Purpose:To assess the clinical efficacy of integrating deep learning reconstruction(DLR)with contrast-enhancement-boost(CE-boost)in 80 kVp head and neck CT angiography(CTA)using substantially lowered radiation and con...Purpose:To assess the clinical efficacy of integrating deep learning reconstruction(DLR)with contrast-enhancement-boost(CE-boost)in 80 kVp head and neck CT angiography(CTA)using substantially lowered radiation and contrast medium(CM)doses,compared to the standard 100 kVp protocol using hybrid iterative reconstruction(HIR).Methods:Sixty-six patients were prospectively enrolled and randomly assigned to one of two groups:the low-dose group(n=33),receiving 80 kVp and 28 mL contrast medium(CM)with a noise index(NI)of 15;and the regular-dose group(n=33),receiving 100 kVp and 40 mL CM with an NI of 10.For the lowdose group,images underwent reconstruction using both hybrid iterative reconstruction(HIR)and deep learning reconstruction(DLR)at mild-,standard-,and strong-strength levels,both before and after combination with contrast enhancement-boost(CE-boost).This generated eight distinct datasets:L-HIR,L-DLR_(mild),L-DLR_(standard),L-DLR_(strong),L-HIR-CE,L-DLR_(mild)-CE,L-DLR_(standard)-CE,and L-DLR_(strong)-CE.Images for the regular-dose group were reconstructed solely with HIR(R-HIR).Quantitative analysis involved calculating and comparing CT attenuation,image noise,signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)within six key vessels:the aortic arch(AA),internal carotid artery(ICA),external carotid artery(ECA),vertebral arteries(VA),basilar artery(BA),and middle cerebral artery(MCA).Two radiologists independently assessed subjective image quality using a 5-point scale,with statistical significance defined as P<0.05.Results:Compared to the regular-dose group,the low-dose protocol achieved a substantial reduction in contrast media volume(28 mL versus 40 mL,a 30%decrease)and radiation exposure((0.41±0.08)mSv versus(1.18±0.12)mSv,a 65%reduction).Both L-DLR_(standard) and L-DLR_(strong) delivered comparable or superior SNR and CNR across all vascular segments relative to R-HIR.However,subjective image quality scores for L-DLR at all strength levels fell below those for R-HIR(all P<0.05 for both readers).Combining CE-boost with the low-dose protocol significantly enhanced the objective image performance of L-DLR_(strong)-CE(all P<0.05)and produced subjective image scores comparable to R-HIR(reader 1:P=0.15;reader 2:P=0.06).Conclusion:When compared to the standard 100 kVp head and neck CTA,the combination of the DLR and CE-boost techniques at 80 kVp can achieve a 30%reduction in contrast dose and a 65%reduction in radiation dose,while maintaining both objective and subjective image quality.展开更多
Three-dimensional (3D) visualization of dynamic biological processes in deep tissue remains challenging due to the trade-off between temporal resolution and imaging depth. Here, we present a novel near-infrared-II (NI...Three-dimensional (3D) visualization of dynamic biological processes in deep tissue remains challenging due to the trade-off between temporal resolution and imaging depth. Here, we present a novel near-infrared-II (NIR-II, 900–1880nm) fluorescence volumetric microscopic imaging method that combines an electrically tunable lens (ETL) with deep learning approaches for rapid 3D imaging. The technology achieves volumetric imaging at 4.2 frames per second (fps) across a 200 μm depth range in live mouse brain vasculature. Two specialized neural networks are utilized: a scale-recurrent network (SRN) for image enhancement and a cerebral vessel interpolation (CVI) network that enables 16-fold axial upsampling. The SRN, trained on two-photon fluorescence microscopic data, improves both lateral and axial resolution of NIR-II fluorescence wide-field microscopic images. The CVI network, adapted from video interpolation techniques, generates intermediate frames between acquired axial planes, resulting in smooth and continuous 3D vessel reconstructions. Using this integrated system, we visualize and quantify blood flow dynamics in individual vessels and are capable of measuring blood velocity at different depths. This approach maintains high lateral resolution while achieving rapid volumetric imaging, and is particularly suitable for studying dynamic vascular processes in deep tissue. Our method demonstrates the potential of combining optical engineering with artificial intelligence to advance biological imaging capabilities.展开更多
Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is u...Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is utilized in each domain.The polarization information represented by the four Stokes parameters currently requires at least two compressions.This work achieves full-Stokes single compression by introducing deep learning reconstruction.The four Stokes parameters are modulated by a quarter-wave plate(QWP)and a liquid crystal tunable filter(LCTF)and then compressed into a single light intensity detected by a complementary metal oxide semiconductor(CMOS).Data processing involves model training and polarization reconstruction.The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework.Unknown Stokes parameters can be reconstructed from a single compression using the trained model.Benefiting from the acquisition simplicity and reconstruction efficiency,this work well facilitates the development and application of polarized hyperspectral imaging.展开更多
The development of 7‐Tesla(7T)magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths,especially in neuroscience research.This review inve...The development of 7‐Tesla(7T)magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths,especially in neuroscience research.This review investigates whether 7T diffusion imaging offers significant benefits over lower field strengths by addressing the following:Technical challenges and corresponding strategies:Challenges include achieving shorter transverse relaxation/effective transverse relaxation times and greater B0 and B1 inhomogeneities.Advanced techniques including high‐performance gradient systems,parallel imaging,multi‐shot acquisition,and parallel transmission can mitigate these issues.Comparison of 3‐Tesla and 7T diffusion imaging:Technologies such as multiplexed sensitivity encoding and deep learning reconstruction(DLR)have been developed to mitigate artifacts and improve image quality.This comparative analysis demonstrates significant improvements in the signal‐to‐noise ratio and spatial resolution at 7T with a powerful gradient system,facilitating enhanced visualization of microstructural changes.Despite greater geometric distortions and signal inhomogeneity at 7T,the system shows clear advantages in high b‐value imaging and highresolution diffusion tensor imaging.Additionally,multiplexed sensitivity encoding significantly reduces image blurring and distortion,and DLR substantially improves the signal‐to‐noise ratio and image sharpness.7T diffusion applications in structural analysis and disease characterization:This review discusses the potential applications of 7T diffusion imaging in structural analysis and disease characterization.展开更多
基金National Natural Science Foundation of China(82001814)National High Level Hospital Clinical Research Funding(grant number 2022-PUMCH-B-067)+1 种基金National High Level Hospital Clinical Research Funding(grant number 2022-PUMCH-B-068)2021 SKY Imaging Research Fund of the Chinese Internatinal Medical Foundation(Z-2014-07-2101).
文摘Purpose:To assess the clinical efficacy of integrating deep learning reconstruction(DLR)with contrast-enhancement-boost(CE-boost)in 80 kVp head and neck CT angiography(CTA)using substantially lowered radiation and contrast medium(CM)doses,compared to the standard 100 kVp protocol using hybrid iterative reconstruction(HIR).Methods:Sixty-six patients were prospectively enrolled and randomly assigned to one of two groups:the low-dose group(n=33),receiving 80 kVp and 28 mL contrast medium(CM)with a noise index(NI)of 15;and the regular-dose group(n=33),receiving 100 kVp and 40 mL CM with an NI of 10.For the lowdose group,images underwent reconstruction using both hybrid iterative reconstruction(HIR)and deep learning reconstruction(DLR)at mild-,standard-,and strong-strength levels,both before and after combination with contrast enhancement-boost(CE-boost).This generated eight distinct datasets:L-HIR,L-DLR_(mild),L-DLR_(standard),L-DLR_(strong),L-HIR-CE,L-DLR_(mild)-CE,L-DLR_(standard)-CE,and L-DLR_(strong)-CE.Images for the regular-dose group were reconstructed solely with HIR(R-HIR).Quantitative analysis involved calculating and comparing CT attenuation,image noise,signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)within six key vessels:the aortic arch(AA),internal carotid artery(ICA),external carotid artery(ECA),vertebral arteries(VA),basilar artery(BA),and middle cerebral artery(MCA).Two radiologists independently assessed subjective image quality using a 5-point scale,with statistical significance defined as P<0.05.Results:Compared to the regular-dose group,the low-dose protocol achieved a substantial reduction in contrast media volume(28 mL versus 40 mL,a 30%decrease)and radiation exposure((0.41±0.08)mSv versus(1.18±0.12)mSv,a 65%reduction).Both L-DLR_(standard) and L-DLR_(strong) delivered comparable or superior SNR and CNR across all vascular segments relative to R-HIR.However,subjective image quality scores for L-DLR at all strength levels fell below those for R-HIR(all P<0.05 for both readers).Combining CE-boost with the low-dose protocol significantly enhanced the objective image performance of L-DLR_(strong)-CE(all P<0.05)and produced subjective image scores comparable to R-HIR(reader 1:P=0.15;reader 2:P=0.06).Conclusion:When compared to the standard 100 kVp head and neck CTA,the combination of the DLR and CE-boost techniques at 80 kVp can achieve a 30%reduction in contrast dose and a 65%reduction in radiation dose,while maintaining both objective and subjective image quality.
基金supported by the National Key R&D Program of China (No. 2024YFF1206700)the National Natural Science Foundation of China (No. U23A20487)the Hangzhou Chengxi Sci-tech Innovation Corridor Management Committee.
文摘Three-dimensional (3D) visualization of dynamic biological processes in deep tissue remains challenging due to the trade-off between temporal resolution and imaging depth. Here, we present a novel near-infrared-II (NIR-II, 900–1880nm) fluorescence volumetric microscopic imaging method that combines an electrically tunable lens (ETL) with deep learning approaches for rapid 3D imaging. The technology achieves volumetric imaging at 4.2 frames per second (fps) across a 200 μm depth range in live mouse brain vasculature. Two specialized neural networks are utilized: a scale-recurrent network (SRN) for image enhancement and a cerebral vessel interpolation (CVI) network that enables 16-fold axial upsampling. The SRN, trained on two-photon fluorescence microscopic data, improves both lateral and axial resolution of NIR-II fluorescence wide-field microscopic images. The CVI network, adapted from video interpolation techniques, generates intermediate frames between acquired axial planes, resulting in smooth and continuous 3D vessel reconstructions. Using this integrated system, we visualize and quantify blood flow dynamics in individual vessels and are capable of measuring blood velocity at different depths. This approach maintains high lateral resolution while achieving rapid volumetric imaging, and is particularly suitable for studying dynamic vascular processes in deep tissue. Our method demonstrates the potential of combining optical engineering with artificial intelligence to advance biological imaging capabilities.
基金supported by the National Key Scientific Instrument and Equipment Development Project of China(No.61527802)。
文摘Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is utilized in each domain.The polarization information represented by the four Stokes parameters currently requires at least two compressions.This work achieves full-Stokes single compression by introducing deep learning reconstruction.The four Stokes parameters are modulated by a quarter-wave plate(QWP)and a liquid crystal tunable filter(LCTF)and then compressed into a single light intensity detected by a complementary metal oxide semiconductor(CMOS).Data processing involves model training and polarization reconstruction.The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework.Unknown Stokes parameters can be reconstructed from a single compression using the trained model.Benefiting from the acquisition simplicity and reconstruction efficiency,this work well facilitates the development and application of polarized hyperspectral imaging.
文摘The development of 7‐Tesla(7T)magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths,especially in neuroscience research.This review investigates whether 7T diffusion imaging offers significant benefits over lower field strengths by addressing the following:Technical challenges and corresponding strategies:Challenges include achieving shorter transverse relaxation/effective transverse relaxation times and greater B0 and B1 inhomogeneities.Advanced techniques including high‐performance gradient systems,parallel imaging,multi‐shot acquisition,and parallel transmission can mitigate these issues.Comparison of 3‐Tesla and 7T diffusion imaging:Technologies such as multiplexed sensitivity encoding and deep learning reconstruction(DLR)have been developed to mitigate artifacts and improve image quality.This comparative analysis demonstrates significant improvements in the signal‐to‐noise ratio and spatial resolution at 7T with a powerful gradient system,facilitating enhanced visualization of microstructural changes.Despite greater geometric distortions and signal inhomogeneity at 7T,the system shows clear advantages in high b‐value imaging and highresolution diffusion tensor imaging.Additionally,multiplexed sensitivity encoding significantly reduces image blurring and distortion,and DLR substantially improves the signal‐to‐noise ratio and image sharpness.7T diffusion applications in structural analysis and disease characterization:This review discusses the potential applications of 7T diffusion imaging in structural analysis and disease characterization.
