TY - GEN
T1 - Cerebral Blood Volume Prediction Based on Multi-modality Magnetic Resonance Imaging
AU - Pan, Yongsheng
AU - Huang, Jingyu
AU - Wang, Bao
AU - Zhao, Peng
AU - Liu, Yingchao
AU - Xia, Yong
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Cerebral blood volume (CBV) refers to the blood volume of a certain brain tissue per unit time, which is the most useful parameter to evaluate intracranial mass lesions. However, the current CBV measurement methods rely on blood perfusion imaging technology which has obvious shortcomings, i.e., long imaging time, high cost, and great discomfort to the patients. To address this, we attempt to utilize some techniques to synthesize the CBV maps from multiple MRI sequences, which is the least harmful imaging technology currently, so as to reduce the time and cost of clinical diagnosis as well as the patients’ discomfort. Two image synthesis techniques are investigated to synthesize the CBV maps on our collection of 103 groups of multiple MRI modalities of 70 subjects. The experimental results on various modality combinations demonstrate that our redesigned algorithms are possible to synthesize promising CBV maps, which is a good start of developing efficient and cheaper CBV prediction system.
AB - Cerebral blood volume (CBV) refers to the blood volume of a certain brain tissue per unit time, which is the most useful parameter to evaluate intracranial mass lesions. However, the current CBV measurement methods rely on blood perfusion imaging technology which has obvious shortcomings, i.e., long imaging time, high cost, and great discomfort to the patients. To address this, we attempt to utilize some techniques to synthesize the CBV maps from multiple MRI sequences, which is the least harmful imaging technology currently, so as to reduce the time and cost of clinical diagnosis as well as the patients’ discomfort. Two image synthesis techniques are investigated to synthesize the CBV maps on our collection of 103 groups of multiple MRI modalities of 70 subjects. The experimental results on various modality combinations demonstrate that our redesigned algorithms are possible to synthesize promising CBV maps, which is a good start of developing efficient and cheaper CBV prediction system.
KW - Cerebral blood volume
KW - Generative adversarial network
KW - Medical image synthesis
UR - http://www.scopus.com/inward/record.url?scp=85115859423&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87592-3_12
DO - 10.1007/978-3-030-87592-3_12
M3 - 会议稿件
AN - SCOPUS:85115859423
SN - 9783030875916
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 121
EP - 130
BT - Simulation and Synthesis in Medical Imaging - 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Svoboda, David
A2 - Burgos, Ninon
A2 - Wolterink, Jelmer M.
A2 - Zhao, Can
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 27 September 2021
ER -