561-04-003 ISMRM Abstract

MR Deep Learning Reconstruction Method with Controllable Frequency Weight After Learning

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling

561-04-003 · Deep Learning Meets k-Space: New Frontiers in Reconstruction · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row B

Keywords: Image Reconstruction Deep learning reconstruction

Accepted

Satoshi ITO ¹, Kotaro ADACHI², Fumiya CHUBACHI²

¹Graduate School of Regional Development and Creativity, Utsunomiya University, Utsunomiya, Japan

²Utsunomiya University, Utsunomiya, Japan

Presenting Author: Satoshi ITO

Synopsis

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References

1. Yamada Y, Tanaka K, Abe Z, NMR Fresnel transform imaging technique using a quadratic nonlinear field gradient, Rev Sci Instrum, vol.63, no.11, pp.5349-5358, 1992

2. Ronneberger O, Fischer P, Brox T, U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer; 2015. p. 234-241

3. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N et al., UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation, IEEE Trans. Med. Imaging 39: no. 6, 1856–1867, 2020

4. Yuan Z, Jian M, Wang Y et al., SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction, frontiers in neuroinformatics, vol.14, 611666, 2020

5. Fan CM, Liu TJ, Liu KH, SUNet: Swin Transformer UNet for Image Denoising, 2022 IEEE International Symposium on Circuits and Systems (ISCAS), Austin, TX, USA, 2022, pp. 2333-2337. doi: 10.1109/ISCAS48785.2022.9937486. [doi]

6. IXIdataset, https://brain-development.org/ixi-dataset/

7. Ito S, Nakamura K, Yamada Y, An Efficient Compressed Sensing Reconstruction Robust to Phase Variation on MR Images, International Society of Magnetic Resonance in Medicine 21th Scientific Meeting, Saltlake, USA, 2604, 2013

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-04-003