Tetiana Dadakova^{1}, Jian Wu^{1}, Hyun-Kyung Chung^{1}, Brian Anhalt^{1}, Dmitry Tkach^{1}, Alexander Graff^{1}, Natalie Marie Schenker-Ahmed^{1}, David Karow^{1}, and Christine Leon Swisher^{1}

^{1}Human Longevity, Inc., San Diego, CA, United States

Many clinical MRI applications in chest and abdomen require low sensitivity to motion. In addition, high acquisition speed is necessary for imaging in non-cooperative patients or those unable to perform breath holds. These applications would benefit from the highly accelerated radial acquisition. Deep learning has been shown to provide good results for image reconstruction from highly under-sampled k-space data. Here we introduce a Projection GAN - a generative adversarial neural network, which is trained to reconstruct highly accelerated MR images from uniformly rotated projections. Our results show that even with aggressive under-sampling the reconstruction has great overall performance.

Compared to Cartesian k-space trajectories, the major benefit of radial sampling

Reconstruction from under-sampled k-space data using deep learning is substantially faster (sub-second) than iterative methods, because it allows to shift the time-consuming computations into training phase

Adapting this approach to MRI, we can leverage the property that the zero-frequency line or partition in k-space corresponds to the mean signal in image space in the corresponding direction for the 2D or 3D image, respectively (Figure 1 shows an example for 2D case). Such signal projection can be acquired at any angle; therefore, MRI allows us to use many more projections than CT, and as many projections necessary to achieve a balance between robust and accurate reconstruction of the image and acquisition time.

Here we present a novel Projection GAN – a generative adversarial neural network, which is trained to reconstruct MR images from a sub-Nyquist number of uniformly rotated projections.

A database

The input for the generator consisted of magnitude image of size 256x256 and the corresponding N

The following average quantitative results were achieved for the test data: NMSE = 0.20, PSNR = 28.06 dB and SSIM = 0.90.

Clinically, the proposed reconstruction method would be useful for applications, which require low sensitivity to motion and high acquisition speed. The examples of such applications include chest and abdominal MRI (pulmonary, cardiac, breast imaging, as well as MRI of pancreas or liver) in patients, who are unable to hold still or to perform breath holds (e.g. unconscious or pediatric patients).

Future research will include extending the Projection GAN to the 3D imaging, where each center partition will correspond to the 2D projection. As any k-space trajectory could be used within each k

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Figure 1 Fully sampled k-space (top row), as well as the center line of k-space (bottom row) and
the corresponding images. The right column shows the mean signal over the vertical
direction of the image. Note that the curves are identical for both cases:
fully sampled image and image from the only center k-space line

Figure 2 Schematic describing (A) how the model was trained and (B) how the generator was used during reconstruction of the
under-sampled projection data

Figure 3 (A) Fully sampled image (ground
truth) and (B) image reconstructed from N_{p} = 16 projections, which corresponds to 16x acceleration compared to fully sampled Cartesian trajectory and 25x acceleration compared to fully sampled radial trajectory