Optical-resolution photoacoustic microscopy (OR-PAM) has rapidly developed and is capable of characterizing optical absorption properties of biological tissue with high contrast and high resolution (micrometer-scale lateral resolution). However, the conventional excitation source of rapidly diverging Gaussian beam imposes limitations on the depth of focus (DOF) in OR-PAM, which in turn affects the depth-resolving ability and detection sensitivity. Here, we proposed a flexible DOF, depth-invariant resolution photoacoustic microscopy (FDIR-PAM) with nondiffraction of Airy beams. The spatial light modulator was incorporated into the optical pathway of the excitation source with matched switching phase patterns, achieving the flexibly adjustable modulation parameters of the Airy beam. We conducted experiments on phantoms and intravital tissue to validate the effectiveness of the proposed approach for high sensitivity and high-resolution characterization of variable topology of tissue, offering a promising DOF of 926 μm with an invariant lateral resolution of 3.2 μm, which is more than 17-fold larger compared to the Gaussian beam. In addition, FDIR-PAM successfully revealed clear individual zebrafish larvae and the pigment pattern of adult zebrafishes, as well as fine morphology of cerebral vasculature in a large depth range with high resolution, which has reached an evident resolving capability improvement of 62% mean value compared with the Gaussian beam.
SignificancePerformance during risky decision making is one of the essential cognitive functions that is impaired in several psychiatric disorders including addiction. However, the cognitive mechanism and neural correlates underlying risky decision making in chronic pain patients are unclear. To our knowledge, this study is among the first to construct computational models to detect the underlying cognitive process of chronic pain patients during risky decision making.AimThis study aimed at inspecting the significantly abnormal risky decision-making patterns of chronic pain patients and its neuro-cognitive correlates.ApproachIn this case-control study, 19 chronic pain patients and 32 healthy controls (HCs) were included to measure the risky decision making in a balloon analogue risk task (BART). Optical neuroimaging using functional near-infrared spectroscopy, together with computational modeling, was carried out to systematically characterize the specific impairments based on BART.ResultsComputational modeling findings on behavioral performance demonstrated that the chronic pain patient group exhibited significant deficits in learning during BART (p < 0.001), tending to make decisions more randomly without deliberation (p < 0.01). In addition, significant brain deactivation alternation in the prefrontal cortex (PFC) during the task was detected for the patient group compared with that from the control group (p < 0.005).ConclusionsLong-term aberrant pain responses significantly disrupted the PFC function and behavioral performance in chronic pain patients. The joint behavioral modeling and neuroimaging techniques open a new avenue for fully understanding the cognitive impairment and brain dysfunction of risky decision making associated with chronic pain.
Without any staining or labeling process, label-free cell imaging has less damage to cells and is suitable for longitudinal observation of cell behaviors. The lensless digital holographic microscopy, which directly uses photoelectric sensor chip to collect holographic images, has the advantages of low cost, non-invasive, large field of view and high resolution, and has been widely used in label-free cell imaging. However, when the classical angular spectrum method is used to recover the complex optical field in lensless digital holography, the twin image shows up and has a serious impact on image quality. To improve the quality of the reconstruction, a multi-wavelength lensless digital holography method is used for cell imaging in this paper. During numerical reconstruction, autofocus is performed firstly, and then the object light field is restored by the multi-wavelength phase retrieval (MWPR) iteration method combined with the compressive sensing (CS) strategy. The experiment results show that the reconstruction method based on multi-wavelength illumination combined with compressive sensing can effectively eliminate the influence of twin image and improve the reconstruction quality. Compared with the reconstruction results of single wavelength hologram, our method can not only obtain highresolution labeled cell images with large field of view, but also obtain high-quality non-labeled cell video, which can be used as an effective technique for cell imaging and dynamic continuous observation in the future.
Flavonoids are natural compounds with diverse structures. This type of nature product is considered to possess a wide range of health beneficial effects. Different skeleton structures and substituent groups lead to different Raman spectral features. In this work, we developed three Raman spectrum analysis methods based on artificial intelligence to classify 18 flavonoids. Firstly, applying principal component analysis (PCA) as dimension reduction method, we compress the 1300cm-1 -1600cm-1 spectral band into several important variables. The results obtained by the preprocessing methods were combined with K-Nearest Neighbor algorithm (KNN), support vector machine (SVM) for classification. Secondly, the combination of relevant features was taken by advanced machine learning method of random forest (RF). In terms of the accuracy of the results, all the methods achieved acceptable classification accuracy, which was almost over 84% on the test set. The experimental results demonstrated that the Raman spectroscopy study based on corresponding unique vibration mode exhibited application prospects in chemical structure classification and pharmacological activity prediction.
Myopia, a global public health problem, is recognized by the World Health Organization as the leading cause of visual impairment in uncorrected people. At present, a large number of reports focus on the pathological manifestations of retinal level myopia. However, corneal histological changes that may be associated with myopia have not been thoroughly investigated. Raman spectroscopy is a rapid and non-destructive analytical technique with the advantages including label-free, non-invasive and highly specific, providing detailed information at the molecular level. Important components of all the human tissue (proteins, nucleic acids, lipids, etc.) have corresponding Raman spectral characteristic peaks, which contribute to the study of myopia at the molecular level. In this study, a microscopic confocal Raman system (MCRS) was built to collect Raman spectrum of corneal stromal samples, which was obtained through femtosecond laser small incision corneal stroma lens extraction (SMLIE). One hundred fifty-nine corneal stromal Raman spectrum data were collected (54 low myopia, 69 moderate myopia and 36 high myopia). Ten characteristic peaks and corresponding components were further identified. K-nearest neighbor (KNN) was used with principal component analysis (PCA) to classify the samples and the classifications were validated by k-fold cross-validation. Three types of samples with different degrees of myopia were differentiated under the PCA-KNN model with an accuracy of 93.1%. Two characteristic peaks (1099 cm-1 and 2940 cm-1) show greatly contribution to the classification results. The results provide that Raman spectroscopy combined with PCA-KNN analysis can effectively distinguish the degree of myopia and is expected to explore the potential causes of myopia.
KEYWORDS: Fluorescence, Autofluorescence, Multispectral imaging, Image segmentation, Matrices, Fluorophores, Fluorescence imaging, Detection and tracking algorithms, Signal to noise ratio, Image processing algorithms and systems
Multispectral imaging is becoming a key technique for biomedical research, but the crosstalk between autofluorescence and fluorescent material severely affects the interpretation of fluorescence images. Spectral unmixing is an effective technique for removing autofluorescence and separating fluorescent targets in multispectral fluorescence imaging. However, the effectiveness of most methods of spectral unmixing has a strong relationship with the noise in the image. In this work, we propose a multispectral fluorescence unmixing method based on a priori information to obtain the pure spectra and their corresponding abundance coefficients in the images. First, the obtained multispectral image is segmented into several superpixels using a superpixel segmentation method, and then the relative pure spectra are extracted using a spectral extraction algorithm on the superpixels. Since the autofluorescence distribution is spread over the whole body, the extracted spectra in which the autofluorescence can be considered as pure spectra are used as a priori knowledge for unmixing. The pixel spectral data that are similar to the set of relatively pure spectra are selected as the pure spectral candidate set. Then the pure spectra can be obtained using the Non-negative matrix factorization method with prior knowledge(NMF-upk). Finally, the abundance corresponding to each spectral feature can be obtained through the least square method. The proposed unmixing method is tested on simulated data and the results show that our unmixing algorithm outperforms other methods.
Coherent anti-Stokes Raman scattering (CARS) microscopy enables the analysis of the chemical composition and distribution within living cells, biomolecules, or living organisms in a label-free manner. Compared with the traditional spontaneous Raman imaging technology, its advantages of high imaging sensitivity and resolution, fast imaging speed and strong signal intensity make it more popular in multiple disciplines. The available CARS microscopes are most adopted advanced crystal solid-state lasers, which are expensive, bulky, and sensitive to the environmental changes. Supercontinuum fiber lasers with a wide spectral tuning range are increasingly used in biomedical applications due to their low cost, small size, and low environmental impact. Here, we homebuilt a CARS microscope based on a supercontinuum fiber laser, a specially tailored laser with a dual-channel time-synchronous outputs. The influence factors were investigated including the objective numerical aperture, laser power, and sample concentration, etc. The feasibility of CARS microscope was then verified by imaging the polystyrene microspheres (PS) and polymethyl methacrylate microspheres (PMMA). Finally, we imaged the lipid droplet distribution of EC109 cell, which revealed the application potential of the supercontinuum fiber laser-based CARS microscope in biomedical applications.
KEYWORDS: Raman spectroscopy, Tomography, Optical tomography, Imaging systems, Signal detection, 3D image processing, 3D image reconstruction, Projection systems
Volumetric imaging enables rapid, quantitative and global measurements of cells, tissues or organisms to obtain their biomolecular information and has become a powerful tool for studying cellular metabolism, brain function and developmental biology. Optical projection tomography (OPT) plays an important role in whole-body imaging of cells, organs, embryos and organisms because it enables three-dimensional (3D) imaging with high spatial and temporal resolution of samples at the millimeter level. However, the OPT technique relies on fluorescent labels for chemical targeting, which can perturb the biological function of living system. As a label-free molecular imaging technique, widefield Raman imaging enables high-resolution analysis of large field-of-view samples. Its combination with projection tomographic strategy enables high-resolution 3D imaging of large-scale samples in a label-free manner. However, this technique was failure to determine the tissue microstructure and specific spatial distribution. Here, we proposed a concept of new label free volumetric imaging, dual-modality of optical-Raman projection tomography. In this concept, Raman projection tomography was assigned to achieve volumetric imaging of chemical composition and distribution in a 3D volume, and the OPT was used to obtain structural information of the 3D volume with micron-level spatial resolution. We further homebuilt a dual-modality imaging system for optical-Raman projection tomography and the feasibility of the system was validated by imaging polystyrene microspheres and dimethyl sulfoxide. Finally, we demonstrated the application potential by a series of bio-sample experiments.
KEYWORDS: Raman spectroscopy, Remote sensing, Gaussian beams, Chemical analysis, Bessel beams, Analytical research, Tablets, Stomach, Skin, Signal to noise ratio
To meet the diversity needs of diagnosis, treatment or prevention of diseases, different pharmaceutical dosage forms are designed and manufactured. The main role of each dosage form is drug carrier. However, changing forms might have some other different effects in clinical usages. For example, the capsule and tablets are absorbed by the intestine and stomach respectively, solutions and patches can act directly on the surface of skin etc. The quantity and quality analysis of the main drug in different form is a key issue in quality control. Therefore, it is a meaningful research of developing a facility method to detect the drug in different dosage forms. The traditional drug detection methods principally analyze and evaluate the performance through chemical reactions, photo-electricity or electrophoresis. However, these methods will cause damage to the samples. Owing to the non-invasive, non-destructive and label-free characteristics, Raman spectroscopy (RS) technique plays an important role in different fields. The current RS setup uses Gaussian beam as the excitation light, which can provide higher signal-to-noise in the thin or transparent sample. However, the Gaussian beam dispersed rapidly in the scattering medium, it is not conducive to in vivo or deep imaging. The Bessel beam having long focusing characteristics and self-reconstructing properties may provide solution to this problem. We here presented a new scheme for RS, which used a Bessel beam as the excitation light. The feasibility and effectiveness of the proposed scheme for detecting the drug in different pharmaceutical dosage forms were verified by series experiments.
Traditional drug detection technique is highly accurate but time consuming and labor intensive. Raman spectroscopy (RS) is a fast and non-destructive detection technique that provides detailed information on chemical composition, phase and morphology, crystallinity and molecular interaction of the sample. The current Raman spectrometer is mainly based on the use of Gaussian light, providing with good signal to noise ratio for a thin or transparent sample. However, owing to the scattering effect, the Gaussian beam will become diffuse in the scattering medium. This makes it not conducive to in vivo or deep imaging. Utilizing the long focusing characteristics and self-reconstructing properties of Bessel beam, we here presented a new scheme for RS, which used a Bessel beam as the excitation light. The Bessel beam-based RS was first verified with the standard samples, and then comparatively tested on several drugs. Taking the acetaminophen as the test sample, we compared the Bessel beam-based RS with the traditional Gaussian beam based one with or without a scattering medium. With the addition of a scattering medium, the signal-to-noise ratio of Raman spectra based on Bessel beam decreases less than that based on the Gaussian light, which demonstrated the great potential of the use of Bessel beam in in vivo or deep RS. This study provides great value for in vivo applications of Raman spectroscopy.
Avoiding adverse effects of staining reagents on cellular viability and cell signaling, label-free cell imaging and analysis is essential to personalized genomics, drug development, and cancer diagnostics. By analyzing the images of cells, imagebased cell analytic methodologies offer a relatively simple and economical way to understand the cell heterogeneities and developments. Owing to the developments in high-resolution image sensors and high-performance computation processors, the emerging lens-less digital holography techniques enable a simple and cost-effective approach to obtain label-free cell images with large field of view and microscopic spatial resolution. In this work, the lens-less digital holography technique is adopted for image-based cell analysis. The holograms of three kinds of cells which are MDA-MB231, EC-109 and MCF-10A respectively were recorded by a lens-less digital holography system composed of a laser diode, a sample holder, a sensor and a laptop computer. The acquired holograms are first high-pass filtered. Then the amplitude images were reconstructed using the angular spectrum method and the sample to sensor distance was determined using the autofocusing criteria based on the sparsity of image edges and corner points. The convolutional neural network (CNN) was used to classify the cells. The experiments show that an accuracy of 97.2% can be achieve for two type cell classification and 91.2% for three type cell classification. It is believed that the lens-less holography combining with machine learning holds great promise in the application of stainless cell imaging and classification.
Raman spectroscopic imaging can provide three-dimensional data set of samples, including two-dimensional spatial image and one-dimensional Raman spectral data. Currently, three strategies can be used to achieve Raman spectroscopic imaging, including point scanning, line scanning, and wide-field illumination. Point scanning method provides the best resolution but has low imaging speed. On the contrary, wide-field illumination can image fast but provides lower spatial resolution. To integrate the advantages of two methods, a new strategy for large-field Raman spectroscopic imaging was proposed, which uses the frequency modulation based spatially encoded light as the excitation. In this method, millions of single beams simultaneously illuminate on the sample to act as the wide-field illumination. Each beam illuminates on different positions of the sample, whose intensity are modulated with different frequencies. Thus, each excitation beam has its own modulation frequency and the excited Raman signal will carry the modulation information. At the detection end, a single point detector was used to collect the time series Raman signals carrying the unique modulation information. Using the sparse reconstruction based on demodulation strategy, the Raman image can be recovered effectively. The feasibility of the method was verified with numerical simulations. The results showed that it is feasible to conduct Raman spectroscopic imaging with high-resolution and high speed under the illumination of frequency modulation based spatially encoded light and the detection of single-point detector.
Raman tomography can provide quantitative distribution of chemicals in a three-dimensional volume with a non-invasive and label-free manner. In view of the problems of existing data collection strategy, a frequency modulation and spatial encoding based Raman tomography was proposed, which aims to improve the data collection scheme and reduce the data collection time. In this scheme, the laser beam was divided into several sub-beams to use as multipoint excitation light sources. These sub-beams were first modulated with different frequencies and then incident on the different points of sample surface simultaneously. Because the excited Raman signals would carry such modulation information, the Raman signals from which excitation position can be distinguished with the demodulation process. In detection end, the Raman scattering light first passed through a spatial-encoding mask and then was directed to the single photomultiplier tube. By changing the pattern of the mask and then performing recovery with sparse reconstruction, the distribution of the Raman signals on the sample surface can be obtained based on compressive sensing theory. Preliminary results showed that our scheme can recover the Raman images to the certain extent with a better signal-to-noise ratio, demonstrating the proposed scheme is feasible.
Stimulated Raman scattering (SRS) microscopy has been increasingly used in biology and medicine in recent years due to its ability to image chemical bonds without labelling. Traditional SRS imaging uses Gaussian beams as the excitation sources, which can achieve high spatial and axial resolutions because of the tight focus of the Gaussian beam. However, the tight focus poses serious problems for observing the scattering media. The Gaussian beam would defocus after propagating through a small distance in scattering media. The SRS microscopy cannot work well in this case. Having the self-healing property, Bessel beams may bring solution to this problem. In our previous work, we applied the Bessel beams to the SRS and implemented three-dimensional SRS imaging with projection concept. Here, we simulated the propagation of Bessel beams and the generation of SRS signals with the beam propagation method (BPM). By adding glass beads on the beam propagation path to simulate scatters, the propagation of the Bessel beams and the generation of the SRS signals would change. We designed a series of simulations to investigate the influence of the size and position of the added glass beads to the generation of SRS signals. Simulation results demonstrated that the SRS signals can generate or be recovered at the certain depth in scattering media.
Frequency domain diffuse optical tomography (FD-DOT) has been considered as a reliable method to quantify the absolute optical properties of tissues. In the conventional FD-DOT, PMTs coupled with optical fiber bundles were used as the detectors. Thus, the imaging system was expensive and complex in system structure. In this study, we propose to utilize the silicon photomultiplier (SiPM) to replace the PMT as the detectors in the FD-DOT system. SiPM can provide the similar level of gain as PMT. Meanwhile its price is much lower than PMT, and the use of optical fiber bundles can be avoided, which makes it possible to build a simple structure system. The feasibility of the SiPM based FD-DOT was studied in the experiment. A 660nm laser diode was utilized as the source to irradiate the phantom, and it was modulated from 10MHz to 40MHz with the step size 10MHz. The SiPM detectors with 1 mm2 detection area were used to collect the photons emitted from the phantom. We measured in several different source-detector distances for each modulation frequency, during which the bias voltage of SiPM remained constant. The results showed that we could restore the linear relationship between the phase lag and the transmission distance. We also obtained the expected linear curve of the logarithm of the product of the amplitude and distance versus transmission distance. In addition, the absorption and scattering coefficients of the phantom were calculated by the slope of the fitting curve, which showed a good consistency at different modulation frequencies. The experiments results illuminated that it is feasible to build a FD-DOT based on SiPM.
Bioluminescence tomography (BLT) is a promising optical imaging tool broadly used in preclinical research to observe and quantify the distribution of bioluminescent markers in small animal models. However, due to the highly scattering property of the biological tissues and the limited surface measurements, fast and precise reconstruction in BLT remains a challenging problem. Permissible source region is a cost-effective strategy to partially solve the problem. In this paper, we present a matched filtering based strategy to extract the permissible region (PSR) adaptively for bioluminescence tomography. First, a digital matched filter is formulated according to the forward weight matrix, then the surface measurements are filtered and the permissible source region is extracted according to the first several biggest outputs of the matched filter larger than a threshold value, and finally the bioluminescent source in the permissible source region is recovered. Numerical simulation experiments are performed to evaluate the performance of the proposed method. The results show that the number of unknowns can be significantly reduced even using a small threshold value and the BLT reconstruction quality can be improved with appropriate PSR.
Cerenkov fluorescence imaging (CLI) has set a bridge between optical and nuclear imaging technologies by using an optical method to detect the distribution of radiotracers. Combining the emerged CLI technique with a clinical endoscope, the Cerenkov luminescence endoscope (CLE) was developed to avoid the problem of the poor penetration depth of the Cerenkov light. However, due to low energy of the Cerenkov light and the transportation loss during endoscopic imaging, the acquisition time of CLE signal is long and the imaging results are poor, which has limited the clinical applications of CLE. There are two ways to improve the availability of the current CLE system. First is to enhance the emitted signals of the Cerenkov light at the source end by developing new kinds of imaging probes or selecting high yield radionuclides. However, this will introduce the in vivo unfriendly problem in clinical translations. The second method is to improve the detection sensitivity of CLE system by optimizing the structure of the system. Here, we customized four endoscopes with different field of view (FOV) angles of endoscope probe and different monofilament diameters of imaging fiber bundles. By comparing the results obtained by different CLE systems, we optimized the parameters of system. The CLE imaging of 18F-FDG showed that when the distance between the probe and radionuclide source was fixed, smaller angle of FOV and lager monofilament diameter will provide higher collection efficiency.
KEYWORDS: Reconstruction algorithms, Image quality, Tomography, Raman spectroscopy, 3D image processing, Head, 3D acquisition, Data modeling, Data acquisition, Sensors
As an emerging volumetric imaging technique, Stimulated Raman projection tomography (SRPT) can provide quantitative distribution of chemical components in a three-dimensional (3D) volume, with a label-free manner. Currently, the filtered back-projection (FBP) algorithm is used to reconstruct the 3D volume in SRPT. However, to obtain a satisfactory reconstruction result, the FBP algorithm requires a certain amount of projection data, usually, at least 180 projections in a half circle. This leads to a long data acquisition time and hence limits dynamic and longitudinal observation of living systems. Iterative reconstruction from sparsely sampled data may reduce the total data acquisition time by reducing the projections used in the reconstruction. In this work, two total variation regularization based iterative reconstruction algorithms were selected and used in SRPT, including the simultaneous algebra reconstruction technique (SART) and the two-step iterative shrinkage/thresholding algorithm (TwIST). The well-known distance-driven model was utilized as the forward and back-projectors. We evaluated these two algorithms with numerical simulations. Using the original image as the reference, we calculated the quality of the reconstructed images. Simulation results showed that both the SART and TwIST performed better than the FBP algorithm, with larger values of the structural similarity (SSIM). Furthermore, the number of projection images can be largely reduced when the iterative reconstruction algorithm was used. Especially when the SART was used, the projection number can be reduced to 15, providing a satisfactory reconstruction image (SSIM is larger than 0.9).
Optical projection tomography(OPT) provides an approach to recreating three-dimensional images of small biological specimens. Light traverses through a straight line to achieve a homogeneous illumination of the specimen. As the specimens in the conventional OPT could not survive or the survival time was too short, this paper proposes a new type of sample fixation method for OPT imaging. The specimen was anaesthetized in a petri dish, and the dish was fixed under the rotational stage of our homemade OPT system for imaging. This method can reduce the damage to the specimen and be more conducive to the continuous observation for in vivo OPT. However, the sample fixation causes the problem of insufficient sampling. To obtain optical projection tomographic image with insufficient samples, this paper uses the iterative reconstruction algorithm combining with the prior information to solve the inverse reconstruction problem.
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.
For fluorescence tomographic imaging of small animals, the liver is usually regarded as a low-scattering tissue and is surrounded by adipose, kidneys, and heart, all of which have a high scattering property. This leads to a breakdown of the diffusion equation (DE)–based reconstruction method as well as a heavy computational burden for the simplified spherical harmonics equation (SPN). Coupling the SPN and DE provides a perfect balance between the imaging accuracy and computational burden. The coupled third-order SPN and DE (CSDE)-based reconstruction method is developed for fluorescence tomographic imaging. This is achieved by doubly using the CSDE for the excitation and emission processes of the fluorescence propagation. At the same time, the finite-element method and hybrid multilevel regularization strategy are incorporated in inverse reconstruction. The CSDE-based reconstruction method is first demonstrated with a digital mouse-based liver cancer simulation, which reveals superior performance compared with the SPN and DE-based methods. It is more accurate than the DE-based method and has lesser computational burden than the SPN-based method. The feasibility of the proposed approach in applications of in vivo studies is also illustrated with a liver cancer mouse-based in situ experiment, revealing its potential application in whole-body imaging of small animals.
Bioluminescence tomography (BLT) has been successfully applied to the detection and therapeutic evaluation of solid cancers. However, the existing BLT reconstruction algorithms are not accurate enough for cavity cancer detection because of neglecting the void problem. Motivated by the ability of the hybrid radiosity-diffusion model (HRDM) in describing the light propagation in cavity organs, an HRDM-based BLT reconstruction algorithm was provided for the specific problem of cavity cancer detection. HRDM has been applied to optical tomography but is limited to simple and regular geometries because of the complexity in coupling the boundary between the scattering and void region. In the provided algorithm, HRDM was first applied to three-dimensional complicated and irregular geometries and then employed as the forward light transport model to describe the bioluminescent light propagation in tissues. Combining HRDM with the sparse reconstruction strategy, the cavity cancer cells labeled with bioluminescent probes can be more accurately reconstructed. Compared with the diffusion equation based reconstruction algorithm, the essentiality and superiority of the HRDM-based algorithm were demonstrated with simulation, phantom and animal studies. An in vivo gastric cancer-bearing nude mouse experiment was conducted, whose results revealed the ability and feasibility of the HRDM-based algorithm in the biomedical application of gastric cancer detection.
Gastric cancer is the second cause of cancer-related death in the world, and it remains difficult to cure because it has
been in late-stage once that is found. Early gastric cancer detection becomes an effective approach to decrease the gastric
cancer mortality. Bioluminescence tomography (BLT) has been applied to detect early liver cancer and prostate cancer
metastasis. However, the gastric cancer commonly originates from the gastric mucosa and grows outwards. The
bioluminescent light will pass through a non-scattering region constructed by gastric pouch when it transports in tissues.
Thus, the current BLT reconstruction algorithms based on the approximation model of radiative transfer equation are not
optimal to handle this problem. To address the gastric cancer specific problem, this paper presents a novel reconstruction
algorithm that uses a hybrid light transport model to describe the bioluminescent light propagation in tissues. The
radiosity theory integrated with the diffusion equation to form the hybrid light transport model is utilized to describe
light propagation in the non-scattering region. After the finite element discretization, the hybrid light transport model is
converted into a minimization problem which fuses an l1 norm based regularization term to reveal the sparsity of
bioluminescent source distribution. The performance of the reconstruction algorithm is first demonstrated with a digital
mouse based simulation with the reconstruction error less than 1mm. An in situ gastric cancer-bearing nude mouse based
experiment is then conducted. The primary result reveals the ability of the novel BLT reconstruction algorithm in early
gastric cancer detection.
Because of the ability of integrating the strengths of different modalities and providing fully integrated information,
multi-modality molecular imaging techniques provide an excellent solution to detecting and diagnosing earlier cancer,
which remains difficult to achieve by using the existing techniques. In this paper, we present an overview of our research
efforts on the development of the optical imaging-centric multi-modality molecular imaging platform, including the
development of the imaging system, reconstruction algorithms and preclinical biomedical applications. Primary
biomedical results show that the developed optical imaging-centric multi-modality molecular imaging platform may
provide great potential in the preclinical biomedical applications and future clinical translation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.