TY - GEN
T1 - Parameter Analysis on Sensitivity Encoding (SENSE) Algorithm for Parallel Imaging of Magnetic Resonance Imaging
AU - Ariani, Fitria
AU - Basari, Basari
N1 - Funding Information:
ACKNOWLEDGMENT The authors would like to thank to The National Research and Innovation Agency, Republic of Indonesia for Grant 2021 No. 121/C1.3/KS.00-PK/2021.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Magnetic Resonance Imaging (MRI) is one of the most prominent medical imaging technologies for examining human bones and soft tissues. However, it has a shortcoming of long scan time. To overcome this problem, parallel imaging is used to reduce scan time by using multiple coil receivers and under-sampled k-space data which lead to aliasing and artifact images. Sensitivity Encoding (SENSE) is one of the parallel imaging algorithms that work in the image domain to unfolding the aliased images. However, the use of SENSE algorithm is limited by its parameter. This study aims to investigate how parameters in the SENSE algorithm influence MRI image reconstruction via simulation. During the simulation, we used a variation of number of coils (nc) and acceleration factor (R). In order to analyze the data, we use structural similarity index measure (SSIM) and mean squared error (MSE) as the image quality assessment (IQA) methods. According to the IQA parameter, the results showed that number of coils and acceleration factor are correlated. The best IQA result in SENSE reconstructed image comes from the variation of nc = 16 and R = 2, which has the lowest MSE value (0.01405) and the highest SSIM value (0.8384). A higher acceleration factor number causes more aliasing and noise while more number of coils can make the reconstruction image better. Nevertheless, number of coils with the right number of acceleration factor will result in a good reconstructed image. This study showed that SENSE is capable of retrieving good reconstruction images by using the right parameters.
AB - Magnetic Resonance Imaging (MRI) is one of the most prominent medical imaging technologies for examining human bones and soft tissues. However, it has a shortcoming of long scan time. To overcome this problem, parallel imaging is used to reduce scan time by using multiple coil receivers and under-sampled k-space data which lead to aliasing and artifact images. Sensitivity Encoding (SENSE) is one of the parallel imaging algorithms that work in the image domain to unfolding the aliased images. However, the use of SENSE algorithm is limited by its parameter. This study aims to investigate how parameters in the SENSE algorithm influence MRI image reconstruction via simulation. During the simulation, we used a variation of number of coils (nc) and acceleration factor (R). In order to analyze the data, we use structural similarity index measure (SSIM) and mean squared error (MSE) as the image quality assessment (IQA) methods. According to the IQA parameter, the results showed that number of coils and acceleration factor are correlated. The best IQA result in SENSE reconstructed image comes from the variation of nc = 16 and R = 2, which has the lowest MSE value (0.01405) and the highest SSIM value (0.8384). A higher acceleration factor number causes more aliasing and noise while more number of coils can make the reconstruction image better. Nevertheless, number of coils with the right number of acceleration factor will result in a good reconstructed image. This study showed that SENSE is capable of retrieving good reconstruction images by using the right parameters.
KW - Image reconstruction
KW - MRI
KW - Non-invasive
KW - Parallel imaging
KW - SENSE algorithm
UR - http://www.scopus.com/inward/record.url?scp=85124519132&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE53823.2021.9655770
DO - 10.1109/ICITISEE53823.2021.9655770
M3 - Conference contribution
AN - SCOPUS:85124519132
T3 - Proceedings - 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Science and Artificial Intelligence Technologies for Global Challenges During Pandemic Era, ICITISEE 2021
SP - 35
EP - 39
BT - Proceedings - 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021
Y2 - 24 November 2021 through 25 November 2021
ER -