gVirtualXray (gVXR) is an open-source framework that relies on the Beer-Lambert law to simulate x-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and x-ray computed tomography (CT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and x-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimization problems and to train machine learning algorithms. This paper presents applications of gVXR related to XCT.
In propagation-based X-ray phase-contrast (PB XPC) imaging, the measured image contains a mixture of
absorption- and phase-contrast. To obtain separate images of the projected absorption and phase (i.e., refractive)
properties of a sample, phase retrieval methods can be employed. It has been suggested that phase-retrieval can
always improve image quality in PB XPC imaging. However, when objective (task-based) measures of image
quality are employed, this is not necessarily true and phase retrieval can be detrimental. In this work, signal
detection theory is utilized to quantify the performance of a Hotelling observer (HO) for detecting a known signal
in a known background. Two cases are considered. In the first case, the HO acts directly on the measured intensity data. In the second case, the HO acts on either the retrieved phase or absorption image. We demonstrate
that the performance of the HO is superior when acting on the measured intensity data. The loss of task-specific
information induced by phase-retrieval is quantified by computing the efficiency of the HO as the ratio of the
test statistic signal-to-noise ratio (SNR) for the two cases. The effect of the system geometry on this efficiency is
systematically investigated. Our findings confirm that phase-retrieval can impair signal detection performance
in XPC imaging.
Positron emission tomography (PET) is an important imaging modality in both clinical usage and research
studies. For small-animal PET imaging, it is of major interest to improve the sensitivity and resolution. We
have developed a compact high-sensitivity PET system that consisted of two large-area panel PET detector
heads. The highly accurate system response matrix can be computed by use of Monte Carlo simulations, and
stored for iterative reconstruction methods. The detector head employs 2.1x2.1x20 mm3 LSO/LYSO crystals of
pitch size equal to 2.4 mm, and thus will produce more than 224 millions lines of response (LORs). By exploiting
the symmetry property in the dual-head system, the computational demands can be dramatically reduced.
Nevertheless, the tremendously large system size and repetitive reading of system response matrix from the hard
drive will result in extremely long reconstruction times. The implementation of an ordered subset expectation
maximization (OSEM) algorithm on a CPU system (four Athlon x64 2.0 GHz PCs) took about 2 days for 1
iteration. Consequently, it is imperative to significantly accelerate the reconstruction process to make it more
useful for practical applications. Specifically, the graphic processing unit (GPU), which possesses highly parallel
computational architecture of computing units can be exploited to achieve a substantial speedup. In this work, we
employed the state-of-art GPU, NVIDIA Tesla C2050 based on the Fermi-generation of the compute united device
architecture (CUDA) architecture, to yield a reconstruction process within a few minutes. We demonstrated
that reconstruction times can be drastically reduced by using the GPU. The OSEM reconstruction algorithms
were implemented employing both GPU-based and CPU-based codes, and their computational performance was
quantitatively analyzed and compared.
X-ray phase-contrast tomography (PCT) methods seek to quantitatively reconstruct separate images that depict
an object's absorption and refractive properties. Most PCT reconstruction algorithms generally operate
by explicitly or implicitly performing the decoupling of the projected absorption and phase properties at each
tomographic view angle by use of a phase-retrieval formula, followed by the inversion of X-ray transform. Tomographic
reconstruction by use of statistical methods can account for the noise model and a priori information,
and thereby can produce images with better quality over conventional filtered backprojection algorithms. We
proposed a weighted least-squares method that takes into account the second-order statistical properties of the
projected phase images and aims to minimize the objective function by employing a conjugate-gradient (CG)
method. A computer-simulation study was carried out to investigate and evaluate the developed method.
KEYWORDS: X-rays, Signal detection, Signal to noise ratio, X-ray imaging, Imaging systems, Tissues, Absorption, Detection theory, Radio propagation, Signal attenuation
Propagation-based X-ray phase-contrast imaging permits the visualization of tissues that have very similar X-ray absorption properties and may benefit a variety of biomedical imaging applications. Unlike conventional radiographic contrast that is related to the projected absorption properties of tissue, image contrast in phasecontrast radiographs contains contributions from absorption-contrast and phase-contrast. In this work, we
develop a general theoretical framework for assessing the contributions of these contrast mechanisms to signal
detectability measures in propagation-based X-ray phase-contrast imaging. Specifically, concepts from signal
detection theory are utilized to analyze the contributions of phase- and absorption-contrast to an ideal observer
figure of merit for a signal-known-exactly/background-known exactly detection task.
Intravascular photoacoustic (IVPA) imaging that aims to detect atherosclerotic plaques with differential
composition is studied computationally and experimentally. IVPA images are usually reconstructed by simply aligning
photoacoustic signals with scan conversion, which results in images with severe blurring and increases the difficulty in
signal detection. The scanning aperture in IVPA, in contrast to other photoacoustic tomography applications, is enclosed
within the imaged object. Consequently, quantitative image reconstruction becomes infeasible, as the data sufficiency
condition for stable image reconstruction is not satisfied in such a limited-view scanning. However, useful information
regarding certain plaque boundaries can still be reconstructed, which can facilitate plaque detection. In this study,
strategies for limited-view reconstruction will be investigated for the IVPA scanning geometry. Computer simulations are
carried out to validate the developed method.
At diagnostic X-ray energies, variations in the real component of the refractive index of tissues are several orders of
magnitude larger than variations in the imaginary component, or equivalently, the X-ray attenuation coefficient.
Consequently, X-ray phase-contrast imaging may permit the visualization of tissues that have identical, or
very similar, X-ray absorption properties. Quantitative in-line
phase-contrast tomography methods seek to
reconstruct the three-dimensional (3D) complex X-ray refractive index distribution of tissue. Almost all existing
image reconstruction algorithms for quantitative phase-contrast tomography make physical assumptions that are
not consistent with benchtop or clinical implementations that employ an X-ray tube. Such assumptions include a
monochromatic plane-wave X-ray beam that possesses perfect coherence properties. In this work, we implement
and investigate a reconstruction theory for quantitative
phase-contrast tomography that is suitable for use with
polychromatic X-ray beams produced by a tube source. An image reconstruction algorithm is implemented that
requires, as input data, two intensity measurements at each tomographic view that correspond to incident X-ray
beams with distinct coherence properties. Computer-simulation studies that emulate polychromatic tube-based
imaging conditions are conducted to assess the effectiveness of the reconstruction method for characterizing soft
tissue structures.
We investigate an algorithm for boundary-enhanced image reconstruction from limited-angle projection data in
X-ray phase-contrast tomography. The algorithm exploits the fact that the imaging model of phase-contrast
tomography establishes a sparse representation of the object that facilitates accurate image reconstruction form
highly incomplete measurement data. The developed algorithm may therefore benefit a wide range of X-ray
phase-contrast tomography applications by dramatically reducing data-acquisition times and limiting radiation
dose.
Quantitative in-line phase-contrast imaging methods seek to reconstruct separate images that depict an object's absorption and real-valued refractive index distributions. An understanding of the statistical properties of images in planar and tomographic implementations of X-ray phase-contrast imaging is required for optimizing system and algorithm designs using task-based measures of image quality. In this work, the statistical properties of phase-contrast imaging are investigated by use of analytical and computer-simulation methods.
X-ray in-line phase-contrast imaging is a technique that aims to reconstruct the projected absorption and refraction
properties of an object. To achieve this, phase retrieval algorithms are employed. The statistical properties
of the reconstructed images in phase-contrast imaging remain largely unexplored. In this work, the covariance
structure of the absorption and refractive index images is derived analytically, to characterize the noise texture in
quantitative in-line phase-contrast imaging. This information is utilized to investigate how object detectability
is affected by specification of imaging geometry.
Photoacoustic tomography (PAT), also referred to optoacoustic tomography, is a hybrid imaging technique that
combines an optical contrast mechanism and ultrasonic detection principles. The laser-induced photoacoustic
signals in PAT are broadband in nature, but only a bandpass approximation of the signal is recorded by use
of a conventional ultrasonic transducers due to its limited bandwidth. To circumvent this, a PAT system
has been developed that records photoacoustic signals by use of multiple ultrasonic transducers that possess
different central frequencies. In this work, we investigate a sensor fusion methodology for combining the multiple
measurements to obtain an estimate of the true photoacoustic signal that is superior to that obtainable by use
of any single transducer measurement. From the estimated photoacoustic signals, three-dimensional images of
the optical absorption distribution are reconstructed and are found to possess improved accuracy and statistical
properties as compared to the single transducer case. Preliminary computer-simulation studies are presented to
demonstrate and investigate the proposed method.
Propagation-based phase-contrast tomography is a coherent imaging method that seeks to reconstruct the
three-dimensional complex-valued refractive index distribution of an object. Measurements of the transmitted
wavefield intensities on two parallel detector-planes at each tomographic view angle are utilized to determine
the wavefield's complex amplitude, which represent the projection data utilized for tomographic reconstruction.
The mathematical formulas employed to determine the complex amplitude contain Fourier domain singularities
that can result in greatly amplified noise levels in the reconstructed images. In this article, statistically optimal
reconstruction methods that employ multiple (>2) detector-planes are developed that mitigate the noise
amplification effects due to singularities in the reconstruction formulas. These reconstruction methods permit
exploitation of statistically complementary information in a collection of in-line holographic measurement data,
resulting in images that can have dramatically reduced noise levels. Computer-simulation studies are conducted
to demonstrate and investigate quantitatively the developed reconstruction methods.
Diffraction enhanced imaging (DEI) is an analyzer-based X-ray phase-contrast imaging method that measures
the absorption and refractive properties of an object. A well-known limitation of DEI is that it does not account
for ultra-small-angle X-ray scattering (USAXS), which is produced commonly by biological tissue. In this work,
an extended DEI (E-DEI) imaging method is described that attempts to circumvent this limitation. The EDEI
method concurrently reconstructs three images that depict an object's projected absorption, refraction,
and USAXS properties, and can be viewed as an implementation of the multiple-image radiography (MIR)
paradigm. Planar and computed tomography (CT) implementations of E-DEI and an existing MIR method are
compared by use of computer-simulation studies that employ statistical models to describe USAXS effects.
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