Distortion of echo planar imaging (EPI) can be corrected using B field maps, which can be estimated with the topup algorithm that requires two EPI images with opposite distortions. In this study, we propose a new algorithm, termed topup algorithm by single K-space (TASK), to generate two input images from a single k-space for the topup algorithm to correct EPI distortions. The centric EPI contains the opposite phase-encoding polarities in one k-space, which can be divided into two halves with opposite distortions. Therefore, two inputs could be extracted by dividing the k-space into halves and processing them using the proposed procedure including an iterative procedure of automatic brain masking and uniformity correction. The efficiency of TASK was evaluated using 3D EPI. Quantitative evaluations showed that TASK corrected EPI distortion at a similar level to the traditional methods. The estimated field maps from the conventional topup and TASK showed a high correlation ([Formula: see text]). An ablation study showed the validity of every suggested step. Furthermore, it was confirmed that TASK was effective for distortion correction of two-shot centric EPI as well, demonstrating its wider applicability. In conclusion, TASK can correct EPI distortions by its own single k-space information with no additional scan.
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http://dx.doi.org/10.1038/s41598-023-46163-3 | DOI Listing |
Proc (Bayl Univ Med Cent)
September 2024
Department of Anesthesiology, Baylor Scott & White Medical Center - Temple, Temple, Texas, USA.
Background: We hypothesized that implementation of a labor epidural management algorithm would increase the labor epidural catheter replacement rate at our hospital.
Methods: Our institutional review board approved this study and waived the requirement for informed consent. Patients who had labor epidural analgesia and delivered vaginally or had replacement of an epidural catheter prior to vaginal delivery from August 1, 2022 to December 31, 2022 and from August 1, 2023 to December 31, 2023 were included in the study.
Magn Reson Med
September 2024
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Purpose: Demonstrate the potential of spatiotemporal encoding (SPEN) MRI to deliver largely undistorted 2D, 3D, and diffusion weighted images on a 110 mT portable system.
Methods: SPEN's quadratic phase modulation was used to subsample the low-bandwidth dimension of echo planar acquisitions, delivering alias-free images with an enhanced immunity to image distortions in a laboratory-built, low-field, portable MRI system lacking multiple receivers.
Results: Healthy brain images with different SPEN time-bandwidth products and subsampling factors were collected.
Sci Data
February 2024
Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK.
Sci Rep
October 2023
MRI Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Distortion of echo planar imaging (EPI) can be corrected using B field maps, which can be estimated with the topup algorithm that requires two EPI images with opposite distortions. In this study, we propose a new algorithm, termed topup algorithm by single K-space (TASK), to generate two input images from a single k-space for the topup algorithm to correct EPI distortions. The centric EPI contains the opposite phase-encoding polarities in one k-space, which can be divided into two halves with opposite distortions.
View Article and Find Full Text PDFMagn Reson Med
January 2024
Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.
Purpose: To introduce an unsupervised deep-learning method for fast and effective correction of susceptibility artifacts in reversed phase-encode (PE) image pairs acquired with echo planar imaging (EPI).
Methods: Recent learning-based correction approaches in EPI estimate a displacement field, unwarp the reversed-PE image pair with the estimated field, and average the unwarped pair to yield a corrected image. Unsupervised learning in these unwarping-based methods is commonly attained via a similarity constraint between the unwarped images in reversed-PE directions, neglecting consistency to the acquired EPI images.
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