{"title":"人工智能在pet/mr成像中的创新:应用和性能分析。","authors":"Hanzhong Wang, Yue Wang, Xing Chen, Zheng Zhang, Zengping Lin, Biao Li, Guowei Feng, Qiu Huang","doi":"10.1177/08953996241313122","DOIUrl":null,"url":null,"abstract":"<p><p>BackgroundThe primary challenges in PET/MR imaging include prolonged scan durations for both PET and MR components and radiation exposure associated with the PET modality. Artificial intelligence (AI)-based techniques offer a promising approach to overcome these limitations.ObjectiveThis study evaluates the AI-based image enhancement methods integrated into the United Imaging PET/MR system, focusing on improvements in image quality, reduced injection dose, and shortened acquisition duration.MethodSixty-three patients underwent <sup>18</sup>F-FDG PET/MR scans using uPMR790 (0.09 ± 0.01 mCi/kg, 5 min/bed, n = 29) and uPMR890 (0.05 ± 0.01 mCi/kg, 2.5 min/bed for PET and accelerated MR protocols, n = 34) with advanced AI-enhanced method. Shortened MR protocols included T1 W and T2 W sequences. Image quality was evaluated subjectively by two physicians and objectively using SNR and artifact ratios.ResultsThe AI-enhanced system achieved high-quality PET and MR images despite reduced PET doses and scan durations for both PET and MR components. AI-based reconstruction images showed higher SNR, fewer artifacts, and reduced noise compared to the conventional system.ConclusionsAI-enhanced PET/MR significantly improves imaging efficiency by reducing PET/MR acquisition durations, lowering radiation dose, and enhancing overall image quality, making it a valuable tool for clinical hybrid imaging.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"33 3","pages":"516-525"},"PeriodicalIF":1.4000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Innovations in artificial intelligence for pet/mr imaging: Application and performance analysis.\",\"authors\":\"Hanzhong Wang, Yue Wang, Xing Chen, Zheng Zhang, Zengping Lin, Biao Li, Guowei Feng, Qiu Huang\",\"doi\":\"10.1177/08953996241313122\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>BackgroundThe primary challenges in PET/MR imaging include prolonged scan durations for both PET and MR components and radiation exposure associated with the PET modality. Artificial intelligence (AI)-based techniques offer a promising approach to overcome these limitations.ObjectiveThis study evaluates the AI-based image enhancement methods integrated into the United Imaging PET/MR system, focusing on improvements in image quality, reduced injection dose, and shortened acquisition duration.MethodSixty-three patients underwent <sup>18</sup>F-FDG PET/MR scans using uPMR790 (0.09 ± 0.01 mCi/kg, 5 min/bed, n = 29) and uPMR890 (0.05 ± 0.01 mCi/kg, 2.5 min/bed for PET and accelerated MR protocols, n = 34) with advanced AI-enhanced method. Shortened MR protocols included T1 W and T2 W sequences. Image quality was evaluated subjectively by two physicians and objectively using SNR and artifact ratios.ResultsThe AI-enhanced system achieved high-quality PET and MR images despite reduced PET doses and scan durations for both PET and MR components. AI-based reconstruction images showed higher SNR, fewer artifacts, and reduced noise compared to the conventional system.ConclusionsAI-enhanced PET/MR significantly improves imaging efficiency by reducing PET/MR acquisition durations, lowering radiation dose, and enhancing overall image quality, making it a valuable tool for clinical hybrid imaging.</p>\",\"PeriodicalId\":49948,\"journal\":{\"name\":\"Journal of X-Ray Science and Technology\",\"volume\":\"33 3\",\"pages\":\"516-525\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2025-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of X-Ray Science and Technology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/08953996241313122\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/2/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of X-Ray Science and Technology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/08953996241313122","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/25 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
Innovations in artificial intelligence for pet/mr imaging: Application and performance analysis.
BackgroundThe primary challenges in PET/MR imaging include prolonged scan durations for both PET and MR components and radiation exposure associated with the PET modality. Artificial intelligence (AI)-based techniques offer a promising approach to overcome these limitations.ObjectiveThis study evaluates the AI-based image enhancement methods integrated into the United Imaging PET/MR system, focusing on improvements in image quality, reduced injection dose, and shortened acquisition duration.MethodSixty-three patients underwent 18F-FDG PET/MR scans using uPMR790 (0.09 ± 0.01 mCi/kg, 5 min/bed, n = 29) and uPMR890 (0.05 ± 0.01 mCi/kg, 2.5 min/bed for PET and accelerated MR protocols, n = 34) with advanced AI-enhanced method. Shortened MR protocols included T1 W and T2 W sequences. Image quality was evaluated subjectively by two physicians and objectively using SNR and artifact ratios.ResultsThe AI-enhanced system achieved high-quality PET and MR images despite reduced PET doses and scan durations for both PET and MR components. AI-based reconstruction images showed higher SNR, fewer artifacts, and reduced noise compared to the conventional system.ConclusionsAI-enhanced PET/MR significantly improves imaging efficiency by reducing PET/MR acquisition durations, lowering radiation dose, and enhancing overall image quality, making it a valuable tool for clinical hybrid imaging.
期刊介绍:
Research areas within the scope of the journal include:
Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants
X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional
Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics
Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes