Learning-based Ensemble Average Propagator Estimation

By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain. The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals. However, accurate EAP estimation using the number of diffusion gradients that is clinically practical can be challenging. In this work, we propose a deep learning algorithm for EAP estimation, which is named learning-based ensemble average propagator estimation (LEAPE). The EAP is commonly represented by a basis and its associated coefficients, and here we choose the SHORE basis and design a deep network to estimate the coefficients. The network comprises two cascaded components. The first component is a multiple layer perceptron (MLP) that simultaneously predicts the unknown coefficients. However, typical training loss functions, such as mean squared errors, may not properly represent the geometry of the possibly non-Euclidean space of the coefficients, which in particular causes problems for the extraction of directional information from the EAP. Therefore, to regularize the training, in the second component we compute an auxiliary output of approximated fiber orientation (FO) errors with the aid of a second MLP that is trained separately. We performed experiments using dMRI data that resemble clinically achievable $q$-space sampling, and observed promising results compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201

Human nephron number: Implications for health and disease

Several studies have shown that total nephron (glomerular) number varies widely in normal human kidneys. Whereas the studies agree that average nephron number is approximately 900,000 to 1 million per kidney, numbers for individual kidneys range from approximately 200,000 to > 2.5 million. Several studies have shown loss of glomeruli due to age-related glomerulosclerosis. The rates of loss vary among individuals depending upon blood pressure, diseases affecting the kidney, and other attributes of health, but most of the variation in nephron number is present at birth and is therefore developmentally determined. For example, in a relatively small study of nephron number in 15 children < 3 months of age, we found that nephron number ranged from approximately 250,000 to 1.1 million. Given that no new nephrons are formed in human kidneys after approximately 36 weeks' gestation, much interest has focused on renal function and health in individuals born with relatively low nephron endowment. Several studies have reported a direct correlation between birth weight and nephron number and an indirect association between nephron number and blood pressure. Associations between low birth weight and cardiovascular disease, including hypertension, have also been widely reported. This report provides an update on our current knowledge of human nephron number and the associations with adult health and disease