@inproceedings{d2759004765d489cbd53897b26f6bbb7,
title = "Improving the Resolution and SNR of Diffusion Magnetic Resonance Images From a Low-Field Scanner",
abstract = "Spatial resolution, signal-to-noise ratio (SNR) and acquisition time are interconnected in magnetic resonance imaging (MRI). Trade-offs are made to keep the SNR at the acceptable level, maximizing the resolution, minimizing the acquisition time and maintaining radiologically useful images. In low-field MRI scanners and especially in diffusion imaging, these trade-offs are even more crucial due to a generally lower image quality. Image post-processing is necessary in such cases to improve image quality. In this work, we alleviate the challenges of low SNR in dMRI at low magnetic fields by performing super-resolution reconstruction (SRR). Our approach combines multiple low-resolution images acquired at different image slice rotations and employs a convolutional neural network to perform the SRR. Training is performed on noisy images. The network learns to extract and compose complementary image details into a super-resolution output image. Because of the properties of noise and the training process, the super-resolution images are less noisy than the directly acquired high-resolution ones, contain more high-resolution details than the input low-resolution images and the total acquisition time is decreased.",
author = "Jakub Jurek and Kamil Ludwisiak and Andzej Materka and Filip Szczepankiewicz",
year = "2023",
month = sep,
day = "11",
doi = "10.1007/978-3-031-38430-1_12",
language = "English",
isbn = "978-3-031-38429-5",
volume = "746",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Nature",
pages = "147--160",
booktitle = "The Latest Developments and Challenges in Biomedical Engineering",
address = "United States",
}