Optical techniques offer a wide variety of applications as light-matter interactions provide extremely sensitive mechanisms to probe or treat target media.Most of these implementations rely on the usage of ballistic o...Optical techniques offer a wide variety of applications as light-matter interactions provide extremely sensitive mechanisms to probe or treat target media.Most of these implementations rely on the usage of ballistic or quasi-ballistic photons to achieve high spatial resolution.However,the inherent scattering nature of light in biological tissues or tissue-like scattering media constitutes a critical obstacle that has restricted the penetration depth of non-scattered photons and hence limited the implementation of most optical techniques for wider applications.In addition,the components of an optical system are usually designed and manufactured for a fixed function or performance.Recent advances in wavefront shaping have demonstrated that scattering-or component-induced phase distortions can be compensated by optimizing the wavefront of the input light pattern through iteration or by conjugating the transmission matrix of the scattering medium.展开更多
Histopathology relies upon the staining and sectioning of biological tissues,which can be laborious and may cause artifacts and distort tissues.We develop label-free volumetric imaging of thick-tissue slides,exploitin...Histopathology relies upon the staining and sectioning of biological tissues,which can be laborious and may cause artifacts and distort tissues.We develop label-free volumetric imaging of thick-tissue slides,exploiting refractive index distributions as intrinsic imaging contrast.The present method systematically exploits label-free quantitative phase imaging techniques,volumetric reconstruction of intrinsic refractive index distributions in tissues,and numerical algorithms for the seamless stitching of multiple three-dimensional tomograms and for reducing scattering-induced image distortion.We demonstrated label-free volumetric imaging of thick tissues with the field of view of 2 mm×1.75 mm×0.2 mm with a spatial resolution of 170 nm×170 nm×1400 nm.The number of optical modes,calculated as the reconstructed volume divided by the size of the point spread function,was∼20 giga voxels.We have also demonstrated that different tumor types and a variety of precursor lesions and pathologies can be visualized with the present method.展开更多
Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate.The lack of reliable biomarkers for diagnosing and prognosis of sepsis i...Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate.The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management.We aimed to investigate the potential of three-dimensional label-free CD8+T cell morphology as a biomarker for sepsis.This study included three-time points in the sepsis recovery cohort(N=8)and healthy controls(N=20).Morphological features and spatial distribution within cells were compared among the patients'statuses.We developed a deep learning model to predict the diagnosis and prognosis of sepsis using the internal cell morphology.Correlation between the morphological features and clinical indices were analysed.Cell morphological features and spatial distribution differed significantly between patients with sepsis and healthy controls and between the survival and non-survival groups.The model for predicting the diagnosis and prognosis of sepsis showed an area under the receiver operating characteristic curve of nearly 100%with only a few cells,and a strong correlation between the morphological features and clinical indices was observed.Our study highlights the potential of three-dimensional label-free CD8+T cell morphology as a promising biomarker for sepsis.This approach is rapid,requires a minimum amount of blood samples,and has the potential to provide valuable information for the early diagnosis and prognosis of sepsis.展开更多
The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections.Microbial infections are a major healthcare issue worldwide,as these widespread diseases often dev...The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections.Microbial infections are a major healthcare issue worldwide,as these widespread diseases often develop into deadly symptoms.While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection,this effective treatment is difficult to practice.The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification,which includes time-consuming sample growth.Here,we propose a microscopy-based framework that identifies the pathogen from single to few cells.Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network.We demonstrate the identification of 19 bacterial species that cause bloodstream infections,achieving an accuracy of 82.5%from an individual bacterial cell or cluster.This performance,comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample,underpins the effectiveness of our framework in clinical applications.Furthermore,our accuracy increases with multiple measurements,reaching 99.9%with seven different measurements of cells or clusters.We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.展开更多
Owing to its unique penetrating power and high-resolution capability,X-ray imaging has been an irreplaceable tool since its discovery.Despite the significance,the resolution of X-ray imaging has largely been limited b...Owing to its unique penetrating power and high-resolution capability,X-ray imaging has been an irreplaceable tool since its discovery.Despite the significance,the resolution of X-ray imaging has largely been limited by the technical difficulties on X-ray lens making.Various lensless imaging methods have been proposed,but are yet relying on multiple measurements or additional constraints on measurements or samples.Here we present coherent specklecorrelation imaging(CSI)using a designed X-ray diffuser.CSI has no prerequisites for samples or measurements.Instead,from a single shot measurement,the complex sample field is retrieved based on the pseudorandomness of the speckle intensity pattern,ensured through a diffuser.We achieve a spatial resolution of 13.9 nm at 5.46 keV,beating the feature size of the diffuser used(300 nm).The high-resolution imaging capability is theoretically explained based on fundamental and practical limits.We expect the CSI to be a versatile tool for navigating the unexplored world of nanometer.展开更多
Objective and Impact Statement.We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index(RI)tomography.Our computational approach that fully utilizes tom...Objective and Impact Statement.We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index(RI)tomography.Our computational approach that fully utilizes tomographic information of bone marrow(BM)white blood cell(WBC)enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research.Introduction.Conventional methods for examining blood cells,such as blood smear analysis by medical professionals and fluorescence-activated cell sorting,require significant time,costs,and domain knowledge that could affect test results.While label-free imaging techniques that use a specimen’s intrinsic contrast(e.g.,multiphoton and Raman microscopy)have been used to characterize blood cells,their imaging procedures and instrumentations are relatively time-consuming and complex.Methods.The RI tomograms of the BM WBCs are acquired via Mach-Zehnder interferometer-based tomographic microscope and classified by a 3D convolutional neural network.We test our deep learning classifier for the four types of bone marrow WBC collected from healthy donors(n=10):monocyte,myelocyte,B lymphocyte,and T lymphocyte.The quantitative parameters of WBC are directly obtained from the tomograms.Results.Our results show>99%accuracy for the binary classification of myeloids and lymphoids and>96%accuracy for the four-type classification of B and T lymphocytes,monocyte,and myelocytes.The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique.Conclusion.We envision that the proposed cell classification framework can be easily integrated into existing blood cell investigation workflows,providing cost-effective and rapid diagnosis for hematologic malignancy.展开更多
A major challenge in three-dimensional(3D)microscopy is to obtain accurate spatial information while simultaneously keeping the microscopic samples in their native states.In conventional 3D microscopy,axial resolution...A major challenge in three-dimensional(3D)microscopy is to obtain accurate spatial information while simultaneously keeping the microscopic samples in their native states.In conventional 3D microscopy,axial resolution is inferior to spatial resolution due to the inaccessibility to side scattering signals.In this study,we demonstrate the isotropic microtomography of free-floating samples by optically rotating a sample.Contrary to previous approaches using optical tweezers with multiple foci which are only applicable to simple shapes,we exploited 3D structured light traps that can stably rotate freestanding complex-shaped microscopic specimens,and side scattering information is measured at various sample orientations to achieve isotropic resolution.The proposed method yields an isotropic resolution of 230 nm and captures structural details of colloidal multimers and live red blood cells,which are inaccessible using conventional tomographic microscopy.We envision that the proposed approach can be deployed for solving diverse imaging problems that are beyond the examples shown here.展开更多
基金supported by National Natural Science Foundation of China(NSFC)(81930048,81627805)Hong Kong Research Grant Council(15217721,R5029-19,C7074-21GF)+3 种基金Hong Kong Innovation and Technology Commission(GHP/043/19SZ,GHP/044/19GD)Guangdong Science and Technology Commission(2019A1515011374,2019BT02X105)National Research Foundation of Korea(2015R1A3A2066550,2021R1A2C3012903)Institute of Information&Communications Technology Planning&Evaluation(IITP,2021-0-00745)grant funded by the Korea government(MSIT).
文摘Optical techniques offer a wide variety of applications as light-matter interactions provide extremely sensitive mechanisms to probe or treat target media.Most of these implementations rely on the usage of ballistic or quasi-ballistic photons to achieve high spatial resolution.However,the inherent scattering nature of light in biological tissues or tissue-like scattering media constitutes a critical obstacle that has restricted the penetration depth of non-scattered photons and hence limited the implementation of most optical techniques for wider applications.In addition,the components of an optical system are usually designed and manufactured for a fixed function or performance.Recent advances in wavefront shaping have demonstrated that scattering-or component-induced phase distortions can be compensated by optimizing the wavefront of the input light pattern through iteration or by conjugating the transmission matrix of the scattering medium.
基金H.H.,R.H.H.,S.-M.H.,and Y.P.conceived the initial idea.H.H.developed the optical system and analysis methods.H.H.and Y.W.K.performed the experiments and analyzed the data.M.L.and S.S.provided the analysis methods and analyzed the data.All authors wrote and revised the manuscript.This work was supported by KAIST,Up Program,BK21+program,Tomocube,and National Research Foundation of Korea(2017M3C1A3013923,2015R1A3A2066550,and 2018K000396).Professor Park and Mr.Moosung Lee have financial interests in Tomocube Inc.,a company that commercializes optical diffraction tomography and quantitative phase imaging instruments and is one of the sponsors of the work.
文摘Histopathology relies upon the staining and sectioning of biological tissues,which can be laborious and may cause artifacts and distort tissues.We develop label-free volumetric imaging of thick-tissue slides,exploiting refractive index distributions as intrinsic imaging contrast.The present method systematically exploits label-free quantitative phase imaging techniques,volumetric reconstruction of intrinsic refractive index distributions in tissues,and numerical algorithms for the seamless stitching of multiple three-dimensional tomograms and for reducing scattering-induced image distortion.We demonstrated label-free volumetric imaging of thick tissues with the field of view of 2 mm×1.75 mm×0.2 mm with a spatial resolution of 170 nm×170 nm×1400 nm.The number of optical modes,calculated as the reconstructed volume divided by the size of the point spread function,was∼20 giga voxels.We have also demonstrated that different tumor types and a variety of precursor lesions and pathologies can be visualized with the present method.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)[grant number 2022R1F1A1064578].
文摘Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate.The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management.We aimed to investigate the potential of three-dimensional label-free CD8+T cell morphology as a biomarker for sepsis.This study included three-time points in the sepsis recovery cohort(N=8)and healthy controls(N=20).Morphological features and spatial distribution within cells were compared among the patients'statuses.We developed a deep learning model to predict the diagnosis and prognosis of sepsis using the internal cell morphology.Correlation between the morphological features and clinical indices were analysed.Cell morphological features and spatial distribution differed significantly between patients with sepsis and healthy controls and between the survival and non-survival groups.The model for predicting the diagnosis and prognosis of sepsis showed an area under the receiver operating characteristic curve of nearly 100%with only a few cells,and a strong correlation between the morphological features and clinical indices was observed.Our study highlights the potential of three-dimensional label-free CD8+T cell morphology as a promising biomarker for sepsis.This approach is rapid,requires a minimum amount of blood samples,and has the potential to provide valuable information for the early diagnosis and prognosis of sepsis.
基金supported by KAIST Up Program,BK21+program,Tomocube,National Research Foundation of Korea(2015R1A3A2066550)KAIST Institute of Technology Value Creation,Industry Liaison Center(G-COFE Project)grant funded by the Ministry of Science and ICT(N11210014.N11220131)+1 种基金Institute of Information&communicarions Technology Planning&Evaluation(ITP:2021-0-00745)grant funded by the Korea government(MSIT)the Commercialzation Promotion Agency for P&D Outcomes(COMPA:055586)funded by the Korea government.
文摘The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections.Microbial infections are a major healthcare issue worldwide,as these widespread diseases often develop into deadly symptoms.While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection,this effective treatment is difficult to practice.The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification,which includes time-consuming sample growth.Here,we propose a microscopy-based framework that identifies the pathogen from single to few cells.Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network.We demonstrate the identification of 19 bacterial species that cause bloodstream infections,achieving an accuracy of 82.5%from an individual bacterial cell or cluster.This performance,comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample,underpins the effectiveness of our framework in clinical applications.Furthermore,our accuracy increases with multiple measurements,reaching 99.9%with seven different measurements of cells or clusters.We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.
基金supported by the Tomocube,KAIST Advanced Institute for Science-X,National Research Foundation of Korea(2015R1A3A2066550,2021R1C1C2009220,2022M3H4A1A02074314)an Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(2021-0-00745)+1 种基金Technology Innovation program(20011661)funded by the Ministry of Trade,Industry&Energy(MOTIE)supported in part by MSIT and POSTECH.
文摘Owing to its unique penetrating power and high-resolution capability,X-ray imaging has been an irreplaceable tool since its discovery.Despite the significance,the resolution of X-ray imaging has largely been limited by the technical difficulties on X-ray lens making.Various lensless imaging methods have been proposed,but are yet relying on multiple measurements or additional constraints on measurements or samples.Here we present coherent specklecorrelation imaging(CSI)using a designed X-ray diffuser.CSI has no prerequisites for samples or measurements.Instead,from a single shot measurement,the complex sample field is retrieved based on the pseudorandomness of the speckle intensity pattern,ensured through a diffuser.We achieve a spatial resolution of 13.9 nm at 5.46 keV,beating the feature size of the diffuser used(300 nm).The high-resolution imaging capability is theoretically explained based on fundamental and practical limits.We expect the CSI to be a versatile tool for navigating the unexplored world of nanometer.
基金supported by KAIST UP program,BK21+program,Tomocube,National Research Foundation of Korea(2015R1A3A2066550)Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(2021-0-00745).
文摘Objective and Impact Statement.We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index(RI)tomography.Our computational approach that fully utilizes tomographic information of bone marrow(BM)white blood cell(WBC)enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research.Introduction.Conventional methods for examining blood cells,such as blood smear analysis by medical professionals and fluorescence-activated cell sorting,require significant time,costs,and domain knowledge that could affect test results.While label-free imaging techniques that use a specimen’s intrinsic contrast(e.g.,multiphoton and Raman microscopy)have been used to characterize blood cells,their imaging procedures and instrumentations are relatively time-consuming and complex.Methods.The RI tomograms of the BM WBCs are acquired via Mach-Zehnder interferometer-based tomographic microscope and classified by a 3D convolutional neural network.We test our deep learning classifier for the four types of bone marrow WBC collected from healthy donors(n=10):monocyte,myelocyte,B lymphocyte,and T lymphocyte.The quantitative parameters of WBC are directly obtained from the tomograms.Results.Our results show>99%accuracy for the binary classification of myeloids and lymphoids and>96%accuracy for the four-type classification of B and T lymphocytes,monocyte,and myelocytes.The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique.Conclusion.We envision that the proposed cell classification framework can be easily integrated into existing blood cell investigation workflows,providing cost-effective and rapid diagnosis for hematologic malignancy.
基金KAIST UP programme,BK21+programme,Tomocube,and National Research Foundation of Korea(2017M3C1A3013923,2015R1A3A2066550,2018K000396).
文摘A major challenge in three-dimensional(3D)microscopy is to obtain accurate spatial information while simultaneously keeping the microscopic samples in their native states.In conventional 3D microscopy,axial resolution is inferior to spatial resolution due to the inaccessibility to side scattering signals.In this study,we demonstrate the isotropic microtomography of free-floating samples by optically rotating a sample.Contrary to previous approaches using optical tweezers with multiple foci which are only applicable to simple shapes,we exploited 3D structured light traps that can stably rotate freestanding complex-shaped microscopic specimens,and side scattering information is measured at various sample orientations to achieve isotropic resolution.The proposed method yields an isotropic resolution of 230 nm and captures structural details of colloidal multimers and live red blood cells,which are inaccessible using conventional tomographic microscopy.We envision that the proposed approach can be deployed for solving diverse imaging problems that are beyond the examples shown here.
