Clinical use of shear-wave elastography for detecting liver fibrosis in children and adolescents with cystic fibrosis.

BACKGROUND: Complications from liver cirrhosis are a leading cause of death in children with cystic fibrosis. Identifying children at risk for developing liver cirrhosis and halting its progression are critical to reducing liver-associated mortality. OBJECTIVE: Quantitative US imaging, such as shear-wave elastography (SWE), might improve the detection of liver fibrosis in children with cystic fibrosis (CF) over gray-scale US alone. We incorporated SWE in our pediatric CF liver disease screening program and evaluated its performance using magnetic resonance (MR) elastography. MATERIALS AND METHODS: Ninety-four children and adolescents with CF underwent 178 SWE exams, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT) and platelet measurements. Of these, 27 children underwent 34 MR elastography exams. We evaluated SWE performance using 6-MHz and 9-MHZ point SWE, and 9-MHz two-dimensional (2-D) SWE. RESULTS: The 6-MHz point SWE was the only method that correlated with MR elastography (r=0.52; 95% confidence interval [CI] 0.20-0.74; P=0.003). SWE of 1.45 m/s distinguished normal from abnormal MR elastography (79% sensitivity, 100% specificity, 100% positive predictive value [PPV], 55% negative predictive value [NPV], area under the receiver operating characteristic [AUROC] curve 0.94). SWE of 1.84 m/s separated mild-moderate (3.00-4.77 kPa) from severe (>4.77 kPa) MR elastography (88% sensitivity, 86% specificity, 78% PPV, 93% NPV, AUROC 0.79). Elevations of AST, ALT, GGT and thrombocytopenia were associated with higher SWE. AST-to-platelet ratio index of 0.42, fibrosis-4 of 0.29, and GGT-to-platelet ratio of 1.43 all had >95% NPV for SWE >1.84 m/s. CONCLUSION: Given its correlation with MR elastography, SWE might be a clinically useful predictor of liver fibrosis. We identified imaging criteria delineating the use of SWE to identify increased liver stiffness in children with CF. With multicenter validation, these data might be used to improve the detection and monitoring of liver fibrosis in children with CF.

Progression of corpus callosum diffusion-tensor imaging values during a period of signal changes consistent with myelination.

OBJECTIVE: Changes in signal intensity on T1- and T2-weighted MR images consistent with myelination in the corpus callosum occur during months 3-9 of postnatal life and occur earlier in the splenium than in the genu. We hypothesized that the rate of change in diffusion-tensor imaging parameters in the first year of life would be greater in the splenium, especially during months 3-9. SUBJECTS AND METHODS: Fifty-two infants (age range, 0-52 weeks) underwent one MRI examination with a six-direction diffusion-tensor imaging sequence. Fractional anisotropy, apparent diffusion coefficient, radial diffusivity, and axial diffusivity were measured in the genu and splenium of the corpus callosum. For each parameter, the slopes of change in the splenium and in the genu were measured for the entire first year of life and for the age period 3-9 months. The ratios of slope of change in the splenium to that in the genu in these two periods were compared. RESULTS: For fractional anisotropy, the ratio of slope of change in the splenium to that in the genu was 1.67 in the first year and 4.00 for 3-9 months; apparent diffusion coefficient, 2.00 in the first year and 4.33 for 3-9 months; radial diffusivity, 1.75 in the first year and 4.40 for 3-9 months; and axial diffusivity, 3.25 in the first year and 4.86 for 3-9 months. CONCLUSION: Rates of change were always greater in the splenium. For the age period 3-9 months, the splenium-to-genu ratio was approximately 1.5- to 2.5-fold as high as that for the entire year. These findings correspond well with the sequence of signal intensity changes in the corpus callosum.

Correlation of apparent diffusion coefficient and fractional anisotropy values in the developing infant brain.

OBJECTIVE: The purpose of our study was to correlate decrease in apparent diffusion coefficient (ADC) and increase in fractional anisotropy (FA) in various white matter (WM) regions using diffusion tenor imaging (DTI) within the first year of life. MATERIALS AND METHODS: We performed DTI on 53 infants and measured FA and ADC within 10 WM regions important in brain development. For each region, we calculated the slope of ADC as a function of FA, the correlation coefficient (r) and correlation of determination (r(2)). We performed a group analysis of r values and r(2)values for six WM regions primarily composed of crossing fibers and four regions primarily having parallel fibers. Upon finding that a strong correlation of FA with age existed, we adjusted for age and calculated partial correlation coefficients. RESULTS: Slopes of FA versus ADC ranged from -1.00711 to -1.67592 (p < 0.05); r values ranged from -0.81 to -0.50 and r(2) values from 0.25 to 0.66. The four greatest r(2) values were within WM regions having large numbers of crossing fibers and the three lowest r(2) values were in regions having predominantly parallel fibers. After adjusting for age, slopes ranged from -1.08095 to 0.09612 (p < 0.05 in five cases); partial correlation coefficients ranged from -0.49 to 0.03 and r(2) values from 0.31 to 0.79. The highest partial correlation coefficients were then relatively equally distributed between the two types of WM regions. CONCLUSION: In various regions, FA and ADC evolved with differing degrees of correlation. We found a strong influence of age on the relationship between FA and ADC.

Diffusion tensor imaging for evaluation of the childhood brain and pediatric white matter disorders.

Magnetic resonance (MR) imaging has been used by investigators and clinicians to assess the development of the brain in childhood to understand both patterns of normal growth and patterns by which a maturing brain may deviate from normal. Advanced MR techniques such as diffusion tensor imaging (DTI) have gained prominence as a means of assessing brain development. This review explains the sequence of brain maturation and the means by which DTI can be used to assess it in normal children.