Quantitative Synthetic MRI in Children: Normative Intracranial Tissue Segmentation Values during Development

儿童定量合成磁共振成像:发育过程中颅内组织分割的正常值

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Abstract

BACKGROUND AND PURPOSE: Synthetic MR imaging is a new technique to create absolute R1 relaxivity (1/T1), R2 relaxivity (1/T2), and proton-density maps using a single multiple-spin-echo saturation recovery sequence. These relaxivity maps allow rapid automated intracranial segmentation of tissue types. To assess its utility in children, we created a normative data base of intracranial volume and brain parenchymal, GM, WM, CSF, and myelin volumes in a pediatric population with normal brain MRI findings using synthetic MR imaging. MATERIALS AND METHODS: All multiple-spin-echo saturation recovery sequences containing brain MR imaging examinations performed during 34 months were retrospectively reviewed. Abnormal examination findings were excluded following a detailed radiographic and clinical chart review. The remaining normal examination findings were then quantitatively analyzed with synthetic MR imaging. Intracranial, brain parenchymal, GM, WM, CSF, and myelin volumes were plotted versus age. Qualitative assessment of segmentation accuracy was performed. Selected abnormal examination findings were compared with these normative curves. RESULTS: One hundred twenty-two MRI examinations with normal findings were included of individuals ranging from 0.1 to 21.5 years of age (median, 11.8 years). Resulting normative data plots compared favorably with previously published data obtained using more onerous techniques. Differentiation from pathologic states was possible using quantitative values in select cases. CONCLUSIONS: A pediatric data base of normal intracranial tissue volumes using a single sequence and rapid software analysis has been compiled and correlates with previously published data. This provides a framework for clinical interpretation of quantitative synthetic MR images during development. Improved age-based segmentation algorithms in young children are needed.

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