Clinical application of deep learning-based synthetic CT from real MRI to improve dose planning accuracy in Gamma Knife radiosurgery: a proof of concept study

Dose planning for Gamma Knife radiosurgery (GKRS) uses the magnetic resonance (MR)-based tissue maximum ratio (TMR) algorithm, which calculates radiation dose without considering heterogeneous radiation attenuation in the tissue. In order to plan the dose considering the radiation attenuation, the C...

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Veröffentlicht in:	Biomedical Engineering Letters Jg. 12; H. 4; S. 359 - 367
Hauptverfasser:	Park, So Hee, Choi, Dong Min, Jung, In-Ho, Chang, Kyung Won, Kim, Myung Ji, Jung, Hyun Ho, Chang, Jin Woo, Kim, Hwiyoung, Chang, Won Seok
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Korea Springer Science and Business Media LLC 01.11.2022 The Korean Society of Medical and Biological Engineering Springer Nature B.V
Schlagworte:	Adenoma Algorithms Artificial intelligence Attenuation Biological and Medical Physics Biomedical Engineering and Bioengineering Biomedicine Biophysics Brain tumors Computed tomography Convolution Deep learning Engineering Feasibility studies Gamma Knife radiosurgery Health services Image acquisition Image contrast Image enhancement Machine learning Magnetic resonance imaging Medical and Radiation Physics Meningioma Metastases Neuro-oncology Original Original Article Pituitary Planning Radiation Radiation dosage Radiation effects Radiology Radiosurgery Synthetic CT Tumors Vascular diseases Deep learning Gamma Knife radiosurgery Neuro-oncology Synthetic CT Artificial intelligence
ISSN:	2093-9868, 2093-985X, 2093-985X
Online-Zugang:	Volltext
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