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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Biomedical Engineering Letters Ročník 12; číslo 4; s. 359 - 367
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Korea Springer Science and Business Media LLC 01.11.2022 The Korean Society of Medical and Biological Engineering Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!