Augmented Neural neTwork with Incoherent Structure

SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for efficient and robust MR image reconstruction Fang

1Department

1 Liu ,

Lihua

2 Chen ,

Kijowski

1 Richard ,

Li

3 Feng

2Department of Radiology, Southwest Hospital, Chongqing, China of Radiology, University of Wisconsin-Madison, Wisconsin, USA 3Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA

INTRODUCTION • Deep learning has recently demonstrated great potentials for efficient image reconstruction from undersampled k-space measurements1-6.

RESULTS Cartesian Knee Imaging

• For most of existing studies, a fixed undersampling pattern is typically employed for the network training, and the trained neural network is applied to reconstruct new images acquired with the same sampling pattern.

• SANTIS achieved better image quality than CNNFix, which used the same sampling pattern for training a network.

• Thus, the robustness of the trained network could be degraded when an image to be reconstructed is acquired with an undersampling pattern different from that for the training7.

• CNN-Fix failed to reconstruct images reliably when the image to be reconstructed has a different undersampling pattern than the one used during training.

• We propose a new deep-learning based image reconstruction framework, termed as SamplingAugmented Neural neTwork with Incoherent Structure (SANTIS), with the following hypotheses: • Deep learning-based image reconstruction can benefit from extensively varying undersampling patterns during the training process • Varying undersampling patterns can improve the robustness and reconstruction performance of the trained neural network • Such a training strategy enforces sampling or k-space trajectory augmentation, representing a great candidate for applications that employ non-repeating k-space acquisitions, such as goldenangle radial MRI8

Golden-Angle Radial Liver Imaging • In liver datasets, SANTIS also achieved better image quality (green arrows) than CNN-Fix when the image to be reconstructed has changing undersampling patterns.

METHODS

CNN Algorithm Design • A Data-Cycle-Consistent GAN algorithm9, with a combination of residual learning U-Net (for CNN mapping) and PatchGAN (for adversarial process).

• SANTIS also outperformed conventional compressed sensing reconstruction (yellow arrows) with a sparsity constraint (wavelet transform).

– A high efficient end-to-end CNN mapping with residual learning design – A data fidelity loss enforcing CNN output images to be consistent with k-space measurements – An adversarial loss ensuring high perceptional quality of reconstructed images

• As shown with the green arrows, SANTIS outperformed both CNN-Fix-Match (consistent undersampling pattern during both training and inference) and CNN-FixUnmatch (different undersampling pattern between training and inference).

• The proposed reconstruction algorithm was tested for accelerated imaging of the knee (with Cartesian sampling) and the liver (with goldenangle radial sampling).

Sampling Augmentation • The undersampling pattern used for training keeps varying in each network training iteration.

• However, we noted that CNNFix-Unmatch is better in liver imaging than knee imaging, potentially due to the more incoherent sampling behavior from the golden-angle radial sampling.

• The trained network tries to learn various artifact structures and thus better generalizes towards removing new artifacts that might occur during the inference process. • The incoherence introduced by varying the sampling patterns can also potentially improve reconstruction performance.

DATASETS AND TRAINING • 25 fully sampled multislice knee images (acquired at UW Hospital, Madison, USA) and 55 postcontrast 3D golden-angle radial liver MR datasets (acquired at the Southwest Hospital, Chongqing, China) were used for the training. • 5 additional knee datasets and 8 additional liver datasets were used for evaluation. • Knee images were retrospectively undersampled with 3-fold acceleration • Each undersampled liver image includes 89 consecutive spokes • During the training iteration, the undersampling pattern was randomly picked from the sampling pattern library. • For comparison, standard training using a fixed undersampling mask was also performed, and this network was used to reconstruct undersampled image with the same training mask (Matched) and with a different mask (Unmatched).

Conclusion and Discussion • The SANTIS framework enforces sampling or k-space trajectory augmentation with extensive variation of sampling patterns during the training process. • Such a training strategy has the potential to improve the robustness of the network for better reconstruction performance. • We have shown the performance of SANTIS in accelerated Cartesian and golden-angle radial imaging for knee and liver imaging, respectively.

References 1. Hammernik et.al. MRM, 2018 Jun;79(6):3055-3071 3. Wang et.al. ISBI 2016, 514-517 5. Zhu et.al. Nature 2018 Mar 21;555(7697):487-492 7. Knoll et.al. MRM, 2018 May 17. doi: 10.1002 9. Liu et.al. ISMRM ML workshop, March 2018

2. Mardani et.al. TMI, 2018 Jul 23. doi: 10.1109 4. Schlemper et.al. TMI, 2018 Feb;37(2):491-503 6. Han et.al. MRM, 2018 Sep;80(3):1189-1205 8. Feng et.al. MRM, 2014 Sep;72(3):707-17