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Med Phys. 2018 Jul;45(7):3120-3131. doi: 10.1002/mp.12945. Epub 2018 May 18.

Improving resolution of MR images with an adversarial network incorporating images with different contrast.

Kim KH1,2, Do WJ1, Park SH1,2.

Author information

1
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea.
2
Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea.

Abstract

PURPOSE:

The routine MRI scan protocol consists of multiple pulse sequences that acquire images of varying contrast. Since high frequency contents such as edges are not significantly affected by image contrast, down-sampled images in one contrast may be improved by high resolution (HR) images acquired in another contrast, reducing the total scan time. In this study, we propose a new deep learning framework that uses HR MR images in one contrast to generate HR MR images from highly down-sampled MR images in another contrast.

MATERIALS AND METHODS:

The proposed convolutional neural network (CNN) framework consists of two CNNs: (a) a reconstruction CNN for generating HR images from the down-sampled images using HR images acquired with a different MRI sequence and (b) a discriminator CNN for improving the perceptual quality of the generated HR images. The proposed method was evaluated using a public brain tumor database and in vivo datasets. The performance of the proposed method was assessed in tumor and no-tumor cases separately, with perceptual image quality being judged by a radiologist. To overcome the challenge of training the network with a small number of available in vivo datasets, the network was pretrained using the public database and then fine-tuned using the small number of in vivo datasets. The performance of the proposed method was also compared to that of several compressed sensing (CS) algorithms.

RESULTS:

Incorporating HR images of another contrast improved the quantitative assessments of the generated HR image in reference to ground truth. Also, incorporating a discriminator CNN yielded perceptually higher image quality. These results were verified in regions of normal tissue as well as tumors for various MRI sequences from pseudo k-space data generated from the public database. The combination of pretraining with the public database and fine-tuning with the small number of real k-space datasets enhanced the performance of CNNs in in vivo application compared to training CNNs from scratch. The proposed method outperformed the compressed sensing methods.

CONCLUSIONS:

The proposed method can be a good strategy for accelerating routine MRI scanning.

KEYWORDS:

convolutional neural network; generative adversarial network; magnetic resonance imaging

PMID:
29729006
DOI:
10.1002/mp.12945
[Indexed for MEDLINE]

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