Age-Related Changes in Tissue Value Properties in Children

Abstract

Objectives The properties of brain tissue undergo dynamic changes during maturation. T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) are now simultaneously quantifiable within a clinically acceptable time, using a synthetic magnetic resonance imaging (MRI) sequence. This study aimed to provide age-specific reference values for T1, T2, and PD in children, using synthetic MRI. Materials and Methods We included 89 children (median age, 18 months; range, 34 weeks of gestational age to 17 years) who underwent quantitative MRI, using a multidynamic, multiecho sequence on 3 T MRI, between December 2015 and November 2016, and had no abnormal MRI/neurologic assessment findings. T1, T2, and PD were simultaneously measured in each of the 22 defined white matter and gray matter regions of interest. The measured values were plotted against age, and a curve fitting model that best explained the age dependence of tissue values was identified. Age-specific regional tissue values were calculated using a fit equation. Results The tissue values of all brain regions, except cortical PD, decreased with increasing age, and the robust negative association was best explained by modified biexponential model of the form Tissue values = T1 × exp (−C1 × age) + T2 × exp (−C2 × age). The quality of fit to the modified biexponential model was high in white matter and deep gray matter (white matter, R 2 = 97%–99% [T1], 88%–95% [T2], 88%–97% [PD]; deep gray matter, R 2 = 96%–97% [T1], 96% [T2], 49%–88% [PD]; cortex, 70%–83% [T1], 87%–90% [T2], 5%–27% [PD]). The white matter and deep gray matter changed the most dynamically within the first year of life. Conclusions Our study provides age-specific regional reference values, from the neonate to adolescent, of T1, T2, and PD, which could be objective tools for assessment of normal/abnormal brain development using synthetic MRI.

Feasibility of a Synthetic MR Imaging Sequence for Spine Imaging

“…Other studies have since assessed the clinical feasibility of synthetic MR imaging across different neurologic conditions 8,9 such as multiple sclerosis 10 and metastatic disease of the brain 11 and in the pediatric population. [5][6][7]12,13 This technique has also been recently used for musculoskeletal imaging. A recent study, focusing on imaging of the normal knee, 14 assessed the feasibility and diagnostic accuracy of synthetic MR imaging compared with conventional MR imaging as well as the utility of synthetic quantitative T2 maps of the different knee structures compared with conventional T2 mapping sequences.…”

“…This offers the possibility of shortening the study duration and the option of relying on more objective parameters to reach a diagnosis. Recently, this emerging technique has been applied to several brain diseases in adults 3,4 and children, [5][6][7] but to our knowledge, it has never been used in spine imaging. The purpose of this work was to apply synthetic MR imaging to the spine 6 and spinal cord and to compare the overall image quality, diagnostic confidence, and lesion conspicuity produced by synthetic MR imaging with conventional sequences.…”

Variability and Standardization of Quantitative Imaging

“…Thus, absolute signal intensity has no direct meaning, and the evaluation of MRI scans mainly involves comparison with surrounding tissues in the same slice. Absolute quantification of longitudinal relaxation time (T1), transverse relaxation time (T2) or their inverse relaxation rates (R1 and R2), and proton density (PD) provides an absolute scale; hence, it enables a more objective evaluation of development, 100 aging, 101 and diseases. 102 …”

SSL‐QALAS: Self‐Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D‐QALAS

“…In addition, such information also finds applications in neuroscientific imaging, including aging research. 9,[12][13][14][15][16][17][18] Quantitative information can be obtained using sequences including DESPOT1, DESPOT2, MP2RAGE, and MPnRAGE, which acquire a single quantitative MR parameter map from the measurements. [19][20][21] Recently, various MRI methods that have been developed are capable of acquiring multiple quantitative MR parameter maps at high resolution within a reasonable timeframe, including MR fingerprinting (MRF), 22 echo planar time-resolved imaging (EPTI), 23 and MR multitasking.…”