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Propagation-based phase-contrast computed tomography has become a valuable tool for visualization of three-dimensional biological samples, due to its high contrast between materials with similar attenuation properties. However, one of the most-widely used phase-retrieval algorithms imposes a homogeneity assumption onto the sample, which leads to artifacts for numerous applications where this assumption is violated. Prominent examples are biological samples with highly-absorbing implants. Using synchrotron radiation, we demonstrate by the example of a guinea pig inner ear with a cochlear implant electrode, how a recently developed model-based iterative algorithm for propagation-based phase-contrast computed tomography yields distinct benefits for such a task. We find that the model-based approach improves the overall image quality, removes the detrimental influence of the implant and accurately visualizes the cochlea.
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Functional near-infrared spectroscopy (fNIRS) is a fast-developing non-invasive functional brain imaging technology widely used in cognitive neuroscience, clinical research and neural engineering. However, it is a challenge to effectively remove the global physiological noise in the fNIRS signal. The global physiological noise in fNIRS arises from multiple physiological origins in both superficial tissues and the brain. It has complex temporal, spatial and frequency characteristics, casting significant influence on the results. In the present study, we developed a novel wavelet-based method for fNIRS global physiological noise removal. The method is data-driven and does not rely on any additional hardware or subjective noise component selection procedure. It consists of two steps. Firstly, we use wavelet transform coherence to automatically detect the time-frequency points contaminated by the global physiological noise. Secondly, we decompose the fNIRS signal by using the wavelet transform, and then suppress the wavelet energy of the contaminated time-frequency points. Finally, we transform the signal back to a time series. We validated the method by using simulation and real data at both task- and resting-state. The results showed that our method can effectively remove the global physiological noise from the fNIRS signal and improve the spatial specificity of the task activation and the resting-state functional connectivity pattern.
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Mesoscopic fluorescence molecular tomography (MFMT) is a novel imaging technique capable of obtaining 3-D distribution of molecular probes inside biological tissues at depths of a few millimeters with a resolution up to ~100 µm. However, the ill-conditioned nature of the MFMT inverse problem severely deteriorates its reconstruction performances. Furthermore, dense spatial sampling and fine discretization of the imaging volume required for high resolution reconstructions make the sensitivity matrix (Jacobian) highly correlated, which prevents even advanced algorithms from achieving optimal solutions. In this work, we propose two computational methods to respectively increase the incoherence of the sensitivity matrix and improve the convergence rate of the inverse solver. We first apply a compressed sensing (CS) based preconditioner on either the whole sensitivity matrix or sub sensitivity matrices to reduce the coherence between columns of the sensitivity matrix. Then we employed a regularization method based on the weight iterative improvement method (WIIM) to mitigate the ill-condition of the sensitivity matrix and to drive the iterative optimization process towards convergence at a faster rate. We performed numerical simulations and phantom experiments to validate the effectiveness of the proposed strategies. In both in silico and in vitro cases, we were able to improve the quality of MFMT reconstructions significantly.
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We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach, which performs truncated singular value decomposition-based preconditioning followed by fast iterative shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation for this approach is that sparsity information could be accounted for within the initialization, while MLEM would accurately model Poisson noise in the FMT system. Simulation experiments show the proposed method significantly improves images qualitatively and quantitatively. The method results in over 20 times faster convergence compared to uniformly initialized MLEM and improves robustness to noise compared to pure sparse reconstruction. We also theoretically justify the ability of the proposed approach to reduce noise in the background region compared to pure sparse reconstruction. Overall, these results provide strong evidence to model Poisson noise in FMT reconstruction and for application of the proposed reconstruction framework to FMT imaging.
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Structural image-guided near-infrared spectral tomography (NIRST) has been developed as a way to use diffuse NIR spectroscopy within the context of image-guided quantification of tissue spectral features. A direct regularization imaging (DRI) method for NIRST has the value of not requiring any image segmentation. Here, we present a comprehensive investigational study to analyze the impact of the weighting function implied when weighting the recovery of optical coefficients in DRI based NIRST. This was done using simulations, phantom and clinical patient exam data. Simulations where the true object is known indicate that changes to this weighting function can vary the contrast by 10%, the contrast to noise ratio by 20% and the full width half maximum (FWHM) by 30%. The results from phantoms and human images show that a linear inverse distance weighting function appears optimal, and that incorporation of this function can generally improve the recovered total hemoglobin contrast of the tumor to the normal surrounding tissue by more than 15% in human cases.
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Rheumatoid arthritis (RA) is an inflammatory joint disease often affecting the hands, which if untreated causes disability. Diffuse optical tomography (DOT) provides information about the underlying functional properties of biological tissue. To detect pathophysiological changes in inflamed RA joints, a good understanding of the baseline values for healthy subjects is first required. Finger joints from healthy subjects were imaged using a non-contact, multispectral, continuous wave DOT system, recovering physiological parameters of oxygen saturation, total haemoglobin, water concentration and scatter amplitude. Reconstructed values across the cohort demonstrated good consistency between finger joints from the same participant, with greater variation seen between subjects.
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Near-infrared diffuse optical tomography (NIR-DOT) is an emerging technology that offers hemoglobin based, functional imaging tumor biomarkers for breast cancer management. The most promising clinical translation opportunities are in the differential diagnosis of malignant vs. benign lesions, and in early response assessment and guidance for neoadjuvant chemotherapy. Accurate quantification of the tissue oxy- and deoxy-hemoglobin concentration across the field of view, as well as repeatability during longitudinal imaging in the context of therapy guidance, are essential for the successful translation of NIR-DOT to clinical practice. The ill-posed and ill-condition nature of the DOT inverse problem makes this technique particularly susceptible to model errors that may occur, for example, when the experimental conditions do not fully match the assumptions built into the image reconstruction process. To evaluate the susceptibility of DOT images to experimental errors that might be encountered in practice for a parallel-plate NIR-DOT system, we simulated 7 different types of errors, each with a range of magnitudes. We generated simulated data by using digital breast phantoms derived from five actual mammograms of healthy female volunteers, to which we added a 1-cm tumor. After applying each of the experimental error types and magnitudes to the simulated measurements, we reconstructed optical images with and without structural prior guidance and assessed the overall error in the total hemoglobin concentrations (HbT) and in the HbT contrast between the lesion and surrounding area vs. the best-case scenarios. It is found that slight in-plane probe misalignment and plate rotation did not result in large quantification errors. However, any out-of-plane probe tilting could result in significant deterioration in lesion contrast. Among the error types investigated in this work, optical images were the least likely to be impacted by breast shape inaccuracies but suffered the largest deterioration due to cross-talk between signal channels. However, errors in optical images could be effectively controlled when experimental parameters were properly estimated during data acquisition and accounted for in the image processing procedure. Finally, optical images recovered using structural priors were, in general, less susceptible to experimental errors; however, lesion contrasts were more sensitive to errors when tumor locations were used as a priori info. Findings in this simulation study can provide guidelines for system design and operation in optical breast imaging studies.
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The ability to simultaneously recover multiple fluorophores within biological tissue (multiplexing) can have important applications for tracking parallel disease processes in vivo. Here we present a novel method for rapid and quantitative multiplexing within a scattering medium, such as biological tissue, based on fluorescence lifetime contrast. This method employs a tomographic inversion of the asymptotic (late) portion of time-resolved spatial frequency (SF) domain measurements. Using Monte Carlo simulations and phantom experiments, we show that the SF-asymptotic time domain (SF-ATD) approach provides a several-fold improvement in relative quantitation and localization accuracy over conventional SF-time domain inversion. We also show that the SF-ATD approach can exploit selective filtering of high spatial frequencies to dramatically improve reconstruction accuracy for fluorophores with subnanosecond lifetimes, which is typical of most near-infrared fluorophores. These results suggest that the SF-ATD approach will serve as a powerful new tool for whole-body lifetime multiplexing.
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Bioluminescence imaging (BLI) is a non-contact, optical imaging technique based on measurement of emitted light due to an internal source, which is then often directly related to cellular activity. It is widely used in pre-clinical small animal imaging studies to assess the progression of diseases such as cancer, aiding in the development of new treatments and therapies. For many applications, the quantitative assessment of accurate cellular activity and spatial distribution is desirable as it would enable direct monitoring for prognostic evaluation. This requires quantitative spatially-resolved measurements of bioluminescence source strength inside the animal to be obtained from BLI images. This is the goal of bioluminescence tomography (BLT) in which a model of light propagation through tissue is combined with an optimization algorithm to reconstruct a map of the underlying source distribution. As most models consider only the propagation of light from internal sources to the animal skin surface, an additional challenge is accounting for the light propagation from the skin to the optical detector (e.g. camera). Existing approaches typically use a model of the imaging system optics (e.g. ray-tracing, analytical optical models) or approximate corrections derived from calibration measurements. However, these approaches are typically computationally intensive or of limited accuracy. In this work, a new approach is presented in which, rather than directly using BLI images acquired at several wavelengths, the spectral derivative of that data (difference of BLI images at adjacent wavelengths) is used in BLT. As light at similar wavelengths encounters a near-identical system response (path through the optics etc.) this eliminates the need for additional corrections or system models. This approach is applied to BLT with simulated and experimental phantom data and shown that the error in reconstructed source intensity is reduced from 49% to 4%. Qualitatively, the accuracy of source localization is improved in both simulated and experimental data, as compared to reconstruction using the standard approach. The outlined algorithm can widely be adapted to all commercial systems without any further technological modifications.
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To fully realize the potential of photoacoustic tomography (PAT) in preclinical and clinical applications, rapid measurements and robust reconstructions are needed. Sparse-view measurements have been adopted effectively to accelerate the data acquisition. However, since the reconstruction from the sparse-view sampling data is challenging, both the effective measurement and the appropriate reconstruction should be taken into account. In this study, we present an iterative sparse-view PAT reconstruction scheme, where a concept of virtual parallel-projection matching the measurement condition is introduced to aid the "compressive sensing" in the reconstruction procedure, and meanwhile, the non-local spatially adaptive filtering exploring the a priori information of the mutual similarities in natural images is adopted to recover the unknowns in the transformed sparse domain. Consequently, the reconstructed images with the proposed sparse-view scheme can be evidently improved in comparison to those with the universal back-projection method, for the cases of same sparse views. The proposed approach has been validated by the simulations and ex vivo experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.
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A portable near infrared spectral tomography (NIRST) system was adapted for breast cancer detection and treatment monitoring with improved speed of acquisition for parallel 12 wavelengths of parallel frequency-domain (FD) and continuous-wavelength (CW) measurement. Using a novel gain adjustment scheme in the Photomultiplier Tube detectors (PMTs), the data acquisition time for simultaneous acquisition involving three FD and three CW wavelengths, has been reduced from 90 to 55 seconds, while signal variation was also reduced from 2.1% to 1.1%. Tomographic images of breast collagen content have been recovered for the first time, and image reconstruction approaches with and without collagen content included have been validated in simulation studies and normal subject exams. Simulations indicate that including collagen content into the reconstruction procedure can significantly reduce the overestimation in total hemoglobin, water and lipid by 8.9µM, 1.8% and 15.8%, respectively, and underestimates in oxygen saturation by 9.5%, given an average 10% background collagen content. A breast cancer patient with invasive ductal carcinoma was imaged and the reconstructed images show that the recovered tumor/background contrast in total hemoglobin increased from 1.5 to 1.7 when collagen was included in reconstruction.
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Image reconstruction in the most model-based biophotonic imaging modalities essentially poses an ill-posed nonlinear inverse problem, which has been effectively tackled in the diffusion-approximation-satisfied scenarios such as diffuse optical tomography. Nevertheless, a nonlinear implementation in high-resolution laminar optical tomography (LOT) is normally computationally-costly due to its strong dependency on a dense source-detector configuration and a physically-rigorous photon-transport model. To circumvent the adversity, we herein propose a practical nonlinear LOT approach to the absorption reconstruction. The scheme takes advantage of the numerical stability of the singular value decomposition (SVD) for the ill-posed linear inversion, and is accelerated by adopting an explicitly recursive strategy for the time-consuming repeated SVD inversion, which is based on a scaled expression of the sensitivity matrix. Experiments demonstrate that the proposed methodology can perform as well as the traditional nonlinear one, while the computation time of the former is merely 26.27% of the later on average.
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Quantitative photoacoustic tomography is an imaging modality in which distributions of optical parameters inside tissue are estimated from photoacoustic images. This optical parameter estimation is an ill-posed problem and it needs to be approached in the framework of inverse problems. In this work, utilising surface light measurements in quantitative photoacoustic tomography is studied. Estimation of absorption and scattering is formulated as a minimisation problem utilising both internal quantitative photoacoustic data and surface light data. The image reconstruction problem is studied with two-dimensional numerical simulations in various imaging situations using the diffusion approximation as the model for light propagation. The results show that quantitative photoacoustic tomography augmented with surface light data can improve both absorption and scattering estimates when compared to the conventional quantitative photoacoustic tomography.
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The purpose of this study is to propose a strategy for organ reconstruction in fluorescence molecular tomography (FMT) without prior information from other imaging modalities, and to overcome the high cost and ionizing radiation caused by the traditional structural prior strategy. The proposed strategy is designed as an iterative architecture to solve the inverse problem of FMT. In each iteration, a short time Fourier transform (STFT) based algorithm is used to extract the self-prior information in the space-frequency energy spectrum with the assumption that the regions with higher fluorescence concentration have larger energy intensity, then the cost function of the inverse problem is modified by the self-prior information, and lastly an iterative Laplacian regularization algorithm is conducted to solve the updated inverse problem and obtains the reconstruction results. Simulations and in vivo experiments on liver reconstruction are carried out to test the performance of the self-prior strategy on organ reconstruction. The organ reconstruction results obtained by the proposed self-prior strategy are closer to the ground truth than those obtained by the iterative Tikhonov regularization (ITKR) method (traditional non-prior strategy). Significant improvements are shown in the evaluation indexes of relative locational error (RLE), relative error (RE) and contrast-to-noise ratio (CNR). The self-prior strategy improves the organ reconstruction results compared with the non-prior strategy and also overcomes the shortcomings of the traditional structural prior strategy. Various applications such as metabolic imaging and pharmacokinetic study can be aided by this strategy.
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Several attempts to achieve the quantitative photoacoustic tomography (q-PAT) have been investigated using point sources or a single-angle wide-field illumination. However, these schemes normally suffer from low signal-to-noise ratio (SNR) or poor quantification in imaging applications on large-size domains, due to the limitation of ANSI-safety incidence and incompleteness in the data acquisition. We herein present a q-PAT implementation that uses multi-angle light-sheet illuminations and calibrated recovering-and-averaging iterations. The scheme can obtain more complete information on the intrinsic absorption from the multi-angle illumination mode, and collect SNR-boosted photoacoustic signals in the selected planes from the wide-field light-sheet excitation. Therefore, the sliced absorption maps over whole body of small-animals can be recovered in a measurement-flexible, noise-robust and computation-economic way. The proposed approach is validated by phantom, ex vivo and in vivo experiments, exhibiting promising performances in image fidelity and quantitative accuracy for practical applications.
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Mesoscopic fluorescence molecular tomography (MFMT) is a novel imaging technique that aims at obtaining the 3-D distribution of molecular probes inside biological tissues at depths of a few millimeters. To achieve high resolution, around 100-150µm scale in turbid samples, dense spatial sampling strategies are required. However, a large number of optodes leads to sizable forward and inverse problems that can be challenging to compute efficiently. In this work, we propose a two-step data reduction strategy to accelerate the inverse problem and improve robustness. First, data selection is performed via signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) criteria. Then principal component analysis (PCA) is applied to further reduce the size of the sensitivity matrix. We perform numerical simulations and phantom experiments to validate the effectiveness of the proposed strategy. In both in silico and in vitro cases, we are able to significantly improve the quality of MFMT reconstructions while reducing the computation times by close to a factor of two.
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Multimodality molecular imaging emerges as a powerful strategy for correlating multimodal information. We developed a pentamodal imaging system which can perform positron emission tomography, bioluminescence tomography, fluorescence molecular tomography, Cerenkov luminescence tomography and X-ray computed tomography successively. Performance of sub-systems corresponding to different modalities were characterized. In vivo multimodal imaging of an orthotopic hepatocellular carcinoma xenograft mouse model was performed, and acquired multimodal images were fused. The feasibility of pentamodal tomographic imaging system was successfully validated with the imaging application on the mouse model. The ability of integrating anatomical, metabolic, and pharmacokinetic information promises applications of multimodality molecular imaging in precise medicine.
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Fluorescence imaging is a widely used in vivo optical imaging technique for preclinical studies, but strong tissue autofluorescence and external excitation light make it suffer from a low signal-to-noise ratio (SNR). Recently, a new optical imaging method using persistent luminescence has become of interest due to its advantage of circumvention of autofluorescence and bleed-through of excitation light during signal acquisition. In this work, we proposed a tomographic imaging method based on persistent luminescence named persistent luminescence tomography (PLT), which can obtain three dimensional distributions of persistent luminescence probes deep inside small animals. Persistent luminescence signals can last several hours after excitation, which makes it possible for PLT to collect signals without interference by autofluorescence and bleed-through of excitation light, and then to reconstruct tomographic images of high quality. Phantom and mouse experiments are implemented to verify the feasiblity of PLT.
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Tissue-equivalent phantoms that mimic the optical properties of human and animal tissues are commonly used in diffuse optical imaging research to characterize instrumentation or evaluate an image reconstruction method. Although many recipes have been produced for generating solid phantoms with specified absorption and transport scattering coefficients at visible and near-infrared wavelengths, the construction methods are generally time-consuming and are unable to create complex geometries. We present a method of generating phantoms using a standard 3D printer. A simple recipe was devised which enables printed phantoms to be produced with precisely known optical properties. To illustrate the capability of the method, we describe the creation of an anatomically accurate, tissue-equivalent premature infant head optical phantom with a hollow brain space based on MRI atlas data. A diffuse optical image of the phantom is acquired when a high contrast target is inserted into the hollow space filled with an aqueous scattering solution.
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Three-dimensional fluorescence laminar optical tomography (FLOT) can achieve resolutions of 100-200 µm and penetration depths of 2-3 mm. FLOT has been used in tissue engineering, neuroscience, as well as oncology. The limited dynamic range of the charge-coupled device-based system makes it difficult to image fluorescent samples with a large concentration difference, limits its penetration depth, and diminishes the quantitative accuracy of 3D reconstruction data. Here, incorporating the high-dynamic-range (HDR) method widely used in digital cameras, we present HDR-FLOT, increasing penetration depth and improving the ability to image fluorescent samples with a large concentration difference. The method was tested using an agar phantom and a B6 mouse for brain imaging in vivo.