Time-resolved diffuse optical tomographic imaging for the provision of both anatomical and functional information about biological tissue

We present in vivo images of near-infrared (NIR) diffuse optical tomography (DOT) of human lower legs and forearm to validate the dual functions of a time-resolved (TR) NIR DOT in clinical diagnosis, i.e., to provide anatomical and functional information simultaneously. The NIR DOT system is composed of time-correlated single-photon-counting channels, and the image reconstruction algorithm is based on the modified generalized pulsed spectral technique, which effectively incorporates the TR data with reasonable computation time. The reconstructed scattering images of both the lower legs and the forearm revealed their anatomies, in which the bones were clearly distinguished from the muscles. In the absorption images, some of the blood vessels were observable. In the functional imaging, a subject was requested to do handgripping exercise to stimulate physiological changes in the forearm tissue. The images of oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentration changes in the forearm were obtained from the differential images of the absorption at three wavelengths between the exercise and the rest states, which were reconstructed with a differential imaging scheme. These images showed increases in both blood volume and oxyhemoglobin concentration in the arteries and simultaneously showed hypoxia in the corresponding muscles. All the results have demonstrated the capability of TR NIR DOT by reconstruction of the absolute images of the scattering and the absorption with a high spatial resolution that finally provided both the anatomical and functional information inside bulky biological tissues.     

Image correction algorithm for functional three-dimensional diffuse optical tomography brain imaging

We outline a computationally efficient image correction algorithm, which we have applied to diffuse optical tomography (DOT) image time series derived from a magnetic resonance imaging (MRI)-based brain model. Results show that the algorithm increases spatial resolution, decreases spatial bias, and only modestly reduces temporal accuracy for noise levels typically seen in experiment, and produces results comparable to image reconstructions that incorporate information from MRI priors. We demonstrate that this algorithm has robust performance in the presence of noise, background heterogeneity, irregular external and internal boundaries, and error in the initial guess. However, the algorithm introduces artifacts when the absorption and scattering coefficients of the reference medium are overestimated--a situation that is easily avoided in practice. The considered algorithm offers a practical approach to improving the quality of images from time-series DOT, even without the use of MRI priors.     

Image correction scheme applied to functional diffuse optical tomography scattering images

We have extended our investigation on the use of a linear algorithm for enhancing the accuracy of diffuse optical tomography (DOT) images, to include spatial maps of the diffusion coefficient. The results show that the corrected images are markedly improved in terms of estimated size, spatial resolution, two-object resolving power, and quantitative accuracy. These image-enhancing effects are significant at expected levels of diffusion-coefficient contrast in tissue and noise levels typical of experimental DOT data. Overall, the types and magnitudes of image-enhancing effects obtained here are qualitatively similar to those seen in previous studies on mu(a) perturbations. The implications for practical implementations of DOT time-series imaging are discussed.     

Improved accuracy of reconstructed diffuse optical tomographic images by means of spatial deconvolution: two-dimensional quantitative characterization

Systematic characterization studies are presented, relating to a previously reported spatial deconvolution operation that seeks to compensate for the information-blurring property of first-order perturbation algorithms for diffuse optical tomography (DOT) image reconstruction. In simulation results that are presented, this deconvolution operation has been applied to two-dimensional DOT images reconstructed by solving a first-order perturbation equation. Under study was the effect on algorithm performance of control parameters in the measurement (number and spatial distribution of sources and detectors, presence of noise, and presence of systematic error), target (medium shape; and number, location, size, and contrast of inclusions), and computational (number of finite-element-method mesh nodes, length of filter-generating linear system, among others) parameter spaces associated with computation and the use of the deconvolution operators. Substantial improvements in reconstructed image quality, in terms of recovered inclusion location, size, and contrast, are found in all cases. A finding of practical importance is that the method is robust to appreciable differences between the optical coefficients of the media used for filter generation and those of the target media to which the filters are subsequently applied.     

Comparison of imaging quality between linear sampling method and time reversal in microwave imaging problems

This research investigated imaging quality of two important methods widely used in electromagnetic inverse scattering problems. The algorithms, time reversal (TR) and linear sampling method (LSM), were compared for resolution of point imaging and a correlation indicator to determine image quality. Comparisons were made in single- and multifrequency modes for 2D scenarios in free-space. Comparisons revealed that resolution of TR is much better than LSM. In order to compare the total reconstructed images, several cases were considered to determine a comprehensive conclusion. The simulations were done based on experimental data. In this case, comparisons showed that in the term of correlation indicator, LSM surpasses TR.     

合成孔径声纳运动补偿算法的局部优化研究

在合成孔径声纳（Synthetic Aperture Sonar,SAS）的成像处理中,声纳基阵的不稳定是影响成像质量的重要因素,运动误差的存在会直接导致图像的畸变和分辨率下降,在实际的SAS成像中必须考虑运动误差的影响。结合SAS成像的特点,分析了传统运动补偿算法的不足,并提出了针对强点目标的局部优化方案,最后通过仿真和试验数据进行了有效性验证。     

Spatial deconvolution technique to improve the accuracy of reconstructed three-dimensional diffuse optical tomographic images

A straightforward spatial deconvolution operation is presented that seeks to invert the information-blurring property of first-order perturbation algorithms for diffuse optical tomography (DOT) image reconstruction. The method that was developed to generate these deconvolving operators, or filters, was conceptually based on the frequency-encoding process used in magnetic resonance imaging. The computation of an image-correcting filter involves the solution of a large system of linear equations, in which known true distributions and the corresponding recovered distributions are compared. Conversely, application of a filter involves only a simple matrix multiplication. Simulation results show that application of this deconvolution operation to three-dimensional DOT images reconstructed by the solution of a first-order perturbation equation (Born approximation) can yield marked enhancement of image quality. In the examples considered, use of image-correcting filters produces obvious improvements in image quality, in terms of both location and mirco(a) of the inclusions. The displacements between the true and recovered locations of an inclusion's centroid location are as small as 1 mm, in an 8-cm-diameter medium with 1.5-cm-diameter inclusions, and the peak value of the recovered micro(a) for the inclusions deviates from the true value by as little as 5%.     

基于高斯拉普拉斯算子的色域映射算法

目的进一步提高色域映射质量,深入研究空间色域映射算法。方法利用高斯拉普拉斯算子对原图的边缘细节进行提取,叠加到映射后图像上再进行二次映射,得到的图像使用结构相关性和图像色差模型进行评价,将数据与最小色差法、CUSP和Bala等人提出的算法进行比较。结果基于高斯拉普拉斯算子的色域映射算法的结构相关性和图像色差都比Bala等人提出的算法要好。对于色彩艳丽、细节丰富的图像,空间色域映射算法结构相关性和图像色差反而不如普通算法。结论基于高斯拉普拉斯算子的色域映射算法能够提高图像的映射质量,但是空间色域映射算法映射质量并不一定优于非空间类色域映射算法。     

High-resolution ultrasonic imaging using fast two-dimensional homomorphic filtering.

A new method for two-dimensional deconvolution of medical ultrasonic images is presented. The spatial resolution of the deconvolved images is much higher compared to the common images of the fundamental and second harmonic. The deconvolution also results in a more distinct speckle pattern. Unlike the most published deconvolution algorithms for ultrasonic images, the presented technique can be implemented using currently available hardware in real-time imaging, with a rate up to 50 frames per second. This makes it attractive for application in the current ultrasound scanners. The algorithm is based on two-dimensional homomorphic deconvolution with simplified assumptions about the point spread function. Broadband radio frequency image data are deconvolved instead of common fundamental harmonic data. Thus, information of both the first and second harmonics is used. The method was validated on image data recorded from a tissue-mimicking phantom and on clinical image data.     

High-resolution ultrasonic imaging using two-dimensional homomorphic filtering

A new method for two-dimensional deconvolution of medical ultrasonic images is presented. The spatial resolution of the deconvolved images is much higher compared to the common images of the fundamental and second harmonic. The deconvolution also results in a more distinct speckle pattern. Unlike the most published deconvolution algorithms for ultrasonic images, the presented technique can be implemented using currently available hardware in real-time imaging, with a rate up to 50 frames per second. This makes it attractive for application in the current ultrasound scanners. The algorithm is based on two-dimensional homomorphic deconvolution with simplified assumptions about the point spread function. Broadband radio frequency image data are deconvolved instead of common fundamental harmonic data. Thus, information of both the first and second harmonics is used. The method was validated on image data recorded from a tissue-mimicking phantom and on clinical image data.     

Synthetic-focusing strategies for real-time annular-array imaging

Annular arrays provide a means to achieve enhanced image quality with a limited number of elements. Synthetic-focusing (SF) strategies that rely on beamforming data from individual transmit-to-receive (TR) element pairs provide a means to improve image quality without specialized TR delay electronics. Here, SF strategies are examined in the context of high-frequency ultrasound (>15 MHz) annular arrays composed of five elements, operating at 18 and 38 MHz. Acoustic field simulations are compared with experimental data acquired from wire and anechoic-sphere phantoms, and the values of lateral beamwidth, SNR, contrast-to-noise ratio (CNR), and depth of field (DOF) are compared as a function of depth. In each case, data were acquired for all TR combinations (25 in total) and processed with SF using all 25 TR pairs and SF with the outer receive channels removed one by one. The results show that removing the outer receive channels led to an overall degradation of lateral resolution, an overall decrease in SNR, and did not reduce the DOF, although the DOF profile decreased in amplitude. The CNR was >1 and remained fairly constant as a function of depth, with a slight decrease in CNR for the case with just the central element receiving. The relative changes between the calculated and measured quantities were nearly identical for the 18- and 38-MHz arrays. B-mode images of the anechoic phantom and an in vivo mouse embryo using full SF with 25 TR pairs or reduced TR-pair approaches showed minimal qualitative difference.     

遥感图像客观质量评价方法研究

为分析图像压缩过程对遥感图像质量的影响,从遥感图像构像质量和几何质量两个方面探讨了遥感图像客观质量评价方法,并改进一种基于Harris角点检测算法的亚像素级角点检测算法。采用JPEG2000、SPIHT两种算法分别对这些评价方法进行验证。实验表明：为满足人眼视觉观察能分辨图像细节的要求,遥感图像压缩比不宜超过16倍,为使压缩后图像满足计算机视觉应用,图像压缩比不宜超过8倍。     

Non-Local DWI Image Super-Resolution with Joint Information Based on GPU Implementation

Since the spatial resolution of diffusion weighted magnetic resonance imaging (DWI) is subject to scanning time and other constraints, its spatial resolution is relatively limited. In view of this, a new non-local DWI image super-resolution with joint information method was proposed to improve the spatial resolution. Based on the non-local strategy, we use the joint information of adjacent scan directions to implement a new weighting scheme. The quantitative and qualitative comparison of the datasets of synthesized DWI and real DWI show that this method can significantly improve the resolution of DWI. However, the algorithm ran slowly because of the joint information. In order to apply the algorithm to the actual scene, we compare the proposed algorithm on CPU and GPU respectively. It is found that the processing time on GPU is much less than on CPU, and that the highest speedup ratio to the traditional algorithm is more than 26 times. It raises the possibility of applying reconstruction algorithms in actual workplaces.     

Quality assurance (QA) procedures for software: evaluation of an ADC quality system

Image viewing and processing software in computed radiography manipulates image contrast in such a way that all relevant image features are rendered to an appropriate degree of visibility, and improves image quality using enhancement algorithms. The purpose of this study was to investigate procedures for the quality assessment of image processing software for computed radiography with the use of existing test objects and to assess the influence that processing introduces on physical image quality characteristics. Measurements of high-contrast resolution, low-contrast resolution, spatial resolution, greyscale (characteristic curve) and geometric distortion were performed 'subjectively' by three independent observers and 'objectively' by the use of criteria based on pixel intensity values. Results show quality assessment is possible without the need for human evaluators, using digital images. It was discovered that the processing software evaluated in this study was able to improve some aspects of image quality, without introducing geometric distortion.     

Image reconstruction algorithm based on the extended regularised total least squares method for electrical capacitance tomography

Electrical capacitance tomography (ECT) is a non-invasive imaging technology that aims at the visualisation of the cross-sectional permittivity distribution of a dielectric object based on the measured capacitance data. Successful applications of ECT depend greatly on the precision and speed of the image reconstruction algorithms. ECT image reconstruction is a typical ill-posed problem, and its solution is unstable, that is, the solution is sensitive to noises in the input data. Methods that ensure the stability of a solution while enhancing the quality of the reconstructed images should be used to obtain a meaningful reconstruction result. An image reconstruction algorithm based on the regularised total least squares (TLS) method that considers the errors in both the sensitivity field matrix and the capacitance data for ECT is presented. The regularised TLS method is extended using a combination robust estimation technique and an extended stabilising functional according to the ill-posed characteristics of ECT, which transforms the image reconstruction problem into an optimisation problem. In addition, the Newton algorithm is employed to solve the objective functional. Numerical simulations indicate that the algorithm is feasible and overcomes the numerical instability of ECT image reconstruction; for the cases of the reconstructed objects considered here, the spatial resolution of the reconstructed images obtained using the algorithm is enhanced; as a result, an efficient method for ECT image reconstruction is introduced.     

Fast multispectral diffuse optical tomography system for in vivo three-dimensional imaging of seizure dynamics

We describe a multispectral continuous-wave diffuse optical tomography (DOT) system that can be used for in vivo three-dimensional (3-D) imaging of seizure dynamics. Fast 3-D data acquisition is realized through a time multiplexing approach based on a parallel lighting configuration--our system can achieve 0.12 ms per source per wavelength and up to a 14 Hz sampling rate for a full set of data for 3-D DOT image reconstruction. The system is validated using both static and dynamic tissue-like phantoms. An initial in vivo experiment using a rat model of seizure is also demonstrated.     

基于双正交小波变换的资源二号卫星与多光谱影像融合方法研究

针对基于Darbechies等正交小波函数的遥感影像融合时引起失真的现象,引入双正交小波函数,提出了基于双正交小波变换的融合方法,对资源二号卫星和TM的多光谱影像的融合进行研究,并把融合结果与主成分、IHS和Brovey等融合结果进行比较。通过对光谱特征曲线、相关系数、光谱扭曲程度等分析和空间细节的对比,说明双正交小波融合方法除了提高空间分辨率外,最好地保持了多光谱影像的光谱特征,有利于专题信息的提取。     

Dynamic estimation of the modeling error statistics in Diffuse Optical Tomography

Diffuse Optical Tomography (DOT) is a non-invasive imaging technique that suffers from a typical large-scale and ill-posed inverse problem with low spatial resolution. In DOT, the inverse problem is computationally intensive and decreasing the computation complexity and making it well-posed is the one of the most challenging research areas. More precisely, one of the well-known complexity reduction techniques is defined as applying modelling error originated from discretization of forward problem. Applying the discretization error in Bayesian inference has already been discussed; the method in which the likelihood is modified by an off-line prior density estimation. This paper implements a new method to enhance the modelling error approach using an iterative scheme to update statistical parameters of modelling discrepancy in DOT. The algorithm is very similar to Ensemble Kalman Filter. Moreover, the reconstruction process in the applied method is conducted by a small sample size rather than off-line method. Hence, the computation complexity is decreased and the algorithm converges in few iterations. The efficiency of the proposed method is illustrated by simulations.     

Spectral multivariate calibration with wavelength selection using variants of Tikhonov regularization

Tikhonov regularization (TR) is a general method that can be used to form a multivariate calibration model and numerous variants of it exist, including ridge regression (RR). This paper reports on the unique flexibility of TR to form a model using full wavelengths (RR), individually selected wavelengths, or multiple bands of selected wavelengths. Of these three TR variants, the one based on selection of wavelength bands is found to produce lower prediction errors. As with most wavelength selection algorithms, the model vector magnitude indicates that this error reduction comes with a potential increase in prediction uncertainty. Results are presented for near-infrared, ultraviolet-visible, and synthetic spectral data sets. While the focus of this paper is wavelength selection, the TR methods are generic and applicable to other variable-selection situations.     

Tactile Sensing Using Force Sensing Resistors and a Super-Resolution Algorithm

This paper presents a tactile sensor consisting of an array of force sensing resistors (FSRs). The tactile sensing array can be seen as a coordinated system of touch sensors. The low spatial resolution measured with the FSRs compared to other force or pressure sensors required the use of a super-resolution algorithm. Super-resolution algorithms are often used in digital image processing to enhance the resolution of images. Multiple images taken from slightly different orientations are superimposed in such a way that a single higher-resolution image is obtained. Different touch sensors are briefly discussed and the use of FSRs is motivated. Image-registration techniques are discussed and the super-resolution algorithm developed for the application is presented. Some tests performed using the tactile sensor in a neck palpation device and the results of these tests are also presented.