Abstract
The work of this paper analyzes the performance of Sensitivity Encoding (SENSE) through actual data sets and determines the problem of computational efficiency. It corrects the error of the detection signal through the calibration function of the percentage signal change, and uses the three-dimensional sensor image reconstruction technology to calibrate the sensitivity of the blood to the magnetic change, enhances the sensitivity of the magnetic susceptibility gradient, and reduces the scanning time of the MRI experiment. The actual data set handles the image resolution. The performance and experimental results of SENSE are analyzed through actual data sets.
Highlights
The magnetic resonance imaging (MRI) to detect blood oxygenation level dependent (BOLD) signal is the most common tool
It corrects the error of the detection signal through the calibration function of the percentage signal change, and uses the three-dimensional sensor image reconstruction technology to calibrate the sensitivity of the blood to the magnetic change, enhances the sensitivity of the magnetic susceptibility gradient, and reduces the scanning time of the MRI experiment
The magnetic susceptibility gradient will be displayed on the k-space trajectory, and the echo time is measured at the origin
Summary
It corrects the error of the detection signal through the calibration function of the percentage signal change, and uses the three-dimensional sensor image reconstruction technology to calibrate the sensitivity of the blood to the magnetic change, enhances the sensitivity of the magnetic susceptibility gradient, and reduces the scanning time of the MRI experiment. The magnetic resonance imaging (MRI) to detect blood oxygenation level dependent (BOLD) signal is the most common tool. The magnetic field gradient affects the BOLD sensitivity of MRI acquisition.
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