Abstract
Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Particularly, recent deep learning-based methods for image reconstruction will be emphasized, in accordance with their methodology designs and performances in handling volumetric imaging data. It is expected that this review can help relevant researchers understand how to adapt AI for medical imaging and which advantages can be achieved with the assistance of AI.
Highlights
Of all the advances in modern medicine, medical imaging is among the most remarkable developments
The Artificial intelligence (AI)-assisted compressed sensing (ACS) technique developed by United Imaging Intelligence (UII) and United Imaging Healthcare (UIH) integrates the advantages of four acceleration techniques, i.e., [1] deep learning-based reconstruction, [2] partial Fourier transform, [3] parallel imaging, and [4] compressed sensing, into a unified framework, and achieves great success in real-world clinical applications for fast magnetic resonance imaging (MRI) imaging
We focus on the applications in MRI, computed tomography (CT), and positron emission tomography (PET)
Summary
Of all the advances in modern medicine, medical imaging is among the most remarkable developments. Hyun et al [117] proposed an under sampling MRI reconstruction method using U-Net, which shows excellent performance and can generate high-quality MR images with a small amount of data. Gong et al [14] designed an iterative reconstruction framework that combines the U-net structure and the residual network for PET denoising by utilizing dynamic data of prior patients.
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