to be compatible with the 6-channel receiver system, was used. For DTI acquisitions, a single-shot spin echo-echo planar sequence (SEEPI) was used, with diffusion gradients applied in 32 non-collinear

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directions (Jones et al., 1999) and b = 700 s/mm2. One reference image with least diffusion weighting (b = 33 s/mm2) was also acquired (called b0 images in this paper). Fifty axial slices were acquired, parallel to the AC – PC line. The field of view (FOV), the size of the acquisition matrix, and the slice thickness were 150  150 mm/80  80/1.9 mm for newborns and 220  220 mm/96  96/2.3 mm for older subjects. All images were zero-filled to the final reconstruction matrix of 256 * 256. Other imaging parameters were: TR = 7859 ms; TE = 80 ms; and SENSE reduction factor = 2.5. To improve the signal-to-noise ratio, two datasets were acquired, leading to a total acquisition time of 10 min. A dualecho T2-weighted sequence was acquired for anatomical guidance. In the adult subjects, a 3D magnetization-prepared rapid gradientecho (MPRAGE) sequence was also acquired. In view of their young age, the pediatric patients were anesthetized to avoid movement artifacts. The anesthesia was induced and maintained by inhalation of sevofluorane. Newborn healthy volunteers were scanned while asleep.

Image processing and analysis The images were processed with DTI Studio (H. Jiang and S. Mori, Johns Hopkins University, cmrm.med.jhmi.edu or godzilla.kennedykrieger.org), a Windows-based program developed in visual C++. For each voxel, the diffusion tensor (i.e., a 3 * 3 matrix) was calculated with a multivariate linear fitting algorithm (Basser et al., 1994). The tensor in each voxel was diagonalized to obtain its eigenvalues and eigenvectors. The fiber direction at each voxel was assumed to be the eigenvector corresponding to the tensor’s main eigenvalue. This vector was color-coded (blue for superior – inferior, red for left – right, and green for anterior – posterior), with a brightness proportional to the voxel’s fractional anisotropy (FA) (Pajevic and Pierpaoli, 1999). The color-coded maps were analyzed on the 50 axial slices. The brightness, the thickness, and the shape of the tracts were visually inspected. Representative cases and time courses of white matter tract maturation were described by consensus of a group of four

Fig. 3. Representative axial color maps at the level of the internal capsule in (A) 0, 3-, 6-, 9-, (B) 12-, 24-, 36-, and 48-month-old children, with the corresponding FA and ADCave maps and T2W anatomical images.

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Fig. 3 (continued).

experienced observers (L.H., C.S-M, G.C., S.M.). The maturation of the tracts in the brainstem, the projection fibers, the association fibers, the callosal fibers, and the fibers of the limbic system were analyzed sequentially.

averaging. The time courses of FA, ADCave, and normalized b0 signal intensity (nb0) were plotted. The relationships between FA and the nb0 were represented on scatter plots and quantified with regression analysis.

Measurements of FA, ADCave, and normalized b0 signal intensity Results Three types of image contrasts (FA, ADCave, b0 signal intensity) were quantified using three representative axial slices. Twelve anatomical regions of interest (ROIs) were first manually defined in one case (see Fig. 4 for the locations of ROIs) and subsequently used as anatomical guide to identify corresponding regions in the other subjects as reproducibly as possible. The twelve regions included the posterior limb of the internal capsule (predominantly projection fibers), the genu and splenium of the corpus callosum (commissural fibers), the stem of the temporal lobe (rich with association fibers such as the inferior frontooccipital fasciculus and the inferior longitudinal fasciculus), the superior longitudinal fasciculus (association fibers), the fornix and cingulum (limbic fibers), a fiber-crossing area between the corpus callosum and the anterior limb of the internal capsule, a frontal Ufiber, the thalamus, the lenticular nucleus (putamen + globus pallidus), and a frontal cortical region. All ROIs were drawn by one observer (L.H.) on color maps. The same ROIs were also used to measure ADCave and the b0 images. The b0 signal intensity (SI), which is heavily weighted by T2, was normalized by the intensity of a homogeneous area in the lateral ventricles for cross-subject

Overview of developing brains The evolution of the DTI color maps is depicted in Fig. 2. Because the most significant changes occur during the first year of life, representative color maps are shown every 3 months during this period and every year in older children. The two last columns are an adult reference. Throughout the entire age range (0 – 54 months), high-quality color maps could be obtained. Almost all prominent white matter tracts could be identified from birth, although they appeared thinner and the FA was lower. For all white matter tracts, the core regions have higher anisotropy (mostly FA = 0.4 – 0.9), while the anisotropy of peripheral regions is much lower (FA = 0.1 – 0.6) and similar to the values obtained in gray matter at birth. The peripheral white matter FA increases drastically in the first 3 months, achieving clear separation of the gray and white matter. The sizes and FAs of entire white matter tracts increase noticeably during the first 24 months, although it is difficult to conclude whether the size increase is due to actual thickening of axonal bundles or to an

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increase in FA. At 48 months, the appearance of the color maps seems almost identical to the one observed in adults, except for the size of the brain. Below, some tract-specific observations are described. Brainstem The inferior, middle, and superior cerebellar peduncles and the decussation of the superior cerebellar peduncles can be identified in newborns. The corticospinal tract is visible in newborn brains,

although its size and intensity are much lower than in older brains. Posterior to the pontine crossing fiber, the blue structure grouping the medial lemniscus, the tegmental tract, the reticular formation, and the medial longitudinal fasciculus are also observed in newborns (Figs. 2A and E). Projection fibers The corona radiata (Figs. 2D and H), the internal capsule (Figs. 2C and G), and the cerebral peduncle (Figs. 2B and F),

Fig. 4. Fractional anisotropy (FA), ADCave, normalized b0 signal intensity (nb0) versus age curves, and FA versus nb0 scatter plots in (A) the posterior limb of the internal capsule (PLIC), the splenium and the genu of the corpus callosum (sCC and gCC), the cingulum (Cg), (B) the fornix (Fx), the inferior frontooccipital fasciculus (IFO), the superior longitudinal fasciculus (SLF), a frontal U-fiber, (C) the crossing area between the corpus callosum and the anterior limb of the internal capsule (Crs), the thalamus (Th), the lenticular nucleus (Lent), and a cortical gray matter area (Ctx). The location of the manually drawn ROIs are indicated on the color maps (A).

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Fig. 4 (continued).

which contains projection fibers, are some of the most prominent white matter structures observed in newborns. Progressive thickening and an increase in anisotropy occur during the first years of life. Callosal fibers The corpus callosum is one of the most prominent white matter structures in the newborns, although its anisotropy and size are apparently lower than in older brains. The major and minor forceps are not well developed at birth. The locations where the forceps meet the corona radiata (called ‘‘fiber crossing areas’’) have low anisotropy for the first 12 months. The major forceps, which initially have an inverse V-shape (explaining its yellow color, which is a combination of red and green), gradually become inversely U-shaped after 9 months. The anterior commissure can also be recognized in the newborns (Figs. 2C, D, G, and H). Association fibers The superior longitudinal fasciculus seems to be one of the least developed tracts in the newborns, and, with the current resolution and SNR, it could not be well delineated before 12 months of age. The inferior fronto-occipital and inferior longitudinal fasciculus are also very faint at birth but become apparent by 3 months. The core region of the uncinate fasciculus is one of the tracts that can be clearly identified in the newborns. However,

short-range cortical U-fibers could only be appreciated after 3 months (Figs. 2B, D, F and H). Limbic fibers The cingulum and fornix can be clearly identified from birth. The body of the fornix (superior region) looks especially prominent, even in newborns, suggesting early formation of limbic fibers (Figs. 2C, D, G, and H). Quantification and comparison of MR parameters Fig. 3 compares axial T2, FA, ADC, and color maps. The difference in anatomical information between the relaxation-based images and DTI is striking. DTI carries more stable gray – white matter contrast, especially after 3 months of age, while T2 undergoes a drastic contrast change. White matter anatomy can be better appreciated by the color maps throughout the development process. By superimposing the tract location information obtained by color maps on other types of parametric maps, various MR parameters (FA, ADC, normalized b0 SI) can be quantified in a tract-specific manner. The results are shown in Fig. 4. The quantification results confirmed our observations described above. Most contrast changes level off within the first 24 months. Most changes have three phases: rapid change in the first 3 – 6 months, followed by slower change until 24 months, and relative stability after 24 months. The FA and nb0

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Fig. 4 (continued).

changes are well correlated (r = 0.72 – 0.89) in white matter tracts, suggesting that the myelination process is one of the dominant factors in tract maturation (Table 1). Differences in the maturation process among white matter locations are also apparent in these plots. Deep white matter structures, such as the corpus callosum (gCC: genu of the corpus callosum, sCC: splenium of the corpus callosum) and the internal capsule (PLIC: the posterior limb of the internal capsule), have relatively high FA (¨0.4) at 0 months and show a rapid rise to high values (around 0.8). On the contrary, frontal lobe white matter (denoted as a fiber-crossing area) and peripheral white matter (Ufibers) have FAs as low as that of the cortex at birth, which increases to intermediate values (around 0.5) during the first 24 months. The right intercept in Table 1 represents the FA value that would be obtained in non-myelinated white matter (i.e., when the normalized SI equals 1). The high values obtained in deep white matter structures (PLIC, gCC, sCC) indicate that myelin is not the sole source of anisotropy. In the cortex, the thalamus, and the lenticular nucleus, the nb0 decrease is not accompanied by a change in FA, which indicates that there is no correlation between the two parameters (Table 1) during aging. Database and viewing software Although Fig. 2 shows the overall trends for anatomical changes across development, these examples are inevitably

representative cases. For diagnostic radiology, it is crucial to see many normal cases to understand the extent of normal variations. We therefore have posted the database on websites (www.pediatricDTI.org, cmrm.med.jhmi.edu, and godzilla.kennedykrieger.org), which are accessible after registration. A program that interfaces between the normal database and individual patient data (MRICompView) has also been developed and has been posted on the same sites.

Table 1 Correlation between FA and normalized b0 signal intensity Correlation coefficient PLIC sCC gCC Cingulum Fornix IFO SLF U-fiber Crossing Thalamus Lenticular Cortex

0.88 0.73 0.87 0.79 0.82 0.72 0.87 0.84 0.89 0.53 0.44 0.00

Slope 0.61 0.43 0.70 0.63 0.98 0.59 0.56 0.61 0.44 0.2 0.15 0.02

Right intercept 0.29 0.54 0.33 0.08 0.15 0.19 0.10 0.08 0.08 0.13 0.06 0.11

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Discussion Potential of DTI for anatomical imaging in infants and the need for normal databases Although MRI has been an essential tool for studying the pathology of pediatric brains, the amount of anatomical information it can offer has often been limited due to rapid contrast changes in the early phases of development. In pre-myelinated brains or during the myelination process, gray and white matter often have similar T1 and T2 relaxation times, which leads to poor anatomical contrast. As previously reported by Miller et al. (2003) and also as seen in Fig. 2, DTI, even at birth, can provide sharp contrasts between white and gray matter, which remains stable throughout the development process. In addition, DTI is capable of delineating the intra-white matter anatomy (white matter tracts). This information will allow study of the maturation process of individual tracts. In this respect, DTI could be an ideal tool to characterize both normal and abnormal pediatric brain anatomy (Lee et al., 2005; Neil et al., 2002). However, DTI can only become important for children if there are sufficient normal data, the establishment of which was the motivation for this multi-institutional effort to use a common DTI protocol and pool data from normal subjects (Paus et al., 2001) or from patients whose brain were shown to be normal. The pediatric patients who meet these criteria are not numerous and the acrossinstitutional collaboration was designed to increase the numbers of these subjects. The imaging protocol is based on state-of-the-art gradient systems (more than 40 mT/cm per axis) and parallel imaging, which ensures high-quality high-resolution (less than 2.3 mm) images within 10 min of scanning time. Although it is of great interest to study brain development after the 5th year of life, this study includes only 0- to 4-year-old subjects for the following reasons. First, the large extent of morphological and contrast changes occur within the first 24 months. Second, studies of the first 48 months have important clinical values for diagnosis of developmental diseases such as cerebral palsy. Third, 0 – 48 months is the age range for which control data are most difficult to obtain. Currently, we have 6 cases for the 0 month group and 24 cases for the 1 – 48 month group (the online database also contains subjects up to 16 years of age). While the current size of the database is small, new cases are being added to the database regularly. These data and the viewing software are downloadable from our website after registration. We hope this effort will enhance the training process for neuroradiologists. MR parameter changes and mechanisms Using a manually defined ROI, we quantified several MR parameters (FA, ADCave, and nb0). Measurement of accurate T2 over the course of development is challenging due to significant changes in T2, proton density, and compartmentalization. The time constraint in pediatric clinical scans is also a limiting factor. In our study, we utilized b0 images to estimate changes in T2 relaxation time. While these images are heavily T2-weighted, it should be noted that the measurements are also influenced by other factors, such as proton density and echo time. As previously reported, based on T1- and T2-based MRI (Barkovich et al., 1988) and DTI (Mukherjee et al., 2002; Schneider et al., 2004), there are roughly three phases in the maturation process observed by FA, ADCave, and nb0. The first 12 months

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(especially the first 3 months) are characterized by fast contrast changes followed by slow maturation during the period from 12 to 24 months. After 24 months, changes in most white matter regions are below the precision level of our measurements. McKinstry et al. (2002a) showed a gradual decrease in the anisotropy of the cortex during the gestational age of 26 – 42 weeks. In our full-term neonate studies, there were no noticeable cortical changes throughout the entire development process, indicating that, after birth, FA changes induced by further axon and dendrite maturation are below the SNR levels of our measurements. In FA measurements of the white matter, large regional differences were observed. They typically follow a ‘‘high FA in core and low FA in peripheral white matter’’ rule (Zhai et al., 2003). There are several exceptions to this rule. First, the area where the corpus callosum and the anterior limb of the internal capsule meet (‘‘crossing’’ region in Fig. 4) has low FA at birth despite its relatively deep location and also lacks an initial steep FA increase. Second, we noticed that the association fibers, especially the superior longitudinal fasciculus, mature at a relatively later stage of development. On the other hand, limbic fibers (fornix and cingulum), regardless of their relatively small size, can be well appreciated in the early phase of development. The early appearance of the limbic fibers and other tracts, such as the core regions of projection fibers, commissural fibers, and the uncinate fasciculus, agrees with histology-based fetal brain atlases (Bayer and Altman, 2004) and DTI studies in premature newborns (Partridge et al., 2004). One possible mechanism for regional differences is the myelination process. Some of our findings agree with previous studies on the sequence of the myelination process (Brody et al., 1987; Kinney et al., 1988). Autopsy studies have shown that the corticospinal tract, the corpus callosum and the superior cerebellar peduncles mature early, which is in concordance with MRI studies. The late maturation of the association tracts was also confirmed by histological analyses. On the other hand, autopsy studies have shown that the fornix, which has relatively high FA in newborns, does not reach full myelination until 2 years of age (Brody et al., 1987; Kinney et al., 1988). To further support the influence of myelination, we found a strong correlation between FA and nb0 changes (Fig. 4 and Table 1). It has been suggested that the T2 shortening is caused by the water loss induced by the development of the hydrophobic inner layer of the myelin sheath (Barkovich et al., 1988). Strong correlations between nb0 and FA in many regions suggest that myelination, or at least axonal properties that mature concomitantly with myelination, are an important facet of the FA increase. However, myelination is not the sole determining factor for anisotropy. Beaulieu and Allen obtained a similar degree of anisotropy in the non-myelinated olfactory as that in the myelinated trigeminal and optic nerves of the garfish (Beaulieu and Allen, 1994). That study concluded that axonal membranes play the primary role and that myelination, although not necessary for significant anisotropy, modulates the degree of anisotropy. Previous studies on humans (Dubois et al., 2004; Neil et al., 1998), monkeys (Pierpaoli and Basser, 1996), and rodents (Mori et al., 2001; Zhang et al., 2003) also reported high anisotropy in pre-myelinated embryonic and neonate brains followed by a 25 – 30% increase upon myelination (Zhang et al., 2003). High anisotropy observed in the core white matter regions at birth in this study is in accord with those previous studies. Other factors that can influence tract anisotropy during

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brain maturation are a reduction in water content, as well as greater cohesiveness and compactness of the fiber tracts (McGraw et al., 2002), and reduced extra-axonal space (Beaulieu, 2002). Nonstructural factors related to sodium-channel activity have also been shown to influence pre-myelination anisotropy (Prayer et al., 2001). In ‘‘fiber merging’’ areas, a mixture of fiber populations will also play a role (Alexander et al., 2001). An important non-biological factor that we should not neglect is the effect of changes in image resolution with respect to the brain size. To obtain the same amount of anatomical information from brains of different sizes, ideally, the number of pixels within the brain should be kept constant. This could be especially important for DTI, in which partial volume effects may occur easily due to the convoluted white matter structures. Low anisotropy in younger brains, therefore, could be in part due to larger partial volume effects. On the other hand, reduction of the voxel size is limited by the necessity to maintain adequate signalto-noise ratio. Because it has been reported that lower SNR leads to higher FA (Pierpaoli and Basser, 1996), this alternative approach may also lead to bias in the measurement. This issue exemplifies the difficulty in absolute quantification studies of developing brains. Limitations of this study and usefulness for future clinical diagnosis In order for DTI to become an indispensable tool for neuroradiological diagnosis in the future, it must fulfil the following two criteria. First, it should be sensitive to abnormalities that are difficult to visualize with other more time-efficient MR techniques, such as T1- and T2-weighted images. Second, such abnormalities must be visually appreciable because routine diagnosis is almost always based on the experience and training of neuroradiologists. The purpose of this database is to provide a source for neuroradiologists to understand the normal appearance of pediatric brain anatomy as revealed by DTI and its normal range of variation. Because the data have been collected from three different sites, even with the effort to establish a common protocol setup, the ‘‘normal range’’ could contain bias. Judging from the distribution of cases among the three sites (Fig. 1) and quantification results (Fig. 4), the large morphological and contrast changes during the first 48 months seem well beyond the cross-institutional variability. One possible pitfall of our database is that it predominantly relies on clinical data. Neither the sedation of normal infants nor the imaging of non-sedated infants was possible in this study. The former was ethically inconceivable, and the latter would have yielded large motion artifacts. ‘‘MR-negative’’ data have already been used in similar studies (Mukherjee et al., 2001). To minimize the possible penetration of abnormal cases, we carefully inspected both conventional T1- and T2-weighted anatomical images and the clinical history of each patient. Even with these efforts, we cannot rule out the possibility of penetration of abnormal cases in our database. Therefore, we would like to stress the following confounds. First, our database may erroneously have a larger range than the typical range for ‘‘normal.’’ This may not affect the confidence level when a subject has an abnormality that is well beyond our normal range, but it lowers the power of detecting subtler abnormalities. Second, our data may not be appropriate for control data in quantitative DTI studies. The second point is also important due to the fact that the absolute quantitative

measurements may vary between sites, scanners, and pulse sequences. Currently, an NIH-initiated multi-center project for a normal pediatric database of MRI is under way, in which DTI is included as an auxiliary project. Because the number of subjects in the NIH study is much larger and the participants receive comprehensive neurological tests, this database would be more suitable for quantitative studies for subtle anatomic differences. On the other hand, the images in our database are based on more modern acquisition technology (parallel imaging reduces image distortion and image blurring) and have higher resolution (3 mm3 vs. 1.9 – 2.3 mm3). Employment of sedation allows longer scanning time and less motion artifacts. We believe that our database, thus, has a complementary value.

Conclusions In conclusion, the reference images presented in this paper and the online database can pave the way for the use of diffusion tensor imaging in the diagnosis of pediatric pathologies, most notably developmental abnormalities, tumors, and white matter diseases. In the past, there were several factors that hindered the application of DTI to pediatric studies, including the long scanning time, a low SNR that precluded the high resolution imaging required for small brains, and the scarcity of scanners equipped with DTI capabilities. These factors are now rapidly diminishing due to the introduction of stronger gradient units, parallel imaging (Bammer et al., 2001; Pruessmann et al., 1999), and high-field magnets (Jaermann et al., 2004). As a consequence, imaging time for DTI has been shortened considerably and is nearing a clinically acceptable time for pediatric studies. MR-compatible incubators and coils optimized for neonatal imaging are also being introduced, which should promote DTI studies of premature infants (Partridge et al., 2004). High quality DTI data from pediatric brains are becoming more and more abundant (Huppi and Inder, 2001; Neil et al., 2002). Once multiple sites have achieved this capability, the use of DTI is expected to become a standard clinical tool.

Acknowledgments This study was supported by NIH grants RO1 AG20012, P41 RR15241, and R21-EB000991. Dr. van Zijl is a paid lecturer for Philips Medical Systems. This arrangement has been approved by Johns Hopkins University in accordance with its conflict of interest policies.

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