Minimally invasive assessment of hepatic function in children with indocyanine green elimination: a validation study.

Objectives: Pediatric liver disease (PLD) covers a variety of etiologies and severities, from mild temporary illness to diseases with fatal outcomes. There is a demand for minimally invasive and reliable measures for assessment of the severity of PLD. Indocyanine green (ICG) elimination kinetics to estimate hepatic function has been used in adults for decades, however, due to invasiveness, the use in PLD is still limited. The aim of the present study was to evaluate minimally invasive estimation of ICG elimination by pulse spectrophotometry (ICGLi), in comparison with traditional spectrophotometry using serial blood samples (ICGbs). Methods: One hundred children aged 0-18 years were included in the study. ICG elimination kinetics was measured with ICGLi and ICGbs, and results compared by failure rates, mean difference, limits of agreement, Bland Altman plots and linear regression analysis. Plasma disappearance rates (PDRLi and PDRbs) were used for comparison. Results: One hundred and twelve simultaneous measurements in 87 patients were performed successfully. Mean difference for PDR (%/min) was 3.58 (95% CI 2.69; 4.47). Limits of agreement were -5.06; 12.22. A linear correlation between the two methods with a regression coefficient of 0.83 (SE 0.02 95% CI 0.80; 0.87) was found. For conversion we computed the following equation; PDRbs = 0.83 × PDRLi. Conclusions: The present study shows that ICG PDR can be obtained by a minimally invasive method and thus replace measures by serial blood samples in children with liver disease of different etiologies and severities. However, a systematic relative difference between the two methods exists. Our proposed correction factor needs to be validated in larger cohorts.

Monte Carlo SURE-based parameter selection for parallel magnetic resonance imaging reconstruction.

PURPOSE: Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein's unbiased risk estimate that minimizes the multichannel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods that preserve the undersampled acquired data, which cannot be accomplished using existing techniques. THEORY: We derive a weighted MSE criterion appropriate for data-preserving regularized parallel imaging reconstruction and the corresponding weighted Stein's unbiased risk estimate. We describe a Monte Carlo approximation of the weighted Stein's unbiased risk estimate that uses two evaluations of the reconstruction method per candidate parameter value. METHODS: We reconstruct images using the denoising sparse images from GRAPPA using the nullspace method (DESIGN) and L1 iterative self-consistent parallel imaging (L1 -SPIRiT). We validate Monte Carlo Stein's unbiased risk estimate against the weighted MSE. We select the regularization parameter using these methods for various noise levels and undersampling factors and compare the results to those using MSE-optimal parameters. RESULTS: Our method selects nearly MSE-optimal regularization parameters for both DESIGN and L1 -SPIRiT over a range of noise levels and undersampling factors. CONCLUSION: The proposed method automatically provides nearly MSE-optimal choices of regularization parameters for data-preserving nonlinear parallel MRI reconstruction methods.

Non-cartesian MRI reconstruction with automatic regularization Via Monte-Carlo SURE.

Magnetic resonance image (MRI) reconstruction from undersampled k-space data requires regularization to reduce noise and aliasing artifacts. Proper application of regularization however requires appropriate selection of associated regularization parameters. In this work, we develop a data-driven regularization parameter adjustment scheme that minimizes an estimate [based on the principle of Stein's unbiased risk estimate (SURE)] of a suitable weighted squared-error measure in k-space. To compute this SURE-type estimate, we propose a Monte-Carlo scheme that extends our previous approach to inverse problems (e.g., MRI reconstruction) involving complex-valued images. Our approach depends only on the output of a given reconstruction algorithm and does not require knowledge of its internal workings, so it is capable of tackling a wide variety of reconstruction algorithms and nonquadratic regularizers including total variation and those based on the l1-norm. Experiments with simulated and real MR data indicate that the proposed approach is capable of providing near mean squared-error optimal regularization parameters for single-coil undersampled non-Cartesian MRI reconstruction.