Numerical analysis of the effect of reduced temporal coherence in quantitative phase microscopy and tomography

We present the numerical analysis of the effect of the temporarily partially coherent illumination on the phase measurement accuracy in digital holography microscopy (DHM) and optical diffraction tomography (ODT), as reconstruction algorithms tend to assume purely monochromatic conditions. In the regime of reduced temporal coherence, we simulate the hologram formation in two different optical setups, representing classical off-axis two-beam and grating common-path configurations. We consider two ODT variants: with sample rotation and angle-scanning of illumination. Besides the coherence degree of illumination, our simulation considers the influence of the sample normal dispersion, shape of the light spectrum, and optical parameters of the imaging setup. As reconstruction algorithms we employ Fourier hologram method and first-order Rytov approximation with direct inversion and nonnegativity constraints. Quantitative evaluation of the measurement results deviations introduced by the mentioned error sources is comprehensively analyzed, for the first time to the best of our knowledge. Obtained outcomes indicate low final DHM/ODT reconstruction errors for the grating-assisted common-path configuration. Nevertheless, dispersion and asymmetric spectrum introduce non-negligible overestimated refractive index values and noise, and should be thus carefully considered within experimental frameworks.

Optimization-based optical diffraction tomography using iODT initialization

Optical diffraction tomography (ODT) is a label-free and noninvasive technique for biological imaging. However, ODT is only applicable to weakly scattering objects. To extend ODT to the multiple-scattering regime, more advanced inversion algorithms have been developed, including optimization-based ODT (Opti-ODT) and iterative ODT (iODT). In this paper, we propose a combined strategy, namely, an iODT initialization for Opti-ODT, based on the observed complementarity of their individual advantages. This study numerically demonstrates that under this combined strategy, the reconstruction can accurately converge to a better local minimum, especially in the case of multiply scattering objects with large optical path differences.

Common-path intrinsically achromatic optical diffraction tomography

In this work we propose an open-top like common-path intrinsically achromatic optical diffraction tomography system. It operates as a total-shear interferometer and employs Ronchi-type amplitude diffraction grating, positioned in between the camera and the tube lens without an additional 4f system, generating three-beam interferograms with achromatic second harmonic. Such configuration makes the proposed system low cost, compact and immune to vibrations. We present the results of the measurements of 3D-printed cell phantom using laser diode (coherent) and superluminescent diode (partially coherent) light sources. Broadband light sources can be naturally employed without the need for any cumbersome compensation because of the intrinsic achromaticity of the interferometric recording (holograms generated by -1st and +1st conjugated diffraction orders are not affected by the illumination wavelength). The results show that the decreased coherence offers much reduced coherent noise and higher fidelity tomographic reconstruction especially when applied nonnegativity constraint regularization procedure.

Fourier Properties of Symmetric-Geometry Computed Tomography and Its Linogram Reconstruction With Neural Network

In this work, we investigate the Fourier properties of a symmetric-geometry computed tomography (SGCT) with linearly distributed source and detector in a stationary configuration. A linkage between the 1D Fourier Transform of a weighted projection from SGCT and the 2D Fourier Transform of a deformed object is established in a simple mathematical form (i.e., the Fourier slice theorem for SGCT). Based on its Fourier slice theorem and its unique data sampling in the Fourier space, a Linogram-based Fourier reconstruction method is derived for SGCT. We demonstrate that the entire Linogram reconstruction process can be embedded as known operators into an end-to-end neural network. As a learning-based approach, the proposed Linogram-Net has capability of improving CT image quality for non-ideal imaging scenarios, a limited-angle SGCT for instance, through combining weights learning in the projection domain and loss minimization in the image domain. Numerical simulations and physical experiments on an SGCT prototype platform showed that our proposed Linogram-based method can achieve accurate reconstruction from a dual-SGCT scan and can greatly reduce computational complexity when compared with the filtered backprojection type reconstruction. The Linogram-Net achieved accurate reconstruction when projection data are complete and significantly suppressed image artifacts from a limited-angle SGCT scan mimicked by using a clinical CT dataset, with the average CT number error in the selected regions of interest reduced from 67.7 Hounsfield Units (HU) to 28.7 HU, and the average normalized mean square error of overall images reduced from 4.21e-3 to 2.65e-3.

Fast multiple-scattering holographic tomography based on the wave propagation method

We develop a time-efficient computation scheme for a holographic tomography reconstruction technique that accounts for multiple scattering by applying the forward model based on the wave propagation method (WPM). The computational efficiency is achieved by employing adjoint equations for calculation of the gradient of the data fidelity term in the gradient descent optimization procedure. In the paper we provide a general computation scheme that is suitable for various forward models that can be represented in the form of an iterative equation. Next, we provide the complete solution for the time-efficient reconstruction utilizing WPM. In the considered reconstruction case, the proposed algorithm enables the 114-fold speed-up of computations with respect to the original tomographic method.

Holographic tomography with scanning of illumination: space-domain reconstruction for spatially invariant accuracy

The paper presents two novel, space-domain reconstruction algorithms for holographic tomography utilizing scanning of illumination and a fixed detector that is highly suitable for imaging of living biomedical specimens. The first proposed algorithm is an adaptation of the filtered backpropagation to the scanning illumination tomography. Its space-domain implementation enables avoiding the error-prone interpolation in the Fourier domain, which is a significant problem of the state-of-the-art tomographic algorithm. The second proposed algorithm is a modified version of the former, which ensures the spatially invariant reconstruction accuracy. The utility of the proposed algorithms is demonstrated with numerical simulations and experimental measurement of a cancer cell.

Interlaced X-ray diffraction computed tomography

An X-ray diffraction computed tomography data-collection strategy that allows, post experiment, a choice between temporal and spatial resolution is reported. This strategy enables time-resolved studies on comparatively short timescales, or alternatively allows for improved spatial resolution if the system under study, or components within it, appear to be unchanging. The application of the method for studying an Mn-Na-W/SiO2 fixed-bed reactor in situ is demonstrated. Additionally, the opportunities to improve the data-collection strategy further, enabling post-collection tuning between statistical, temporal and spatial resolutions, are discussed. In principle, the interlaced scanning approach can also be applied to other pencil-beam tomographic techniques, like X-ray fluorescence computed tomography, X-ray absorption fine structure computed tomography, pair distribution function computed tomography and tomographic scanning transmission X-ray microscopy.

Three-dimensional quantitative phase imaging via tomographic deconvolution phase microscopy

The field of three-dimensional quantitative phase imaging (3D QPI) is expanding rapidly with applications in biological, medical, and industrial research, development, diagnostics, and metrology. Much of this research has centered on developing optical diffraction tomography (ODT) for biomedical applications. In addition to technical difficulties associated with coherent noise, ODT is not congruous with optical microscopy utilizing partially coherent light, which is used in most biomedical laboratories. Thus, ODT solutions have, for the most part, been limited to customized optomechanical systems which would be relatively expensive to implement on a wide scale. In the present work, a new phase reconstruction method, called tomographic deconvolution phase microscopy (TDPM), is described which makes use of commercial microscopy hardware in realizing 3D QPI. TDPM is analogous to methods used in deconvolution microscopy which improve spatial resolution and 3D-localization accuracy of fluorescence micrographs by combining multiple through-focal scans which are deconvolved by the system point spread function. TDPM is based on the 3D weak object transfer function theory which is shown here to be capable of imaging "nonweak" phase objects with large phase excursions. TDPM requires no phase unwrapping and recovers the entire object spectrum via object rotation, mitigating the need to fill in the "missing cone" of spatial frequencies algorithmically as in limited-angle ODT. In the present work, TDPM is demonstrated using optical fibers, including single-mode, polarization-maintaining, and photonic-crystal fibers as well as an azimuthally varying CO2-laser-induced long-period fiber grating period as test phase objects.

Realistic wave-optics simulation of X-ray phase-contrast imaging at a human scale

X-ray phase-contrast imaging (XPCI) can dramatically improve soft tissue contrast in X-ray medical imaging. Despite worldwide efforts to develop novel XPCI systems, a numerical framework to rigorously predict the performance of a clinical XPCI system at a human scale is not yet available. We have developed such a tool by combining a numerical anthropomorphic phantom defined with non-uniform rational B-splines (NURBS) and a wave optics-based simulator that can accurately capture the phase-contrast signal from a human-scaled numerical phantom. Using a synchrotron-based, high-performance XPCI system, we provide qualitative comparison between simulated and experimental images. Our tool can be used to simulate the performance of XPCI on various disease entities and compare proposed XPCI systems in an unbiased manner.

Time domain compressive beam forming of ultrasound signals

Ultrasound imaging is a wide spread technique used in medical imaging as well as in non-destructive testing. The technique offers many advantages such as real-time imaging, good resolution, prompt acquisition, ease of use, and low cost compared to other techniques such as x-ray imaging. However, the maximum frame rate achievable is limited as several beams must be emitted to compute a single image. For each emitted beam, one must wait for the wave to propagate back and forth, thus imposing a limit to the frame rate. Several attempts have been made to use less beams while maintaining image quality. Although efficiently increasing the frame rate, these techniques still use several transmit beams. Compressive Sensing (CS), a universal data completion scheme based on convex optimization, has been successfully applied to a number of imaging modalities over the past few years. Using a priori knowledge of the signal, it can compute an image using less data allowing for shorter acquisition times. In this paper, it is shown that a valid CS framework can be derived from ultrasound propagation theory, and that this framework can be used to compute images of scatterers using only one plane wave as a transmit beam.

Improved regularization reconstruction from sparse angle data in optical diffraction tomography

In this paper, we propose an improved deterministic regularization algorithm to handle the sparse angle data problem in optical diffraction tomography. Based on optical diffraction tomography and the deterministic regularization algorithm, the regularization iteration is performed in the space domain and the frequency domain simultaneously, which greatly reduces the computational cost. By applying piecewise-smoothness and positivity constraints as the penalty function, the missing frequency spectrum is effectively recovered and the internal refractive index distribution of the specimen is accurately reconstructed. Using simulated and experimental results, we show that the proposed regularization algorithm allows accurate refractive index reconstruction from very sparse angle data in optical diffraction tomography.

A projection-based approach to diffraction tomography on curved boundaries

An approach to diffraction tomography is investigated for two-dimensional image reconstruction of objects surrounded by an arbitrarily-shaped curve of sources and receivers. Based on the integral theorem of Helmholtz and Kirchhoff, the approach relies upon a valid choice of the Green's functions for selected conditions along the (possibly-irregular) boundary. This allows field projections from the receivers to an arbitrary external location. When performed over all source locations, it will be shown that the field caused by a hypothetical source at this external location is also known along the boundary. This field can then be projected to new external points that may serve as a virtual receiver. Under such a reformation, data may be put in a form suitable for image construction by synthetic aperture methods. Foundations of the approach are shown, followed by a mapping technique optimized for the approach. Examples formed from synthetic data are provided.

Light ray field capture using focal plane sweeping and its optical reconstruction using 3D displays

We propose a method to capture light ray field of three-dimensional scene using focal plane sweeping. Multiple images are captured using a usual camera at different focal distances, spanning the three-dimensional scene. The captured images are then back-projected to four-dimensional spatio-angular space to obtain the light ray field. The obtained light ray field can be visualized either using digital processing or optical reconstruction using various three-dimensional display techniques including integral imaging, layered display, and holography.

Sparse-view ultrasound diffraction tomography using compressed sensing with nonuniform FFT

Accurate reconstruction of the object from sparse-view sampling data is an appealing issue for ultrasound diffraction tomography (UDT). In this paper, we present a reconstruction method based on compressed sensing framework for sparse-view UDT. Due to the piecewise uniform characteristics of anatomy structures, the total variation is introduced into the cost function to find a more faithful sparse representation of the object. The inverse problem of UDT is iteratively resolved by conjugate gradient with nonuniform fast Fourier transform. Simulation results show the effectiveness of the proposed method that the main characteristics of the object can be properly presented with only 16 views. Compared to interpolation and multiband method, the proposed method can provide higher resolution and lower artifacts with the same view number. The robustness to noise and the computation complexity are also discussed.

Noise suppressed optical diffraction tomography with autofocus correction

We propose a novel tomographic measurement approach that enables a noise suppressed characterization of microstructures. The idea of this work is based on a finding that coherent noise in the input phase data generates an artificial circular structure whose magnitude is the highest at the centre of tomographic reconstruction. This method decreases the noise level by applying an unconventional tomographic measurement configuration with an object deliberately shifted with respect to the rotation axis. This enables a spatial separation between the reconstructed sample structure and the area of the largest refractive index perturbations. The input phase data defocusing that is a by-product of the introduced modification is numerically corrected with an automatic focus correction algorithm. The proposed method is validated with simulations and experimental measurements of an optical microtip.

Low-rank modeling of local k-space neighborhoods (LORAKS) for constrained MRI

Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-rank matrices when the image has limited spatial support or slowly varying phase. Based on this, we develop a novel and flexible framework for constrained image reconstruction that uses low-rank matrix modeling of local k-space neighborhoods (LORAKS). A new regularization penalty and corresponding algorithm for promoting low-rank are also introduced. The potential of LORAKS is demonstrated with simulated and experimental data for a range of denoising and sparse-sampling applications. LORAKS is also compared against state-of-the-art methods like homodyne reconstruction, l1-norm minimization, and total variation minimization, and is demonstrated to have distinct features and advantages. In addition, while calibration-based support and phase constraints are commonly used in existing methods, the LORAKS framework enables calibrationless use of these constraints.

Plane-wave ultrasound beamforming using a nonuniform fast Fourier transform

Beamforming of plane-wave ultrasound echo signals in the Fourier domain provides fast and accurate image reconstruction. Conventional implementations perform a k-space interpolation from the uniform sampled grid to a nonuniform acoustic dispersion grid. In this paper, we demonstrate that this step can be replaced by a nonuniform Fourier transform. We study the performance of the nonuniform fast Fourier transform (NUFFT) in terms of signal-to-noise ratio and computational cost, and show that the NUFFT offers an advantage in the trade-off between speed and accuracy, compared with other frequency-domain beamforming strategies.

Non-Cartesian sampled centric scan SPRITE imaging with magnetic field gradient and B0(t) field measurements for MRI in the vicinity of metal structures

This paper proposes the possibility of spatially resolved MRI measurements undertaken inside metallic cells. MRI has been rarely usable inside conducting vessels due to the eddy currents in the walls caused by switching magnetic field gradients, which render most advanced MRI pulse sequences impossible. We propose magnetic field gradient waveform monitoring (MFGM) for MRI of samples inside metallic cells. In this work the MFGM method was extended to measure the B(0) field temporal evolution associated with gradient waveforms. MFGM was used to observe and correct eddy current effects associated with a metallic cell. High quality centric scan SPRITE images result from such corrections. MRI of samples held under pressure, most notably rock core samples, traditionally employs cells that are non-magnetic and fabricated from polymeric materials. The natural material for high-pressure MRI is however non-ferromagnetic metal given their high tensile strengths and high thermal conductivity. MRI measurement of macroscopic samples at high pressure would be generally possible if metallic pressure vessels could be employed. This study will form the basis of new MRI compatible metallic pressure vessels, which will permit MRI of macroscopic systems at high pressure.

Navigator accuracy requirements for prospective motion correction

Prospective motion correction in MRI is becoming increasingly popular to prevent the image artifacts that result from subject motion. Navigator information is used to update the position of the imaging volume before every spin excitation so that lines of acquired k-space data are consistent. Errors in the navigator information, however, result in residual errors in each k-space line. This paper presents an analysis linking noise in the tracking system to the power of the resulting image artifacts. An expression is formulated for the required navigator accuracy based on the properties of the imaged object and the desired resolution. Analytical results are compared with computer simulations and experimental data.

A relationship between the far field diffraction pattern and the axial pressure radiating from a two-dimensional aperture

The diffraction of an acoustic wave by a two-dimensional aperture produces a sound field that can generally be represented at any point in space as a superposition of a continuum of plane waves. The mathematical formulation that facilitates this representation is known as the angular spectrum of plane waves method. The spectrum, in this representation, is a wavenumber spectrum obtained from a two-dimensional Fourier transform of the acoustic pressure (or velocity) distribution over the surface of the aperture boundary; a quantity which is also known to characterize the Fraunhofer diffraction pattern of the aperture. In this article, the angular spectrum method is used to formulate a mathematical relationship for two-dimensional apertures between the Fraunhofer diffraction pattern and a one-dimensional Fourier transform of the axial pressure. This relationship can be used to rapidly compute the axial pressure profile of the aperture if the boundary condition on the aperture is known and, in some cases, can be used as an inverse method. The approach is demonstrated for the cases of a flat circular piston and a flat rectangular piston undergoing harmonic motion in an infinite, rigid baffle. In the latter case, an analytical solution is also obtained.

Exploitation of symmetries for image reconstruction in linearized variable density diffraction tomography

The problem of reconstructing an object's weakly varying compressibility and density distributions in three-dimensional (3D) acoustic diffraction tomography is studied. Based on the Fourier diffraction projection theorem for acoustic media, it is demonstrated that the 3D Fourier components of an object's compressibility and density distributions can be decoupled algebraically, thereby providing a method for separately reconstructing the distributions. This is facilitated by the identification and exploitation of tomographic symmetries and the rotational invariance of the imaging model. The developed reconstruction methods are investigated by use of computer- simulation studies. The application of the proposed image reconstruction strategy to other tomography problems is discussed.

Multispectral intensity diffraction tomography: single material objects with variable densities

A reconstruction theory for multispectral intensity diffraction tomography (I-DT) is established and investigated for use with single material objects whose dispersion characteristics are known a priori. Instead of varying the object-to-detector distance, as prescribed by the original I-DT method and other classic in-line holographic reconstruction methods, the temporal frequency of the illuminating plane wave represents the degree of freedom of the imaging system that is varied to acquire two independent intensity measurements at each tomographic view angle. Unlike previous multispectral I-DT methods, the proposed method does not require a nondispersive assumption. A computer-simulation study is presented to demonstrate and corroborate the method.

Accurate image reconstruction from few-view and limited-angle data in diffraction tomography

We present a method for obtaining accurate image reconstruction from highly sparse data in diffraction tomography (DT). A practical need exists for reconstruction from few-view and limited-angle data, as this can greatly reduce required scan times in DT. Our method does this by minimizing the total variation (TV) of the estimated image, subject to the constraint that the Fourier transform of the estimated image matches the measured Fourier data samples. Using simulation studies, we show that the TV-minimization algorithm allows accurate reconstruction in a variety of few-view and limited-angle situations in DT. Accurate image reconstruction is obtained from far fewer data samples than are required by common algorithms such as the filtered-backpropagation algorithm. Overall our results indicate that the TV-minimization algorithm can be successfully applied to DT image reconstruction under a variety of scan configurations and data conditions of practical significance.

Interferometric Synthetic Aperture Microscopy: Computed Imaging for Scanned Coherent Microscopy

Three-dimensional image formation in microscopy is greatly enhanced by the use of computed imaging techniques. In particular, Interferometric Synthetic Aperture Microscopy (ISAM) allows the removal of out-of-focus blur in broadband, coherent microscopy. Earlier methods, such as optical coherence tomography (OCT), utilize interferometric ranging, but do not apply computed imaging methods and therefore must scan the focal depth to acquire extended volumetric images. ISAM removes the need to scan the focus by allowing volumetric image reconstruction from data collected at a single focal depth. ISAM signal processing techniques are similar to the Fourier migration methods of seismology and the Fourier reconstruction methods of Synthetic Aperture Radar (SAR). In this article ISAM is described and the close ties between ISAM and SAR are explored. ISAM and a simple strip-map SAR system are placed in a common mathematical framework and compared to OCT and radar respectively. This article is intended to serve as a review of ISAM, and will be especially useful to readers with a background in SAR.

A modified view ordering for artifact reduction in MRI

Fast spin echo (FSE) is a means of rapidly acquiring k-space data in magnetic resonance imaging (MRI). Unfortunately, images obtained using FSE often contain artifacts. These are caused primarily by patient motion and transversal magnetisation decay, the latter being characterised by the time constant T2. This paper presents a study of the effect of data acquisition order on the severity of these effects.

A computer simulation study of imaging flexural inhomogeneities using plate-wave diffraction tomography

This paper investigates the feasibility of plate-wave diffraction tomography for the reconstruction of flexural inhomogeneities in plates using the results of computer simulation studies. The numerical implementation of the fundamental reconstruction algorithm, which has recently been developed by Wang and Rose [C.H. Wang, L.R.F. Rose, Plate-wave diffraction tomography for structural health monitoring, Rev. Quant. Nondestr. Eval. 22 (2003) 1615-1622] is investigated addressing the essential effects of applying the discrete form of the Fourier diffraction theorem for solving the inverse problem as discussed by Kak and Slaney [A.C. Kak, M. Slaney, Principles of Computerized Tomographic Imaging, IEEE Press, New York, 1988] for the acoustic case, viz. diffraction limited sensitivity, influence of weak scatterer assumption, damage location and scatter field data processing in time and Fourier space as well as experimental limitations such as finite receiver length and limited views. The feasibility of the imaging technique is investigated for cylindrical inhomogeneities of various severities and relative position within the interrogation space and a normal incident interrogation configuration. The results show that plate-wave diffraction tomography enables the quantitative reconstruction of location, size and severity of plate damage with excellent sensitivity and offers the potential for detecting corrosion thinning, disbonds and delamination damage in structural integrity management applications.

Improved bulk rotation detection and correction in MRI

Bulk motion occurring during the acquisition of magnetic resonance images (MRI) remains a significant limitation in image quality. The paper presents an extension to TRELLIS, a recently developed method of detecting and correcting for bulk motion. Accurate determination of the relative orientations of overlapping strips of k-space is demonstrated. Reconstructions for both simulated and actual MRI acquisitions are presented.

Multispectral intensity diffraction tomography reconstruction theory: quasi-nondispersive objects

A multispectral intensity diffraction tomography (I-DT) reconstruction theory for quasi-nondispersive scattering objects is developed and investigated. By "quasi-nondispersive" we refer to an object that is characterized by a refractive index distribution that is approximately nondispersive over a predefined finite temporal frequency interval in which the tomographic measurements are acquired. The scanning requirements and measurement data are shown to be different than in conventional I-DT. Unlike conventional I-DT that requires intensity measurements on a pair of detector planes for each probing wave field, this new method uses measurements on a single detector plane at two frequencies. Computer simulation studies are conducted to demonstrate the method.

High-resolution ab initio three-dimensional x-ray diffraction microscopy

Coherent x-ray diffraction microscopy is a method of imaging nonperiodic isolated objects at resolutions limited, in principle, by only the wavelength and largest scattering angles recorded. We demonstrate x-ray diffraction imaging with high resolution in all three dimensions, as determined by a quantitative analysis of the reconstructed volume images. These images are retrieved from the three-dimensional diffraction data using no a priori knowledge about the shape or composition of the object, which has never before been demonstrated on a nonperiodic object. We also construct two-dimensional images of thick objects with greatly increased depth of focus (without loss of transverse spatial resolution). These methods can be used to image biological and materials science samples at high resolution with x-ray undulator radiation and establishes the techniques to be used in atomic-resolution ultrafast imaging at x-ray free-electron laser sources.

A comparison of truncated total least squares with Tikhonov regularization in imaging by ultrasound inverse scattering

For good image quality using ultrasound inverse scattering, one alternately solves the well-posed forward scattering equation for an estimated total field and the ill-posed inverse scattering equation for the desired object property function. In estimating the total field, error or noise contaminates the coefficients of both matrix and data of the inverse scattering equation. Previous work on ill-posed inverse ultrasonic scattering commonly used Tikhonov regularization, which considers error only in the data. The solution so obtained is not precise enough to reconstruct the quantitative internal structure of a large or high-contrast object. This paper adopts the truncated total least squares method, simultaneously considering error and noise on both sides of the inverse scattering equation, and compares it with the classical Tikhonov regularization. We show that it can substantially improve reconstruction fit and image quality when the inverse scattering equation system is strongly ill-posed.

Image reconstruction with shift-variant filtration and its implication for noise and resolution properties in fan-beam computed tomography

In computed tomography (CT), the fan-beam filtered backprojection (FFBP) algorithm is used widely for image reconstruction. It is known that the FFBP algorithm can significantly amplify data noise and aliasing artifacts in situations where the focal lengths are comparable to or smaller than the size of the field of measurement (FOM). In this work, we propose an algorithm that is less susceptible to data noise, aliasing, and other data inconsistencies than is the FFBP algorithm while retaining the favorable resolution properties of the FFBP algorithm. In an attempt to evaluate the noise properties in reconstructed images, we derive analytic expressions for image variances obtained by use of the FFBP algorithm and the proposed algorithm. Computer simulation studies are conducted for quantitative evaluation of the spatial resolution and noise properties of images reconstructed by use of the algorithms. Numerical results of these studies confirm the favorable spatial resolution and noise properties of the proposed algorithm and verify the validity of the theoretically predicted image variances. The proposed algorithm and the derived analytic expressions for image variances can have practical implications for both estimation and detection/classification tasks making use of CT images, and they can readily be generalized to other fan-beam geometries.

Reconstruction in diffraction ultrasound tomography using nonuniform FFT

We show an iterative reconstruction framework for diffraction ultrasound tomography. The use of broad-band illumination allows significant reduction of the number of projections compared to straight ray tomography. The proposed algorithm makes use of forward nonuniform fast Fourier transform (NUFFT) for iterative Fourier inversion. Incorporation of total variation regularization allows the reduction of noise and Gibbs phenomena while preserving the edges. The complexity of the NUFFT-based reconstruction is comparable to the frequency-domain interpolation (gridding) algorithm, whereas the reconstruction accuracy (in sense of the L2 and the L(infinity) norm) is better.

Vector entropy imaging theory with application to computerized tomography

Medical imaging theory for x-ray CT and PET is based on image reconstruction from projections. In this paper a novel vector entropy imaging theory under the framework of multiple criteria decision making is presented. We also study the most frequently used image reconstruction methods, namely, least square, maximum entropy, and filtered back-projection methods under the framework, of the single performance criterion optimization. Finally, we introduce some of the results obtained by various reconstruction algorithms using computer-generated noisy projection data from the Hoffman phantom and real CT scanner data. Comparison of the reconstructed images indicates that the vector entropy method gives the best in error (difference between the original phantom data and reconstruction), smoothness (suppression of noise), grey value resolution and is free of ghost images.

Exact frequency-domain reconstruction for thermoacoustic tomography--II: Cylindrical geometry

Microwave-induced thermoacoustic tomography (TAT) in a cylindrical configuration is developed to image biological tissue. Thermoacoustic signals are acquired by scanning a flat ultrasonic transducer. Using a new expansion of a spherical wave in cylindrical coordinates, we apply the Fourier and Hankel transforms to TAT and obtain an exact frequency-domain reconstruction method. The effect of discrete spatial sampling on image quality is analyzed. An aliasing-proof reconstruction method is proposed. Numerical and experimental results are included.

Exact frequency-domain reconstruction for thermoacoustic tomography--I: Planar geometry

We report an exact and fast Fourier-domain reconstruction algorithm for thermoacoustic tomography in a planar configuration assuming thermal confinement and constant acoustic speed. The effects of the finite size of the detector and the finite length of the excitation pulse are explicitly included in the reconstruction algorithm. The algorithm is numerically and experimentally verified. We also demonstrate that the blurring caused by the finite size of the detector surface is the primary limiting factor on the resolution and that it can be compensated for by deconvolution.

Nondestructive imaging of shallow buried objects using acoustic computed tomography

The nondestructive three-dimensional acoustic tomography concept of the present investigation combines computerized tomography image reconstruction algorithms using acoustic diffracting waves together with depth information to produce a three-dimensional (3D) image of an underground section. The approach illuminates the underground area of interest with acoustic plane waves of frequencies 200-3000 Hz. For each transmitted pulse, the reflected-refracted signals are received by a line array of acoustic sensors located at a diametrically opposite point from the acoustic source line array. For a stratified underground medium and for a given depth, which is represented by a time delay in the received signal, a horizontal tomographic 2D image is reconstructed from the received projections. Integration of the depth dependent sequence of cross-sectional reconstructed images provides a complete three-dimensional overview of the inspected terrain. The method has been tested with an experimental system that consists of a line array of four-acoustic sources, providing plane waves, and a receiving line array of 32-acoustic sensors. The results indicate both the potential and the challenges facing the new methodology. Suggestions are made for improved performance, including an adaptive noise cancellation scheme and a numerical interpolation technique.

Quantitative imaging using a time-domain eigenfunction method

An inverse scattering method that uses eigenfunctions of a scattering operator at a single frequency is extended to include the full range of frequencies present in the incident pulse waveform. The resulting so-called time-domain eigenfunction method is shown to yield a modulated version of the scattering potential. The potential is recovered by a demodulation process using cross correlation with a reference. Including an adaptive delay in the reference is shown to compensate partially for the linearization of the Born approximation and to extend its valid range. The k-space window of the time-domain solution is expressed in terms of the incident waveform and shown to be smoother than that of a single-frequency solution. The time-domain method is examined using both calculated and measured data. In the calculations, an exact solution for scattering from one or multiple nonconcentric cylinders is used to obtain the scattered field. In the measurements, a novel ring-transducer system was employed to obtain the incident and total fields. The results of simulations and experiments show that the method is robust and accurate for the size of objects considered and that the point resolution approaches one-half the wavelength at the pulse center frequency.

On the optimality of the gridding reconstruction algorithm

Gridding reconstruction is a method to reconstruct data onto a Cartesian grid from a set of nonuniformly sampled measurements. This method is appreciated for being robust and computationally fast. However, it lacks solid analysis and design tools to quantify or minimize the reconstruction error. Least squares reconstruction (LSR), on the other hand, is another method which is optimal in the sense that it minimizes the reconstruction error. This method is computationally intensive and, in many cases, sensitive to measurement noise. Hence, it is rarely used in practice. Despite their seemingly different approaches, the gridding and LSR methods are shown to be closely related. The similarity between these two methods is accentuated when they are properly expressed in a common matrix form. It is shown that the gridding algorithm can be considered an approximation to the least squares method. The optimal gridding parameters are defined as the ones which yield the minimum approximation error. These parameters are calculated by minimizing the norm of an approximation error matrix. This problem is studied and solved in the general form of approximation using linearly structured matrices. This method not only supports more general forms of the gridding algorithm, it can also be used to accelerate the reconstruction techniques from incomplete data. The application of this method to a case of two-dimensional (2-D) spiral magnetic resonance imaging shows a reduction of more than 4 dB in the average reconstruction error.

Computationally efficient and statistically robust image reconstruction in three-dimensional diffraction tomography

Diffraction tomography (DT) is an inversion scheme used to reconstruct the spatially variant refractive-index distribution of a scattering object. We developed computationally efficient algorithms for image reconstruction in three-dimensional (3D) DT. A unique and important aspect of these algorithms is that they involve only a series of two-dimensional reconstructions and thus greatly reduce the prohibitively large computational load required by conventional 3D reconstruction algorithms. We also investigated the noise characteristics of these algorithms and developed strategies that exploit the statistically complementary information inherent in the measured data to achieve a bias-free reduction of the reconstructed image variance. We performed numerical studies that corroborate our theoretical assertions.

Consistency conditions and linear reconstruction methods in diffraction tomography

Because an image can be reconstructed from knowledge of its Radon transform (RT), the task of reconstructing an image is tantamount to that of estimating its RT. Based upon the Fourier diffraction projection (FDP) theorem, from the statistical perspective of unbiased reduction of image variance, we previously proposed an infinite family of estimation methods for obtaining the RT from the scattered data in diffraction tomography (DT). In this work, using the FDP theorem, we define the diffraction Radon transform (DRT), which can be treated as the data function in DT. Subsequently, using strategies similar to those that analyze the consistency conditions on the exponential Radon transform in two-dimensional (2-D) single-photon emission computed tomography with uniform attenuation, we studied the consistency condition on the DRT and we show that there is a hierarchy of estimation methods that actually project the noisy data function onto its consistency space in different ways. In terms of a weighted inner product of the consistency and inconsistency parts of a noisy data function, we further demonstrate that a subset of the family of estimation methods can be interpreted as orthogonal projections onto the consistency space of the DRT. In particular, the statistically suboptimal estimation method in the family corresponds to an orthogonal projection associated with an ordinary inner product of the consistency and inconsistency parts of a noisy data function.

Higher-order (nonlinear) diffraction tomography: reconstruction algorithms and computer simulation

The usual propagation transform of diffraction tomography is generalized into higher-order (nonlinear) propagation transforms via use of the Born series as the data-generating model in scattering experiments. Nonlinear tomographic reconstruction algorithms are developed for inversion of scattered field data modeled up to an arbitrarily large (possibly infinite) number of terms in the Born series. A computer simulation study is included to illustrate the performance of the algorithms for the case of scattering objects with cylindrical symmetry.

A new reconstruction approach for reflection mode diffraction tomography

Reflection mode diffraction tomography (RM DT) is an inversion scheme used to reconstruct the acoustical refractive index distribution of a scattering object. In this work, we reveal the existence of statistically complementary information inherent in the backscattered data and propose reconstruction algorithms that exploit this information for achieving a bias-free reduction of image variance in RM DT images. Such a reduction of image variance can potentially enhance the detectability of subtle image features when the signal-to-noise ratio of the measured scattered data is low in RM DT. The proposed reconstruction algorithms are mathematically identical, but they propagate noise and numerical errors differently. We investigate theoretically, and validate numerically, the noise properties of images reconstructed using one of the reconstruction algorithms for several different multifrequency sources and uncorrelated data noise.

Minimal-scan filtered backpropagation algorithms for diffraction tomography

The filtered backpropagation (FBPP) algorithm, originally developed by Devaney [Ultrason. Imaging 4, 336 (1982)], has been widely used for reconstructing images in diffraction tomography. It is generally known that the FBPP algorithm requires scattered data from a full angular range of 2 pi for exact reconstruction of a generally complex-valued object function. However, we reveal that one needs scattered data only over the angular range 0 < or = phi < or = 3 pi/2 for exact reconstruction of a generally complex-valued object function. Using this insight, we develop and analyze a family of minimal-scan filtered backpropagation (MS-FBPP) algorithms, which, unlike the FBPP algorithm, use scattered data acquired from view angles over the range 0 < or = phi < or = 3 pi/2. We show analytically that these MS-FBPP algorithms are mathematically identical to the FBPP algorithm. We also perform computer simulation studies for validation, demonstration, and comparison of these MS-FBPP algorithms. The numerical results in these simulation studies corroborate our theoretical assertions.

Unified reconstruction theory for diffraction tomography, with consideration of noise control

In diffraction tomography, the spatial distribution of the scattering object is reconstructed from the measured scattered data. For a scattering object that is illuminated with plane-wave radiation, under the condition of weak scattering one can invoke the Born (or the Rytov) approximation to linearize the equation for the scattered field (or the scattered phase) and derive a relationship between the scattered field (or the scattered phase) and the distribution of the scattering object. Reconstruction methods such as the Fourier domain interpolation methods and the filtered backpropagation method have been developed previously. However, the underlying relationship among and the noise properties of these methods are not evident. We introduce the concepts of ideal and modified sinograms. Analysis of the relationships between, and the noise properties of the two sinograms reveals infinite classes of methods for image reconstruction in diffraction tomography that include the previously proposed methods as special members. The methods in these classes are mathematically identical, but they respond to noise and numerical errors differently.

Influence of digital audio filters on image reconstruction in MRI

This paper deals with the influence of the transient response and group delay of digital filters on the MRI signal and its aspects in image reconstruction. The consequence of digital filtration on the acquired signal will be shown in the time domain (k-space) for three basic imaging methods-echo scan, radial scan and spiral scan. The influence of the group delay and transient response of filters will be explained and a method will be proposed which compensates both these phenomena while retaining all the advantages of digital filtration. The proposed method is based on applying the principle of signal superposition and on using the consequences of the sampling principle. The method works in the time domain. It is very simple and rapid and does not depend on the properties of the acquired signal or reconstruction algorithm. It will be shown and explained in which cases the transient response can be neglected and in which it has to be compensated. In the end, the results of the proposed methods will be shown for mentioned cases on a simulated signal in the image domain.