Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks
This addresses motion correction in MRI for medical diagnosis, offering a faster alternative to existing methods.
The paper tackles motion artifacts in multishot MRI by proposing a GAN-based method that reconstructs high-fidelity images, reducing reconstruction time by two orders of magnitude.
Multishot Magnetic Resonance Imaging (MRI) has recently gained popularity as it accelerates the MRI data acquisition process without compromising the quality of final MR image. However, it suffers from motion artifacts caused by patient movements which may lead to misdiagnosis. Modern state-of-the-art motion correction techniques are able to counter small degree motion, however, their adoption is hindered by their time complexity. This paper proposes a Generative Adversarial Network (GAN) for reconstructing motion free high-fidelity images while reducing the image reconstruction time by an impressive two orders of magnitude.