A Deep Learning Approach Using Masked Image Modeling for Reconstruction of Undersampled K-spaces

• URL: http://arxiv.org/abs/2208.11472v1
• Date: Wed, 24 Aug 2022 12:27:54 GMT
• Title: A Deep Learning Approach Using Masked Image Modeling for Reconstruction of Undersampled K-spaces
• Authors: Kyler Larsen, Arghya Pal and Yogesh Rathi
• Abstract summary: This study makes use of 11161 reconstructed MRI and k spaces of knee MRI images from Facebook's fastmri dataset. The model was evaluated through L1 loss, gradient normalization, and structural similarity values. The reconstructed k spaces yielded structural similarity values of over 99% for both training and validation with the fully sampled k spaces.
• Score: 7.227671880690971
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Magnetic Resonance Imaging (MRI) scans are time consuming and precarious, since the patients remain still in a confined space for extended periods of time. To reduce scanning time, some experts have experimented with undersampled k spaces, trying to use deep learning to predict the fully sampled result. These studies report that as many as 20 to 30 minutes could be saved off a scan that takes an hour or more. However, none of these studies have explored the possibility of using masked image modeling (MIM) to predict the missing parts of MRI k spaces. This study makes use of 11161 reconstructed MRI and k spaces of knee MRI images from Facebook's fastmri dataset. This tests a modified version of an existing model using baseline shifted window (Swin) and vision transformer architectures that makes use of MIM on undersampled k spaces to predict the full k space and consequently the full MRI image. Modifications were made using pytorch and numpy libraries, and were published to a github repository. After the model reconstructed the k space images, the basic Fourier transform was applied to determine the actual MRI image. Once the model reached a steady state, experimentation with hyperparameters helped to achieve pinpoint accuracy for the reconstructed images. The model was evaluated through L1 loss, gradient normalization, and structural similarity values. The model produced reconstructed images with L1 loss values averaging to <0.01 and gradient normalization values <0.1 after training finished. The reconstructed k spaces yielded structural similarity values of over 99% for both training and validation with the fully sampled k spaces, while validation loss continually decreased under 0.01. These data strongly support the idea that the algorithm works for MRI reconstruction, as they indicate the model's reconstructed image aligns extremely well with the original, fully sampled k space.

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