Investig Magn Reson Imaging.  2019 Jun;23(2):81-99. 10.13104/imri.2019.23.2.81.

Deep Learning in MR Image Processing

• Lee D
• Lee J
• Ko J
• Yoon J
• Ryu K
• Nam Y

• KMID: 2452523
• DOI: http://doi.org/10.13104/imri.2019.23.2.81

Abstract

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

Keyword

Deep learning; Machine learning; Image processing

MeSH Terms

Complement System Proteins
Image Processing, Computer-Assisted
Learning*
Machine Learning
Magnetic Resonance Imaging
Quality Improvement
Complement System Proteins

Figure

• 

  Fig. 1 (a) Overall process of the learning process for the simplified version of the neural network, which is the most widely used deep learning architecture in image processing applications. The nodes of the previous layer can be connected to each node of the next layer in various ways. (b) Fully-connected layer. (c) Locally-connected layer. (d) Locally-connected layer with multiple channels.

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  Fig. 2 Examples showing the ability of deep learning to generate realistic fake images. (a) Representative test images from the trained network for generating either pizza images from T1-weighted MR images or T1-weighted MR images from pizza images. (b) Representative test images from the tr ained network for generating MR diffusion-weighted images from actual CT images. The network generated the realistic synthesized diffusion-weighted image from the actual CT image. However, this generated diffusion-weighted image does not include the pathologic information which appears on the corresponding actual diffusion-weighted image. These two examples were generated using the generative adversarial networks, which are popular deep neural networks used for image-to-image translation tasks (181920).

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  Fig. 3 Splitting data into training, valid, and test sets is generally recommended so as to avoid overfitting and evaluate the performance objectively. The optimally fitted network shows similarly good performances for all three data sets.

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  Fig. 4 Simple example of the effect of data augmentation on deep learning. Data augmentation is generally recommended in order to increase the robustness for input data variances. The slightly modified architecture of automated transform by manifold approximation (21) was used in this example.

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  Fig. 5 Tools for MRI image reconstruction. The intersection of the individual solution distributions from the tools may represent the most likely solution.

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  Fig. 6 Retrospective motion correction using deep learning. Motion corrupted image (left), compensated images with 1D navigator (center), and deep learning approach (right). The second row shows the enlarged images of the cervical spinal cord region.

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  Fig. 7 Artifact correction for synthetic fluid attenuated inversion recovery (FLAIR) images using deep learning. Several artifacts are common on conventional model-based synthetic FLAIR images (68). The deep learning method suggested by Ryu et al. (69) successfully corrected these artifacts, thereby preserving the contrast of conventional FLAIR images (70).

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  Fig. 8 Multilayer perceptron method for T2 mapping using a multi-echo spin-echo sequence. The effects of B1 inhomogeneity were also considered, and the complexity of the model has been overcome using the proposed method (75).

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  Fig. 9 Deep neural network trained to map the gold standard QSM (COSMOS) reconstructed from multiple scans) from a single orientation phase data (78).

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  Fig. 10 Synthesized magnetization-prepared rapid gradient-echo (MPRAGE) images from multiecho gradient-echo images. The deep neural network (3D U-Net) was used in this example (98).

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