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Objectives: We studied in a clinical setting the age dependent T1 relaxation time as a marker of normal late brain maturation and compared it to conventional techniques, namely the apparent diffusion coefficient (ADC). Materials and methods: Forty-two healthy subjects ranging from ages 1 year to 20 years were included in our study. T1 brain maps in which the intensity of each pixel corresponded to T1 relaxation times were generated based on MR imaging data acquired using a MP2RAGE sequence. During the same session, diffusion tensor imaging data was collected. T1 relaxation times and ADC in white matter and grey matter were measured in seven clinically relevant regions of interest and were correlated to subjects’ age. Results: In the basal ganglia, there was a small, yet significant, decrease in T1 relaxation time (-0.45 ≤R≤-0.59, p<10−2) and ADC (-0.60≤R≤-0.65, p<10−4) as a function of age. In the frontal and parietal white matter, there was a significant decrease in T1 relaxation time (-0.62≤R≤-0.68, p<10−4) and ADC (-0.81≤R≤-0.85, p<10−4) as a function of age. T1 relaxation time changes in the corpus callosum and internal capsule were less relevant for this age range. There was no significant difference between the correlation of T1 relaxation time and ADC with respect to age (p-value = 0.39). The correlation between T1 relaxation and ADC is strong in the white matter but only moderate in basal ganglia over this age period. Conclusions: T1 relaxation time is a marker of brain maturation or myelination during late brain development. Between the age of 1 and 20 years, T1 relaxation time decreases as a function of age in the white matter and basal ganglia. The greatest changes occur in frontal and parietal white matter. These regions are known to mature in the final stage of development and are mainly composed of association circuits. Age-correlation is not significantly different between T1 relaxation time and ADC. Therefore, T1 relaxation time does not appear to be a superior marker of brain maturation than ADC but may be considered as complementary owing the intrinsic differences in bio-physical sensitivity. This work may serve as normative ranges in clinical imaging routines. [ABSTRACT FROM AUTHOR]

Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed. [ABSTRACT FROM AUTHOR]

Introduction: Pineal cysts are a common incidental finding on brain MRI with resulting difficulties in differentiation between normal glands and pineal pathologies. The aim of this study was to assess the size and morphology of the cystic pineal gland in children (0-5 years) and compare the findings with published pineoblastoma cases. Methods: In this retrospective multicenter study, 257 MR examinations (232 children, 0-5 years) were evaluated regarding pineal gland size (width, height, planimetric area, maximal cyst(s) size) and morphology. We performed linear regression analysis with 99 % prediction intervals of gland size versus age for the size parameters. Results were compared with a recent meta-analysis of pineoblastoma by de Jong et al. Results: Follow-up was available in 25 children showing stable cystic findings in 48 %, cyst size increase in 36 %, and decrease in 16 %. Linear regression analysis gave 99 % upper prediction bounds of 10.8 mm, 10.9 mm, 7.7 mm and 66.9 mm, respectively, for cyst size, width, height, and area. The slopes (size increase per month) of each parameter were 0.030, 0.046, 0.021, and 0.25, respectively. Most of the pineoblastomas showed a size larger than the 99 % upper prediction margin, but with considerable overlap between the groups. Conclusion: We presented age-adapted normal values for size and morphology of the cystic pineal gland in children aged 0 to 5 years. Analysis of size is helpful in discriminating normal glands from cystic pineal pathologies such as pineoblastoma. We also presented guidelines for the approach of a solid or cystic pineal gland in hereditary retinoblastoma patients. [ABSTRACT FROM AUTHOR]

Introduction: Differentiation between normal solid (non-cystic) pineal glands and pineal pathologies on brain MRI is difficult. The aim of this study was to assess the size of the solid pineal gland in children (0-5 years) and compare the findings with published pineoblastoma cases. Methods: We retrospectively analyzed the size (width, height, planimetric area) of solid pineal glands in 184 non-retinoblastoma patients (73 female, 111 male) aged 0-5 years on MRI. The effect of age and gender on gland size was evaluated. Linear regression analysis was performed to analyze the relation between size and age. Ninety-nine percent prediction intervals around the mean were added to construct a normal size range per age, with the upper bound of the predictive interval as the parameter of interest as a cutoff for normalcy. Results: There was no significant interaction of gender and age for all the three pineal gland parameters (width, height, and area). Linear regression analysis gave 99 % upper prediction bounds of 7.9, 4.8, and 25.4 mm, respectively, for width, height, and area. The slopes (size increase per month) of each parameter were 0.046, 0.023, and 0.202, respectively. Ninety-three percent (95 % CI 66-100 %) of asymptomatic solid pineoblastomas were larger in size than the 99 % upper bound. Conclusion: This study establishes norms for solid pineal gland size in non-retinoblastoma children aged 0-5 years. Knowledge of the size of the normal pineal gland is helpful for detection of pineal gland abnormalities, particularly pineoblastoma. [ABSTRACT FROM AUTHOR]

Abstract: Fragile X-associated tremor/ataxia syndrome (FXTAS), a late-onset movement disorder affecting FMR1 premutation carriers, is associated with cerebral and cerebellar lesions. The aim of this study was to test whether computational anatomy can detect similar patterns in asymptomatic FMR1 premutation carriers (mean age 46.7 years) with qualitatively normal -appearing grey and white matter on brain MRI. We used a multimodal imaging protocol to characterize brain anatomy by automated assessment of gray matter volume and white matter properties. Structural changes in the hippocampus and in the cerebellar motor network with decreased gray matter volume in lobule VI and white matter alterations of the corresponding afferent projections through the middle cerebellar peduncles are demonstrated. Diffuse subcortical white matter changes in both hemispheres, without corresponding gray matter alterations, are only identified through age × group interactions. We interpret the hippocampal fimbria and cerebellar changes as early alterations with a possible neurodevelopmental origin. In contrast, progression of the diffuse cerebral hemispheric white matter changes suggests a neurodegenerative process, leading to late-onset lesions, which may mark the imminent onset of FXTAS. [Copyright &y& Elsevier]