Abstract
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.
Y. Pan and J. Huang contribute equally.
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Acknowledgment
This work was supported in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grants JCYJ20180306171334997, in part by the National Natural Science Foundation of China under Grants 61771397, in part by the China Postdoctoral Science Foundation under Grants BX2021333, and in part by the Taishan Scholars Program under Grants tsqn20161070.
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Pan, Y., Huang, J., Wang, B., Zhao, P., Liu, Y., Xia, Y. (2021). Cerebral Blood Volume Prediction Based on Multi-modality Magnetic Resonance Imaging. In: Svoboda, D., Burgos, N., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2021. Lecture Notes in Computer Science(), vol 12965. Springer, Cham. https://doi.org/10.1007/978-3-030-87592-3_12
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DOI: https://doi.org/10.1007/978-3-030-87592-3_12
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