`` FastMRI '' which makes MRI scan 10 times faster reveals a method to dramatically improve the quality of MRI images
[2001.08699] MRI Banding Removal via Adversarial Training
https://arxiv.org/abs/2001.08699
FastMRI leverages adversarial learning to remove image artifacts
https://ai.facebook.com/blog/fastmri-leverages-adversarial-learning-to-remove-image-artifacts/
Research results have already been published on FastMRI, and codes and datasets for building reference models on GitHub have been made public so that a wider community can engage in FastMRI. Following these, on February 25, 2020, the research team announced a technology to solve the problem of FastMRI using deep learning and improve the quality of the entire MRI scan image.
According to the research team, one of the challenges in producing accurate MRI scans from raw data using deep learning is the extra streaks in the scanned images. The 'noise' in MRI scans is obvious to trained professionals, but it is not easily discernible by laymen. `` When evaluating accelerated MRI scan images, artificial streaks entering the horizontal direction can significantly degrade the image quality and obscure the disease, '' said Michael Lecht of the New York University Langone Medical Center I noticed that there is a potential. '
To address this issue, the team used hostile learning to create a deep learning model that 'obtains raw data from MRI scans and produces accurate MRI images without artificial streaks.'
Hostile learning is often likened to 'the relationship between banknote counterfeiters and police.' In other words, counterfeiters create counterfeit bills that are as real as possible, and the police judge whether it is real or fake, but as the police's ability to judge increases, so does the skill of the counterfeiter to fool its eyes ... It can be said that the mechanism is similar to the form. In the case of fastMRI, the research team stated that the goal of hostile learning was to 'predict the streak pattern orientation'. At this time, since the hostile model and the reconstructed model of the MRI image were trained simultaneously, the reconstructed model was improved until the streak disappeared, and the accuracy of detecting the streak of the hostile model was continuously improved.
The following image is a normal MRI scan image on the left, an MRI scan image accelerated by AI in the middle, and an image created by hostile learning on the right. In the image on the right, the extra diagonal lines disappear, indicating that the image is clearer.
Because artificial streaks are a major issue in MRI accelerated by AI, the method announced this time may be a step to make FastMRI usable in clinical settings. In addition, the research team believes that this technology may be useful for improving functions, as the latest MRI scanner '3 Tesla MRI' has also been reported to be easy to create artificial streaks.
Related Posts:
