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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

Authors
 So Hee Park  ;  Dong Min Choi  ;  In-Ho Jung  ;  Kyung Won Chang  ;  Myung Ji Kim  ;  Hyun Ho Jung  ;  Jin Woo Chang  ;  Hwiyoung Kim  ;  Won Seok Chang 
Citation
 BIOMEDICAL ENGINEERING LETTERS, Vol.12(4) : 359-367, 2022-06 
Journal Title
BIOMEDICAL ENGINEERING LETTERS
ISSN
 2093-9868 
Issue Date
2022-06
Keywords
Artificial intelligence ; Deep learning ; Gamma Knife radiosurgery ; Neuro-oncology ; Synthetic CT
Abstract
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.
Full Text
https://link.springer.com/article/10.1007/s13534-022-00227-x
DOI
10.1007/s13534-022-00227-x
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hwiyoung(김휘영)
Park, So Hee(박소희)
Chang, Kyung Won(장경원)
Chang, Won Seok(장원석) ORCID logo https://orcid.org/0000-0003-3145-4016
Chang, Jin Woo(장진우) ORCID logo https://orcid.org/0000-0002-2717-0101
Jung, In-Ho(정인호)
Jung, Hyun Ho(정현호)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191472
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