Multispectral Photoacoustic Tomography (MSPAT) is capable of visualizing the concentration distribution of various chromophores in biological tissues, where the unmixing process is usually performed with the spectral fitting method that requires the absorption spectral signatures of all chromophores in the tissue to be known. However, due to the changes of spectral signatures of some exogenous contrast agents in vitro and in vivo, the conventional fitting method will be hindered. Although the blind unmixing algorithms do not require the exact absorption spectrum of each chromophore in advance, it is often sensitive to noise, which may lead to low quantitative results. Considering that the non-negativity of concentration distribution and absorption spectra of the chromophores as well as the sparsity of concentration image of exogenous contrast agent in the gradient domain, we herein propose a modified MSPAT implementation that utilizes a non-negative matrix factorization iterative reconstruction framework with the support of a priori information of spectral signature of oxy-/deoxy-hemoglobin and valid sparsity regularization during the iteration. Consequently, the spectral signature of the exogenous contrast agent and the concentration distribution of each chromophore can be recovered simultaneously. The proposed approach has been validated by simulation and in vivo experiments, exhibiting promising performances in image fidelity even when the multi-wavelength photoacoustic tomographic images used for spectral unmixing are affected by noise or reconstruction artifacts.
Near infrared diffuse optical tomography (DOT) is a significant potential means of detecting breast cancer. Compared with other system structures, the parallel-plate scanning mode has such advantages like adapting to different breast size, as well as increasing the transmission of light by compressing. Traditional parallel-plate DOT systems utilized the fibers for photon transmission and photomultiplier tube (PMT) or CCD for photon detection, which resulted in the high complexity and cost. In this study, we propose a fiber-free parallel-plate continuous-wave DOT system for breast cancer detection based on Silicon photomultiplier (SiPM) and multi-wavelength light emitting diode (LED). 50 three-wavelength (660 nm, 750nm and 840nm) LEDs are arranged in a printed circuit board (PCB) array as the source plate. Parallel to this plate, the other plate with 56 silicon photomultipliers (SiPM) arranged is designed as the detection plate. The control of the light source excitation and the detection of the SiPMs output are implemented by a module based on a data acquisition card. The structure of proposed system is very simple, and the acquisition time is no more than 5 minutes. The feasibility of the system was verified by polyoxymethylene and agar phantom experiments, which indicated that the parallel-plate system can accurately reconstruct optical parameters.
The characteristics of the transducer, such as the transducer shape, have a significant impact on the image performance in optoacoustic (photoacoustic) imaging. Several reconstruction algorithms have considered the shape of the transducer in the optoacoustic reconstruction process, showing the improvement in image quality compared to reconstruction procedures with the point detector approximation. One flexible approach assumes the surface of transducer that consists of a set of surface elements. However, this approach suffers from long computation time and excessive memory consumption, especially for model-based reconstruction strategies. Herein, we present a modified model-based reconstruction algorithm using a virtual parallel-projection method, for the optoacoustic imaging system with flat detector. In this case, the sum of the surface elements' model matrixes can be replaced by a virtual parallel-projection model matrix, in order to reduce the reconstruction time and memory consumption. The proposed method has been performed on numerical simulations, phantom experiments of microspheres with the diameter of 200 μm and in vivo experiments in mice. The reconstruction results of proposed method show the similar image quality as the results of the traditional reconstruction method setting surface elements, while the computation time and memory requirements have been efficiently decreased.
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 of 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 virtual parallel-projection concept matching for the proposed measurement condition is introduced to help to achieve the “compressive sensing” procedure of the reconstruction, and meanwhile the spatially adaptive filtering fully considering the a priori information of the mutually similar blocks existing in natural images is introduced to effectively recover the partial unknown coefficients in the transformed domain. Therefore, the sparse-view PAT images can be reconstructed with higher quality compared with the results obtained by the universal back-projection (UBP) algorithm in the same sparse-view cases. The proposed approach has been validated by simulation experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.
Quantitative photoacoustic tomography (q-PAT) is a nontrivial technique can be used to reconstruct the absorption image with a high spatial resolution. Several attempts have been investigated by setting point sources or fixed-angle illuminations. However, in practical applications, these schemes normally suffer from low signal-to-noise ratio (SNR) or poor quantification especially for large-size domains, due to the limitation of the ANSI-safety incidence and incompleteness in the data acquisition. We herein present a q-PAT implementation that uses multi-angle light-sheet illuminations and a calibrated iterative multi-angle reconstruction. The approach can acquire more complete information on the intrinsic absorption and SNR-boosted photoacoustic signals at selected planes from the multi-angle wide-field excitations of light-sheet. Therefore, the sliced absorption maps over whole body can be recovered in a measurementflexible, noise-robust and computation-economic way. The proposed approach is validated by the phantom experiment, exhibiting promising performances in image fidelity and quantitative accuracy.
Photoacoustic mesoscopy (PAMe), offering high-resolution (sub-100-μm) and high optical contrast imaging at the depth of 1-10 mm, generally obtains massive collection data using a high-frequency focused ultrasonic transducer. The spatial impulse response (SIR) of this focused transducer causes the distortion of measured signals in both duration and amplitude. Thus, the reconstruction method considering the SIR needs to be investigated in the computation-economic way for PAMe. Here, we present a modified back-projection algorithm, by introducing a SIR-dependent calibration process using a non-satationary convolution method. The proposed method is performed on numerical simulations and phantom experiments of microspheres with diameter of both 50 μm and 100 μm, and the improvement of image fidelity of this method is proved to be evident by methodology parameters. The results demonstrate that, the images reconstructed when the SIR of transducer is accounted for have higher contrast-to-noise ratio and more reasonable spatial resolution, compared to the common back-projection algorithm.
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.
The underdeterminedness of the inverse problems encountered in diffuse optical tomography (DOT) becomes especially severe when detecting breast cancers, because much more variables are needed to be reconstructed due to the big-size. With the addition of ill-condition caused by the diffusive nature of light propagation, the ill-posedness makes it very difficult to improve the image reconstruction. Fortunately, from the anatomy viewpoint, we have known that the cancer is distributed locally and only amounts to a small percentage of the whole breast. This makes it possible to employ the compressive sensing theory to mitigate the ill-posedness, based on the prior knowledge about the sparsity of the signal to be reconstructed. Specifically speaking, sparsity regularizations can be used in DOT to improve the image reconstruction under the premise that un-increase the number of measurements required in the reconstruction. In this paper, we primarily focus on comparing the performances of different kinds of Lp-norm-based regularizations in terms of theory and real effects, respectively. The numerical and phantom experiments have proven that the sparsity regularizations can dramatically improve the image reconstruction. Furthermore, as the p in the Lp-norm decreasing to zero, the solutions become sparser and the corresponding image quality gets higher, with smooth L0-norm-based regularization providing the highest image quality.
Of the three measurement schemes established for diffuse fluorescence tomography (DFT), the time-domain scheme is well known to provide the richest information about the distribution of the targeting fluorophore in living tissues. However, the explicit use of the full time-resolved data usually leads to a considerably lengthy time for image reconstruction, limiting its applications to three-dimensional or small-volume imaging. To cope with the adversity, we propose herein a computationally efficient scheme for DFT image reconstruction where the time-dependent photon density is expanded to a Fourier-series and calculated by solving the independent frequency-domain diffusion equations at multiple sampling frequencies with the support of a combined multicore CPU-based coarse-grain and multithread GPU-based fine-grain parallelization strategy. With such a parallelized Fourier-series truncated diffusion approximation, both the time- and frequency-domain inversion procedures are developed and validated for their effectiveness and accuracy using simulative and phantom experiments. The results show that the proposed method can generate reconstructions comparable to the explicit time-domain scheme, with significantly reduced computational time.
Diffuse optical tomography (DOT) is a biomedical imaging technology for noninvasive visualization of spatial variation
about the optical properties of tissue, which can be applied to in vivo small-animal disease model. However, traditional
DOT suffers low spatial resolution due to tissue scattering. To overcome this intrinsic shortcoming, multi-modal
approaches that incorporate DOT with other imaging techniques have been intensively investigated, where a priori
information provided by the other modalities is normally used to reasonably regularize the inverse problem of DOT.
Nevertheless, these approaches usually consider the anatomical structure, which is different from the optical structure.
Photoacoustic tomography (PAT) is an emerging imaging modality that is particularly useful for visualizing lightabsorbing
structures embedded in soft tissue with higher spatial resolution compared with pure optical imaging. Thus, we
present a PAT-guided DOT approach, to obtain the location a priori information of optical structure provided by PAT
first, and then guide DOT to reconstruct the optical parameters quantitatively. The results of reconstruction of phantom
experiments demonstrate that both quantification and spatial resolution of DOT could be highly improved by the
regularization of feasible-region information provided by PAT.
Shape-parameterized diffuse optical tomography (DOT), which is based on a priori that assumes the uniform distribution
of the optical properties in the each region, shows the effectiveness of complex biological tissue optical heterogeneities
reconstruction. The priori tissue optical structure could be acquired with the assistance of anatomical imaging methods
such as X-ray computed tomography (XCT) which suffers from low-contrast for soft tissues including different optical
characteristic regions. For the mouse model, a feasible strategy of a priori tissue optical structure acquisition is proposed
based on a non-rigid image registration algorithm. During registration, a mapping matrix is calculated to elastically align
the XCT image of reference mouse to the XCT image of target mouse. Applying the matrix to the reference atlas which
is a detailed mesh of organs/tissues in reference mouse, registered atlas can be obtained as the anatomical structure of
target mouse. By assigning the literature published optical parameters of each organ to the corresponding anatomical
structure, optical structure of the target organism can be obtained as a priori information for DOT reconstruction
algorithm. By applying the non-rigid image registration algorithm to a target mouse which is transformed from the
reference mouse, the results show that the minimum correlation coefficient can be improved from 0.2781 (before
registration) to 0.9032 (after fine registration), and the maximum average Euclid distances can be decreased from
12.80mm (before registration) to 1.02mm (after fine registration), which has verified the effectiveness of the algorithm.
Diffuse florescence tomography (DFT) as a high-sensitivity optical molecular imaging tool, can be applied to in vivo
visualize interior cellular and molecular events for small-animal disease model through quantitatively recovering
biodistributions of specific molecular probes. In DFT, the radiative transfer equation (RTE) and its approximation, such
as the diffuse equation (DE), have been used as the forward models. The RTE-based DFT methodology is more suitable
for biological tissue having void-like regions and the near-source area as in the situations of small animal imaging. We
present a RTE-based scheme for the steady state DFT, which combines the discrete solid angle method and the finite
difference method to obtain numerical solutions of the 2D steady RTE, with the natural boundary condition and
collimating light source model. The approach is validated using the forward data from the Monte Carlo simulation for its
better performances in the spatial resolution and reconstruction fidelity compared to the DE-based scheme.
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