دورية أكاديمية
A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning.
| العنوان: | A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning. |
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| المؤلفون: | Hou Z; The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China., Kong Y; School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China.; Centre de Recherche en Information, BioMdicale Sino-Franais, Nanjing, China.; Centre de Recherche en Information, BioMdicale Sino-Franais, 35000, Rennes, France., Wu J; School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China., Gu J; School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China., Liu J; The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China., Gao S; The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China., Yin Y; The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China., Zhang L; The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China., Han Y; The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China., Zhu J; Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250000, Shandong, China. zhujian@sdfmu.edu.cn.; Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250000, Shandong, China. zhujian@sdfmu.edu.cn.; Centre de Recherche en Information, BioMdicale Sino-Franais, Nanjing, China. zhujian@sdfmu.edu.cn.; Centre de Recherche en Information, BioMdicale Sino-Franais, 35000, Rennes, France. zhujian@sdfmu.edu.cn., Li S; The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China. lishuangshuang@njglyy.com. |
| المصدر: | Japanese journal of radiology [Jpn J Radiol] 2024 Jul; Vol. 42 (7), pp. 765-776. Date of Electronic Publication: 2024 Mar 27. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Country of Publication: Japan NLM ID: 101490689 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-108X (Electronic) Linking ISSN: 18671071 NLM ISO Abbreviation: Jpn J Radiol Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Tokyo : Springer |
| مواضيع طبية MeSH: | Deep Learning* , Radiotherapy Planning, Computer-Assisted*/methods , Lung Neoplasms*/radiotherapy , Lung Neoplasms*/diagnostic imaging , Four-Dimensional Computed Tomography*/methods, Humans ; Female ; Male ; Aged ; Carcinoma, Non-Small-Cell Lung/radiotherapy ; Carcinoma, Non-Small-Cell Lung/diagnostic imaging ; Middle Aged ; Tomography, X-Ray Computed/methods ; Lung/diagnostic imaging |
| مستخلص: | Purpose: Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning. Materials and Methods: Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI Results: CTVI Conclusion: Using deep-learning techniques, CTVI (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.) |
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| معلومات مُعتمدة: | 82202300 National Natural Science Foundation of China |
| فهرسة مساهمة: | Keywords: 4DCT; Deep learning; Lung cancer; Radiotherapy; Synthetic imaging; Ventilation imaging |
| تواريخ الأحداث: | Date Created: 20240327 Date Completed: 20240701 Latest Revision: 20240701 |
| رمز التحديث: | 20240702 |
| DOI: | 10.1007/s11604-024-01550-2 |
| PMID: | 38536558 |
| قاعدة البيانات: | MEDLINE |
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