Abstract
Recent years have seen increased research interest in replacing the computationally intensive Magnetic resonance (MR) image reconstruction process with deep neural networks. We claim in this paper that the traditional image reconstruction methods and deep learning (DL) are mutually complementary and can be combined to achieve better image reconstruction quality. To test this hypothesis, a hybrid DL image reconstruction method was proposed by combining a state-of-the-art deep learning network, namely a generative adversarial network with cycle loss (CycleGAN), with a traditional data reconstruction algorithm: Projection Onto Convex Set (POCS). The output of the first iteration's training results of the CycleGAN was updated by POCS and used as the extra training data for the second training iteration of the CycleGAN. The method was validated using sub-sampled Magnetic resonance imaging data. Compared with other state-of-the-art, DL-based methods (e.g., U-Net, GAN, and RefineGAN) and a traditional method (compressed sensing), our method showed the best reconstruction results.
Highlights
Magnetic resonance imaging (MRI) has become a standard diagnosis tool in clinic service due to the non-invasiveness, multiple contrasts, and high temporal and spatial resolutions it can provide
We extended our previous work in integrating traditional MR image reconstruction
The former is a popular deep learning (DL) network that has been successfully applied to many research fields [34,35], and ular DL network that has been successfully applied to many research fields [34,35], and popular DL network that has been successfully applied to many research fields [34,35], and the latter is a standard optimization method that has been widely used in MR image reconstruction [36,37]
Summary
Magnetic resonance imaging (MRI) has become a standard diagnosis tool in clinic service due to the non-invasiveness, multiple contrasts, and high temporal and spatial resolutions it can provide. The speed of MRI is still much slower compared with the other imaging modalities such as computed tomography (CT) because of the sequential encoding paradigm used for acquiring the frequency- or phase-encoded raw data in the socalled k-space (the Fourier transform domain) [1]. Without sacrificing the image resolution and without hardware upgrading, an essential way to shorten the data acquisition time is to acquire a subset of the fully sampled k-space determined by the Nyquist sampling theory. An imperative step is to reconstruct the MR image from the incomplete k-space data, as it was from a fully sampled k-space dataset. Parallel imaging is a popular choice which relies on recovering the missing k-space information using the spatially localized sensitivity profiles of the elements within a phase array coil [2,3,4,5,6]
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