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
Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible ligh...Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible light,whereas PCCT utilizes photon-counting detectors that directly transform X-ray photons into electric signals.This direct conversion allows photon-counting detectors to sort photons into discrete energy levels,thereby enhancing image quality through superior noise reduction,improved spatial and contrast resolution,and reduced artifacts.In pediatric applications,PCCT offers substantial benefits,including lower radiation doses,which may help reduce the risk of malignancy in pediatric patients,with perhaps greater potential to benefit those with repeated exposure from a young age.Enhanced spatial resolution facilitates better visualization of small structures,vital for diagnosing congenital heart defects.Additionally,PCCT’s spectral capabilities improve tissue characterization and enable the creation of virtual monoenergetic images,which enhance soft-tissue contrast and potentially reduce contrast media doses.Initial clinical results indicate that PCCT provides superior image quality and diagnostic accuracy compared to conven-tional CT,particularly in challenging pediatric cardiovascular cases.As PCCT technology matures,further research and standardized protocols will be essential to fully integrate it into pediatric imaging practices,ensuring optimized diagnostic outcomes and patient safety.展开更多
目的:研究基于光子计数探测器CT(photon-counting detector CT,PCD-CT)采集的能谱定位像(spectral localizer radiograph,SLR)定量检测股骨颈的面积骨密度(areal bone mineral density,aBMD)的效能。方法:于2024年7月至2025年4月前瞻性...目的:研究基于光子计数探测器CT(photon-counting detector CT,PCD-CT)采集的能谱定位像(spectral localizer radiograph,SLR)定量检测股骨颈的面积骨密度(areal bone mineral density,aBMD)的效能。方法:于2024年7月至2025年4月前瞻性纳入需接受双能量X射线吸收法(dual-energy X-ray absorptiometry,DXA)以及CT扫描这2种检查的受试者(≥18岁)。这些患者在PCD-CT上接受检查获取SLR,由2名观察者在SLR上独立、盲法测量患者左侧股骨颈的aBMD。以DXA的测量结果为标准,评估SLR对aBMD的定量准确性及针对异常骨量(T值<-1.0)的诊断效能。结果:本研究共纳入63名受试者(其中女性36人),平均年龄(64.30±13.20)岁,DXA测得的中位aBMD值为0.889[四分位间距(interquartile range,IQR)为0.749~1.031]g/cm^(2),其中23人(36.51%)表现出异常骨量。2名观察者测量的aBMD值[中位数(IQR)表示]分别为0.879(0.760~0.985)g/cm^(2)和0.891(0.784~0.977)g/cm^(2),基于SLR测量的aBMD值具有极好的观察者间一致性(组内相关系数为0.98)。以DXA结果为参考,SLR测量aBMD的中位百分比绝对误差为6.66%(IQR为3.64%~9.80%),基于SLR诊断异常骨量的准确率、灵敏度、特异度分别为95.24%(50/63)、95.65%(22/23)、95.00%(38/40)。结论:基于PCD-CT采集的能谱定位像可以准确定量股骨颈的骨密度,并表现出较高的异常骨量诊断效能。展开更多
One of the issues in the aluminum-alloy die casting industry is the space occurring inside the casting, and the improvement of the verification technology is expected. The purpose of this research is to seal holes ins...One of the issues in the aluminum-alloy die casting industry is the space occurring inside the casting, and the improvement of the verification technology is expected. The purpose of this research is to seal holes inside the aluminum metal by resin and verify them by photon-counting X-ray computed tomography (CT) using an energy-discrimination 64-channel cadmium-telluride (CdTe) line detector. Moreover, it is important to estimate the image of the effective atomic number and the electronic density by the energy mapping of the attenuation coefficient utilizing photon-counting X-ray CTto distinguish both the aluminum metal and the resin filler in the aluminum hole. As a result, the energy discrimination of the resin filler in the space of aluminum casting has been attained. We could observe the atomic number image utilizing dual-energyX-ray CTmethod with the 64-channel CdTe photon-counting detector.展开更多
Deep learning(DL)has proven to be important for computed tomography(CT)image denoising.However,such models are usually trained under supervision,requiring paired data that may be difficult to obtain in practice.Diffus...Deep learning(DL)has proven to be important for computed tomography(CT)image denoising.However,such models are usually trained under supervision,requiring paired data that may be difficult to obtain in practice.Diffusion models offer unsupervised means of solving a wide range of inverse problems via posterior sampling.In particular,using the estimated unconditional score function of the prior distribution,obtained via unsupervised learning,one can sample from the desired posterior via hijacking and regularization.However,due to the iterative solvers used,the number of function evaluations(NFE)required may be orders of magnitudes larger than for single-step samplers.In this paper,we present a novel image denoising technique for photon-counting CT by extending the unsupervised approach to inverse problem solving to the case of Poisson flow generative models(PFGM)++.By hijacking and regularizing the sampling process we obtain a single-step sampler,that is NFE=1.Our proposed method incorporates posterior sampling using diffusion models as a special case.We demonstrate that the added robustness afforded by the PFGM++framework yields significant performance gains.Our results indicate competitive performance compared to popular supervised,including state-of-the-art diffusion-style models with NFE=1(consistency models),unsupervised,and non-DL-based image denoising techniques,on clinical low-dose CT data and clinical images from a prototype photon-counting CT system developed by GE HealthCare.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.
文摘Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible light,whereas PCCT utilizes photon-counting detectors that directly transform X-ray photons into electric signals.This direct conversion allows photon-counting detectors to sort photons into discrete energy levels,thereby enhancing image quality through superior noise reduction,improved spatial and contrast resolution,and reduced artifacts.In pediatric applications,PCCT offers substantial benefits,including lower radiation doses,which may help reduce the risk of malignancy in pediatric patients,with perhaps greater potential to benefit those with repeated exposure from a young age.Enhanced spatial resolution facilitates better visualization of small structures,vital for diagnosing congenital heart defects.Additionally,PCCT’s spectral capabilities improve tissue characterization and enable the creation of virtual monoenergetic images,which enhance soft-tissue contrast and potentially reduce contrast media doses.Initial clinical results indicate that PCCT provides superior image quality and diagnostic accuracy compared to conven-tional CT,particularly in challenging pediatric cardiovascular cases.As PCCT technology matures,further research and standardized protocols will be essential to fully integrate it into pediatric imaging practices,ensuring optimized diagnostic outcomes and patient safety.
文摘One of the issues in the aluminum-alloy die casting industry is the space occurring inside the casting, and the improvement of the verification technology is expected. The purpose of this research is to seal holes inside the aluminum metal by resin and verify them by photon-counting X-ray computed tomography (CT) using an energy-discrimination 64-channel cadmium-telluride (CdTe) line detector. Moreover, it is important to estimate the image of the effective atomic number and the electronic density by the energy mapping of the attenuation coefficient utilizing photon-counting X-ray CTto distinguish both the aluminum metal and the resin filler in the aluminum hole. As a result, the energy discrimination of the resin filler in the space of aluminum casting has been attained. We could observe the atomic number image utilizing dual-energyX-ray CTmethod with the 64-channel CdTe photon-counting detector.
基金supported by MedTechLabs,GE HealthCare,the Swedish Research council,No.2021-05103the Göran Gustafsson foundation,No.2114.
文摘Deep learning(DL)has proven to be important for computed tomography(CT)image denoising.However,such models are usually trained under supervision,requiring paired data that may be difficult to obtain in practice.Diffusion models offer unsupervised means of solving a wide range of inverse problems via posterior sampling.In particular,using the estimated unconditional score function of the prior distribution,obtained via unsupervised learning,one can sample from the desired posterior via hijacking and regularization.However,due to the iterative solvers used,the number of function evaluations(NFE)required may be orders of magnitudes larger than for single-step samplers.In this paper,we present a novel image denoising technique for photon-counting CT by extending the unsupervised approach to inverse problem solving to the case of Poisson flow generative models(PFGM)++.By hijacking and regularizing the sampling process we obtain a single-step sampler,that is NFE=1.Our proposed method incorporates posterior sampling using diffusion models as a special case.We demonstrate that the added robustness afforded by the PFGM++framework yields significant performance gains.Our results indicate competitive performance compared to popular supervised,including state-of-the-art diffusion-style models with NFE=1(consistency models),unsupervised,and non-DL-based image denoising techniques,on clinical low-dose CT data and clinical images from a prototype photon-counting CT system developed by GE HealthCare.
