Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning.

Background: Acceleration of MR imaging (MRI) is a popular research area, and usage of deep learning for acceleration has become highly widespread in the MR community. Joint acceleration of multiple-acquisition MRI was proven to be effective over a single-acquisition approach. Also, optimization in the sampling pattern demonstrated its advantage over conventional undersampling pattern.

Varying undersampling directions for accelerating multiple acquisition magnetic resonance imaging.

In this study, we propose a new sampling strategy for efficiently accelerating multiple acquisition MRI. The new sampling strategy is to obtain data along different phase-encoding directions across multiple acquisitions. The proposed sampling strategy was evaluated in multicontrast MR imaging (T1, T2, proton density) and multiple phase-cycled (PC) balanced steady-state free precession (bSSFP) imaging by using convolutional neural networks with central and random sampling patterns.

Reconstruction of multicontrast MR images through deep learning.

Purpose: Magnetic resonance (MR) imaging with a long scan time can lead to degraded images due to patient motion, patient discomfort, and increased costs. For these reasons, the role of rapid MR imaging is important. In this study, we propose the joint reconstruction of multicontrast brain MR images from down-sampled data to accelerate the data acquisition process using a novel deep-learning network.

Artificial neural network for Slice Encoding for Metal Artifact Correction (SEMAC) MRI.

Purpose: To develop new artificial neural networks (ANNs) to accelerate slice encoding for metal artifact correction (SEMAC) MRI.

Methods: Eight titanium phantoms and 77 patients after brain tumor surgery involving metallic neuro-plating instruments were scanned using SEMAC at a 3T Skyra scanner. For the phantoms, proton-density, T1-, and T2-weighted images were acquired for developing both multilayer perceptron (MLP) and convolutional neural network (CNN).