Generating Synthetic MR Spectroscopic Imaging Data with Generative Adversarial Networks to Train Machine Learning Models.

Purpose: To develop a new method to generate synthetic MR spectroscopic imaging (MRSI) data for training machine learning models.

Methods: This study targeted routine MRI examination protocols with single voxel spectroscopy (SVS). A novel model derived from pix2pix generative adversarial networks was proposed to generate synthetic MRSI data using MRI and SVS data as inputs.

A fast and practical computation method for magnetic resonance simulators.

Purpose: This work aims to develop a fast and practical computation method for MR simulations. The computational cost of MR simulations is often high because magnetizations of many isochromats are updated using a small step size on the order of microseconds. There are two types of subsequences to be processed for the simulations: subsequences with and without RF pulses.

Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview.

This article presents an overview of deep learning (DL) and its applications to function approximation for MR in medicine. The aim of this article is to help readers develop various applications of DL. DL has made a large impact on the literature of many medical sciences, including MR.

Aliasing layers for processing parallel imaging and EPI ghost artifacts efficiently in convolutional neural networks.

Purpose: The purposes of this work are to develop a method for efficiently processing MR-specific artifacts using a convolutional neural network (CNN), and to present its applications for the removal of the artifacts without suppressing actual signals. In MR images that are acquired using parallel imaging and/or EPI, the locations of aliasing artifacts and/or N-half ghost artifacts can be analytically calculated. However, existing methods using CNNs do not take the structures of the artifacts into account, and therefore need a large number of convolution layers for processing the artifacts.

Estimation of Spatiotemporal Sensitivity Using Band-limited Signals with No Additional Acquisitions for k-t Parallel Imaging.

Purpose: Dynamic MR techniques, such as cardiac cine imaging, benefit from shorter acquisition times. The goal of the present study was to develop a method that achieves short acquisition times, while maintaining a cost-effective reconstruction, for dynamic MRI. k - t sensitivity encoding (SENSE) was identified as the base method to be enhanced meeting these two requirements.

Facial deblur inference using subspace analysis for recognition of blurred faces.

This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images. The main issue is how to infer a Point Spread Function (PSF) representing the process of blur on faces. Inferring a PSF from a single facial image is an ill-posed problem.