Showing papers on "Iterative reconstruction published in 2004"

Fast and robust multiframe super resolution

Abstract: Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate approach using L/sub 1/ norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.

Artifacts in CT: Recognition and Avoidance

Abstract: Artifacts can seriously degrade the quality of computed tomographic (CT) images, sometimes to the point of making them diagnostically unusable. To optimize image quality, it is necessary to understand why artifacts occur and how they can be prevented or suppressed. CT artifacts originate from a range of sources. Physics-based artifacts result from the physical processes involved in the acquisition of CT data. Patient-based artifacts are caused by such factors as patient movement or the presence of metallic materials in or on the patient. Scanner-based artifacts result from imperfections in scanner function. Helical and multisection technique artifacts are produced by the image reconstruction process. Design features incorporated into modern CT scanners minimize some types of artifacts, and some can be partially corrected by the scanner software. However, in many instances, careful patient positioning and optimum selection of scanning parameters are the most important factors in avoiding CT artifacts.

A unified treatment of some iterative algorithms in signal processing and image reconstruction

Abstract: Let T be a (possibly nonlinear) continuous operator on Hilbert space . If, for some starting vector x, the orbit sequence {Tkx,k = 0,1,...} converges, then the limit z is a fixed point of T; that is, Tz = z. An operator N on a Hilbert space is nonexpansive?(ne) if, for each x and y in , Even when N has fixed points the orbit sequence {Nkx} need not converge; consider the example N = ?I, where I denotes the identity operator. However, for any the iterative procedure defined by converges (weakly) to a fixed point of N whenever such points exist. This is the Krasnoselskii?Mann (KM) approach to finding fixed points of ne operators. A wide variety of iterative procedures used in signal processing and image reconstruction and elsewhere are special cases of the KM iterative procedure, for particular choices of the ne operator N. These include the Gerchberg?Papoulis method for bandlimited extrapolation, the SART algorithm of Anderson and Kak, the Landweber and projected Landweber algorithms, simultaneous and sequential methods for solving the convex feasibility problem, the ART and Cimmino methods for solving linear systems of equations, the CQ algorithm for solving the split feasibility problem and Dolidze's procedure for the variational inequality problem for monotone operators.

Emission Tomography: The Fundamentals of PET and SPECT

Abstract: PET and SPECT are two of todays most important medical-imaging methods, providing images that reveal subtle information about physiological processes in humans and animals. "Emission Tomography: The Fundamentals of PET and SPECT" explains the physics and engineering principles of these important functional-imaging methods. The technology of emission tomography is covered in detail, including historical origins, scientific and mathematical foundations, imaging systems and their components, image reconstruction and analysis, simulation techniques, and clinical and laboratory applications. The book describes the state of the art of emission tomography, including all facets of conventional SPECT and PET, as well as contemporary topics, such as iterative image reconstruction, small-animal imaging, and PET/CT systems. This book is intended as a textbook and reference resource for graduate students, researchers, medical physicists, biomedical engineers, and professional engineers and physicists in the medical-imaging industry. Thorough tutorials of fundamental and advanced topics are presented by dozens of the leading researchers in PET and SPECT. SPECT has long been a mainstay of clinical imaging, and PET is now one of the worlds fastest growing medical imaging techniques, owing to its dramatic contributions to cancer imaging and other applications. "Emission Tomography: The Fundamentals of PET and SPECT" is an essential resource for understanding the technology of SPECT and PET, the most widely used forms of molecular imaging. The features are: contains thorough tutorial treatments, coupled with coverage of advanced topics; three of the four holders of the prestigious Institute of Electrical and Electronics Engineers Medical Imaging Scientist Award are chapter contributors; and includes color artwork.

Fundamental limits of reconstruction-based superresolution algorithms under local translation

Abstract: Superresolution is a technique that can produce images of a higher resolution than that of the originally captured ones. Nevertheless, improvement in resolution using such a technique is very limited in practice. This makes it significant to study the problem: "Do fundamental limits exist for Superresolution?" In this paper, we focus on a major class of superresolution algorithms, called the reconstruction-based algorithms, which compute high-resolution images by simulating the image formation process. Assuming local translation among low-resolution images, this paper is the first attempt to determine the explicit limits of reconstruction-based algorithms, under both real and synthetic conditions. Based on the perturbation theory of linear systems, we obtain the superresolution limits from the conditioning analysis of the coefficient matrix. Moreover, we determine the number of low-resolution images that are sufficient to achieve the limit. Both real and synthetic experiments are carried out to verify our analysis.

Three-dimensional volumetric object reconstruction using computational integral imaging

Abstract: We propose a three-dimensional (3D) imaging technique that can sense a 3D scene and computationally reconstruct it as a 3D volumetric image. Sensing of the 3D scene is carried out by obtaining elemental images optically using a pickup microlens array and a detector array. Reconstruction of volume pixels of the scene is accomplished by computationally simulating optical reconstruction according to ray optics. The entire pixels of the recorded elemental images contribute to volumetric reconstruction of the 3D scene. Image display planes at arbitrary distances from the display microlens array are computed and reconstructed by back propagating the elemental images through a computer synthesized pinhole array based on ray optics. We present experimental results of 3D image sensing and volume pixel reconstruction to test and verify the performance of the algorithm and the imaging system. The volume pixel values can be used for 3D image surface reconstruction.

Reconstructions in limited-view thermoacoustic tomography

Abstract: The limited-view problem is studied for thermoacoustic tomography, which is also referred to as photoacoustic or optoacoustic tomography depending on the type of radiation for the induction of acoustic waves. We define a “detection region,” within which all points have sufficient detection views. It is explained analytically and shown numerically that the boundaries of any objects inside this region can be recovered stably. Otherwise some sharp details become blurred. One can identify in advance the parts of the boundaries that will be affected if the detection view is insufficient. If the detector scans along a circle in a two-dimensional case, acquiring a sufficient view might require covering more than a π-, or less than a π-arc of the trajectory depending on the position of the object. Similar results hold in a three-dimensional case. In order to support our theoretical conclusions, three types of reconstruction methods are utilized: a filtered backprojection (FBP) approximate inversion, which is shown to work well for limited-view data, a local-tomography-type reconstruction that emphasizes sharp details (e.g., the boundaries of inclusions), and an iterative algebraic truncated conjugate gradient algorithm used in conjunction with FBP. Computations are conducted for both numerically simulated and experimental data. The reconstructions confirm our theoretical predictions.

Self-gated cardiac cine MRI

Abstract: The need for ECG gating presents many difficulties in cardiac magnetic resonance imaging (CMRI) Real-time imaging techniques eliminate the need for ECG gating in cine CMRI, but they cannot offer the spatial and temporal resolution provided by segmented acquisition techniques Previous MR signal-based techniques have demonstrated an ability to provide cardiac gating information; however, these techniques result in decreased imaging efficiency The purpose of this work was to develop a new "self-gated" (SG) acquisition technique that eliminates these efficiency deficits by extracting the motion synchronization signal directly from the same MR signals used for image reconstruction Three separate strategies are proposed for deriving the SG signal from data acquired using radial k-space sampling: echo peak magnitude, kymogram, and 2D correlation The SG techniques were performed on seven normal volunteers A comparison of the results showed that they provided cine image series with no significant differences in image quality compared to that obtained with conventional ECG gating techniques SG techniques represent an important practical advance in clinical MRI because they enable the acquisition of high temporal and spatial resolution cardiac cine images without the need for ECG gating and with no loss in imaging efficiency

Exact image reconstruction on PI-lines from minimum data in helical cone-beam CT.

Abstract: The development of accurate and efficient algorithms for image reconstruction from helical cone-beam projections remains a subject of active research. In the last few years, a number of quasi-exact and exact algorithms have been developed. Among them, the Katsevich algorithms are of filtered backprojection type and thus possess computational advantages over other existing exact algorithms. In this work, we propose an alternative approach to reconstructing exactly an image from helical cone-beam projections. Based on this approach, we develop an algorithm that requires less data than do the existing quasi-exact and exact algorithms, including the Katsevich algorithms. Our proposed algorithm is also of filtered backprojection type with one-dimensional filtering performed along a PI-line in image space. Therefore, it is (at least) computationally as efficient as the Katsevich algorithms. We have performed a preliminary numerical study to demonstrate and validate the proposed algorithm using computer-simulation data. The implication of the proposed approach and algorithm appears to be significant in that they can naturally address the long object problem as well as the super-short scan problem and, most importantly, in that they provide the opportunity to reconstruct images within any selected region of interest from minimum data, allowing the use of detector with a reduced size, the selection of a minimum number of rotation angles and thus the reduction of radiation dose delivered to the imaged subject.

Parameter-optimized digital holographic microscope for high-resolution living-cell analysis

Abstract: A parameter-optimized off-axis setup for digital holographic microscopy is presented for simultaneous, high-resolution, full-field quantitative amplitude and quantitative phase-contrast microscopy and the detection of changes in optical path length in transparent objects, such as undyed living cells. Numerical reconstruction with the described nondiffractive reconstruction method, which suppresses the zero order and the twin image, requires a mathematical model of the phase-difference distribution between the object wave and the reference wave in the hologram plane. Therefore an automated algorithm is explained that determines the parameters of the mathematical model by carrying out the discrete Fresnel transform. Furthermore the relationship between the axial position of the object and the reconstruction distance, which is required for optimization of the lateral resolution of the holographic images, is derived. The lateral and the axial resolutions of the system are discussed and quantified by application to technical objects and to living cells.

A two-step Hilbert transform method for 2D image reconstruction.

Abstract: The paper describes a new accurate two-dimensional (2D) image reconstruction method consisting of two steps. In the first step, the backprojected image is formed after taking the derivative of the parallel projection data. In the second step, a Hilbert filtering is applied along certain lines in the differentiated backprojection (DBP) image. Formulae for performing the DBP step in fanbeam geometry are also presented. The advantage of this two-step Hilbert transform approach is that in certain situations, regions of interest (ROIs) can be reconstructed from truncated projection data. Simulation results are presented that illustrate very similar reconstructed image quality using the new method compared to standard filtered backprojection, and that show the capability to correctly handle truncated projections. In particular, a simulation is presented of a wide patient whose projections are truncated laterally yet for which highly accurate ROI reconstruction is obtained.

A comparison of reconstruction algorithms for breast tomosynthesis.

Abstract: Three algorithms for breast tomosynthesis reconstruction were compared in this paper, including (1) a back-projection (BP) algorithm (equivalent to the shift-and-add algorithm), (2) a Feldkamp filtered back-projection (FBP) algorithm, and (3) an iterative Maximum Likelihood (ML) algorithm. Our breast tomosynthesis system acquires 11 low-dose projections over a 50 degree angular range using an a-Si (CsI:Tl) flat-panel detector. The detector was stationary during the acquisition. Quality metrics such as signal difference to noise ratio (SDNR) and artifact spread function (ASF) were used for quantitative evaluation of tomosynthesis reconstructions. The results of the quantitative evaluation were in good agreement with the results of the qualitative assessment. In patient imaging, the superimposed breast tissues observed in two-dimensional (2D) mammograms were separated in tomosynthesis reconstructions by all three algorithms. It was shown in both phantom imaging and patient imaging that the BP algorithm provided the best SDNR for low-contrast masses but the conspicuity of the feature details was limited by interplane artifacts; the FBP algorithm provided the highest edge sharpness for microcalcifications but the quality of masses was poor; the information of both the masses and the microcalcifications were well restored with balanced quality by the ML algorithm, superior to the results from the other two algorithms.

X-ray scatter correction algorithm for cone beam CT imaging

Abstract: Developing and optimizing an x-ray scatter control and reduction technique is one of the major challenges for cone beam computed tomography (CBCT) because CBCT will be much less immune to scatter than fan-beam CT. X-ray scatter reduces image contrast, increases image noise and introduces reconstruction error into CBCT. To reduce scatter interference, a practical algorithm that is based upon the beam stop array technique and image sequence processing has been developed on a flat panel detector-based CBCT prototype scanner. This paper presents a beam stop array-based scatter correction algorithm and the evaluation results through phantom studies. The results indicate that the beam stop array-based scatter correction algorithm is practical and effective to reduce and correct x-ray scatter for a CBCT imaging task.

Prediction of permeability for porous media reconstructed using multiple-point statistics

Abstract: To predict multiphase flow through geologically realistic porous media, it is necessary to have a three-dimensional (3D) representation of the pore space. We use multiple-point statistics based on two-dimensional (2D) thin sections as training images to generate geologically realistic 3D pore-space representations. Thin-section images can provide multiple-point statistics, which describe the statistical relation between multiple spatial locations and use the probability of occurrence of particular patterns. Assuming that the medium is isotropic, a 3D image can be generated that preserves typical patterns of the void space seen in the thin sections. The method is tested on Berea sandstone for which a 3D image from micro-CT (Computerized Tomography) scanning is available and shows that the use of multiple-point statistics allows the long-range connectivity of the structure to be preserved, in contrast to two-point statistics methods that tend to underestimate the connectivity. Furthermore, a high-resolution 2D thin-section image of a carbonate reservoir rock is used to reconstruct 3D structures by the proposed method. The permeabilities of the statistical images are computed using the lattice-Boltzmann method (LBM). The results are similar to the measured values, to the permeability directly computed on the micro-CT image for Berea and to predictions using analysis of the 2D images and the effective medium approximation.

Nonlinear sinogram smoothing for low-dose X-ray CT

Abstract: When excessive quantum noise is present in extremely low dose X-ray CT imaging, statistical properties of the data has to be considered to achieve a satisfactory image reconstruction. Statistical iterative reconstruction with accurate modeling of the noise, rather than a filtered back-projection (FBP) with low-pass filtering, is one way to deal with the problem. Estimating a noise-free sinogram to satisfy the FBP reconstruction for the Radon transform is another way. The benefits of the latter include a higher computation efficiency, more uniform spatial resolution in the reconstructed image, and less modification of the current machine configurations. In a clinic X-ray CT system, the acquired raw data must be calibrated, in addition to the logarithmic transform, to achieve the high diagnostic image quality. The calibrated projection data or sinogram no longer follow a compound Poisson distribution in general, but are close to a Gaussian distribution with signal-dependent variance. In this paper, we first investigated a relatively accurate statistical model for the sinogram data, based on several phantom experiments. Then we developed a penalized likelihood method to smooth the sinogram, which led to a set of nonlinear equations that can be solved by iterated conditional mode (ICM) algorithm within a reasonable computing time. The method was applied to several experimental datasets acquired at 120 kVp, 10 mA/20 mA/50 mA protocols with a GE HiSpeed multi-slice detector CT scanner and demonstrated a significant noise suppression without noticeable sacrifice of the spatial resolution.

Controlling image size as a function of distance and wavelength in Fresnel-transform reconstruction of digital holograms

Abstract: A method for controlling the size of amplitude and phase images reconstructed from digital holograms by the Fresnel-transform method is proposed and demonstrated. The method can provide a constant reconstruction pixel width in the reconstructed image plane, independent of the recording and reconstruction distance. The proposed method makes it possible to maintain the size of an object for a sequence of digital holograms recorded at different distances and, therefore, to subtract phase maps for an object recorded at different distances. Furthermore, the method solves the problem of superimposition in multiwavelength digital holography for color display and holographic interferometry applications.

Reconstruction of a high-resolution image on a compound-eye image-capturing system.

Abstract: The authors have proposed an architecture for a compact image-capturing system called TOMBO (thin observation module by bound optics), which uses compound-eye imaging for a compact hardware configuration [Appl. Opt. 40, 1806 (2001)]. The captured compound image is decomposed into a set of unit images, then the pixels in the unit images are processed with digital processing to retrieve the target image. A new method for high-resolution image reconstruction, called a pixel rearrange method, is proposed. The relation between the target object and the captured signals is estimated and utilized to rearrange the original pixel information. Experimental results show the effectiveness of the proposed method. In the experimental TOMBO system, the resolution obtained is four times higher than that of the unit image that did not undergo reconstruction processing.

The influence of antiscatter grids on soft-tissue detectability in cone-beam computed tomography with flat-panel detectors.

Abstract: The influence of antiscatter x-ray grids on image quality in cone-beam computed tomography (CT) is evaluated through broad experimental investigation for various anatomical sites (head and body), scatter conditions (scatter-to-primary ratio (SPR) ranging from approximately 10% to 150%), patient dose, and spatial resolution in three-dimensional reconstructions. Studies involved linear grids in combination with a flat-panel imager on a system for kilovoltage cone-beam CT imaging and guidance of radiation therapy. Grids were found to be effective in reducing x-ray scatter "cupping" artifacts, with heavier grids providing increased image uniformity. The system was highly robust against ring artifacts that might arise in CT reconstructions as a result of gridline shadows in the projection data. The influence of grids on soft-tissue detectability was evaluated quantitatively in terms of absolute contrast, voxel noise, and contrast-to-noise ratio (CNR) in cone-beam CT reconstructions of 16 cm "head" and 32 cm "body" cylindrical phantoms. Imaging performance was investigated qualitatively in observer preference tests based on patient images (pelvis, abdomen, and head-and-neck sites) acquired with and without antiscatter grids. The results suggest that although grids reduce scatter artifacts and improve subject contrast, there is little strong motivation for the use of grids in cone-beam CT in terms of CNR and overall image quality under most circumstances. The results highlight the tradeoffs in contrast and noise imparted by grids, showing improved image quality with grids only under specific conditions of high x-ray scatter (SPR> 100%), high imaging dose (Dcenter> 2 cGy), and low spatial resolution (voxel size > or = 1 mm).

Conceptual design of a proton computed tomography system for applications in proton radiation therapy

Abstract: Proton computed tomography (pCT) has the potential to improve the accuracy of dose calculations for proton treatment planning, and will also be useful for pretreatment verification of patient positioning relative to the proton beam. A design study was performed to define the optimal approach to a pCT system based on specifications for applications in proton therapy. Conceptual and detailed design of a pCT system is presented; the system consists of a silicon-based particle tracking system and a crystal calorimeter to measure energy loss of individual protons. We discuss the formation of pCT images based on the reconstruction of volume electron density maps and the suitability of analytic and statistical algorithms for image reconstruction.

Time Reversal and Its Application to Tomography with Diffracting Sources

Abstract: An exact time-domain method is proposed to time reverse a transient scalar wave using only the field measured on an arbitrary closed surface enclosing the initial source. Under certain conditions, a time-reversed field can be approximated by retransmitting the measured signals in a reversed temporal order. Exact reconstruction for three-dimensional broadband diffraction tomography (a linearized inverse scattering problem) is proposed by time-reversing the measured field back to the time when each secondary source is excited. The algorithm is verified by a numerical simulation. Extension to the case using Green's function in a heterogeneous medium is discussed.

Analysis of 3-D myocardial motion in tagged MR images using nonrigid image registration

Abstract: Tagged magnetic resonance imaging (MRI) is unique in its ability to noninvasively image the motion and deformation of the heart in vivo, but one of the fundamental reasons limiting its use in the clinical environment is the absence of automated tools to derive clinically useful information from tagged MR images. In this paper, we present a novel and fully automated technique based on nonrigid image registration using multilevel free-form deformations (MFFDs) for the analysis of myocardial motion using tagged MRI. The novel aspect of our technique is its integrated nature for tag localization and deformation field reconstruction using image registration and voxel based similarity measures. To extract the motion field within the myocardium during systole we register a sequence of images taken during systole to a set of reference images taken at end-diastole, maximizing the normalized mutual information between the images. We use both short-axis and long-axis images of the heart to estimate the full four-dimensional motion field within the myocardium. We also present validation results from data acquired from twelve volunteers.

A novel reconstruction algorithm to extend the CT scan field-of-view.

Abstract: For various reasons, a projection dataset acquired on a computed tomography (CT) scanner can be truncated. That is, a portion of the scanned object is positioned outside the scan field-of-view (SFOV) and the line integrals corresponding to those regions are not measured. A projection truncation problem causes imaging artifacts that lead to suboptimal image quality. In this paper, we propose a reconstruction algorithm that enables an adequate estimation of the projection outside the SFOV. We make use of the fact that the total attenuation of each ideal projection in a parallel sampling geometry remains constant over views. We use the magnitudes and slopes of the projection samples at the location of truncation to estimate water cylinders that can best fit to the projection data outside the SFOV. To improve the robustness of the algorithm, continuity constraints are placed on the fitting parameters. Extensive phantom and patient experiments were conducted to test the robustness and accuracy of the proposed algorithm.

Algorithm for reconstruction of digital holograms with adjustable magnification.

Abstract: A new algorithm that allows for reconstruction of digital holograms with adjustable magnification is proposed. The algorithm involves two reconstruction steps implemented by a conventional single Fourier-transform algorithm. The advantages of the algorithm lie in its adaptability to various object sizes and recording distances as well as in its capability to maintain the pitch of a reconstructed image, independent of the reconstruction distance and wavelength for objects larger than a CCD. The feasibility of the algorithm is demonstrated by experiments. The algorithm is especially useful for reconstructing color holograms and for metrological applications.

Adaptive finite element based tomography for fluorescence optical imaging in tissue.

Abstract: A three-dimensional fluorescence-enhanced optical tomography scheme based upon an adaptive finite element formulation is developed and employed to reconstruct fluorescent targets in turbid media from frequency-domain measurements made in reflectance geometry using area excitation illumination. The algorithm is derived within a Lagrangian framework by treating the photon diffusion model as a constraint to the optimization problem. Adaptively refined meshes are used to separately discretize maps of the forward/adjoint variables and the unknown parameter of fluorescent yield. A truncated Gauss-Newton method with simple bounds is used as the optimization method. Fluorescence yield reconstructions from simulated measurement data with added Gaussian noise are demonstrated for one and two fluorescent targets embedded within a 512ml cubical tissue phantom. We determine the achievable resolution for the area-illumination/area-detection reflectance measurement geometry by reconstructing two 0.4cm diameter spherical targets placed at at a series of decreasing lateral spacings. The results show that adaptive techniques enable the computationally efficient and stable solution of the inverse imaging problem while providing the resolution necessary for imaging the signals from molecularly targeting agents.

High-Resolution, Real-time 3D Shape Acquisition

Abstract: In this paper we describe a high-resolution, real-time 3D shape acquisition system based on structured light techniques. This system uses a color pattern whose RGB channels are coded with either sinusoidal or trapezoidal fringe patterns. When projected by a modified DLP projector (color filters removed), this color pattern results in three grayscale patterns projected sequentially at a frequency of 240 Hz. A high-speed B/W CCD camera synchronized with the projector captures the three images, from which the 3D shape of the object is reconstructed. A color CCD camera is also used to capture images for texture mapping. The maximum 3D shape acquisition speed is 120 Hz (532 × 500 pixels), which is high enough for capturing the 3D shapes of moving objects. Two coding methods, sinusoidal phase-shifting method and trapezoidal phase-shifting method, were tested and results with good accuracy were obtained. The trapezoidal phase-shifting algorithm also makes real-time 3D reconstruction possible.

Three-dimensional nonlinear image reconstruction for microwave biomedical imaging

Abstract: Active microwave imaging has attracted significant interests in biomedical applications, in particular for breast imaging. However, the high electrical contrasts in breast tissue also increases the difficulty of forming an accurate image because of the increased multiple scattering. To model such strong three-dimensional (3-D) multiple scattering effects in biomedical imaging applications, we develop a full 3-D inverse scattering algorithm based on the combination of the contrast source inversion and the fast Fourier transform algorithm. Numerical results show that our algorithm can accurately invert for the high-contrast media in breast tissue.

Sagittal laser optical tomography for imaging of rheumatoid finger joints.

Abstract: We present a novel optical tomographic imaging system that was designed to determine two-dimensional spatial distribution of optical properties in a sagittal plane through finger joints. The system incorporates a single laser diode and a single silicon photodetector into a scanning device that records spatially resolved light intensities as they are transmitted through a finger. These data are input to a model-based iterative image reconstruction (MOBIIR) scheme, which uses the equation of radiative transfer (ERT) as a forward model for light propagation through tissue. We have used this system to obtain tomographic images of six proximal interphalangeal finger joints from two patients with rheumatoid arthritis. The optical images were compared to clinical symptoms and ultrasound images.

Use of spherical harmonic deconvolution methods to compensate for nonlinear gradient effects on MRI images.

Abstract: Spatial encoding in MR techniques is achieved by sampling the signal as a function of time in the presence of a magnetic field gradient. The gradients are assumed to generate a linear magnetic field gradient, and typical image reconstruction relies upon this approximation. However, high-speed gradients in the current generation of MRI scanners often sacrifice linearity for improvements in speed. Such nonlinearity results in distorted images. The problem is presented in terms of first principles, and a correction method based on a gradient field spherical harmonic expansion is proposed. In our case, the amount of distortion measured within a typical field of view (FOV) required for head imaging is sufficiently large that without the use of some distortion correction technique, the images would be of limited use for stereotaxy or longitudinal studies, where precise volumetric information is required.

Laminar optical tomography: demonstration of millimeter-scale depth-resolved imaging in turbid media.

Abstract: Laminar optical tomography (LOT) is a new technique that combines the advantages of diffuse optical tomography image reconstruction and a microscopy-based setup to allow noncontact imaging with 100–200‐µm resolution effective over depths of 0–2.5 mm. LOT is being developed primarily for multispectral imaging of rat cortex, for which resolving functional dynamics in various layers of the brain’s cortex (to depths of 1500 µm) is of increasing interest to neurophysiologists. System design and image reconstruction techniques are described, along with simulation and phantom results that demonstrate the characteristics and limitations of system accuracy and resolution.

Real-time dynamic 3-D object shape reconstruction and high-fidelity texture mapping for 3-D video

Abstract: Three-dimensional (3-D) video is a real 3-D movie recording the object's full 3-D shape, motion, and precise surface texture. This paper first proposes a parallel pipeline processing method for reconstructing a dynamic 3-D object shape from multiview video images, by which a temporal series of full 3-D voxel representations of the object behavior can be obtained in real time. To realize the real-time processing, we first introduce a plane-based volume intersection algorithm: first represent an observable 3-D space by a group of parallel plane slices, then back-project observed multiview object silhouettes onto each slice, and finally apply two-dimensional silhouette intersection on each slice. Then, we propose a method to parallelize this algorithm using a PC cluster, where we employ five-stage pipeline processing in each PC as well as slice-by-slice parallel silhouette intersection. Several results of the quantitative performance evaluation are given to demonstrate the effectiveness of the proposed methods. In the latter half of the paper, we present an algorithm of generating video texture on the reconstructed dynamic 3-D object surface. We first describe a naive view-independent rendering method and show its problems. Then, we improve the method by introducing image-based rendering techniques. Experimental results demonstrate the effectiveness of the improved method in generating high fidelity object images from arbitrary viewpoints.