BACKGROUND Photon-counting detector(PCD)CT represents a transformative advancement in radiological imaging,offering superior spatial resolution,enhanced contrast-tonoise ratio,and reduced radiation dose compared with ...BACKGROUND Photon-counting detector(PCD)CT represents a transformative advancement in radiological imaging,offering superior spatial resolution,enhanced contrast-tonoise ratio,and reduced radiation dose compared with the conventional energyintegrating detector CT.AIM To evaluate PCD CT in oncologic imaging,focusing on its role in tumor detection,staging,and treatment response assessment.METHODS We performed a systematic PubMed search from January 1,2017 to December 31,2024,using the keywords“photon-counting CT”,“cancer”,and“tumor”to identify studies on its use in oncologic imaging.We included experimental studies on humans or human phantoms and excluded reviews,commentaries,editorials,non-English,animal,and non-experimental studies.Study selection followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.Out of 175 initial studies,39 met the inclusion criteria after screening and full-text review.Data extraction focused on study type,country of origin,and oncologic applications of photon-counting CT.No formal risk of bias assessment was performed,and the review was not registered in PROSPERO as it did not include a meta-analysis.RESULTS Key findings highlighted the advantages of PCD CT in imaging renal masses,adrenal adenomas,ovarian cancer,breast cancer,prostate cancer,pancreatic tumors,hepatocellular carcinoma,metastases,multiple myeloma,and lung cancer.Additionally,PCD CT has demonstrated improved lesion characterization and enhanced diagnostic accuracy in oncology.Despite its promising capabilities challenges related to data processing,storage,and accessibility remain.CONCLUSION As PCD CT technology evolves,its integration into routine oncologic imaging has the potential to significantly enhance cancer diagnosis and patient management.展开更多
Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection ...Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection scheme.Sparse-view CT collection reduces the radiation dose,but with reduced resolution and reconstructed artifacts particularly in analytical reconstruction methods.Recently,deep learning has been employed in sparse-view CT reconstruction and achieved stateof-the-art results.Nevertheless,its low generalization performance and requirement for abundant training datasets have hindered the practical application of deep learning in phase-contrast CT.In this study,a CT model was used to generate a substantial number of simulated training datasets,thereby circumventing the need for experimental datasets.By training a network with simulated training datasets,the proposed method achieves high generalization performance in attenuationbased CT and phase-contrast CT,despite the lack of sufficient experimental datasets.In experiments utilizing only half of the CT data,our proposed method obtained an image quality comparable to that of the filtered back-projection algorithm with full-view projection.The proposed method simultaneously addresses two challenges in phase-contrast three-dimensional imaging,namely the lack of experimental datasets and the high exposure dose,through model-driven deep learning.This method significantly accelerates the practical application of phase-contrast CT.展开更多
文摘BACKGROUND Photon-counting detector(PCD)CT represents a transformative advancement in radiological imaging,offering superior spatial resolution,enhanced contrast-tonoise ratio,and reduced radiation dose compared with the conventional energyintegrating detector CT.AIM To evaluate PCD CT in oncologic imaging,focusing on its role in tumor detection,staging,and treatment response assessment.METHODS We performed a systematic PubMed search from January 1,2017 to December 31,2024,using the keywords“photon-counting CT”,“cancer”,and“tumor”to identify studies on its use in oncologic imaging.We included experimental studies on humans or human phantoms and excluded reviews,commentaries,editorials,non-English,animal,and non-experimental studies.Study selection followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.Out of 175 initial studies,39 met the inclusion criteria after screening and full-text review.Data extraction focused on study type,country of origin,and oncologic applications of photon-counting CT.No formal risk of bias assessment was performed,and the review was not registered in PROSPERO as it did not include a meta-analysis.RESULTS Key findings highlighted the advantages of PCD CT in imaging renal masses,adrenal adenomas,ovarian cancer,breast cancer,prostate cancer,pancreatic tumors,hepatocellular carcinoma,metastases,multiple myeloma,and lung cancer.Additionally,PCD CT has demonstrated improved lesion characterization and enhanced diagnostic accuracy in oncology.Despite its promising capabilities challenges related to data processing,storage,and accessibility remain.CONCLUSION As PCD CT technology evolves,its integration into routine oncologic imaging has the potential to significantly enhance cancer diagnosis and patient management.
基金supported by the National Natural Science Foundation of China(Nos.U2032148,U2032157,11775224)USTC Research Funds of the Double First-Class Initiative(No.YD2310002008)the National Key Research and Development Program of China(No.2017YFA0402904),the Youth Innovation Promotion Association,CAS(No.2020457)。
文摘Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection scheme.Sparse-view CT collection reduces the radiation dose,but with reduced resolution and reconstructed artifacts particularly in analytical reconstruction methods.Recently,deep learning has been employed in sparse-view CT reconstruction and achieved stateof-the-art results.Nevertheless,its low generalization performance and requirement for abundant training datasets have hindered the practical application of deep learning in phase-contrast CT.In this study,a CT model was used to generate a substantial number of simulated training datasets,thereby circumventing the need for experimental datasets.By training a network with simulated training datasets,the proposed method achieves high generalization performance in attenuationbased CT and phase-contrast CT,despite the lack of sufficient experimental datasets.In experiments utilizing only half of the CT data,our proposed method obtained an image quality comparable to that of the filtered back-projection algorithm with full-view projection.The proposed method simultaneously addresses two challenges in phase-contrast three-dimensional imaging,namely the lack of experimental datasets and the high exposure dose,through model-driven deep learning.This method significantly accelerates the practical application of phase-contrast CT.
