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Analisis Perbedaan Contrast to Noise Ratio (CNR) terhadap Variasi K-Space Filling pada Pemeriksaan MRI Brain Sekuen T2WI Axial dengan Klinis Space Occupying Lession (SOL)
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K-space functions to store digital data generated from time to time during scanning, then the data will be converted into image information via Fourier transformation. Radial trajectory at the Siemens vendor is called BLADE, which is a k-space filling method that starts from the central k-space axis and then rotates so that the data block is obtained. Cartesian trajectory is a method for filling k-space which is done linearly from top to bottom or from bottom to top. The aim of this research is to analyze the effect of the k-space filling method using the radial trajectory (BLADE) and Cartesian trajectory methods on the quality of axial MRI Brain T2WI sequence images in Space Occupying Lession (SOL) cases. This study uses a quantitative research method with an experimental study approach. The population and sample of the study were 10 patients with SOL clinical MRI brain examinations, and each sample underwent variations in k-space filling using the radial trajectory (BLADE) and Cartesian trajectory methods on axial T2WI sequences. The data was processed using radiant DICOM and then analyzed using the normality test and the Wilcoxon test on SPSS 25 software. The results of the study show a significance value (p-value) of 0.000 < 0.05, so it can be concluded that Ha is accepted and H0 is rejected, which means there is a difference in the Contrast to Noise Ratio (CNR) in the axial T2WI sequence MRI Brain examination using variations of k -space filling cartesian trajectory and radial trajectory (BLADE) techniques. The radial trajectory (BLADE) technique in the MRI Brain T2WI axial sequence examination is considered capable of producing a more optimal CNR compared to the Cartesian trajectory technique, seen from the mean rank value for the radial trajectory (BLADE) technique it’s 32.88, while for the Cartesian trajectory technique it’s 18. 7.
Politeknik Pratama Purwokerto
Title: Analisis Perbedaan Contrast to Noise Ratio (CNR) terhadap Variasi K-Space Filling pada Pemeriksaan MRI Brain Sekuen T2WI Axial dengan Klinis Space Occupying Lession (SOL)
Description:
K-space functions to store digital data generated from time to time during scanning, then the data will be converted into image information via Fourier transformation.
Radial trajectory at the Siemens vendor is called BLADE, which is a k-space filling method that starts from the central k-space axis and then rotates so that the data block is obtained.
Cartesian trajectory is a method for filling k-space which is done linearly from top to bottom or from bottom to top.
The aim of this research is to analyze the effect of the k-space filling method using the radial trajectory (BLADE) and Cartesian trajectory methods on the quality of axial MRI Brain T2WI sequence images in Space Occupying Lession (SOL) cases.
This study uses a quantitative research method with an experimental study approach.
The population and sample of the study were 10 patients with SOL clinical MRI brain examinations, and each sample underwent variations in k-space filling using the radial trajectory (BLADE) and Cartesian trajectory methods on axial T2WI sequences.
The data was processed using radiant DICOM and then analyzed using the normality test and the Wilcoxon test on SPSS 25 software.
The results of the study show a significance value (p-value) of 0.
000 < 0.
05, so it can be concluded that Ha is accepted and H0 is rejected, which means there is a difference in the Contrast to Noise Ratio (CNR) in the axial T2WI sequence MRI Brain examination using variations of k -space filling cartesian trajectory and radial trajectory (BLADE) techniques.
The radial trajectory (BLADE) technique in the MRI Brain T2WI axial sequence examination is considered capable of producing a more optimal CNR compared to the Cartesian trajectory technique, seen from the mean rank value for the radial trajectory (BLADE) technique it’s 32.
88, while for the Cartesian trajectory technique it’s 18.
7.
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