Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences.

Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood-brain barrier (BBB) permeability. Extraction of the pharmacokinetic (PK) parameters from the DCE-MRI concentration curves allows quantitative assessment of the integrity of the BBB functionality. However, curve fitting required for the analysis of DCE-MRI data is error-prone as the dynamic scans are subject to non-white, spatially-dependent and anisotropic noise. We present a novel spatio-temporal framework based on Deep Neural Networks (DNNs) to address the DCE-MRI denoising challenges. This is accomplished by an ensemble of expert DNNs constructed as deep autoencoders, where each is trained on a specific subset of the input space to accommodate different noise characteristics and curve prototypes. Spatial dependencies of the PK dynamics are captured by incorporating the curves of neighboring voxels in the entire process. The most likely reconstructed curves are then chosen using a classifier DNN followed by a quadratic programming optimization. As clean signals (ground-truth) for training are not available, a fully automatic model for generating realistic training sets with complex nonlinear dynamics is introduced. The proposed approach has been successfully applied to full and even temporally down-sampled DCE-MRI sequences, from two different databases, of stroke and brain tumor patients, and is shown to favorably compare to state-of-the-art denoising methods.

Ezetimibe is effective in the treatment of persistent hyperlipidemia of renal allograft recipients.

INTRODUCTION: Ezetimibe is a hypolipidemic agent acting via inhibition of cholesterol absorption from the small intestine. The effectiveness and safety of long-term administration of ezetimibe was evaluated in renal allograft recipients with persistent hyperlipidemia. PATIENTS AND METHODS: 67 renal allograft recipients with post-transplantation hyperlipidemia resistant to statins were included in the study; 11 were treated with ezetimibe (10 mg/day) alone and 56 with ezetimibe and statin. The effectiveness of ezetimibe was assessed by determination of total cholesterol (TC), LDL cholesterol (LDL-C), HDL cholesterol (HDL-C) and triglycerides (TR). Its safety was determined by liver enzymes (ALT, AST), LDH, CPK, serum creatinine and blood levels of immunosuppressive drugs (cyclosporine, tacrolimus, everolimus, sirolimus) over the follow-up period of 18±6 months. RESULTS: A significant reduction of TC and LDL-C blood levels by 25% and 34% respectively, was observed during the first month of treatment with ezetimibe (p<0.001). This reduction was maintained for the whole period of ezetimibe administration. Renal function remained stable over the follow-up period, while no changes of the blood levels of immunosuppressive drugs were observed. Liver enzymes, LDH and CPK remained normal in all patients except for one diabetic patient who developed rhabdomyolysis. Apart from gastrointestinal symptoms in 2 patients, no other side effects were observed. CONCLUSION: Combination of ezetimibe with statins represents an effective and safe regimen for treatment of persistent hyperlipidemia in renal allograft recipients.