Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction.
Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep le...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 1; pp. 60 - 72 |
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| Autores principales: | , , , , , , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175966521&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966521 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966521 175966521 175966521 10.1007/s10278-023-00930-w 175966521 ppf: 60 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction. aug: au: Zou, Zhongsheng Gong, Changfei Zeng, Lingpeng Guan, Yu Huang, Bin Yu, Xiuwen Liu, Qiegen Zhang, Minghui affil: https://ror.org/042v6xz23 Department of Electronic Information Engineering, Nanchang University, Nanchang, China sug: subj: Quality Assurance Radiation Dosage Deep Learning Neural Networks (Computer) Prediction Models Human Cancer Patients Radiotherapy, Conformal Patient Centered Care ab: Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as "IVPSQA." The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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