Development of image reconstruction algorithms for fluorescence diffuse optical tomography using total light approach

Fluorescence diffuse optical tomography (fDOT) is a noninvasive imaging technique that makes it possible to quantify the spatial distribution of fluorescent tracers in small animals. fDOT image reconstruction is commonly performed by means of iterative methods such as the algebraic reconstruction technique (ART). The useful results yielded by more advanced l1-regularized techniques for signal recovery and image reconstruction, together with the recent publication of Split Bregman (SB) procedure, led us to propose a new approach to the fDOT inverse problem, namely, ART-SB. This method alternates a cost-efficient reconstruction step (ART iteration) with a denoising filtering step based on minimization of total variation of the image using the SB method, which can be solved efficiently and quickly. We applied this method to simulated and experimental fDOT data and found that ART-SB provides substantial benefits over conventional ART.

A comparison of regularization operators for noisy gamma-ray tomographic reconstruction

Resolution in ART: An experimental investigation of the resolving power of an algebraic picture reconstruction technique

Disorders of lung growth, maturation and con- trol of breathing are among the most important challenges faced by the pediatric pulmonologists and neonatologists. Objective, non-invasive measures, suitable for use in small infants, are urgently required to characterise the nature and severity of lung disease in early life. Electrical Impedance Tomography (EIT) has the potential to provide these; however the data must be analysed and presented in a manner that clinicians can utilise to obtain quantitative data to aid interpre- tation and to relate the functional imaging of ventilation dis- tribution to other lung function results. We present a new data processing interface, designed to address the clinical requirements of imaging neonatal lung function. This incorporates breath detection software and end- expiratory gated baseline correction. Boundary voltage data are presented as Waterfall plots, allowing much temporal- spatial information to be displayed in a very compact manner, immune from reconstruction artifacts. Data are presented for an unsedated neonate, showing posture dependant changes in regional ventilation and temporal differences between tidal and maximal breaths. Images are also produced, using a trun- cated singular value decomposition (TSVD) reconstruction algorithm. Images are also produced, based on a truncated singular value decomposition (TSVD) reconstruction algo- rithm

BackgroundAfter the release of compressed sensing (CS) theory, reconstruction algorithms from sparse and incomplete data have shown great improvements in diminishing artifacts of missing data. Following this progress, both local and non-local regularization induced iterative reconstructions have been actively used in limited view angle imaging problems.MethodsIn this study, a 3D iterative image reconstruction method (ART + TV)NLM was introduced by combining local total variation (TV) with non-local means (NLM) filter. In the first step, TV minimization was applied to the image obtained by algebraic reconstruction technique (ART) for background noise removal with preserving edges. In the second step, NLM is used in order to suppress the out of focus slice blur which is the most existent image artifact in tomosynthesis imaging. NLM exploits the similar structures to increase the smoothness in the image reconstructed by ART + TV.ResultsA tomosynthesis system and a 3D phantom were designed to perform simulations to show the superior performance of our proposed (ART + TV)NLM over ART and widely used ART + TV methods. Visual inspections show a significant improvement in image quality compared to ART and ART + TV.ConclusionsRMSE, Structure SIMilarity (SSIM) value and SNR of a specific layer of interest (LOI) showed that by proper selection of NLM parameters, significant improvements can be achieved in terms of convergence rate and image quality.

Objectives Global navigation satellite system(GNSS) tomography, characterized by reconstructing the three-dimensional distribution of the atmospheric water vapor, has proved its capacity for studying the extreme weather events. The ill-conditioned problem resulting from the GNSS acquisition geometry is the critical issue of the GNSS tomography system. Algebraic reconstruction techniques, with the advantages of simple iteration and fast convergence, has been widely applied to the tropospheric tomography. However, the error allocation principle based on the intersection length of GNSS rays with each ray-voxel affects the accuracy of tomographic results. An improved adaptive algebraic reconstruction techniques are proposed to address this problem. Methods In view of the unreasonable error allocation in the traditional algorithm, we suggest a new error allocation principle based on the variation of water vapor density(WVD) in voxels for the adaptive algorithms, in which the product of the intersection length and WVD is considered as a new principle to redistribute the difference between the GNSS slant water vapor(SWV)and the reconstructed SWV. Besides, the weight matrix model of elevation angles is introduced to the improved algorithm to optimize the tomographic results. Results The new algorithms are tested by measured data from Xuzhou continuously operating reference stations(CORS) network and radiosonde during July2016. Experimental results reveal that the WVD derived from the adaptive algorithms performs better than that of the general algorithms in root mean squared error(RMSE), standard deviation(STD) and mean absolute error(MAE), and the RMSE is decreased by 25.91%, 15.81%, and 24.64% for adaptive algebraic reconstruction technique(AART), adaptive multiplicative algebraic reconstruction technique(AMART)and adaptive simultaneous iterative reconstruction technique(ASIRT) respectively. Under the conditions of light rain, moderate rain and heavy rain, the adaptive algorithms retrieve better water vapor profile than the traditional ones. Conclusions Overall, the water vapor profile derived from the adaptive algorithms agrees with the radiosonde distribution better than the traditional algorithms, which reveals the advantage of the proposed method on optimizing the tomography solutions.

It is well known that the reconstruction problem in fluorescence diffuse optical tomography is badly conditioned and requires the knowledge of medium optical properties. Its principle is to measure the fluorescence light emerging at different positions of the surface when the biological medium is excited with point sources. In this paper, we evaluate the influence of the medium optical properties and the noise on the fluorescence reconstruction, and we introduce a new regularized fluorescence reconstruction method using an <i>a priori</i> on contours. The fluorescence reconstruction improvement is studied when using this method.

ULTRASONIC TOMOGRAPHY IN COMPOSITE MATERIALS

Mapping chemicals in air using an environmental CAT scanning system: evaluation of algorithms

In this contribution the inverse source problem for a multipath environment is addressed, with multipath being created by a grid of point scatterers appositely deployed in the scene. By adopting a weighted back-propagation imaging algorithm, we study how resolution improvement and artefacts depend on the grid layout. Successively, back-propagation is compared with a truncated singular value decomposition (TSVD) inverse filtering. It is shown that TSVD in principle allows for a larger resolution improvement and is less prone to artefacts. However, TSVD is more computationally demanding and when noise corrupts data, back-propagation definitely performs better.

Limited-angle computed tomography (CT) may appear in restricted CT scans. Since the available projection data is incomplete, the images reconstructed by filtered back-projection (FBP) or algebraic reconstruction technique (ART) often encounter shading artifacts. However, using the anisotropy property of the shading artifacts that coincide with the characteristic of limited-angle CT images can reduce the shading artifacts. Considering this concept, we combine the anisotropy property of the shading artifacts with the anisotropic structure property of an image to develop a new algorithm for image reconstruction. Specifically, we propose an image reconstruction method based on adaptive weighted anisotropic total variation (AwATV). This method, termed as AwATV method for short, is designed to preserve image structures and then remove the shading artifacts. It characterizes both of above properties. The anisotropy property of the shading artifacts accounts for reducing artifacts, and the anisotropic structure property of an image accounts for preserving structures. In order to evaluate the performance of AwATV, we use the simulation projection data of FORBILD head phantom and real CT data for image reconstruction. Experimental results show that AwATV can always reconstruct images with higher SSIM and PSNR, and smaller RMSE, which means that AwATV enables to reconstruct images with higher quality in term of artifact reduction and structure preservation.

To investigate algebraic reconstruction technique (ART) for parallel imaging reconstruction of radial data, applied to accelerated cardiac cine. A graphics processing unit (GPU)-accelerated ART reconstruction was implemented and applied to simulations, point spread functions and in 12 subjects imaged with radial cardiac cine acquisitions. Cine images were reconstructed with radial ART at multiple undersampling levels (192 Nr × Np = 96 to 16). Images were qualitatively and quantitatively analyzed for sharpness and artifacts, and compared to filtered back-projection, and conjugate gradient SENSE. Radial ART provided reduced artifacts and mainly preserved spatial resolution, for both simulations and in vivo data. Artifacts were qualitatively and quantitatively less with ART than filtered back-projection using 48, 32, and 24 Np , although filtered back-projection provided quantitatively sharper images at undersampling levels of 48-24 Np (all P < 0.05). Use of undersampled radial data for generating auto-calibrated coil-sensitivity profiles resulted in slightly reduced quality. ART was comparable to conjugate gradient SENSE. GPU-acceleration increased ART reconstruction speed 15-fold, with little impact on the images. GPU-accelerated ART is an alternative approach to image reconstruction for parallel radial MR imaging, providing reduced artifacts while mainly maintaining sharpness compared to filtered back-projection, as shown by its first application in cardiac studies.

ART: Mathematics and applications: A report on the mathematical foundations and on the applicability to real data of the algebraic reconstruction techniques

The prime motivation of this work is to devise a projection algorithm that makes the Algebraic Reconstruction Technique (ART) and related methods more efficient for routine clinical use without compromising their accuracy. While we focus mostly on a fast implementation of ART-type methods in the context of 3D cone-beam reconstruction, most of the material presented here is also applicable to speed up 2D slice reconstruction from fan-beam data. In this paper, we utilize the concepts of the splatting algorithm, which is a well known and very efficient voxel-driven projection technique for parallel projection, and devise an extension for perspective cone-beam projection that is considerably more accurate than previously outlined extensions. Since this new voxel-driven splatting algorithm must make great sacrifices with regards to computational speed, we describe a new 3D ray-driven projector that uses similar concepts than the voxel-driven projector but is considerably faster, and, at the same time, also more accurate. We conclude that with the proposed fast projection algorithm the computational cost of cone-beam ART can be reduced significantly with the added benefit of slight gains in accuracy. A further conclusion of our studies is that for parallel-beam reconstruction, on the other hand, a simple voxel-driven splatting algorithm provides for more efficient projection.

Towards a preclinical prototype for diagnostic and monitoring of cerebral pathologies, here we present the 3D electromagnetic (EM) virtual prototyping of different clinical scenarios as an instrument for studying the interaction of biological tissues with EM waves, for designing a microwave brain imaging scanner and for generating a set EM fields usually required by imaging algorithms. We employ a full-wave modelling, which uses a Method of Moment (MoM) solver with high order basis functions and includes frequency variable electrical parameters for each component. The model of the microwave imaging system consists of 24-element conformal antennas, an anthropomorphic adult human head, and a spherical shape blood-filled as stroke. Here, the simulated system and data are tested applying an imaging algorithm based on Truncated Singular Value Decomposition (TSVD) and Born approximation, but they can be combined with other microwave imaging algorithms.