To minimize slice excitation leakage to adjacent slices, interleaved slice acquisition is nowadays performed regularly in fMRI scanners. In interleaved slice acquisition, the number of slices skipped between two consecutive slice acquisitions is often referred to as the 'interleave parameter'; the loss of this parameter can be catastrophic for the analysis of fMRI data. In this article we present a method to retrospectively detect the interleave parameter and the axis in which it is applied. Our method relies on the smoothness of the temporal-distance correlation function, which becomes disrupted along the axis on which interleaved slice acquisition is applied. We examined this method on simulated and real data in the presence of fMRI artifacts such as physiological noise, motion, etc. We also examined the reliability of this method in detecting different types of interleave parameters and demonstrated an accuracy of about 94% in more than 1000 real fMRI scans.
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http://dx.doi.org/10.1109/ISBI.2014.6867803 | DOI Listing |
Magn Reson Med Sci
November 2024
Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan.
Micromachines (Basel)
October 2024
School of Integrated Circuits and Electronics, Beijing Institute of Technology (BIT), Beijing 100081, China.
With the development of mobile communication, digital signatures with low latency, low area, and high security are in increasing demand. Elliptic curve cryptography (ECC) is widely used because of its security and lightweight. Elliptic curve scalar multiplication (ECSM) is the basic arithmetic in ECC.
View Article and Find Full Text PDFNMR Biomed
December 2024
Department of Medical Imaging, The University of Arizona, Tucson, Arizona, USA.
Efficient abdominal coverage with T1-mapping methods currently available in the clinic is limited by the breath hold period (BHP) and the time needed for T1 recovery. This work develops a T1-mapping framework for efficient abdominal coverage based on rapid T1 recovery curve (T1RC) sampling, slice-selective inversion, optimized slice interleaving, and a convolutional neural network (CNN)-based T1 estimation. The effect of reducing the T1RC sampling was evaluated by comparing T1 estimates from T1RC ranging from 0.
View Article and Find Full Text PDFSci Rep
September 2024
Department of Medicinal Chemistry, Uppsala University, Uppsala, Sweden.
In this work, echo-planar spectroscopic imaging (EPSI) with flyback readout gradient-echo train was implemented in a preclinical MR scanner. The aim of this study is to visualize and quantify the ghost spectral lines produced by two, three and four interleaved echo trains with different amplitudes of the readout gradients, and to investigate the feasibility of the flyback data acquisition in micro-imaging of small animals. Applied multi-slice EPSI sequence utilizes asymmetric gradient-echo train that combines the shortest possible rewind gradients with readout gradients.
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