Model-based reconstruction for looping-star MRI.

Purpose: The aim of this study was to develop a reconstruction method that more fully models the signals and reconstructs gradient echo (GRE) images without sacrificing the signal to noise ratio and spatial resolution, compared to conventional gridding and model-based image reconstruction method.

Methods: By modeling the trajectories for every spoke and simplifying the scenario to only echo-in and echo-out mixture, the approach explicitly models the overlapping echoes. After modeling the overlapping echoes with two system matrices, we use the conjugate gradient algorithm (CG-SENSE) with the nonuniform FFT (NUFFT) to optimize the image reconstruction cost function.

A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions.

Purpose: A major challenge for accurate quantitative SPECT imaging of some radionuclides is the inadequacy of simple energy window-based scatter estimation methods, widely available on clinic systems. A deep learning approach for SPECT/CT scatter estimation is investigated as an alternative to computationally expensive Monte Carlo (MC) methods for challenging SPECT radionuclides, such as Y.

Methods: A deep convolutional neural network (DCNN) was trained to separately estimate each scatter projection from the measured Y bremsstrahlung SPECT emission projection and CT attenuation projection that form the network inputs.