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Abstract #3852

Self-supervised deep learning model for denioising and distortion correction in accelerated echo planar imaging

Jeewon Kim1, Beomgu Kang1, HyunWook Park2, and Hyunseok Seo1
1Korea Institute of Science and Technology (KIST), Seoul, Korea, Republic of, 2Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, Republic of

Synopsis

Keywords: AI/ML Image Reconstruction, Artifacts, Denoising

Motivation: Accelerated EPI can improve temporal resolution but leads to low SNR, which exacerbates geometry distortions and aliasing artifacts.

Goal(s): Our objective is to simultaneously denoise and correct distortions in accelerated EPI using self-supervised learning.

Approach: An raw image generation of accelerated EPI block reproduces distorted images from distortion-corrected output of the network. The model is trained to reduce the error between the original distorted image and the synthesized image, enabling effective artifact correction with denoising based on J-invariant characteristic.

Results: Even in the absence of ground truth images, our model effectively corrects aliasing artifacts and geometric distortions in accelerated EPI images, providing denoising.

Impact: The proposed self-supervised approach achieves a significant advancement by enabling simultaneous denoising and distortion correction in accelerated EPI without ground truth images, thereby enhancing image quality in accelerated imaging.

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