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This paper draws attention to the study of performance of a new Molecular Breast Imaging (MBI) device, whose purpose is the early diagnosis of breast cancer, using Monte Carlo simulations. ...

A complete software platform based on anthropomorphic breast models used with both planar and three-dimensional phase contrast breast imaging is presented and subjectively validated. For the development of the platform, tests with three anthropomorphic breast phantoms, available both in computational and physical form, were designed and implemented. The models are characterized with different complexity: two phantoms are with spheres and one anthropomorphic. Further on, two of the physical breast models were created with the use of 3D printing techniques. These phantoms with thickness of 40 mm and 31 mm, respectively, were based on digital phantoms created with in-house developed software tools. The third physical breast phantom is the L1 phantom developed at Katholieke Universiteit Leuven with 58 mm thickness. Based on this physical phantom, a computational one was created. The three physical breast phantoms were imaged at ID17 biomedical imaging line at ESRF. Two acquisition setups were used: planar and limited angle tomography modes. Simulated and experimental planar and three-dimensional images were compared in terms of visual reproducibility. Results showed that phantoms characterized with more simple structure produce subjectively similar experimental and simulation appearance in terms of object reproduction and similar edge effects. The thicker phantom demonstrated lower visual coincidence between the two types of planar images, due to higher thickness and higher energy incident beam. The results of this study will be used in the design of new experimental study, to be conducted at lower incident beam energy as well as improving the modelling of phase contrast imaging by using Monte Carlo techniques.

Attenuation of ultrasound waves varies with tissue composition, hence its estimation offers great potential for tissue characterization and diagnosis and staging of pathology. We recently proposed a method that allows to spatially reconstruct the distribution of the overall ultrasound attenuation in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode and a calibration protocol using water measurements, thus it can be implemented on conventional ultrasound systems with minor adaptations. Herein, we extend this method by additionally estimating and imaging the frequency-dependent nature of local ultrasound attenuation for the first time. Spatial distributions of attenuation coefficient and exponent are reconstructed, enabling an elaborate and expressive tissue-specific characterization. With simulations, we demonstrate that our proposed method yields a low reconstruction error of 0.04 dB/cm at 1 MHz for attenuation coefficient and 0.08 for the frequency exponent. With tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast as well as reproducibility are demonstrated. Attenuation exponents of a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.4 and 0.5 on average, consistently from different images of their heterogeneous compositions. Such frequency-dependent parametrization could enable novel imaging and diagnostic techniques, as well as facilitate attenuation compensation of other ultrasound-based imaging techniques. © 2020, Medical Image Analysis, 67, ISSN:1361-8415, ISSN:1361-8423

The purpose of the present work is the study of reconstruction properties of a new Molecular Breast Imaging (MBI) device for the early diagnosis of breast cancer, in Limited Angle Tomography (LAT), by using two asymmetric detector heads with different collimators. The detectors face each other in anti-parallel viewing direction and, mild-compressing the breast phantom, they are able to reconstruct the inner tumour of the phantoms with only a limited number of projections using a dedicated maximum-likelihood expectation maximization (ML-EM) algorithm. Phantoms, MBI system, as well as Monte Carlo simulator using Geant 4 Application for Tomographic Emission (GATE) software, are briefly described. MBI system's model has been implemented in IDL (Interactive Data Visualization), in order to evaluate the best LAT configuration of the system and its reconstruction ability by varying tumour's size, depth and uptake. LAT setup in real and simulated configurations, as well as the ML-EM method and the preliminary reconstruction results, are discussed., 10 pages, 10 figures, proceeding conference

A deep-learning method for limited angle tomography in synchrotron radiation transmission X-ray microscopies and a demonstration of its application in 3D visualization of a chlorella cell., In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision with other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts because of missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. In particular, U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100° limited angle tomography. For synthetic test data, U-Net significantly reduces the root-mean-square error (RMSE) from 2.55 × 10−3 µm−1 in the FBP reconstruction to 1.21 × 10−3 µm−1 in the U-Net reconstruction and also improves the structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least-square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 × 10−3 µm−1 and 0.932, respectively. For real test data, the proposed method remarkably improves the 3D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nanoscale imaging in biology, nanoscience and materials science.

This work aims to study and implement a reconstruction method of X-ray tomography able to reconstruct the image from insufficent number of projections. We proposed a new algorithm using the sinogram interpolation. The sinogram restoration is based on the incomplete sinogram available and generates additional data. The reconstructed images were obtained from the interpolated projections, using the classical FBP method, the quality of the reconstructed image depend on the quality and quantity of the projections. Our study showed image restoration performance in case of missing data and shows improved quality of CT images with reduced artifacts in the reconstruction results.

The aim of this work is to obtain estimates of the accuracy of the x-ray limited-angle tomography method for determining the shape and size of internal cracks in large-sized reinforced concrete structures. A fan-beam registration scheme with a source moving along a circular arc is considered. For reconstruction, we use the fan-beam transform inversion formula and the algebraic method ART. By means of numerical simulation, the dependences of errors of the first and second kind on the mutual orientation of the central beam and the direction of the crack, the angular size of the source trajectory and the noise level are obtained.

In computed tomography, image reconstruction from an insufficient angular range of projection data is called limited angle tomography. Due to missing data, reconstructed images suffer from artifacts, which cause boundary distortion, edge blurring, and intensity biases. Recently, deep learning methods have been applied very successfully to this problem in simulation studies.

The application of traditional machine learning techniques, in the form of regression models based on conventional, "hand-crafted" features, to artifact reduction in limited angle tomography is investigated.Mean-variation-median (MVM), Laplacian, Hessian, and shift-variant data loss (SVDL) features are extracted from the images reconstructed from limited angle data. The regression models linear regression (LR), multilayer perceptron (MLP), and reduced-error pruning tree (REPTree) are applied to predict artifact images.REPTree learns artifacts best and reaches the smallest root-mean-square error (RMSE) of 29 HU for the Shepp-Logan phantom in a parallel-beam study. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. In fan-beam, the SVDL features are also beneficial. A preliminary experiment on clinical data in a fan-beam study demonstrates that REPTree can reduce some artifacts for clinical data. However, it is not sufficient as a lot of incorrect pixel intensities still remain in the estimated reconstruction images.REPTree has the best performance on learning artifacts in limited angle tomography compared with LR and MLP. The features of MVM, Hessian, and SVDL are beneficial for artifact prediction in limited angle tomography. Preliminary experiments on clinical data suggest that the investigation on more features is necessary for clinical applications of REPTree.

[EN] In this contribution, large SiPM arrays (8 x 8 elements of 6 x 6 mm(2) each) are processed with an ASIC-based readout and coupled to a monolithic LYSO crystal to explore their potential use for TOF-PET applications. The aim of this work is to study the integration of this technology in the development of clinical PET systems reaching sub-300 ps coincidence resolving time (CRT). The SiPM and readout electronics have been evaluated first, using a small size 1.6 mm (6 mm height) crystal array (32 x 32 elements). All pixels were well resolved and they exhibited an energy resolution of about 20% (using Time-over-Threshold methods) for the 511 keV photons. Several parameters have been scanned to achieve the optimum readout system performance, obtaining a CRT as good as 330 +/- 5 ps FWHM. When using a black-painted monolithic block, the spatial resolution was measured to be on average 2.6 +/- 0.5 mm, without correcting for the source size. Energy resolution appears to be slightly above 20%. CRT measurements with the monolithic crystal detector were also carried out. Preliminary results as well as calibration methods specifically designed to improve timing performance, are being analyzed in the present manuscript., This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 695536). It has also been supported by the Spanish Ministerio de Economia, Industria y Competitividad under Grants No. FIS2014-62341-EXP and TEC2016-79884-C2-1-R.

In computed tomography, image reconstruction from an insufficient angular range of projection data is called limited angle tomography. Due to missing data, reconstructed images suffer from artifacts, which cause boundary distortion, edge blurring, and intensity biases. Recently, deep learning methods have been applied very successfully to this problem in simulation studies. However, the robustness of neural networks for clinical applications is still a concern. It is reported that most neural networks are vulnerable to adversarial examples. In this paper, we aim to investigate whether some perturbations or noise will mislead a neural network to fail to detect an existing lesion. Our experiments demonstrate that the trained neural network, specifically the U-Net, is sensitive to Poisson noise. While the observed images appear artifact-free, anatomical structures may be located at wrong positions, e.g. the skin shifted by up to 1 cm. This kind of behavior can be reduced by retraining on data with simulated Poisson noise. However, we demonstrate that the retrained U-Net model is still susceptible to adversarial examples. We conclude the paper with suggestions towards robust deep-learning-based reconstruction.

Limited angle tomography is a severely ill-posed problem yet practically significant in x-ray computed tomography. In recent development, limited angle tomography problem is casted in the form of convex optimization problem. In this paper, incorporating anisotropic total variation with Mumford-Shah functional, we propose a novel objective functional and develop the corresponding alternating optimization algorithm. The experimental results demonstrate the efficiency of Mumford-Shah functional minimization for limited angle tomography. Furthermore, we also get the segmentations of reconstructed images.

A method of solving two-dimensional limited-angle tomography problems, based on the Cavalieri condition for the Radon transform, is investigated. Two algorithms realizing this approach are compared between themselves and also with the algorithm calculating missing projections from approximate tomograms. Numerical simulations show that all three algorithms allow the reconstruction accuracy to be improved in the case under consideration. Each of them, however, has certain specific features, which should be taken into account in solving practical tasks.

Digital breast tomosynthesis (DBT) has been investigated as a promising alternative to conventional X-ray mammography for breast cancer screening. By reconstructing 3D volumetric images from multiple 2D projections measured over a limited angular range, it can offer depth-directional information and improve both sensitivity and specificity of cancer detection in dense breasts. The diagnostic performance of DBT can be affected by a number of imaging parameters. The angular range of scan orbit is one of the most crucial factors, since it determines the depth-directional resolution. Recently, we proposed the wide angle tomosynthesis based on voltage modulations of X-ray source. By using X-rays with large penetration power on exterior positions, it can acquire high-SNR projections over a wide angular range. In this paper, we present comparative studies on exposure conditions in DBT, including narrow and wide angle scan using an invariant tube voltage of X-ray source, and wide angle scan with the voltage modulation technique. In addition, we compared the conventional reconstruction methods with recently proposed IDIR algorithms. In preliminary studies, the wide-angle scheme with proposed IDIR algorithm showed superior performances in detecting abnormal lesions over conventional approaches.

Digital breast tomosynthesis (DBT), an emerging imaging modality, provides a pseudo-3D image of the breast. Algorithms to aid the human observer process these large datasets involve two key tasks: reconstruction and registration. Previous studies separated these steps, solving each task independently. This can be effective if reconstructing using a complete set of data, e.g., in cone beam CT, assuming that only simple deformations exist. However, for ill-posed limited-angle problems such as DBT, estimating the deformation is complicated by the significant artefacts associated with DBT reconstructions, leading to severe inaccuracies in the registration. In this paper, we present an innovative algorithm, which combines reconstruction of a pair of temporal DBT acquisitions with their simultaneous registration. Using various computational phantoms and in vivo DBT simulations, we show that, compared to the conventional sequential method, jointly estimating image intensities and transformation parameters gives superior results with respect to reconstruction fidelity and registration accuracy.

We are developing a dedicated high resolution (sub-mm), high efficiency, and very fast (with live reconstruction) prostate PET imager composed of an endorectal PET probe and two PET panel modules placed close to the patient on the opposite side of the prostate, and operating in coincidence with the probe. The immediate live image feedback will be primarily useful in biopsy guidance. PET images will be co-registered with the images from the Transrectal Ultrasound (TRUS) probe that will provide the usual structural 2D or 3D information, while the PET imager will provide the metabolic information related to the biological state of the prostate. We are reporting on preliminary data acquired with the prototype imager. The major highlight is that we are achieving ∼1mm FWHM DOI resolution with the PET probe, using new monolithic MPPC arrays from Hamamatsu. But even with a non-DOI probe, we obtained good performance in this limited angle tomography problem.

We investigate the reconstruction problem of limited angle tomography. Such problems arise naturally in applications like digital breast tomosynthesis, dental tomography, electron microscopy, etc. Since the acquired tomographic data is highly incomplete, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, e.g. filtered backprojection (FBP), do not perform well in such situations. To stabilize the reconstruction procedure additional prior knowledge about the unknown object has to be integrated into the reconstruction process. In this work, we propose the use of the sparse regularization technique in combination with curvelets. We argue that this technique gives rise to an edge-preserving reconstruction. Moreover, we show that the dimension of the problem can be significantly reduced in the curvelet domain. To this end, we give a characterization of the kernel of the limited angle Radon transform in terms of curvelets and derive a characterization of solutions obtained through curvelet sparse regularization. In numerical experiments, we will show that the theoretical results directly translate into practice and that the proposed method outperforms classical reconstructions.

The design of limited angle tomography systems requires the optimization of various imaging parameters in order to achieve useful as well as reliable results. Algebraic reconstruction techniques, specifically the SART algorithm, have given excellent results in CT and are being actively considered for limited angle commercial applications such as tomosynthesis. In this study, we simulate a range of limited angle scenarios by systematically varying a number of key imaging parameters, and examine the performance of the SART algorithm under these variations. The phantoms used are basic ellipsoids in 2D, yielding analytical projections, and an MR-derived breast phantom in 3D.

Computed tomography (CT) with x-rays or γ-rays has the potential of being a powerful tool in assessing the degree of damage in reinforced concrete structures. The traditional CT method consists of taking measurements from all direction around the structure. However in reinforced concrete structures the measurement device cannot be positioned around all the structure, and the measurement data are thus available only from a limited range of angles. In this paper a new algorithm is presented for reducing the effects of the limited angle problem. Computer simulations of limited angle tomography of reinforced concrete columns show that the proposed algorithm is robust and can significantly increase the power of tomography in civil engineering applications.

In nondestructive testing (NDT) of microelectronic components many applications using X-ray radiography are well established. This method is based on the attenuation of radiation intensities of x-rays transmitting an object. Computer tomography (CT), however, is a visualization method which is based on reconstructing three-dimensional models from several two-dimensional X-ray projections of the object. It is only recently used for NDT because it is more expensive and time consuming than conventional X-ray imaging. Nevertheless, there are applications where simple radiography provides only poor results because of superimposed object layers. This article discusses NDT specific problems of CT such as beam hardening and shows some microelectronic applications benefiting from CT as well as examples where modifications of the standard CT procedure are necessary to gain depth information about the object. This so called limited angle tomography reaches a higher image resolution than CT when flat modules are tested.

A method is presented for reconstructing a function from its line integrals. This problem arises in imaging whenever the physical constraints of the system prohibit the gathering of the line integrals over the angles - pi /2 >

We investigate the reconstruction problem for limited angle tomography. Such problems arise naturally in applications like digital breast tomosynthesis, dental tomography, etc. Since the acquired tomographic data is highly incomplete, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, such as filtered backprojection (FBP), do not perform well in such situations. To stabilize the inversion we propose the use of a sparse regularization technique in combination with curvelets. We argue that this technique has the ability to preserve edges. As our main result, we present a characterization of the kernel of the limited angle Radon transform in terms of curvelets. Moreover, we characterize reconstructions which are obtained via curvelet sparse regularizations at a limited angular range. As a result, we show that the dimension of the limited angle problem can be significantly reduced in the curvelet domain. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)

We consider the reconstruction problem for limited angle tomography using filtered backprojection (FBP) and lambda tomography. We use microlocal analysis to explain why the well-known streak artifacts are present at the end of the limited angular range. We explain how to mitigate the streaks and prove that our modified FBP and lambda operators are standard pseudodifferential operators, and so they do not add artifacts. We provide reconstructions to illustrate our mathematical results.

One approach to the three-dimensional, tomographic reconstruction of data from a planar dual-detector stationary positron camera necessitates frequency-space filtering of the image obtained from back-projection. It is therefore useful to have a closed-form expression for the appropriate filter, but the derivation is complicated by the fact that the camera measures only a limited angular range of projections. Nevertheless, suitable expressions can be obtained and are here derived for detectors of rectangular and circular cross-section.

SPECT imaging of [123I]IMP is reviewed. Methods for radiopharmaceutical production are discussed with an emphasis on labeling small quantities of IMP. Limited angle tomography and full angle SPECT with standard cameras and special imaging systems are reviewed. Selection of collimator and methods of reconstruction are discussed. Clinical studies are described with emphasis on stroke, epilepsy and dementia. The efforts to perform quantitative imaging of rCBF with [123I]IMP are reviewed.