Objective: In vivo bioluminescence imaging (BLI) is a promising tool for monitoring the growth and metastasis of tumors. However, quantitative BLI research based on intravenous (IV) injection is limited, which greatly restricts its further application. To address this problem, we designed a pharmacokinetic (PK) model which is suitable for applying on IV administration of small amounts of D-Luciferin. Methods : After three weeks of post-implantation, mkn28-luc xenografted mice were subjected to 40 min dynamic BLI immediately following D-Luciferin intravenous injection on days 1, 3, 5, 7 and 9. Further, the PK model was applied on dynamic BLI data to obtain the sum of kinetic rate constants (SKRC). Results : Results showed that the SKRC values decreased rapidly with the growth of the tumor. There was a statistical difference between the SKRC values measured at different time points, while the time point of luminous intensity peak (TLP) was unaffected by the growth of the tumor. Conclusions : In short, our results imply that dynamic BLI combined with our PK model can predict tumor growth earlier and with higher sensitivity compared to the conventional method, which is crucial for improving drug evaluation efficacy. In addition, the dynamic BLI may provide a valuable reference for the noninvasive acquiring arterial input function (AIF), which may also provide a new application prospect for hybrid PET-optical imaging.
In this paper, we propose a novel weakly supervised semantic segmentation (WSSS) method that uses image tags as supervision to achieve joint pixel-level localization of the key local structure (KLS) and image-level classification of the aurora images captured by the ground-based optical all-sky imager. First, a patch-scale model (PSM) based on the small-scale structure of aurora is designed to identify the type-specific regions for each training image. Second, a region-scale model is trained with the identified type-specific regions to coarsely localize the KLS from multiple sizes of field of view, based on which the aurora image is classified. Finally, given the predicted image type, the PSM further refines the KLS in a pixel level. By localizing KLS from coarse to fine, the proposed method captures both overall shape with a bottom-up processing and local structure details of aurora in a top-down manner. Extensive experiments on the expert labeled data sets have demonstrated the efficacy of the proposed method in benchmarking with the state-of-the-art WSSS methods.
Manganese-based nanoparticles (NPs) have recently attracted much attention in the field of biomedical imaging due to their impressive enhanced T-1 contrast ability. Although the reported manganese-based NPs have exhibited good imaging capabilities as contrast agents, it is still urgent to develop novel multifunctional manganese-based imaging probes for future biomedical imaging, especially PET/MRI probes. Herein, we present chelator-free zirconium-89 (Zr-89, t(1/2): 78.4 h) labeling of manganese oxide NPs (Mn3O4@PEG) with similar to 78% labeling yield and good stability. Serial positron emission tomography (PET) and magnetic resonance imaging (MRI) studies non-invasively assessed the biodistribution patterns of the NPs and the feasibility of in vivo dual-modality imaging and lymph-node mapping. Since Mn3O4 NPs exhibited desirable properties for enhanced T-1 imaging and the simplicity of chelator-free radiolabeling, [Zr-89]Mn3O4@PEG NPs offer a novel, simple, safe and accurate nanoplatforms for future precise cancer imaging and diagnosis.
Cancer metastasis is one of the biggest challenges in cancer treatments since it increases the likelihood that a patient will die from the disease. Therefore, the availability of techniques for the early detection and quantification of tumors is very important. We have prepared cyanine 7.5 NHS ester (Cy7.5) and folic acid (FA) conjugated biodegradable mesoporous silica nanoparticles (bMSN@Cy7.5-FA NPs) ( 100 nm) for visualizing tumors in vivo. The fluorescence spectra revealed that the emission peak of bMSN@Cy7.5-FA NPs had a red-shift of 1 nm. Confocal immunofluorescent images showed that bMSN@Cy7.5-FA NPs had an excellent targeting ability for visualizing cancer cells. In vivo fluorescence imaging has been conducted using an orthotopic model for pancreatic cancer within 48 h, and the fluorescence intensity reached a maximum at a post injection time-point of 12 h, which demonstrated that the use of bMSN@Cy7.5-FA NPs provides an excellent imaging platform for tumor precision therapy in mice.
Coronary artery disease (CAD) is one of the leading causes of death worldwide. The computed tomography angiography (CTA) is increasingly used to diagnose CAD due to its non-invasive nature and high-resolution three-dimensional (3D) imaging capability of the coronary artery anatomy. CTA allows for identification and grading of stenosis by evaluating the degree of narrowing of the blood-filled coronary artery lumen. Both identification and grading rely on the precise segmentation of the coronary arteries on CTA images. In this paper, a fully automatic segmentation framework is proposed to extract the coronary arteries from the whole cardiac CTA images. The framework adopts a paired multi-scale 3D deep convolutional neural networks (CNNs) to identify which voxels belong to the vessel lumen. Voxels that may belong to coronary artery lumen are recognized by the first CNN in the pair and both artery positives and artery-like negatives are distinguished by the second one. Each CNN is assigned to a different task. They share the same architecture in common but with different weights. In order to combine local and larger contextual information, we adopt a dual pathway architecture that can process the input image simultaneously on multiple scales. The experiments were performed on a CTA dataset from 44 patients. 35 CTA scans are used for training and the rests for testing. The proposed segmentation framework achieved a mean Dice similarity coefficient (DSC) of 0.8649 and mean surface distance (MSD) of 0.5571 with reference to manual annotations. Experimental results show that the proposed framework is capable of performing complete, accurate and robust segmentation of the coronary arteries.
Volumetric imaging allows global understanding of three-dimensional (3D) complex systems. Light-sheet fluorescence microscopy and optical projection tomography have been reported to image 3D volumes with high resolutions and at high speeds. Such methods, however, usually rely on fluorescent labels for chemical targeting, which could perturb the biological functionality in living systems. We demonstrate Bessel-beam-based stimulated Raman projection (SRP) microscopy and tomography for label-free volumetric chemical imaging. Our SRP microscope enables fast quantitation of chemicals in a 3D volume through a two-dimensional lateral scan. Furthermore, combining SRP and sample rotation, we demonstrate the SRP tomography that can reconstruct the 3D distribution of chemical compositions with optical spatial resolution at a higher speed than the Gaussian-beam-based stimulated Raman scattering sectioning imaging can. We explore the potential of our SRP technology by mapping polymer particles in 3D volumes and lipid droplets in adipose cells.
Most previous studies exploring the neural mechanism of psychogenic erectile dysfunction (pED) focused on brain activity under tasks. We suggest that the resting brain activity is equally important in pED studies, in that the patterns of spontaneous neural activities is independent of modalities of sensory input, therefore providing substantial information regarding the central mechanism of pED. Our previous study reported the altered baseline activity in right anterior insula (aINS) in pED patients. Also, the insula is a pivotal region in sexual behavior, which is suggested to be able to directly mediate erection. Therefore, the current study employed resting-state fMRI to examine alterations in functional connectivity of the aINS comparing pED patients with matched control subjects. After rigorous participant inclusion procedure, 27 pED patients and 27 healthy male controls were enrolled. Our results elucidated the disrupted homogeneity within the right aINS and aberrant connection patterns between the right aINS and the right dorsolateral prefrontal cortex (dlPFC), as well as the right aINS and the right temporoparietal junction (TPJ) respectively in pED group, as compared with the healthy controls. In conclusion, our results demonstrated the aberrant insula-centered functional connectivity in pED, which may be related to the abnormal representation of internal bodily state or needs in pED patients and thus further affect the inhibitory control in the sexual context. We hope that these findings may shed new light on the understanding of the central mechanism of pED.
For a rotatory dual-head positron emission tomography (PET) system, how to determine the rotation increments is an open problem. In this study, we simulated the characteristics of a rotatory dual-head PET system. The influences of different rotation increments were compared and analyzed. Based on this simulation, the imaging performance of a prototype system was verified. A reconstruction flowchart was proposed based on a precalculated system response matrix (SRM). The SRM made the relationships between the voxels and lines of response (LORs) fixed; therefore, we added the interpolation method into the flowchart. Five metrics, including spatial resolution, normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), contrast-to-noise (CNR), and structure similarity (SSIM), were applied to assess the reconstructed image quality. The results indicated that the 60 degree rotation increments with the bilinear interpolation had advantages in resolution, PSNR, NMSE, and SSIM. In terms of CNR, the 90 degree rotation increments were better than other increments. In addition, the reconstructed images of 90 degree rotation increments were also flatter than that of 60 degree increments. Therefore, both the 60 and 90 degree rotation increments could be used in the real experiments, and which one to choose may depend on the application requirement.
The combination of different modality images can provide detailed and comprehensive information for the prognostic assessment and therapeutic strategy of patients with ischemic heart disease. In this study, a 3D fusion framework is designed to integrate coronary computed tomography (CT) angiography (CTA), 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]DG) positron emission tomography (PET)/CT, and [(68)Ga]-1,4,7-triazacyclononane-1,4,7-triacetic acid-(Arg-Gly-Asp)2 ([(68)Ga]-NOTA-PRGD2) PET/CT images of the myocardial infarction model in minipigs. First, the structural anatomy of the heart in coronary CTA and CT is segmented using a multi-atlas-based method. Then, the hearts are registered using the B-spline-based free form deformation. Finally, the [(18)F]DG and [(68)Ga]-NOTA-PRGD2 signals are mapped into the heart in coronary CTA, which produces a single fusion image to delineate both the cardiac structural anatomy and the functional information of myocardial viability and angiogenesis. Heart segmentation demonstrates high accuracy with good agreement between manual delineation and automatic segmentation. The fusion result intuitively reflects the extent of the [(18)F]DG uptake defect as well as the location where the [(68)Ga]-NOTA-PRGD2 signal appears. The fusion result verified the occurrence of angiogenesis based on the in vivo noninvasive molecular imaging approach. The presented framework is helpful in facilitating the study of the relationship between infarct territories and blocked coronary arteries as well as angiogenesis.
The Cerenkov luminescence imaging (CLI) has built a bridge between the nuclear and optical imaging, and opened a new direction of optical imaging in clinic translation. However, the wide applications of CLI are limited by the weak intensity and poor penetration capacity. By coupling highly sensitive charged coupled device camera with optical fiber bundle based endoscope, Cerenkov luminescence endoscopy (CLE) has recently been developed to overcome these limitations. Using the CLE, diseases deep buried in the body, such as the gastrointestinal (GI) tract cancer, can be identified by radiotracer, which can hardly be detected by CLI from the surface of the body. Here, we review recent progress on CLI and CLE, including the principle, imaging instrument, as well as applications. In addition, the intensity enhanced strategies for Cerenkov luminescence are presented, which may provide an ideal solution to the obstacle of CLE in the translation studies. In the end, we present possible future directions of the CLE technology.
Quantitative analysis of myocardial infarction is important for the prognostic assessment and therapeutic strategy of patients with ischemic heart disease (IHD). 18 F-fluorodeoxyglucose (FDG) PET is generally regarded as the gold standard for in vivo assessing myocardial viability. However, currently PET scans can only be interpreted by well trained radiologists because the morphological variation and location of infarct myocardium is hard to reflect visually on 2D slices or polar plot. In this paper, a 3D non-rigid coronary CTA (CCTA)-PET registration strategy is presented to quantitative analysis the extent of myocardial infarction. Whole heart is firstly segmented from both CCTA and PET/CT. Then the myocardium of left ventricle (LV) is separated from CCTA and PET/CT, respectively. After morphological post-processing, the myocardium image is integrated with the whole heart image both for CCTA and PET/CT to conduct the myocardial registration. A random forest classifier is trained to identify the infarct area of LV wall. The myocardial infarction analysis method proposed in this paper is compared with the cardiac tissue histological study. The result demonstrates good agreement with the TTC stain result in both infarct size and location, and suggests a potential value for clinic application in the prognosis of myocardial infarction.
OBJECTIVE: The existence of void regions and the complexity of structural heterogeneity and optical specificity are two challenges encountered in three-dimensional (3-D) optical imaging of in situ gastric cancer. An adaptively alternative light-transport-model-based 3-D optical imaging method was developed for addressing these challenges. METHODS: In this newly developed imaging method, both the anatomical structure and optical properties are considered as a priori information, which makes the full use of the specificity of the biological tissues and improves both the quality and efficiency of the reconstructed images. The soul of the adaptively alternative technique is technique is configured to first classify the tissues based on the anatomical structure and optical properties and then select various equations to specifically characterize the light transport in different categories of tissues. RESULTS: A series of rigorous simulations were conducted to demonstrate the performance of the hybrid light transport model, and the quality of the reconstructed images was then evaluated using a digital-mouse-based gastric cancer mimicing simulation, which showed that the localization error was less than 1 mm and the quantification error was approximately 10%. Finally, the applicability of the proposed method for in vivo imaging of gastric cancer was illustrated using groups of gastric cancer-bearing mice, which demonstrated the ability of the proposed method for longitudinal and quantitative monitoring of gastric cancer. CONCLUSION: The developed method offers improved probability and great potential in the applications of earlier detecting in situ gastric cancer and longitudinal and quantitative observation of its development.
We quantified molecular specificity of Cy5.5-GX1 in vivo with dynamic fluorescence imaging to better understand its kinetic properties. According to whether or not free GX1 was injected and when it was injected, twelve of BGC-823 xenografted mice were randomly divided into three groups and underwent a 60 minute dynamic fluorescence scanning. Combined with a principal-component analysis, the binding potential (Bp) of the probe was determined by both Logan graphical analysis with reference tissue model (GARTM) and Lammertsma simplified reference tissue model (SRTM). The sum of the pharmacokinetic rate constants (SKRC) was quantified by the Gurfinkel exponential model (GEXPM). Cy5.5-GX1 specifically targeted tumor both in vitro and in vivo. We obtained similar quantification results of Bp (GARTM Bp = 0.582 +/- 0.2655, SRTM Bp = 0.618 +/- 0.2923), and obtained a good linear relation between the Bp value and the SKRC value. Our results indicate that the SKRC value is more suitable for an early-stage kinetic data analysis, and the Bp value depicts kinetic characteristics under the equilibrium state. Dynamic fluorescence imaging in conjunction with various kinetic models are optimal tools to quantify molecular specificity of the Cy5.5-GX1 probe in vivo.
The aim of this article is to investigate the influence of a tracer injection dose (ID) and camera integration time (IT) on quantifying pharmacokinetics of Cy5.5-GX1 in gastric cancer BGC-823 cell xenografted mice. Based on three factors, including whether or not to inject free GX1, the ID of Cy5.5-GX1, and the camera IT, 32 mice were randomly divided into eight groups and received 60-min dynamic fluorescence imaging. Gurfinkel exponential model (GEXPM) and Lammertsma simplified reference tissue model (SRTM) combined with a singular value decomposition analysis were used to quantitatively analyze the acquired dynamic fluorescent images. The binding potential (Bp) and the sum of the pharmacokinetic rate constants (SKRC) of Cy5.5-GX1 were determined by the SRTM and EXPM, respectively. In the tumor region, the SKRC value exhibited an obvious trend with change in the tracer ID, but the Bp value was not sensitive to it. Both the Bp and SKRC values were independent of the camera IT. In addition, the ratio of the tumor-To-muscle region was correlated with the camera IT but was independent of the tracer ID. Dynamic fluorescence imaging in conjunction with a kinetic analysis may provide more quantitative information than static fluorescence imaging, especially for a priori information on the optimal ID of targeted probes for individual therapy.
Bioluminescence tomography (BLT) has been a valuable optical molecular imaging technique to non-invasively depict the cellular and molecular processes in living animals with high sensitivity and specificity. Due to the inherent ill-posedness of BLT, a priori information of anatomical structure is usually incorporated into the reconstruction. The structural information is usually provided by computed tomography (CT) or magnetic resonance imaging (MRI). In order to obtain better quantitative results, BLT reconstruction with heterogeneous tissues needs to segment the internal organs and discretize them into meshes with the finite element method (FEM). It is time-consuming and difficult to handle the segmentation and discretization problems. In this paper, we present a fast reconstruction method for BLT based on multi-atlas registration and adaptive voxel discretization to relieve the complicated data processing procedure involved in the hybrid BLT/CT system. A multi-atlas registration method is first adopted to estimate the internal organ distribution of the imaged animal. Then, the animal volume is adaptively discretized into hexahedral voxels, which are fed into FEM for the following BLT reconstruction. The proposed method is validated in both numerical simulation and an in vivo study. The results demonstrate that the proposed method can reconstruct the bioluminescence source efficiently with satisfactory accuracy.
Simplified spherical harmonics approximation (SPN) equations are widely used in modeling light propagation in biological tissues. However, with the increase of order N, its computational burden will severely aggravate. We propose a graphics processing unit (GPU) accelerated framework for SPN equations. Compared with the conventional central processing unit implementation, an increased performance of the GPU framework is obtained with an increase in mesh size, with the best speed-up ratio of 25 among the studied cases. The influence of thread distribution on the performance of the GPU framework is also investigated.
The widespread use of whole-body small animal in vivo imaging in preclinical research has proposed the new demands on imaging processing and analysis. Micro-CT provides detailed anatomical structural information for continuous detection and different individual comparison, but the body deformation happened during different data acquisition needs sophisticated registration. In this paper, we propose a hybrid method for registering micro-CT mice images, which combines the strengths of point-based and intensity-based registration methods. Point-based non-rigid method using thin-plate spline robust point matching algorithm is utilized to acquire a coarse registration. And then intensity-based non-rigid method using normalized mutual information, Halton sampling and adaptive stochastic gradient descent optimization is used to acquire precise registration. Two accuracy metrics, Dice coefficient and average surface distance are used to do the quantitative evaluation. With the intra- and intersubject micro-CT mice images registration assessment, the hybrid method has been proven capable of excellent performance on micro-CT mice images registration.
Cancer is a common cause of death worldwide. Despite significant advances in cancer treatments, the morbidity and mortality are still enormous. Tumor heterogeneity, especially intratumoral heterogeneity, is a significant reason underlying difficulties in tumor treatment and failure of a number of current therapeutic modalities, even of molecularly targeted therapies. The development of a virtually noninvasive "liquid biopsy" from the blood has been attempted to characterize tumor heterogeneity. This review focuses on cell-free circulating tumor DNA (ctDNA) in the bloodstream as a versatile biomarker. ctDNA analysis is an evolving field with many new methods being developed and optimized to be able to successfully extract and analyze ctDNA, which has vast clinical applications. ctDNA has the potential to accurately genotype the tumor and identify personalized genetic and epigenetic alterations of the entire tumor. In addition, ctDNA has the potential to accurately monitor tumor burden and treatment response, while also being able to monitor minimal residual disease, reducing the need for harmful adjuvant chemotherapy and allowing more rapid detection of relapse. There are still many challenges that need to be overcome prior to this biomarker getting wide adoption in the clinical world, including optimization, standardization, and large multicenter trials.
Cerenkov luminescence imaging (CLI) can provide information of medical radionuclides used in nuclear imaging based on Cerenkov radiation, which makes it possible for optical means to image clinical radionuclide labeled probes. However, the exceptionally weak Cerenkov luminescence (CL) from Cerenkov radiation is susceptible to lots of impulse noises introduced by high energy gamma rays generating from the decays of radionuclides. In this work, a temporal median filter is proposed to remove this kind of impulse noises. Unlike traditional CLI collecting a single CL image with long exposure time and smoothing it using median filter, the proposed method captures a temporal sequence of CL images with shorter exposure time and employs a temporal median filter to smooth a temporal sequence of pixels. Results of in vivo experiments demonstrated that the proposed temporal median method can effectively remove random pulse noises induced by gamma radiation and achieve a robust CLI image.
This paper proposes an effective scale invariant texture representation based on frequency decomposition and gradient orientation. First, the image intensities are decomposed into different orientations by using wedge filters in the frequency domain, and the N-nary coding method is adopted for the vector quantization. Second, the scale invariant gradient orientation is generated by selecting the most stable value of the gradient orientation with different Gaussian scales. Finally, the 2D joint distribution of the two types of local descriptors is used as the representation. The performance was evaluated on texture classification using a nearest neighbor classifier. Simple but not ordinary, our method achieves state of the art classification performance on the KTH-TIPS dataset under the traditional experimental design. Moreover, the main experiments were conducted on the KTH-TIPS and KTH-TIPS2-b datasets with the experimental designs of scale invariance validation. Compared with the methods of basic image features (BIFs) and local energy pattern (LEP), the proposed representation achieves superior performance with much lower dimension of representation.
Object detection methods based on keypoint localization are widely used, and the rotation invariance is one of the fundamental issues to consider. This paper proposes a novel shape prior model with rotation invariance. The proposed shape prior model discards all orientation-involved features and only uses the distance features among keypoints, hence it is competent to detect objects with a rotation of the arbitrary angle when combined with local appearance description with rotation invariance. In the stage of detection, belief propagation algorithm is employed, so that our method no longer needs the initial position of the keypoints. Furthermore, we generalize the classical distance transforms, the generalized distance transforms make the beliefs to be calculated in a nearly linear time. Experiments were carried out on face category and touring-bike category in the Caltech-256 database. The results demonstrated that the proposed method achieved a strong robustness of rotation.
By recording a time series of tomographic images, dynamic fluorescence molecular tomography (FMT) allows exploring perfusion, biodistribution, and pharmacokinetics of labeled substances in vivo. Usually, dynamic tomographic images are first reconstructed frame by frame, and then unmixing based on principle component analysis (PCA) or independent component analysis (ICA) is performed to detect and visualize functional structures with different kinetic patterns. PCA and ICA assume sources are statistically uncorrelated or independent and donot perform well when correlated sources are present. In this paper, we deduce the relationship between the measured imaging data and the kinetic patterns and present a temporal unmixing approach, which is based on nonnegative blind source separation (BSS) method with a convex analysis framework to separate the measured data. The presented method requires no assumption on source independence or zero correlations. Several numerical simulations and phantom experiments are conducted to investigate the performance of the proposed temporal unmixing method. The results indicate that it is feasible to unmix the measured data before the tomographic reconstruction and the BSS based method provides better unmixing quality compared with PCA and ICA.
Weak intensity and poor penetration depth are two big obstacles toward clinical use of Cerenkov luminescence imaging (CLI). In this proof-of-concept study, we overcame these limitations by using lanthanides-based radioluminescent microparticles (RLMPs), called terbium doped Gd2O2S. The characterization experiment showed that the emission excited by Cerenkov luminescence can be neglected whereas the spectrum experiment demonstrated that the RLMPs can actually be excited by gamma-rays. A series of in vitro experiments demonstrated that RLMPs significantly improve the intensity and the penetration capacity of CLI, which has been extended to as deep as 15 mm. In vivo pseudotumor study further prove the huge potential of this enhancement strategy for Cerenkov luminescence imaging in living animal studies.
Our previous study showed a great attenuation for the Cerenkov luminescence endoscope (CLE), resulting in relatively low detection sensitivity of radiotracers. Here, a kind of radioluminescence nanoparticles (RLNPs), terbium doped Gd2O2S was mixed with the radionuclide68Ga to enhance the intensity of emitted luminescence, which finally improved the detection sensitivity of the CLE by using the radioluminescence imaging technique. With the in vitro and in vivo pseudotumor experiments, we showed that the use of RLNPs mixed with the radionuclide 68Ga enabled superior sensitivity compared with the radionuclide68Ga only, with 50-fold improvement on detection sensitivity, which guaranteed meeting the demands of the clinical diagnosis of gastrointestinal tract tumors.
Aiming at the limitations of the simplified spherical harmonics approximation (SPN) and diffusion equation (DE) in describing the light propagation in tissues, a hybrid simplified spherical harmonics with diffusion equation (HSDE) based diffuse light transport model is proposed. In the HSDE model, the living body is first segmented into several major organs, and then the organs are divided into high scattering tissues and other tissues. DE and SPN are employed to describe the light propagation in these two kinds of tissues respectively, which are finally coupled using the established boundary coupling condition. The HSDE model makes full use of the advantages of SPN and DE, and abandons their disadvantages, so that it can provide a perfect balance between accuracy and computation time. Using the finite element method, the HSDE is solved for light flux density map on body surface. The accuracy and efficiency of the HSDE are validated with both regular geometries and digital mouse model based simulations. Corresponding results reveal that a comparable accuracy and much less computation time are achieved compared with the SPN model as well as a much better accuracy compared with the DE one.
X-ray luminescence computed tomography (XLCT) has become a promising imaging technology for biological application based on phosphor nanoparticles. There are mainly three kinds of XLCT imaging systems: pencil beam XLCT, narrow beam XLCT and cone beam XLCT. Narrow beam XLCT can be regarded as a balance between the pencil beam mode and the cone-beam mode in terms of imaging efficiency and image quality. The collimated X-ray beams are assumed to be parallel ones in the traditional narrow beam XLCT. However, we observe that the cone beam X-rays are collimated into X-ray beams with fan-shaped broadening instead of parallel ones in our prototype narrow beam XLCT. Hence we incorporate the distribution of the X-ray beams in the physical model and collected the optical data from only two perpendicular directions to further speed up the scanning time. Meanwhile we propose a depth related adaptive regularized split Bregman (DARSB) method in reconstruction. The simulation experiments show that the proposed physical model and method can achieve better results in the location error, dice coefficient, mean square error and the intensity error than the traditional split Bregman method and validate the feasibility of method. The phantom experiment can obtain the location error less than 1.1 mm and validate that the incorporation of fan-shaped X-ray beams in our model can achieve better results than the parallel X-rays.
Fluorescent gold nanoclusters (AuNCs) have been extensively studied due to their unique construction and distinctive properties, which place them between single metal atoms and larger nanoparticles. The dimension of AuNCs is comparable to the Fermi wavelength of ...
The third-order simplified harmonic spherical approximation (SP3) and diffusion approximation (DA) equations have been widely used in the three-dimensional (3D) whole-body optical imaging of small animals. With different types of tissues, which were classified by the ratio of , the two equations have their own application scopes. However, the classification criterion was blurring and unreasonable, and the scope has not been systematically investigated until now. In this study, a new criterion for classifying tissues was established based on the absolute value of absorption and reduced scattering coefficients. Using the newly defined classification criterion, the performance and applicability of the SP3 and DA equations were evaluated with a series of investigation experiments. Extensive investigation results showed that the SP3 equation exhibited a better performance and wider applicability than the DA one in most of the observed cases, especially in tissues of low-scattering-low-absorption and low-scattering-high-absorption range. For the case of tissues with the high-scattering-low-absorption properties, a similar performance was observed for both the SP3 and the DA equations, in which case the DA was the preferred option for 3D whole-body optical imaging. Results of this study would provide significant reference for the study of hybrid light transport models.
Cerenkov luminescence imaging (CLI) provides potential to use clinical radiotracers for optical imaging. The goal of this study was to present a newly developed endoscopic CLI (ECLI) system and illustrate its feasibility and potential in distinguishing and quantifying cancerous lesions of the GI tract. The ECLI system was established by integrating an electron-multiplying charge-coupled device camera with a flexible fibre endoscope. Phantom experiments and animal studies were conducted to test and illustrate the system in detecting and quantifying the presence of radionuclide in vitro and in vivo. A pilot clinical study was performed to evaluate our system in clinical settings. Phantom and mice experiments demonstrated its ability to acquire both the luminescent and photographic images with high accuracy. Linear quantitative relationships were also obtained when comparing the ECLI radiance with the radiotracer activity (r (2) = 0.9779) and traditional CLI values (r (2) = 0.9025). Imaging of patients revealed the potential of ECLI in the identification and quantification of cancerous tissue from normal, which showed good consistence with the clinical PET examination. The new ECLI system shows good consistence with the clinical PET examination and has great potential for clinical translation and in aiding detection of the GI tract disease.
Combining two or more imaging modalities to provide complementary information has become commonplace in clinical practice and in preclinical and basic biomedical research. By incorporating the structural information provided by computed tomography (CT) or magnetic resonance imaging (MRI), the ill poseness nature of bioluminescence tomography (BLT) can be reduced significantly, thus improve the accuracies of reconstruction and in vivo quantification. In this paper, we present a small animal imaging system combining multi-view and multi-spectral BLT with MRI. The independent MRI-compatible optical device is placed at the end of the clinical MRI scanner. The small animal is transferred between the light tight chamber of the optical device and the animal coil of MRI via a guide rail during the experiment. After the optical imaging and MRI scanning procedures are finished, the optical images are mapped onto the MRI surface by interactive registration between boundary of optical images and silhouette of MRI. Then, incorporating the MRI structural information, a heterogeneous reconstruction algorithm based on finite element method (FEM) with L 1 normalization is used to reconstruct the position, power and region of the light source. In order to validate the feasibility of the system, we conducted experiments of nude mice model implanted with artificial light source and quantitative analysis of tumor inoculation model with MDA-231-GFP-luc. Preliminary results suggest the feasibility and effectiveness of the prototype system.
Local energy pattern, a statistical histogram-based representation, is proposed for texture classification. First, we use normalized local-oriented energies to generate local feature vectors, which describe the local structures distinctively and are less sensitive to imaging conditions. Then, each local feature vector is quantized by self-adaptive quantization thresholds determined in the learning stage using histogram specification, and the quantized local feature vector is transformed to a number by N-nary coding, which helps to preserve more structure information during vector quantization. Finally, the frequency histogram is used as the representation feature. The performance is benchmarked by material categorization on KTH-TIPS and KTH-TIPS2-a databases. Our method is compared with typical statistical approaches, such as basic image features, local binary pattern (LBP), local ternary pattern, completed LBP, Weber local descriptor, and VZ algorithms (VZ-MR8 and VZ-Joint). The results show that our method is superior to other methods on the KTH-TIPS2-a database, and achieving competitive performance on the KTH-TIPS database. Furthermore, we extend the representation from static image to dynamic texture, and achieve favorable recognition results on the University of California at Los Angeles (UCLA) dynamic texture database.
PURPOSE: The appearance of x-ray luminescence computed tomography (XLCT) opens new possibilities to perform molecular imaging by x ray. In the previous XLCT system, the sample was irradiated by a sequence of narrow x-ray beams and the x-ray luminescence was measured by a highly sensitive charge coupled device (CCD) camera. This resulted in a relatively long sampling time and relatively low utilization of the x-ray beam. In this paper, a novel cone beam x-ray luminescence computed tomography strategy is proposed, which can fully utilize the x-ray dose and shorten the scanning time. The imaging model and reconstruction method are described. The validity of the imaging strategy has been studied in this paper. METHODS: In the cone beam XLCT system, the cone beam x ray was adopted to illuminate the sample and a highly sensitive CCD camera was utilized to acquire luminescent photons emitted from the sample. Photons scattering in biological tissues makes it an ill-posed problem to reconstruct the 3D distribution of the x-ray luminescent sample in the cone beam XLCT. In order to overcome this issue, the authors used the diffusion approximation model to describe the photon propagation in tissues, and employed the sparse regularization method for reconstruction. An incomplete variables truncated conjugate gradient method and permissible region strategy were used for reconstruction. Meanwhile, traditional x-ray CT imaging could also be performed in this system. The x-ray attenuation effect has been considered in their imaging model, which is helpful in improving the reconstruction accuracy. RESULTS: First, simulation experiments with cylinder phantoms were carried out to illustrate the validity of the proposed compensated method. The experimental results showed that the location error of the compensated algorithm was smaller than that of the uncompensated method. The permissible region strategy was applied and reduced the reconstruction error to less than 2 mm. The robustness and stability were then evaluated from different view numbers, different regularization parameters, different measurement noise levels, and optical parameters mismatch. The reconstruction results showed that the settings had a small effect on the reconstruction. The nonhomogeneous phantom simulation was also carried out to simulate a more complex experimental situation and evaluated their proposed method. Second, the physical cylinder phantom experiments further showed similar results in their prototype XLCT system. With the discussion of the above experiments, it was shown that the proposed method is feasible to the general case and actual experiments. CONCLUSIONS: Utilizing numerical simulation and physical experiments, the authors demonstrated the validity of the new cone beam XLCT method. Furthermore, compared with the previous narrow beam XLCT, the cone beam XLCT could more fully utilize the x-ray dose and the scanning time would be shortened greatly. The study of both simulation experiments and physical phantom experiments indicated that the proposed method was feasible to the general case and actual experiments.
Modeling light propagation in the whole body is essential and necessary for optical imaging. However, non-scattering, low-scattering and high absorption regions commonly exist in biological tissues, which lead to inaccuracy of the existing light transport models. In this paper, a novel hybrid light transport model that couples the simplified spherical harmonics approximation (SPN) with the radiosity theory (HSRM) was presented, to accurately describe light transport in turbid media with non-scattering, low-scattering and high absorption heterogeneities. In the model, the radiosity theory was used to characterize the light transport in non-scattering regions and the SPN was employed to handle the scattering problems, including subsets of low-scattering and high absorption. A Neumann source constructed by the light transport in the non-scattering region and formed at the interface between the non-scattering and scattering regions was superposed into the original light source, to couple the SPN with the radiosity theory. The accuracy and effectiveness of the HSRM was first verified with both regular and digital mouse model based simulations and a physical phantom based experiment. The feasibility and applicability of the HSRM was then investigated by a broad range of optical properties. Lastly, the influence of depth of the light source on the model was also discussed. Primary results showed that the proposed model provided high performance for light transport in turbid media with non-scattering, low-scattering and high absorption heterogeneities.
In in vivo optical projection tomography (OPT), object motion will significantly reduce the quality and resolution of the reconstructed image. Based on the well-known Helgason-Ludwig consistency condition (HLCC), we propose a novel method for motion correction in OPT under parallel beam illumination. The method estimates object motion from projection data directly and does not require any other additional information, which results in a straightforward implementation. We decompose object movement into translation and rotation, and discuss how to correct for both translation and general motion simultaneously. Since finding the center of rotation accurately is critical in OPT, we also point out that the system's geometrical offset can be considered as object translation and therefore also calibrated through the translation estimation method. In order to verify the algorithm effectiveness, both simulated and in vivo OPT experiments are performed. Our results demonstrate that the proposed approach is capable of decreasing movement artifacts significantly thus providing high quality reconstructed images in the presence of object motion.
The naturally occurring aurora phenomenon is a dynamically evolving process. Taking temporal information into consideration, the auroral image sequence analysis is more reasonable and desirable than using static images only. However, the enormous richness of space structures and temporal variations make automatic auroral sequence analysis a particularly challenging task. In this paper, a hidden Markov model (HMM) based representation method including features of spatial texture and dynamic evolution is presented to characterize auroral image sequences captured by all-sky imagers (ASIs). The uniform local binary patterns are employed to describe the 2-D space structures of ASI images. HMM is feasible to characterize the doubly stochastic process involved in the auroral evolution-measurable polar light activities and hidden dynamic plasma processes. We present an affine log-likelihood normalization technique to manage the sequences with different lengths. The proposed method is used in the automatic recognition of four primary categories of ASI auroral observations between the years 2003 and 2009 at the Yellow River Station, Ny-Alesund, Svalbard. The supervised classification results on manually labeled data in 2003 demonstrate the effectiveness of the proposed technique. Compared with frame-based classification, the higher accuracies and the lower rejection rates show the advantages of the sequence-based-method. The occurrence distributions of the four aurora categories were obtained through automatic classification of data gathered from 2004 to 2009. Their agreement with the multiple-wavelength intensity distribution of the dayside aurora and the conclusions made from the frame-based method further illustrate the validity of our method on auroral representation and classification.
A spatial texture based representation method including features of intensity, shape and texture, was utilized to characterize all-sky auroral images. The combination of the local binary pattern (LBP) operator and a delicately designed block partition scheme achieved both global shapes and local textures capabilities. The representation method was used in automatic recognition of four primary categories of discrete dayside aurora using observations between years 2003-2009 at the Yellow River Station, Nylesund, Svalbard. The supervised classification results on labeled data in 2003 were in accordance with the labeling by scientists considering both spectral and morphological information. The occurrence distributions of the four categories were obtained through automatic classification of data between 2004-2009, which confirm the multiple-wavelength intensity distribution of dayside aurora, and further provide morphological interpretation of auroral types.