Adapted sampling for 3D X-ray computed tomography

International audience—In this paper, we introduce a method to build an adapted mesh representation of a 3D object for X-Ray tomogra-phy reconstruction. Using this representation, we provide means to reduce the computational cost of reconstruction by way of iterative algorithms. The adapted sampling of the reconstruction space is directly obtained from the projection dataset and prior to any reconstruction. It is built following two stages : firstly, 2D structural information is extracted from the projection images and is secondly merged in 3D to obtain a 3D pointcloud sampling the interfaces of the object. A relevant mesh is then built from this cloud by way of tetrahedralization. Critical parameters selections have been automatized through a statistical framework, thus avoiding dependence on users expertise. Applying this approach on geometrical shapes and on a 3D Shepp-Logan phantom, we show the relevance of such a sampling-obtained in a few seconds-and the drastic decrease in cells number to be estimated during reconstruction when compared to the usual regular voxel lattice. A first iterative reconstruction of the Shepp-Logan using this kind of sampling shows the relevant advantages in terms of low dose or sparse acquisition sampling contexts. The method can also prove useful for other applications such as finite element method computations

A virtual world of paleontology

Computer-aided visualization and analysis of fossils has revolutionized the study of extinct organisms. Novel techniques allow fossils to be characterized in three dimensions and in unprecedented detail. This has enabled paleontologists to gain important insights into their anatomy, development, and preservation. New protocols allow more objective reconstructions of fossil organisms, including soft tissues, from incomplete remains. The resulting digital reconstructions can be used in functional analyses, rigorously testing long-standing hypotheses regarding the paleobiology of extinct organisms. These approaches are transforming our understanding of long-studied fossil groups, and of the narratives of organismal and ecological evolution that have been built upon them

Evaluation of Single-Chip, Real-Time Tomographic Data Processing on FPGA - SoC Devices

A novel approach to tomographic data processing has been developed and evaluated using the Jagiellonian PET (J-PET) scanner as an example. We propose a system in which there is no need for powerful, local to the scanner processing facility, capable to reconstruct images on the fly. Instead we introduce a Field Programmable Gate Array (FPGA) System-on-Chip (SoC) platform connected directly to data streams coming from the scanner, which can perform event building, filtering, coincidence search and Region-Of-Response (ROR) reconstruction by the programmable logic and visualization by the integrated processors. The platform significantly reduces data volume converting raw data to a list-mode representation, while generating visualization on the fly.Comment: IEEE Transactions on Medical Imaging, 17 May 201

Challenges in imaging and predictive modeling of rhizosphere processes

Background Plant-soil interaction is central to human food production and ecosystem function. Thus, it is essential to not only understand, but also to develop predictive mathematical models which can be used to assess how climate and soil management practices will affect these interactions. Scope In this paper we review the current developments in structural and chemical imaging of rhizosphere processes within the context of multiscale mathematical image based modeling. We outline areas that need more research and areas which would benefit from more detailed understanding. Conclusions We conclude that the combination of structural and chemical imaging with modeling is an incredibly powerful tool which is fundamental for understanding how plant roots interact with soil. We emphasize the need for more researchers to be attracted to this area that is so fertile for future discoveries. Finally, model building must go hand in hand with experiments. In particular, there is a real need to integrate rhizosphere structural and chemical imaging with modeling for better understanding of the rhizosphere processes leading to models which explicitly account for pore scale processes

Development of Spect and Ct Tomographic Image Reconstruction

The purpose of this study was to contribute to the advancement of statistically-based iterative reconstruction algorithms and protocols for both SPECT and micro CT data. Major contributions of this work to SPECT reconstruction include formulation and implementation of fully three-dimensional voxel-based system matrix in parallel-beam, fan-beam, and cone-beam collimator geometries while modeling the process of attenuation, system resolution and sensitivity. This is achieved by casting rays through a volume of voxels and using ray-voxel intersection lengths to determine approximate volume contributions. Qualitative and quantitative analysis of reconstructed Monte Carlo data sets show that this is a very effective and efficient method. Using this method, three SPECT studies were conducted. First, the reconstruction performance was studied for a triple-head cone-beam SPECT system using a helical orbit acquisition. We looked at various subset groupings for the ordered-subsets expectation maximization (OSEM) algorithm. We also examined how rotational and translational sampling affects reconstructed image quality when constrained by total injected dose and scan time. We conclude the following: When reconstructing noiseless datasets, varying the rotational sampling from 90 views to 360 views over 360 degrees does not affect the reconstructed activity regardless of the object size in terms of both convergence and accuracy. When using ordered subsets, the subset group arrangement is important in terms of both image quality and accuracy. The smaller the object is that is being reconstructed, the rate of convergence decreases, the spatial resolution decreases, and accuracy decreases. Second, we examined a system composed of three, possibly different, converging collimators using a circular orbit. We conclude the following: When reconstructing noiseless datasets, using a triple-cone beam system resulted in distortion artifacts along the axial direction and diminished resolution along the transaxial direction. Using a triple-fan beam system resulted in the best reconstructed image quality in terms of bias, noise, and contrast. When noisy datasets were reconstructed, a cone-cone-fan beam system resulted in best reconstructed image quality in terms of mean-to-actual ratio for small lesions and a triple-fan beam system for large lesions. Finally, a two-dimensional mesh-based system matrix for parallel-beam collimation with attenuation and resolution modeling was designed, implemented, and studied. We conclude that no more than two divisions per detector bin width are needed for satisfactory reconstruction. Also, using more than two divisions per detector bin does not significantly improve reconstructed images. A chapter on iterative micro-CT reconstruction is also included. Our contribution to micro-CT reconstruction is the formulation and implementation of a cone-beam system matrix that reduces ring artifacts associated with sampling of the reconstruction space. This new approach reduces the common 3 D ray-tracing technique into 2-D, making it very efficient. The images obtained using our approach are compared to images reconstructed by means of analytical techniques. We observe significant improvement in image quality for the images reconstructed using our iterative method

Advanced tomographic image reconstruction algorithms for Diffuse Optical Imaging

Diffuse Optical Imaging is relatively new set of imaging modality that use infrared and near infrared light to characterize the optical properties of biological tissue. The technology used is less expensive than other imaging modalities such as X-ray mammography, it is portable and can be used to monitor brain activation and cancer diagnosis, besides to aid to other imaging modalities and therapy treatments in the characterization of diseased tissue, i. e. X-ray, Magnetic Resonance Imaging and Radio Frequency Ablation. Due the optical properties of biological tissue near-infrared light is highly scattered, as a consequence, a limited amount of light is propagated thus making the image reconstruction process very challenging. Typically, diffuse optical image reconstructions require from several minutes to hours to produce an accurate image from the interaction of the photons and the chormophores of the studied medium. To this day, this limitation is still under investigation and there are several approaches that are close to the real-time image reconstruction operation. Diffuse Optical Imaging includes a variety of techniques such as functional Near-Infrared Spectroscopy (fNIRS), Diffuse Optical Tomography (DOT), Fluorescence Diffuse Optical Tomography (FDOT) and Spatial Frequency Domain imaging (SFDI). These emerging image reconstruction modalities aim to become routine modalities for clinical applications. Each technique presents their own advantages and limitations, but they have been successfully used in clinical trials such as brain activation analysis and breast cancer diagnosis by mapping the response of the vascularity within the tissue through the use of models that relate the interaction between the tissue and the path followed by the photons. One way to perform the image reconstruction process is by separating it in two stages: the forward problem and the inverse problem; the former is used to describe light propagation inside a medium and the latter is related to the reconstruction of the spatio-temporal distribution of the photons through the tissue. Iterative methods are used to solve both problems but the intrinsic complexity of photon transport in biological tissue makes the problem time-consuming and computationally expensive. The aim of this research is to apply a fast-forward solver based on reduced order models to Fluorescence Diffuse Optical Tomography and Spatial Frequency Domain Imaging to contribute to these modalities in their application of clinical trials. Previous work showed the capabilities of the reduced order models for real-time reconstruction of the absorption parameters in the brain of mice. Results demonstrated insignificant loss of quantitative and qualitative accuracy and the reconstruction was performed in a fraction of the time normally required on this kind of studies. The forward models proposed in this work, offer the capability to run three-dimensional image reconstructions in CPU-based computational systems in a fraction of the time required by image reconstructions methods that use meshes generated using the Finite Element Method. In the case of SFMI, the proposed approach is fused with the approach of the virtual sensor for CCD cameras to reduce the computational burden and to generate a three-dimensional map of the distribution of tissue optical properties. In this work, the use case of FDOT focused on the thorax of a mouse model with tumors in the lungs as the medium under investigation. The mouse model was studied under two- and three- dimension conditions. The two-dimensional case is presented to explain the process of creating the Reduced-Order Models. In this case, there is not a significant improvement in the reconstruction considering NIRFAST as the reference. The proposed approach reduced the reconstruction time to a quarter of the time required by NIRFAST, but the last one performed it in a couple of seconds. In contrast, the three-dimensional case exploited the capabilities of the Reduced-Order Models by reducing the time of the reconstruction from a couple of hours to several seconds, thus allowing a closer real-time reconstruction of the fluorescent properties of the interrogated medium. In the case of Spatial Frequency Domain Imaging, the use case considered a three-dimensional section of a human head that is analysed using a CCD camera and a spatially modulated light source that illuminates the mentioned head section. Using the principle of the virtual sensor, different regions of the CCD camera are clustered and then Reduced Order Models are generated to perform the image reconstruction of the absorption distribution in a fraction of the time required by the algorithm implemented on NIRFAST. The ultimate goal of this research is to contribute to the field of Diffuse Optical Imaging and propose an alternative solution to be used in the reconstruction process to those models already used in three-dimensional reconstructions of Fluorescence Diffuse Optical Tomography and Spatial Frequency Domain Imaging, thus offering the possibility to continuously monitor tissue obtaining results in a matter of seconds

Fast Microwave Tomography Algorithm for Breast Cancer Imaging

Microwave tomography has shown promise for breast cancer imaging. The microwaves are harmless to body tissues, which makes microwave tomography a safe adjuvant screening to mammography. Although many clinical studies have shown the effectiveness of regular screening for the detection of breast cancer, the anatomy of the breast and its critical tissues challenge the identification and diagnosis of tumors in this region. Detection of tumors in the breast is more challenging in heterogeneously dense and extremely dense breasts, and microwave tomography has the potential to be effective in such cases. The sensitivity of microwaves to various breast tissues and the comfort and safety of the screening method have made microwave tomography an attractive imaging technique. Despite the need for an alternative screening technique, microwave tomography has not yet been introduced as a screening modality in regular health care, and is still subject to research. The main obstacles are imperfect hardware systems and inefficient imaging algorithms. The immense computational costs for the image reconstruction algorithm present a crucial challenge. 2D imaging algorithms are proposed to reduce the amount of hardware resources required and the imaging time. Although 2D microwave tomography algorithms are computationally less expensive, few imaging groups have been successful in integrating the acquired 3D data into the 2D tomography algorithms for clinical applications. The microwave tomography algorithms include two main computation problems: the forward problem and the inverse problem. The first part of this thesis focuses on a new fast forward solver, the 2D discrete dipole approximation (DDA), which is formulated and modeled. The effect of frequency, sampling number, target size, and contrast on the accuracy of the solver are studied. Additionally, the 2D DDA time efficiency and computation time as a single forward solver are investigated.\ua0 The second part of this thesis focuses on the inverse problem. This portion of the algorithm is based on a log-magnitude and phase transformation optimization problem and is formulated as the Gauss-Newton iterative algorithm. The synthetic data from a finite-element-based solver (COMSOL Multiphysics) and the experimental data acquired from the breast imaging system at Chalmers University of Technology are used to evaluate the DDA-based image reconstruction algorithm. The investigations of modeling and computational complexity show that the 2D DDA is a fast and accurate forward solver that can be embedded in tomography algorithms to produce images in seconds. The successful development and implementation in this thesis of 2D tomographic breast imaging with acceptable accuracy and high computational cost efficiency has provided significant savings in time and in-use memory and is a dramatic improvement over previous implementations