Showing papers on "Iterative reconstruction published in 2010"
TL;DR: A novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images, which outperforms all others submitted so far for four out of the six data sets.
Abstract: This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints. The proposed approach automatically detects and discards outliers and obstacles and does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges. We have tested our algorithm on various data sets including objects with fine surface details, deep concavities, and thin structures, outdoor scenes observed from a restricted set of viewpoints, and "crowded" scenes where moving obstacles appear in front of a static structure of interest. A quantitative evaluation on the Middlebury benchmark [1] shows that the proposed method outperforms all others submitted so far for four out of the six data sets.
2,863 citations
TL;DR: By comparison with one-step, FFT-based reconstruction, time reversal is shown to be sufficiently general that it can also be used for finite-sized planar measurement surfaces and the optimization of computational speed is demonstrated through parallel execution using a graphics processing unit.
Abstract: A new, freely available third party MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields is described. The toolbox, named k-Wave, is designed to make realistic photoacoustic modeling simple and fast. The forward simulations are based on a k-space pseudo-spectral time domain solution to coupled first-order acoustic equations for homogeneous or heterogeneous media in one, two, and three dimensions. The simulation functions can additionally be used as a flexible time reversal image reconstruction algorithm for an arbitrarily shaped measurement surface. A one-step image reconstruction algorithm for a planar detector geometry based on the fast Fourier transform (FFT) is also included. The architecture and use of the toolbox are described, and several novel modeling examples are given. First, the use of data interpolation is shown to considerably improve time reversal reconstructions when the measurement surface has only a sparse array of detector points. Second, by comparison with one-step, FFT-based reconstruction, time reversal is shown to be sufficiently general that it can also be used for finite-sized planar measurement surfaces. Last, the optimization of computational speed is demonstrated through parallel execution using a graphics processing unit.
1,629 citations
TL;DR: A comprehensive optimization method to arrive at the spatial and spectral layout of the color filter array of a GAP camera is presented and a novel algorithm for reconstructing the under-sampled channels of the image while minimizing aliasing artifacts is developed.
Abstract: We propose the concept of a generalized assorted pixel (GAP) camera, which enables the user to capture a single image of a scene and, after the fact, control the tradeoff between spatial resolution, dynamic range and spectral detail. The GAP camera uses a complex array (or mosaic) of color filters. A major problem with using such an array is that the captured image is severely under-sampled for at least some of the filter types. This leads to reconstructed images with strong aliasing. We make four contributions in this paper: 1) we present a comprehensive optimization method to arrive at the spatial and spectral layout of the color filter array of a GAP camera. 2) We develop a novel algorithm for reconstructing the under-sampled channels of the image while minimizing aliasing artifacts. 3) We demonstrate how the user can capture a single image and then control the tradeoff of spatial resolution to generate a variety of images, including monochrome, high dynamic range (HDR) monochrome, RGB, HDR RGB, and multispectral images. 4) Finally, the performance of our GAP camera has been verified using extensive simulations that use multispectral images of real world scenes. A large database of these multispectral images has been made available at http://wwwl.cs.columbia.edu/ CAVE/projects/gap_camera/ for use by the research community.
833 citations
TL;DR: A new approach to autocalibrating, coil‐by‐coil parallel imaging reconstruction, is presented, a generalized reconstruction framework based on self‐consistency that can accurately reconstruct images from arbitrary k‐space sampling patterns.
Abstract: A new approach to autocalibrating, coil-by-coil parallel imaging reconstruction, is presented. It is a generalized reconstruction framework based on self-consistency. The reconstruction problem is formulated as an optimization that yields the most consistent solution with the calibration and acquisition data. The approach is general and can accurately reconstruct images from arbitrary k-space sampling patterns. The formulation can flexibly incorporate additional image priors such as off-resonance correction and regularization terms that appear in compressed sensing. Several iterative strategies to solve the posed reconstruction problem in both image and k-space domain are presented. These are based on a projection over convex sets and conjugate gradient algorithms. Phantom and in vivo studies demonstrate efficient reconstructions from undersampled Cartesian and spiral trajectories. Reconstructions that include off-resonance correction and nonlinear l(1)-wavelet regularization are also demonstrated.
793 citations
TL;DR: This paper model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework and develops a constructive (greedy) algorithm designed for fast reconstruction useful in practical settings.
Abstract: In this paper, we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework. We utilize a hierarchical form of the Laplace prior to model the sparsity of the unknown signal. We describe the relationship among a number of sparsity priors proposed in the literature, and show the advantages of the proposed model including its high degree of sparsity. Moreover, we show that some of the existing models are special cases of the proposed model. Using our model, we develop a constructive (greedy) algorithm designed for fast reconstruction useful in practical settings. Unlike most existing CS reconstruction methods, the proposed algorithm is fully automated, i.e., the unknown signal coefficients and all necessary parameters are estimated solely from the observation, and, therefore, no user-intervention is needed. Additionally, the proposed algorithm provides estimates of the uncertainty of the reconstructions. We provide experimental results with synthetic 1-D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.
718 citations
TL;DR: A bayesian regularization approach that adds spatial priors from the MR magnitude image is formulated for susceptibility imaging, introducing a new quantitative contrast in MRI that is directly linked to iron in the brain.
Abstract: The diagnosis of many neurologic diseases benefits from the ability to quantitatively assess iron in the brain. Paramagnetic iron modifies the magnetic susceptibility causing magnetic field inhomogeneity in MRI. The local field can be mapped using the MR signal phase, which is discarded in a typical image reconstruction. The calculation of the susceptibility from the measured magnetic field is an ill-posed inverse problem. In this work, a bayesian regularization approach that adds spatial priors from the MR magnitude image is formulated for susceptibility imaging. Priors include background regions of known zero susceptibility and edge information from the magnitude image. Simulation and phantom validation experiments demonstrated accurate susceptibility maps free of artifacts. The ability to characterize iron content in brain hemorrhage was demonstrated on patients with cavernous hemangioma. Additionally, multiple structures within the brain can be clearly visualized and characterized. The technique introduces a new quantitative contrast in MRI that is directly linked to iron in the brain.
639 citations
TL;DR: This paper proposes the use of the alternating direction method - a classic approach for optimization problems with separable variables - for signal reconstruction from partial Fourier measurements, and runs very fast (typically in a few seconds on a laptop) because it requires a small number of iterations.
Abstract: Recent compressive sensing results show that it is possible to accurately reconstruct certain compressible signals from relatively few linear measurements via solving nonsmooth convex optimization problems. In this paper, we propose the use of the alternating direction method - a classic approach for optimization problems with separable variables (D. Gabay and B. Mercier, ?A dual algorithm for the solution of nonlinear variational problems via finite-element approximations,? Computer and Mathematics with Applications, vol. 2, pp. 17-40, 1976; R. Glowinski and A. Marrocco, ?Sur lapproximation par elements finis dordre un, et la resolution par penalisation-dualite dune classe de problemes de Dirichlet nonlineaires,? Rev. Francaise dAut. Inf. Rech. Oper., vol. R-2, pp. 41-76, 1975) - for signal reconstruction from partial Fourier (i.e., incomplete frequency) measurements. Signals are reconstructed as minimizers of the sum of three terms corresponding to total variation, ?1-norm of a certain transform, and least squares data fitting. Our algorithm, called RecPF and published online, runs very fast (typically in a few seconds on a laptop) because it requires a small number of iterations, each involving simple shrinkages and two fast Fourier transforms (or alternatively discrete cosine transforms when measurements are in the corresponding domain). RecPF was compared with two state-of-the-art algorithms on recovering magnetic resonance images, and the results show that it is highly efficient, stable, and robust.
591 citations
TL;DR: The ASIR reconstruction algorithm is a promising technique for providing diagnostic quality CT images at significantly reduced radiation doses in comparison with low-dose and standard-dose filtered back projection CT.
Abstract: OBJECTIVE. The purpose of this article is to discuss the application of a new CT reconstruction algorithm, adaptive statistical iterative reconstruction (ASIR), to reduce radiation dose at body CT and to provide imaging examples in comparison with low-dose and standard-dose filtered back projection CT.CONCLUSION. The ASIR reconstruction algorithm is a promising technique for providing diagnostic quality CT images at significantly reduced radiation doses.
562 citations
TL;DR: In this paper, a generalized normalization technique for MAR is proposed, which can reduce metal artifacts to a minimum, even close to metal regions, even for patients with dental fillings, which cause most severe artifacts.
Abstract: Purpose: While modern clinical CT scanners under normal circumstances produce high quality images, severe artifacts degrade the image quality and the diagnostic value if metal prostheses or other metal objects are present in the field of measurement. Standard methods for metal artifact reduction (MAR) replace those parts of the projection data that are affected by metal (the so-called metal trace or metal shadow) by interpolation. However, while sinogram interpolation methods efficiently remove metal artifacts, new artifacts are often introduced, as interpolation cannot completely recover the information from the metal trace. The purpose of this work is to introduce a generalized normalization technique for MAR, allowing for efficient reduction of metal artifacts while adding almost no new ones. The method presented is compared to a standard MAR method, as well as MAR using simple length normalization.
Methods: In the first step, metal is segmented in the image domain by thresholding. A 3D forward projection identifies the metal trace in the original projections. Before interpolation, the projections are normalized based on a 3D forward projection of a prior image. This prior image is obtained, for example, by a multithreshold segmentation of the initial image. The original rawdata are divided by the projection data of the prior image and, after interpolation, denormalized again. Simulations and measurements are performed to compare normalized metal artifact reduction (NMAR) to standard MAR with linear interpolation and MAR based on simple length normalization.
Results: Promising results for clinical spiral cone-beam data are presented in this work. Included are patients with hip prostheses, dental fillings, and spine fixation, which were scanned at pitch values ranging from 0.9 to 3.2. Image quality is improved considerably, particularly for metal implants within bone structures or in their proximity. The improvements are evaluated by comparing profiles through images and sinograms for the different methods and by inspecting ROIs. NMAR outperforms both other methods in all cases. It reduces metal artifacts to a minimum, even close to metal regions. Even for patients with dental fillings, which cause most severe artifacts, satisfactory results are obtained with NMAR. In contrast to other methods, NMAR prevents the usual blurring of structures close to metal implants if the metal artifacts are moderate.
Conclusions: NMAR clearly outperforms the other methods for both moderate and severe artifacts. The proposed method reliably reduces metal artifacts from simulated as well as from clinical CT data. Computationally efficient and inexpensive compared to iterative methods, NMAR can be used as an additional step in any conventional sinogram inpainting-based MAR method.
505 citations
TL;DR: Compared with standard FBP reconstruction, an ASIR algorithm improves image quality and has the potential to decrease radiation dose at low-Tube-voltage, high-tube-current multidetector abdominal CT during the late hepatic arterial phase.
Abstract: Our study results demonstrate that an adaptive statistical iterative reconstruction (ASIR) algorithm yields significantly lower noise and improved image quality at low-tube-voltage (80-kVp), high-tube-current (675-mA) multidetector abdominal CT during the late hepatic arterial phase.
489 citations
13 Jun 2010
TL;DR: This work takes point-based real-time structure from motion (SFM) as a starting point, generating accurate 3D camera pose estimates and a sparse point cloud and warp the base mesh into highly accurate depth maps based on view-predictive optical flow and a constrained scene flow update.
Abstract: We present a method which enables rapid and dense reconstruction of scenes browsed by a single live camera. We take point-based real-time structure from motion (SFM) as our starting point, generating accurate 3D camera pose estimates and a sparse point cloud. Our main novel contribution is to use an approximate but smooth base mesh generated from the SFM to predict the view at a bundle of poses around automatically selected reference frames spanning the scene, and then warp the base mesh into highly accurate depth maps based on view-predictive optical flow and a constrained scene flow update. The quality of the resulting depth maps means that a convincing global scene model can be obtained simply by placing them side by side and removing overlapping regions. We show that a cluttered indoor environment can be reconstructed from a live hand-held camera in a few seconds, with all processing performed by current desktop hardware. Real-time monocular dense reconstruction opens up many application areas, and we demonstrate both real-time novel view synthesis and advanced augmented reality where augmentations interact physically with the 3D scene and are correctly clipped by occlusions.
TL;DR: In this article, compressive sensing (CS) methods for tomographic reconstruction of a building complex from the TerraSAR-X spotlight data are presented, and the theory of 4-D (differential, i.e., space-time) CS TomoSAR and compares it with parametric (nonlinear least squares) and nonparametric (singular value decomposition) reconstruction methods.
Abstract: Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. The resolution in the elevation direction depends on the size of the elevation aperture, i.e., on the spread of orbit tracks. Since the orbits of modern meter-resolution spaceborne SAR systems, like TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution reconstruction algorithms are desired. The high anisotropy of the 3-D tomographic resolution element renders the signals sparse in the elevation direction; only a few pointlike reflections are expected per azimuth-range cell. This property suggests using compressive sensing (CS) methods for tomographic reconstruction. This paper presents the theory of 4-D (differential, i.e., space-time) CS TomoSAR and compares it with parametric (nonlinear least squares) and nonparametric (singular value decomposition) reconstruction methods. Super-resolution properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented.
TL;DR: Comparison of image quality and lesion conspicuity on abdominal computed tomographic images acquired with different x-ray tube current-time products and reconstructed with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) techniques shows ASIR lowers noise and improves diagnostic confidence in and conspicuity of subtle abdominal lesions at 8.4 mGy.
Abstract: Our study results show that reduction of radiation dose down to 8.4 mGy is possible when abdominal CT images are reconstructed with 30% adaptive statistical iterative reconstruction (ASIR) blending and reduction of dose down to 4.2 mGy is possible for patients weighing 90 kg or less when images are reconstructed with 50% or 70% ASIR blending.
TL;DR: Compared with routine-dose CT with FBP, abdominal low-doseCT with ASIR significantly reduces noise, thereby permitting diagnostic abdominal examinations with lower radiation doses and diagnostic acceptability comparable to that of routine- dose CT withFBP.
Abstract: OBJECTIVE. The purpose of this article is to retrospectively compare radiation dose, noise, and image quality of abdominal low-dose CT reconstructed with adaptive statistical iterative reconstruction (ASIR) and routine-dose CT reconstructed with filtered back projection (FBP).MATERIALS AND METHODS. Fifty-three patients (37 men and 16 women; mean age, 60.8 years) underwent contrast-enhanced abdominal low-dose CT with 40% ASIR. All 53 patients had previously undergone contrast-enhanced routine-dose CT with FBP. With the scanning techniques masked, two radiologists independently graded images for sharpness, image noise, diagnostic acceptability, and artifacts. Quantitative measures of radiation dose and image noise were also obtained. All results were compared on the basis of body mass index (BMI).RESULTS. The volume CT dose index (CTDIvol), dose–length product, and radiation dose for low-dose CT with ASIR were 17 mGy, 860 mGy, and 13 mSv, respectively, compared with 25 mGy, 1,193 mGy, and 18 mSv for routine...
24 Mar 2010
TL;DR: Block-based random image sampling is coupled with a projection-driven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image, yielding images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation.
Abstract: Recent years have seen significant interest in the paradigm of compressed sensing (CS) which permits, under certain conditions, signals to be sampled at sub-Nyquist rates via linear projection onto a random basis while still enabling exact reconstruction of the original signal. As applied to 2D images, however, CS faces several challenges including a computationally expensive reconstruction process and huge memory required to store the random sampling operator. Recently, several fast algorithms have been developed for CS reconstruction, while the latter challenge was addressed by Gan using a block-based sampling operation as well as projection-based Landweber iterations to accomplish fast CS reconstruction while simultaneously imposing smoothing with the goal of improving the reconstructed-image quality by eliminating blocking artifacts. In this technique, smoothing is achieved by interleaving Wiener filtering with the Landweber iterations, a process facilitated by the relative simple implementation of the Landweber algorithm. In this work, we adopt Gan's basic framework of block-based CS sampling of images coupled with iterative projection-based reconstruction with smoothing. Our contribution lies in that we cast the reconstruction in the domain of recent transforms that feature a highly directional decomposition. These transforms---specifically, contourlets and complex-valued dual-tree wavelets---have shown promise to overcome deficiencies of widely-used wavelet transforms in several application areas. In their application to iterative projection-based CS recovery, we adapt bivariate shrinkage to their directional decomposition structure to provide sparsity-enforcing thresholding, while a Wiener-filter step encourages smoothness of the result. In experimental simulations, we find that the proposed CS reconstruction based on directional transforms outperforms equivalent reconstruction using common wavelet and cosine transforms. Additionally, the proposed technique usually matches or exceeds the quality of total-variation (TV) reconstruction, a popular approach to CS recovery for images whose gradient-based operation also promotes smoothing but runs several orders of magnitude slower than our proposed algorithm.
TL;DR: A unique method for real‐time MRI that reduces image acquisition times to only 20 ms is described, approaching the ultimate limit of MRI technology, and yields high image quality in terms of spatial resolution, signal‐to‐noise ratio and the absence of artifacts.
Abstract: The desire to visualize noninvasively physiological processes at high temporal resolution has been a driving force for the development of MRI since its inception in 1973. In this article, we describe a unique method for real-time MRI that reduces image acquisition times to only 20 ms. Although approaching the ultimate limit of MRI technology, the method yields high image quality in terms of spatial resolution, signal-to-noise ratio and the absence of artifacts. As proposed previously, a fast low-angle shot (FLASH) gradient-echo MRI technique (which allows for rapid and continuous image acquisitions) is combined with a radial encoding scheme (which offers motion robustness and moderate tolerance to data undersampling) and, most importantly, an iterative image reconstruction by regularized nonlinear inversion (which exploits the advantages of parallel imaging with multiple receiver coils). In this article, the extension of regularization and filtering to the temporal domain exploits consistencies in successive data acquisitions and thereby enhances the degree of radial undersampling in a hitherto unexpected manner by one order of magnitude. The results obtained for turbulent flow, human speech production and human heart function demonstrate considerable potential for real-time MRI studies of dynamic processes in a wide range of scientific and clinical settings. Copyright © 2010 John Wiley & Sons, Ltd.
TL;DR: ASIR technique allows radiation dose reduction for abdominal CT examinations whereas improving image noise compared with the FBP technique, which however, was mild and did not affect the diagnostic acceptability of images.
Abstract: Purpose:To assess radiation dose reduction for abdominal computed tomography (CT) examinations with adaptive statistical iterative reconstruction (ASIR) technique.Materials and Methods:With institutional review board approval, retrospective review of weight adapted abdominal CT exams were performed
TL;DR: This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal.
Abstract: Compressed sensing (CS) holds considerable promise to accelerate the data acquisition in magnetic resonance imaging by exploiting signal sparsity. Prior knowledge about the signal can be exploited in some applications to choose an appropriate sparsifying transform. This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal. The approach is presented and evaluated in simulations and in in vivo T(1) and T(2) mapping experiments in the brain. Accurate T(1) and T(2) maps are obtained from highly reduced data. This model-based reconstruction could also be applied to other MR parameter mapping applications like diffusion and perfusion imaging.
TL;DR: Investigation and evaluation of image reconstruction from data collected at projection views significantly fewer than what is used in current CBCT imaging demonstrate that, depending upon scanning conditions and imaging tasks, algorithms based on constrained TV-minimization can reconstruct images of potential utility from a small fraction of the data used in typical, currentCBCT applications.
Abstract: Flat-panel-detector x-ray cone-beam computed tomography (CBCT) is used in a rapidly increasing host of imaging applications, including image-guided surgery and radiotherapy. The purpose of the work is to investigate and evaluate image reconstruction from data collected at projection views significantly fewer than what is used in current CBCT imaging. Specifically, we carried out imaging experiments using a bench-top CBCT system that was designed to mimic imaging conditions in image-guided surgery and radiotherapy; we applied an image reconstruction algorithm based on constrained total-variation (TV)-minimization to data acquired with sparsely sampled view-angles and conducted extensive evaluation of algorithm performance. Results of the evaluation studies demonstrate that, depending upon scanning conditions and imaging tasks, algorithms based on constrained TV-minimization can reconstruct images of potential utility from a small fraction of the data used in typical, current CBCT applications. A practical implication of the study is that the optimization of algorithm design and implementation can be exploited for considerably reducing imaging effort and radiation dose in CBCT.
13 Jun 2010
TL;DR: This paper proposes an approach to extend edge-directed super-resolution to include detail from an image/texture example provided by the user (e.g., from the Internet), and can achieve quality results at very large magnification, which is often problematic for both edge- directed and learning-based approaches.
Abstract: Edge-directed image super resolution (SR) focuses on ways to remove edge artifacts in upsampled images. Under large magnification, however, textured regions become blurred and appear homogenous, resulting in a super-resolution image that looks unnatural. Alternatively, learning-based SR approaches use a large database of exemplar images for “hallucinating” detail. The quality of the upsampled image, especially about edges, is dependent on the suitability of the training images. This paper aims to combine the benefits of edge-directed SR with those of learning-based SR. In particular, we propose an approach to extend edge-directed super-resolution to include detail from an image/texture example provided by the user (e.g., from the Internet). A significant benefit of our approach is that only a single exemplar image is required to supply the missing detail – strong edges are obtained in the SR image even if they are not present in the example image due to the combination of the edge-directed approach. In addition, we can achieve quality results at very large magnification, which is often problematic for both edge-directed and learning-based approaches.
TL;DR: A novel technique based on a slice acquisition model, which enables the reconstruction of a volumetric image from multiple-scan slice acquisitions and a robust M-estimation solution which minimizes a robust error norm function between the model-generated slices and the acquired slices are developed.
Abstract: Fast magnetic resonance imaging slice acquisition techniques such as single shot fast spin echo are routinely used in the presence of uncontrollable motion These techniques are widely used for fetal magnetic resonance imaging (MRI) and MRI of moving subjects and organs Although high-quality slices are frequently acquired by these techniques, inter-slice motion leads to severe motion artifacts that are apparent in out-of-plane views Slice sequential acquisitions do not enable 3-D volume representation In this study, we have developed a novel technique based on a slice acquisition model, which enables the reconstruction of a volumetric image from multiple-scan slice acquisitions The super-resolution volume reconstruction is formulated as an inverse problem of finding the underlying structure generating the acquired slices We have developed a robust M-estimation solution which minimizes a robust error norm function between the model-generated slices and the acquired slices The accuracy and robustness of this novel technique has been quantitatively assessed through simulations with digital brain phantom images as well as high-resolution newborn images We also report here successful application of our new technique for the reconstruction of volumetric fetal brain MRI from clinically acquired data
TL;DR: This paper derives the 1-D MPI signal, resolution, bandwidth requirements, signal-to-noise ratio (SNR), specific absorption rate, and slew rate limitations, and concludes with experimental data measuring the point spread function for commercially available SPIO nanoparticles.
Abstract: The magnetic particle imaging (MPI) imaging process is a new method of medical imaging with great promise. In this paper we derive the 1-D MPI signal, resolution, bandwidth requirements, signal-to-noise ratio (SNR), specific absorption rate, and slew rate limitations. We conclude with experimental data measuring the point spread function for commercially available SPIO nanoparticles and a demonstration of the principles behind 1-D imaging using a static offset field. Despite arising from the nonlinear temporal response of a magnetic nanoparticle to a changing magnetic field, the imaging process is linear in the magnetization distribution and can be described as a convolution. Reconstruction in one dimension is exact and has a well-behaved quasi-Lorentzian point spread function. The spatial resolution improves cubically with increasing diameter of the SPIO domain, inverse to absolute temperature, linearly with saturation magnetization, and inversely with gradient. The bandwidth requirements approach a megahertz for reasonable imaging parameters and millimeter scale resolutions, and the SNR increases with the scanning rate. The limit to SNR as we scale MPI to human sizes will be patient heating. SAR and magnetostimulation limits give us surprising relations between optimal scanning speeds and scanning frequency for different types of scanners.
TL;DR: An edge-preserving maximum a posteriori (MAP) estimation based super-resolution algorithm using a weighted directional Markov image prior model for a ROI from more than one low-resolution surveillance image is proposed.
TL;DR: In this article, a method to compensate for the effect of acoustic absorption on the measured time domain signals is described, where the reconstruction is regularized by filtering the absorption and dispersion terms in the spatial frequency domain using a Tukey window.
Abstract: The reconstruction of photoacoustic images typically neglects the effect of acoustic absorption on the measured time domain signals. Here, a method to compensate for acoustic absorption in photoacoustic tomography is described. The approach is based on time-reversal image reconstruction and an absorbing equation of state which separately accounts for acoustic absorption and dispersion following a frequency power law. Absorption compensation in the inverse problem is achieved by reversing the absorption proportionality coefficient in sign but leaving the equivalent dispersion parameter unchanged. The reconstruction is regularized by filtering the absorption and dispersion terms in the spatial frequency domain using a Tukey window. This maintains the correct frequency dependence of these parameters within the filter pass band. The method is valid in one, two and three dimensions, and for arbitrary power law absorption parameters. The approach is verified through several numerical experiments. The reconstruction of a carbon fibre phantom and the vasculature in the abdomen of a mouse are also presented. When absorption compensation is included, a general improvement in the image magnitude and resolution is seen, particularly for deeper features.
TL;DR: A fast model-based inversion algorithm for quantitative 2-D and 3-D optoacoustic tomography based on an accurate and efficient forward model, which eliminates the need for regularization in the inversion process while providing modeling flexibility essential for quantitative image formation.
Abstract: We present a fast model-based inversion algorithm for quantitative 2-D and 3-D optoacoustic tomography. The algorithm is based on an accurate and efficient forward model, which eliminates the need for regularization in the inversion process while providing modeling flexibility essential for quantitative image formation. The resulting image-reconstruction method eliminates stability problems encountered in previously published model-based techniques and, thus, enables performing image reconstruction in real time. Our model-based framework offers a generalization of the forward solution to more comprehensive optoacoustic propagation models, such as including detector frequency response, without changing the inversion procedure. The reconstruction speed and other algorithmic performances are demonstrated using numerical simulation studies and experimentally on tissue-mimicking optically heterogeneous phantoms and small animals. In the experimental examples, the model-based reconstructions manifested correctly the effect of light attenuation through the objects and did not suffer from the artifacts which usually afflict the commonly used filtered backprojection algorithms, such as negative absorption values.
TL;DR: It is shown that the alternating direction method is very efficient for solving image restoration and reconstruction problems and allows us to solve problems of image restoration, impulse noise removal, inpainting, and image cartoon+texture decomposition.
Abstract: In this paper, we study alternating direction methods for solving constrained total-variation image restoration and reconstruction problems. Alternating direction methods can be implementable variants of the classical augmented Lagrangian method for optimization problems with separable structures and linear constraints. The proposed framework allows us to solve problems of image restoration, impulse noise removal, inpainting, and image cartoon+texture decomposition. As the constrained model is employed, we need only to input the noise level, and the estimation of the regularization parameter is not required in these imaging problems. Experimental results for such imaging problems are presented to illustrate the effectiveness of the proposed method. We show that the alternating direction method is very efficient for solving image restoration and reconstruction problems.
TL;DR: The use of iterative algorithms for model-based MR image reconstruction based on appropriate models can improve image quality, but at the price of increased computation.
Abstract: Magnetic resonance imaging (MRI) is a sophisticated and versatile medical imaging modality. The inverse FFT has served the MR community very well as the conventional image reconstruction method for k-space data with full Cartesian sampling. And for well sampled non-Cartesian data, the gridding method with appropriate density compensation factors is fast and effective. But when only under-sampled data is available, or when non-Fourier physical effects like field inhomogeneity are important, then gridding/FFT methods for image reconstruction are suboptimal, and iterative algorithms based on appropriate models can improve image quality, rat the price of increased computation. This article reviews the use of iterative algorithms for model-based MR image reconstruction.
TL;DR: A novel data acquisition scheme and an imaging algorithm for TWI radar based on compressive sensing, which states that a signal having a sparse representation can be reconstructed from a small number of nonadaptive randomized projections by solving a tractable convex program is presented.
Abstract: To achieve high-resolution 2-D images, through-wall imaging (TWI) radar with ultra-wideband and long antenna arrays faces considerable technical challenges such as a prolonged data collection time, a huge amount of data, and a high hardware complexity. This paper presents a novel data acquisition scheme and an imaging algorithm for TWI radar based on compressive sensing (CS), which states that a signal having a sparse representation can be reconstructed from a small number of nonadaptive randomized projections by solving a tractable convex program. Instead of measuring all spatial-frequency data, a few samples, by employing an overcomplete dictionary, are sufficient to obtain reliable target space images even at high noise levels. Preliminary simulated and experimental results show that the proposed algorithm outperforms the conventional delay-and-sum beamforming method even though many fewer CS measurements are used.
21 Jun 2010
TL;DR: By avoiding explicit reconstruction, this work is able to perform skeleton-driven topology repair of acquired point clouds in the presence of large amounts of missing data and show that the curve skeletons the authors extract provide an intuitive and easy-to-manipulate structure for effective topology modification, leading to more faithful surface reconstruction.
Abstract: We present an algorithm for curve skeleton extraction via Laplacian-based contraction. Our algorithm can be applied to surfaces with boundaries, polygon soups, and point clouds. We develop a contraction operation that is designed to work on generalized discrete geometry data, particularly point clouds, via local Delaunay triangulation and topological thinning. Our approach is robust to noise and can handle moderate amounts of missing data, allowing skeleton-based manipulation of point clouds without explicit surface reconstruction. By avoiding explicit reconstruction, we are able to perform skeleton-driven topology repair of acquired point clouds in the presence of large amounts of missing data. In such cases, automatic surface reconstruction schemes tend to produce incorrect surface topology. We show that the curve skeletons we extract provide an intuitive and easy-to-manipulate structure for effective topology modification, leading to more faithful surface reconstruction.
TL;DR: It is found that high quality CBCT image can be reconstructed from undersampled and potentially noisy projection data by using the proposed method, and it is demonstrated that compressed sensing outperforms the traditional algorithm when dealing with sparse, and possibly noisy, CBCT projection views.
Abstract: Purpose: This article considers the problem of reconstructingcone-beam computed tomography(CBCT)images from a set of undersampled and potentially noisy projection measurements. Methods: The authors cast the reconstruction as a compressed sensing problem based on l 1 norm minimization constrained by statistically weighted least-squares of CBCT projection data. For accurate modeling, the noise characteristics of the CBCT projection data are used to determine the relative importance of each projection measurement. To solve the compressed sensing problem, the authors employ a method minimizing total-variation norm, satisfying a prespecified level of measurement consistency using a first-order method developed by Nesterov. Results: The method converges fast to the optimal solution without excessive memory requirement, thanks to the method of iterative forward and back-projections. The performance of the proposed algorithm is demonstrated through a series of digital and experimental phantom studies. It is found a that high quality CBCTimage can be reconstructed from undersampled and potentially noisy projection data by using the proposed method. Both sparse sampling and decreasing x-ray tube current (i.e., noisy projection data) lead to the reduction of radiationdose in CBCTimaging. Conclusions: It is demonstrated that compressed sensing outperforms the traditional algorithm when dealing with sparse, and potentially noisy, CBCT projection views.