Abstract

Convolutional neural networks (CNNs) demonstrate excellent performance when employed to reconstruct the images obtained by compressed-sensing magnetic resonance imaging (CS-MRI). Our study aimed to enhance image quality by developing a novel iterative reconstruction approach that utilizes image-based CNNs and k-space correction to preserve original k-space data. In the proposed method, CNNs represent a priori information concerning image spaces. First, the CNNs are trained to map zero-filling images onto corresponding full-sampled images. Then, they recover the zero-filled part of the k-space data. Subsequently, k-space corrections, which involve the replacement of unfilled regions by original k-space data, are implemented to preserve the original k-space data. The above-mentioned processes are used iteratively. The performance of the proposed method was validated using a T2-weighted brain-image dataset, and experiments were conducted with several sampling masks. Finally, the proposed method was compared with other noniterative approaches to demonstrate its effectiveness. The aliasing artifacts in the reconstructed images obtained using the proposed approach were reduced compared to those using other state-of-the-art techniques. In addition, the quantitative results obtained in the form of the peak signal-to-noise ratio and structural similarity index demonstrated the effectiveness of the proposed method. The proposed CS-MRI method enhanced MR image quality with high-throughput examinations.

Highlights

  • Magnetic resonance imaging (MRI) is a noninvasive imaging modality for acquiring biological information at high spatial resolution

  • Compared to X-ray computed tomography, MRI scan times are longer owing to the use of a data-acquisition scheme that is sequentially sampled over the Fourier domain— referred to as the k-space

  • This paper presents a compressed-sensing magnetic resonance imaging (CS-MRI) reconstruction approach that combines image-based Convolutional neural networks (CNNs) and k-space correction, wherein the two methods are iteratively implemented, with the CNNs representing a priori image space information

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Summary

Introduction

Magnetic resonance imaging (MRI) is a noninvasive imaging modality for acquiring biological information at high spatial resolution. Compared to X-ray computed tomography, MRI scan times are longer owing to the use of a data-acquisition scheme that is sequentially sampled over the Fourier domain— referred to as the k-space. This shortcoming has resulted in the proposal of several hardware-based and software-based techniques, such as asymmetric Fourier imaging [1], parallel imaging [2,3,4], and echo-planar imaging [5,6], to reduce the time required to obtain an MRI scan. Undersampled data are subject to aliasing artifacts

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