A Synergized Pulsing-Imaging Network (SPIN)

TL;DR

This paper introduces a novel integrated MRI approach that jointly optimizes pulse sequences and image reconstruction using deep learning, demonstrating promising simulation results and potential for extension to other imaging modalities.

Contribution

It presents the first unified framework combining data acquisition and reconstruction optimization in MRI through deep learning.

Findings

Successful simulation of the integrated MRI strategy

Potential extension to ultrasound and emission-transmission tomography

Seamless optimization of pulse sequence and reconstruction

Abstract

Currently, the deep neural network is the mainstream for machine learning, and being actively developed for biomedical imaging applications with an increasing emphasis on tomographic reconstruction for MRI, CT, and other imaging modalities. Multiple deep-learning-based approaches were applied to MRI image reconstruction from k-space samples to final images. Each of these studies assumes a given pulse sequence that produces incomplete and/or inconsistent data in the Fourier space, and targets a trained neural network that recovers an underlying image as close as possible to the ground truth. For the first time, in this paper we view data acquisition and the image reconstruction as the two key parts of an integrated MRI process, and optimize both the pulse sequence and the reconstruction scheme seamlessly in the machine learning framework. Our pilot simulation results show an exemplary embodiment of our new MRI strategy. Clearly, this work can be extended to other imaging modalities and their combinations as well, such as ultrasound imaging, and also potentially simultaneous emission-transmission tomography aided by polarized radiotracers.