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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Vydáno v:Biomedical Engineering Letters Ročník 12; číslo 4; s. 359 - 367
Hlavní autoři: 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:angličtina
Vydáno: Korea Springer Science and Business Media LLC 01.11.2022
The Korean Society of Medical and Biological Engineering
Springer Nature B.V
Témata:
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
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Shrnutí: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 Convolution algorithm should be used, and additional radiation exposure for computed tomography (CT) and registration errors between MR and CT are entailed. This study investigated the clinical feasibility of synthetic CT (sCT) from GKRS planning MR using deep learning. The model was trained using frame-based contrast-enhanced T1-weighted MR images and corresponding CT slices from 54 training subjects acquired for GKRS planning. The model was applied prospectively to 60 lesions in 43 patients including benign tumor such as meningioma and pituitary adenoma, metastatic brain tumors, and vascular disease of various location for evaluating the model and its application. We evaluated the sCT and compared between treatment plans made with MR only (TMR 10 plan), MR and real CT (rCT; Convolution with rCT [Conv-rCT] plan), and MR and synthetic CT (Convolution with sCT [Conv-sCT] plan). The mean absolute error (MAE) of 43 sCT was 107.35 ± 16.47 Hounsfield units. The TMR 10 treatment plan differed significantly from plans made by Conv-sCT and Conv-rCT. However, the Conv-sCT and Conv-rCT plans were similar. This study showed the practical applicability of deep learning based on sCT in GKRS. Our results support the possibility of formulating GKRS treatment plans while considering radiation attenuation in the tissue using GKRS planning MR and no radiation exposure.
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ISSN:2093-9868
2093-985X
2093-985X
DOI:10.1007/s13534-022-00227-x