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
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
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
Large field-of-view（FoV） three-dimensional（3 D） photon-counting imaging is demonstrated with a single-pixel single-photon detector based on a Geiger-mode Si-avalanche photodiode. By removing the collecting lens（C...Large field-of-view（FoV） three-dimensional（3 D） photon-counting imaging is demonstrated with a single-pixel single-photon detector based on a Geiger-mode Si-avalanche photodiode. By removing the collecting lens（CL）before the detector, the FoV is expanded to ±10°. Thanks to the high detection efficiency, the signal-to-noise ratio of the imaging system is as high as 7.8 dB even without the CL when the average output laser pulse energy is about 0.45 pJ/pulse for imaging the targets at a distance of 5 m. A 3 D image overlaid with the reflectivity data is obtained according to the photon-counting time-of-flight measurement and the return photon intensity.展开更多
Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperatur...Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings.Current computed tomography(CT)thermometry is based on energy-integrated CT,tissue-specific experimental data,and linear relationships between attenuation and temperature.In this paper,we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest.In our feasibility study,distilled water,50 mmol/L CaCl2,and 600 mmol/L CaCl2 are chosen as the base materials.Their attenuations are measured in four discrete energy bins at various temperatures.The neural network trained on the experimental data achieves a mean absolute error of 3.97°C and 1.80°C on 300 mmol/L CaCl2 and a milk-based protein shake respectively.These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dis-similar to our base materials.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
We present the results of using a photon-eounting full-waveform lidar to obtain detailed target information with high accuracy.The parameters of the waveforms(i.e.,vertical structure,peak position,peak amplitude,peak ...We present the results of using a photon-eounting full-waveform lidar to obtain detailed target information with high accuracy.The parameters of the waveforms(i.e.,vertical structure,peak position,peak amplitude,peak width and backscatter cross section)are derived with a high resolution limit of 31 mm to establish the vertical structure and scattering properties of targets,which contribute to the recognition and classification of various scatterers.The photon-counting full-waveform lidar has higher resolution than linear-mode full-waveform lidar,and it can obtain more specific target information compared to photon-counting discrete-point lidar,which can provide a potential alternative technique for tomographic surveying and mapping.展开更多
Spaceborne photon-counting LiDAR is significantly affected by noise,and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions.Accordin...Spaceborne photon-counting LiDAR is significantly affected by noise,and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions.Accordingly,a new denoising method is presented to extract signal photons adaptively.The method includes two steps.First,the local neighborhood radius is calculated according to photons’density,then thefirst-step denoising process is completed via photons’curvature feature based on KNN search and covariance matrix.Second,the local photonfiltering direction and threshold are obtained based on thefirst-step denoising results by RANSAC and elevation frequency histogram,and the local dense noise photons that thefirst-step cannot be identified are further eliminated.The following results are drawn:(1)experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94,and the accuracy is effectively improves with the number of denoising times;(2)experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies.Overall,the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons,and the denoising results can provide more reasonable and reliable data for sustainable urban development.展开更多
Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under differen...Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under different energy ranges,which can be helpful for material decomposition studies.However,there is a considerable amount of inherent quantum noise in narrow energy bins,resulting in a low signal-to-noise ratio,which can consequently affect the material decomposition performance in the image domain.Deep learning technology is currently widely used in medical image segmentation,denoising,and recognition.In order to improve the results of material decomposition,we propose an attention-based global convolutional generative adversarial network(AGC-GAN)to decompose different materials for spectral CT.Specifically,our network is a global convolutional neural network based on an attention mechanism that is combined with a generative adversarial network.The global convolutional network based on the attention mechanism is used as the generator,and a patchGAN discriminant network is used as the discriminator.Meanwhile,a clinical spectral CT image dataset is used to verify the feasibility of our proposed approach.Extensive experimental results demonstrate that AGC-GAN achieves a better material decomposition performance than vanilla U-Net,fully convolutional network,and fully convolutional denseNet.Remarkably,the mean intersection over union,structural similarity,mean precision,PAcc,and mean F1-score of our method reach up to 87.31%,94.83%,93.22%,97.39%,and 93.05%,respectively.展开更多
By introducing the thermal entangled state representation, we investigate the time evolution of distribution functions in the dissipative channels by bridging the relation between the initial distribution function and...By introducing the thermal entangled state representation, we investigate the time evolution of distribution functions in the dissipative channels by bridging the relation between the initial distribution function and the any time distribution function. We find that most of them are expressed as such integrations over the Laguerre Gaussian function. Furthermore, as applications, we derive the time evolution of photon-counting distribution by bridging the relation between the initial distribution function and the any time photon-counting distribution, and the time evolution of Rfunction characteristic of nonclassicality depth.展开更多
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 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.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11774095,11722431 and 11621404the Shanghai Basic Research Project under Grant No 18JC1412200+2 种基金the National Key R&D Program of China under Grant No2016YFB0400904the Program of Introducing Talents of Discipline to Universities under Grant No B12024the Shanghai International Cooperation Project under Grant No 16520710600
文摘Large field-of-view（FoV） three-dimensional（3 D） photon-counting imaging is demonstrated with a single-pixel single-photon detector based on a Geiger-mode Si-avalanche photodiode. By removing the collecting lens（CL）before the detector, the FoV is expanded to ±10°. Thanks to the high detection efficiency, the signal-to-noise ratio of the imaging system is as high as 7.8 dB even without the CL when the average output laser pulse energy is about 0.45 pJ/pulse for imaging the targets at a distance of 5 m. A 3 D image overlaid with the reflectivity data is obtained according to the photon-counting time-of-flight measurement and the return photon intensity.
基金the Johns Hopkins University Leong Research Award for Undergraduates.
文摘Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings.Current computed tomography(CT)thermometry is based on energy-integrated CT,tissue-specific experimental data,and linear relationships between attenuation and temperature.In this paper,we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest.In our feasibility study,distilled water,50 mmol/L CaCl2,and 600 mmol/L CaCl2 are chosen as the base materials.Their attenuations are measured in four discrete energy bins at various temperatures.The neural network trained on the experimental data achieves a mean absolute error of 3.97°C and 1.80°C on 300 mmol/L CaCl2 and a milk-based protein shake respectively.These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dis-similar to our base materials.
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 the National Natural Science Foundation of China under Grant Nos 11774095,11804099 and 11621404the Shanghai Basic Research Project under Grant No 18JC1412200the Program of Introducing Talents of Discipline to Universities under Grant No B12024
文摘We present the results of using a photon-eounting full-waveform lidar to obtain detailed target information with high accuracy.The parameters of the waveforms(i.e.,vertical structure,peak position,peak amplitude,peak width and backscatter cross section)are derived with a high resolution limit of 31 mm to establish the vertical structure and scattering properties of targets,which contribute to the recognition and classification of various scatterers.The photon-counting full-waveform lidar has higher resolution than linear-mode full-waveform lidar,and it can obtain more specific target information compared to photon-counting discrete-point lidar,which can provide a potential alternative technique for tomographic surveying and mapping.
基金supported by the National Key R&D Program of China under[grant number 2021YFF0704600]the National Natural Science Foundation of China under[grant number 42171352,42271365,U22A20566]the High-Level Talent Aggregation Project in Hunan Province,China-Innovation Team under[grant number 2019RS1060].
文摘Spaceborne photon-counting LiDAR is significantly affected by noise,and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions.Accordingly,a new denoising method is presented to extract signal photons adaptively.The method includes two steps.First,the local neighborhood radius is calculated according to photons’density,then thefirst-step denoising process is completed via photons’curvature feature based on KNN search and covariance matrix.Second,the local photonfiltering direction and threshold are obtained based on thefirst-step denoising results by RANSAC and elevation frequency histogram,and the local dense noise photons that thefirst-step cannot be identified are further eliminated.The following results are drawn:(1)experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94,and the accuracy is effectively improves with the number of denoising times;(2)experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies.Overall,the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons,and the denoising results can provide more reasonable and reliable data for sustainable urban development.
基金supported by National Natural Science Foundation of China (No.62101136)Shanghai Sailing Program (No.21YF1402800)+3 种基金Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01)ZJLab,Shanghai Municipal of Science and Technology Project (No.20JC1419500)Natural Science Foundation of Chongqing (No.CSTB2022NSCQ-MSX0360)Shanghai Center for Brain Science and Brain-inspired Technology.
文摘Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under different energy ranges,which can be helpful for material decomposition studies.However,there is a considerable amount of inherent quantum noise in narrow energy bins,resulting in a low signal-to-noise ratio,which can consequently affect the material decomposition performance in the image domain.Deep learning technology is currently widely used in medical image segmentation,denoising,and recognition.In order to improve the results of material decomposition,we propose an attention-based global convolutional generative adversarial network(AGC-GAN)to decompose different materials for spectral CT.Specifically,our network is a global convolutional neural network based on an attention mechanism that is combined with a generative adversarial network.The global convolutional network based on the attention mechanism is used as the generator,and a patchGAN discriminant network is used as the discriminator.Meanwhile,a clinical spectral CT image dataset is used to verify the feasibility of our proposed approach.Extensive experimental results demonstrate that AGC-GAN achieves a better material decomposition performance than vanilla U-Net,fully convolutional network,and fully convolutional denseNet.Remarkably,the mean intersection over union,structural similarity,mean precision,PAcc,and mean F1-score of our method reach up to 87.31%,94.83%,93.22%,97.39%,and 93.05%,respectively.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.11047133 and 60967002)the Key Programs Foundation of Ministry of Education of China (Grant No.210115)+1 种基金the Research Foundation of the Education Department of Jiangxi Province of China (Grant Nos.GJJ10097 and GJJ10404)the Natural Science Foundation of Jiangxi Province of China (Grant No.2010GQW0027)
文摘By introducing the thermal entangled state representation, we investigate the time evolution of distribution functions in the dissipative channels by bridging the relation between the initial distribution function and the any time distribution function. We find that most of them are expressed as such integrations over the Laguerre Gaussian function. Furthermore, as applications, we derive the time evolution of photon-counting distribution by bridging the relation between the initial distribution function and the any time photon-counting distribution, and the time evolution of Rfunction characteristic of nonclassicality depth.
