We have developed VSHARP®, a suite of scatter correction solutions that have been incorporated into the commercially available cone-beam software development toolkit, CST (Varex Imaging, Salt Lake City, UT) enabling scatter correction to be applied as part of an entire CBCT reconstruction pipeline. The suite includes 2D VSHARP®, a deconvolution correction using asymmetric Gaussian kernels, 2D VSHARP-ML, a U-NET machine-learning correction, and 3D VSHARP®, a correction using a rapid finite-element Linear Boltzmann Transport Equation (LBTE) solver to estimate scatter in a manner similar to traditional stochastic Monte Carlo (MC) simulations. Of the three corrections, 3D VSHARP is the most accurate and flexible since it can be readily applied to arbitrary scanner geometries, protocols, and scan parts while the 2D VSHARP models may need to be regenerated for each configuration. On the other hand, 3D VSHARP is inherently slower since a minimum of two reconstruction passes are needed and the LBTE solver, while much faster than traditional MC, is still computationally intensive. The goal of this work was to minimize LBTE run times for (typically large) industrial datasets by optimizing parameter settings, particularly the choice of the sampling grid dimensions. This was achieved by applying a multi-objective genetic algorithm to find the Pareto front characterizing the tradeoff between speed and accuracy and identifying key operating points on the curve. Testing with 720 frames of 3720x3720 projection data to make a reconstruction volume of size 500x500x600, we found that excellent image quality can be obtained by using a coarse scatter grid size of 27x27x32 volume and 44x44 detector and a primary grid size of 246x246 x295 volume and 295x295 detector, both over 42 frames for a grand total of 21 seconds LBTE computation time. We show the Pareto characterization, as well as demonstrations of 3D VSHARP image quality with significantly reduced scatter-induced artifacts such as streaking and shading.
Chest radiography (CXR) plays an important role in triage, management, and monitoring of patients with COVID-19. In this work, we use a dual-layer (DL) flat-panel detector to perform artifact-free single-exposure DE CXR for COVID-19 detection. A simulation study was performed to generate DE CXRs from CT volumes of 13 patients diagnosed with COVID-19, which were compared with simulated conventional CXR by two chest radiologists. A phantom study was also conducted using an anthropomorphic chest phantom with synthetic opacifications. Improved visualization of opacities was observed for DE CXRs in both studies, indicating that the proposed DL detector could be a powerful new tool for COVID-19 diagnosis and patient management.
High quality cone-beam tomography (CBCT) reconstruction requires accurately estimating and subtracting the (often) large amount of scatter from the raw projection data. Although considerable attention has been paid to scatter correction algorithm development over the past several years, there still exists the need for a practical, general-purpose tool that is accurate, fast, and requires minimal calibration. Here, we introduce 3D VSHARP® which utilizes a finite element solver of the Linear Boltzmann Transport Equation (LBTE) to accurately and rapidly simulate photon transport through a model of the object being scanned and then scale and subtract the estimated scatter from raw projections. 3D VSHARP has been incorporated into the commercially available reconstruction software development toolkit, CST (Varex Imaging, Salt Lake City, UT) enabling scatter correction to be applied to arbitrary scanner configurations and geometries as part of an entire reconstruction pipeline. To set parameters for 3D VSHARP, the user chooses from a library of files that describe key physical aspects of the CT system, including its x-ray spectrum, detector response, and, if they exist, bowtie filter, and anti-scatter grid. The object model, which characterizes the spatial distribution of the atomic number and density of the scanned object, is automatically generated from the first-pass reconstruction which may, if desired, include CST’s existing kernel-based scatter correction 2D VSHARP®. We describe the new correction tool and show example reconstructions. High accuracy of scatter correction and excellent image quality were achieved with total reconstruction times on the order of 1 minute.
In this work, we present a novel model-based material decomposition (MBMD) approach for x-ray CT that includes system blur in the measurement model. Such processing has the potential to extend spatial resolution in material density estimates - particularly in systems where different spectral channels exhibit different spatial resolutions. We illustrate this new approach for a dual-layer detector x-ray CT and compare MBMD algorithms with and without blur in the reconstruction forward model. Both qualitative and quantitative comparisons of performance with and without blur modeling are reported. We find that blur modeling yields images with better recovery of high-resolution structures in an investigation of reconstructed line pairs as well as lower cross-talk bias between material bases that is ordinarily found due to mismatches in spatial resolution between spectral channels. The extended spatial resolution of the material decompositions has potential application in a range of high-resolution clinical tasks and spectral CT systems where spectral channels exhibit different spatial resolutions.
Cone-beam CT (CBCT) is widely used in diagnostic imaging and image-guided procedures, leading to an increasing need for advanced CBCT techniques, such as dual energy (DE) imaging. Previous studies have shown that DECBCT can perform quantitative material decomposition, including quantification of contrast agents, electron density, and virtual monoenergetic images. Currently, most CBCT systems perform DE imaging using a kVp switching technique. However, the disadvantages of this method are spatial and temporal misregistration as well as total scan time increase, leading to errors in the material decomposition. DE-CBCT with a dual layer flat panel detector potentially overcomes these limitations by acquiring the dual energy images simultaneously. In this work, we investigate the DE imaging performance of a prototype dual layer detector by evaluating its material decomposition capability and comparing its performance to that of the kVp switching method. Two sets of x-ray spectra were used for kVp switching: 80/120 kVp and 80/120 kVp + 1 mm Cu filtration. Our results show the dual layer detector outperforms kVp switching at 80/120 kVp with matched dose. The performance of kVp switching was better by adding 1 mm copper filtration to the high energy images (80/120 kVp + 1 mm Cu), though the dual layer detector still provided comparable performance for material decomposition tasks. Overall, both the dual layer detector and kVp switching methods provided quantitative material decomposition images in DE-CBCT, with the dual layer detector having additional potential advantages.
Metal artifact remains a challenge in cone-beam CT images. Many two-pass metal artifact reduction methods have been proposed, which work fairly well, but are limited when the metal is outside the scan field-of-view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Furthermore, two-pass methods increase the total reconstruction time. In this study, a projection-based metal detection and correction method with a dual layer detector is investigated. The dual layer detector provides dual energy images with perfect temporal and spatial registration in each projection, which aid in the identification of metal. A simple phantom with metal wires (copper) and a needle (steel) is used to evaluate the projection-based metal artifact reduction method from a dual layer scan and compared with that of a single layer scan. Preliminary results showed enhanced ability to identify metal regions, leading to substantially reduced metal artifact in reconstructed images. In summary, an effective single-pass, projection-domain method using a dual layer detector has been demonstrated, and it is expected to be robust against truncation and motion.
Dual Energy (DE) imaging has been widely used in digital radiography and fluoroscopy, as has dual energy CT for various medical applications. In this study, the imaging performance of a dynamic dual-layer a-Si flat panel detector (FPD) prototype was characterized for dual energy imaging tasks. Dual energy cone beam CT (DE CBCT) scans were acquired and used to perform material decomposition in the projection domain, followed by reconstruction to generate material specific and virtual monoenergetic (VM) images. The dual-layer FPD prototype was built on a Varex XRD 4343RF detector by adding a 200 μm thick CsI scintillator and a-Si panel of 150 μm pixel size on top as a low energy detector. A 1 mm copper filter was added as a middle layer to increase energy separation with the bottom layer as a high energy detector. The imaging performance, such as Modulation Transfer Function (MTF), Conversion Factor (CF), and Detector Quantum Efficiency (DQE) of both the top and bottom detector layers were characterized and compared with those of the standard single layer XRD4343 RF detector. Several tissue equivalent cylinders (solid water, liquid water, bone, acrylic, polyethylene, etc.) were placed on a rotating stand, and two separate 450-projection CBCT scans were performed under continuous 120 kV and 80 kV X-ray beams. After an empirical material decomposition calibration, water and bone images were generated for each projection, and their respective volumes were reconstructed using Varex’s CBCT Software Tools (CST 2.0). A VM image, which maximized the contrast-to-noise ratio of water to polyethylene, was generated based on the water and bone images. The MTF at 1.0 lp/mm from the low energy detector was 32% and 22% higher than the high energy detector and the standard detector, respectively; the DQE of both high and low energy detectors is much lower than that of the standard XRD 4343RF detector. The CNR of water to polyethylene from the VM image improved by 50% over that from the low energy image alone at 120 kV, and by 80% at 80 kV. This study demonstrates the feasibility of using a dual-layer FPD in applications such as DE CBCT for contrast enhancement and material decomposition. Further evaluations are underway.
Modern amorphous silicon flat panel-based electronic portal imaging devices that utilize thin gadolinium oxysulfide scintillators suffer from low quantum efficiencies (QEs). Thick two dimensionally (2D) pixelated scintillator arrays offer an effective but expensive option for increasing QE. To reduce costs, we have investigated the possibility of combining a thick one dimensional (1D) pixelated scintillator (PS) with an orthogonally placed 1D structured optical filter to provide for overall good 2D spatial resolution. In this work, we studied the potential for using a 1D video screen privacy film (PF) to serve as a directional optical attenuator and filter. A Geant4 model of the PF was built based on reflection and transmission measurements taken with a laser-based optical reflectometer. This information was incorporated into a Geant4-based x-ray detector simulator to generate modulation transfer functions (MTFs), noise power spectra (NPS), and detective quantum efficiencies (DQEs) for various 1D and 2D configurations. It was found that the 1D array with PF can provide the MTFs and DQEs of 2D arrays. Although the PF significantly reduced the amount of optical photons detected by the flat panel, we anticipate using a scintillator with an inherently high optical yield (e.g. cesium iodide) for MV imaging, where fluence rates are inherently high, will still provide adequate signal intensities for the imaging tasks associated with radiotherapy.
The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was −7%, with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.
The overall goal of this work is to develop a rapid, accurate and fully automated software tool to
estimate patient-specific organ doses from computed tomography (CT) scans using a deterministic
Boltzmann Transport Equation solver and automated CT segmentation algorithms. This work
quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm.
The investigated algorithm uses a combination of feature-based and atlas-based methods. A multiatlas
approach was also investigated. We hypothesize that the auto-segmentation algorithm is
sufficiently accurate to provide organ dose estimates since random errors at the organ boundaries
will average out when computing the total organ dose. To test this hypothesis, twenty head-neck CT
scans were expertly segmented into nine regions. A leave-one-out validation study was performed,
where every case was automatically segmented with each of the remaining cases used as the expert
atlas, resulting in nineteen automated segmentations for each of the twenty datasets. The segmented
regions were applied to gold-standard Monte Carlo dose maps to estimate mean and peak organ
doses. The results demonstrated that the fully automated segmentation algorithm estimated the mean
organ dose to within 10% of the expert segmentation for regions other than the spinal canal, with
median error for each organ region below 2%. In the spinal canal region, the median error was 7%
across all data sets and atlases, with a maximum error of 20%. The error in peak organ dose was
below 10% for all regions, with a median error below 4% for all organ regions. The multiple-case
atlas reduced the variation in the dose estimates and additional improvements may be possible with
more robust multi-atlas approaches. Overall, the results support potential feasibility of an automated
segmentation algorithm to provide accurate organ dose estimates.
Striped ratio grids are a new concept for scatter management in cone-beam CT. These grids are a modification of
conventional anti-scatter grids and consist of stripes which alternate between high grid ratio and low grid ratio. Such a
grid is related to existing hardware concepts for scatter estimation such as blocker-based methods or primary
modulation, but rather than modulating the primary, the striped ratio grid modulates the scatter. The transitions between
adjacent stripes can be used to estimate and subtract the remaining scatter. However, these transitions could be
contaminated by variation in the primary radiation. We describe a simple nonlinear image processing algorithm to
estimate scatter, and proceed to validate the striped ratio grid on experimental data of a pelvic phantom. The striped ratio
grid is emulated by combining data from two scans with different grids. Preliminary results are encouraging and show a
significant reduction of scatter artifact.
Flat panel imagers based on amorphous silicon technology (a-Si) for digital radiography are accepted by the medical
and industrial community as having several advantages over radiographic film-based systems. Use of Mega-voltage
x-rays with these flat panel systems is applicable to both portal imaging for radiotherapy and for nondestructive
testing (NDT) and security applications. In the medical field, one potential application that has not been greatly
explored is to radiotherapy treatment planning. Currently, such conventional computed tomographic (CT) data
acquired at kV energies is used to help delineate tumor targets and normal structures that are to be spared during
treatment. CT number accuracy is crucial for radiotherapy dose calculations. Conventional CT scanners operating at
kV X-ray energies typically exhibit significant image reconstruction artifacts in the presence of metal implants in
human body. Using the X-ray treatment beams, having energies typically ≥6MV, to acquire the CT data may not be
practical if it is desired to maintain contrast sensitivity at a sufficiently low dose. Nondestructive testing imaging
systems can expand their application space with the development of the higher energy accelerator for use in
pipeline, and casting inspection as well as certain cargo screening applications that require more penetration. A new
prototype x-band BCL designed to operate up to 1.75 MV has been designed built and tested. The BCL was tested
with a prototype portal imager and medical phantoms to determine artifact reductions and a PaxScan 2530HE
industrial imager to demonstrate resolution is maintained and penetration is improved.
KEYWORDS: Breast, Mammography, Digital mammography, Monte Carlo methods, Sensors, Image quality, Signal detection, Signal attenuation, Image restoration, Point spread functions
Scattered radiation remains one of the primary challenges for digital mammography, resulting in decreased image contrast and visualization of key features. While anti-scatter grids are commonly used to reduce scattered radiation in digital mammography, they are an incomplete solution that can add radiation dose, cost, and complexity. Instead, a software-based scatter correction method utilizing asymmetric scatter kernels is developed and evaluated in this work, which improves upon conventional symmetric kernels by adapting to local variations in object thickness and attenuation that result from the heterogeneous nature of breast tissue. This fast adaptive scatter kernel superposition (fASKS) method was applied to mammography by generating scatter kernels specific to the object size, x-ray energy, and system geometry of the projection data. The method was first validated with Monte Carlo simulation of a statistically-defined digital breast phantom, which was followed by initial validation on phantom studies conducted on a clinical mammography system. Results from the Monte Carlo simulation demonstrate excellent agreement between the estimated and true scatter signal, resulting in accurate scatter correction and recovery of 87% of the image contrast originally lost to scatter. Additionally, the asymmetric kernel provided more accurate scatter correction than the conventional symmetric kernel, especially at the edge of the breast. Results from the phantom studies on a clinical system further validate the ability of the asymmetric kernel correction method to accurately subtract the scatter signal and improve image quality. In conclusion, software-based scatter correction for mammography is a promising alternative to hardware-based approaches such as anti-scatter grids.
In order to predict and improve the performance of pixelated detectors, it is important to understand the optical
properties of the basic unit of the scintillating structure in the detector. To measure one of the essential optical properties,
reflectance, we have used a device composed of a laser and photodiode array. We have also developed an analytical
model of the optical phenomena based on Snell's law and the Fresnel equations to simply analyze measured results and
reflectance parameters at the interface. The computed and experimentally measured results typically have good
agreement, validating the analytical model and measurements. The optical parameters are used as inputs to GEANT4 [1].
The simulations are then leveraged to optimize an imager design before a prototype is built.
The optical reflectance was measured by using relatively inexpensive samples. A sample has scintillator, glue, and
septum (reflector) layers, and each sample has a different scintillator surface (polished/rough) and/or reflector [ESR
film/aluminum-sputtered (coated) ESR film] condition. A high-refractive-index hemisphere was attached on the top
surface of a sample to increase the maximum incidence angle at the scintillator-glue interface from 27° to 52°. The
sample including ESR film demonstrated average reflectance approximately 1.3 times higher than that from the sample
with aluminum-sputtered ESR film as a reflector, and the polished surface condition showed higher reflectance than the
rough-cut surface condition.
KEYWORDS: Monte Carlo methods, Reconstruction algorithms, Sensors, Photons, Computer simulations, Data modeling, Detection and tracking algorithms, Computed tomography, Superposition, 3D modeling
Scatter in cone-beam computed tomography (CBCT) is a significant problem that degrades image contrast, uniformity and CT number accuracy. One means of estimating and correcting for detected scatter is through an iterative deconvolution process known as scatter kernel superposition (SKS). While the SKS approach is efficient, clinically significant errors on the order 2-4% (20-40 HU) still remain. We have previously shown that the kernel method can be improved by perturbing the kernel parameters based on reference data provided by limited Monte Carlo simulations of a first-pass reconstruction. In this work, we replace the Monte Carlo modeling with a deterministic Boltzmann solver (AcurosCTS) to generate the reference scatter data in a dramatically reduced time. In addition, the algorithm is improved so that instead of adjusting kernel parameters, we directly perturb the SKS scatter estimates. Studies were conducted on simulated data and on a large pelvis phantom scanned on a tabletop system. The new method reduced average reconstruction errors (relative to a reference scan) from 2.5% to 1.8%, and significantly improved visualization of low contrast objects. In total, 24 projections were simulated with an AcurosCTS execution time of 22 sec/projection using an 8-core computer. We have ported AcurosCTS to the GPU, and current run-times are approximately 4 sec/projection using two GPU’s running in parallel.
Scintillating Fiber Optic Plates (SFOP) or Fiber Optic Scintillator (FOS) made with scintillating fiber-glass, were
investigated for x-ray imaging. Two different samples (T x W x L = 2cm x 5cm x 5cm) were used; Sample A: 10μm
fibers, Sample B: 50μm fibers both with statistically randomized light absorbing fibers placed in the matrix. A
customized holder was used to place the samples in close contact with photodiodes in an amorphous silicon flat panel
detector (AS1000, Varian), typically used for portal imaging. The detector has a 392μm pixel pitch and in the standard
configuration uses a gadolinium oxy-sulphide (GOS) screen behind a copper plate. X-ray measurements were performed
at 120kV (RQA 9 spectrum), 1MeV (5mm Al filtration) and 6MeV (Flattening Filter Free) for Sample A and the latter 2
spectra for Sample B. A machined edge was used for MTF measurements. The measurements showed the MTF
degraded with increased X-ray energies because of the increase in Compton scattering. However, at the Nyquist
frequency of 1.3lp/mm, the MTF is still high (FOS value vs. Cu+GOS): (a) 37% and 21% at 120kVp for the 10μm FOS
and the Cu+GOS arrays, (b) 31%, 20% and 20% at 1MeV and (c) 17%, 11% and 14% at 6MeV for the 10μm FOS,
50μm FOS and the Cu+GOS arrays. The DQE(0) value comparison were (a) at 120kV ~24% and ~13 % for the 10μm
FOS and the Cu+GOS arrays (b) at 1MV 10%, 10% and 7% and (c) at 6MV 12%, ~19% and 1.6% for the 10μm FOS ,
50μm FOS and Cu+GOS arrays.
X-ray scatter degrades image contrast, uniformity and CT number accuracy in cone-beam computed tomography
(CBCT). Correction methods based on the scatter kernel superposition (SKS) technique are efficient and suitable for
many clinical applications but still produce residual errors due to limitations in the scatter kernel models. To reduce
these errors, we propose to generate a first-pass reconstruction using a set of default SKS parameters followed by limited
Monte Carlo simulations that are then used to perturb and refine key kernel parameters in order to obtain an improved
second-pass correction. To test the approach, we used the fast adaptive scatter kernel model (fASKS) employing
asymmetric kernels for the first-pass scatter correction and then used GEANT4 to simulate scatter-to-primary ratios in
selected projections allowing for refined scatter estimates. The results show that a minimal number of projections require
simulation in order to adequately perturb scatter kernel parameters for all projections. Compared to the default
asymmetric kernels, the refined kernels reduced CT number errors from 24 HU to 15 HU in a large pelvis phantom
resulting in a more uniform and accurate image.
Development of the indirect scintillating detector is hindered not only by the cost and lead-time of manufacturing
but also the computational resources required for numerical modeling. The simulation is bogged down by
the number of x-ray photons (gammas) required to duplicate the experimental flood image ensemble necessary
to characterize the noise power spectrum (NPS), a key input into the detective quantum efficiency (DQE). The
simulation approach presented in this work exploits our previously reported procedure named Fujita-Lubberts-
Swank (FLS)6 . This novel technique computes the Lubberts NPS from an ensemble of single gamma point spread
functions (PSF) and, as a result, allows for a significant reduction in the number of simulated particles, enabling
full DQE(f) simulations with optical transport in less than one CPU-hour. For a given detector and spectrum,
the FLS execution time is determined primarily by the number of gamma and optical photons initiated. The optimal
number of each varies with the detector specifics. In this work, we present a different simulation paradigm
in which Geant4 was customized to allow for the user to specify the quantities of detected gammas, and detected
opticals per gamma. These quantities were empirically shown to be constant over a small selection of different
detector types. While work still needs to be done to explore the range of detectors for which this technique will
work, we demonstrate a concept which brings added convenience and efficiency to FLS detector simulations.
A challenge in using on-board cone beam computed tomography (CBCT) to image lung tumor motion prior to radiation
therapy treatment is acquiring and reconstructing high quality 4D images in a sufficiently short time for practical use.
For the 1 minute rotation times typical of Linacs, severe view aliasing artifacts, including streaks, are created if a
conventional phase-correlated FDK reconstruction is performed. The McKinnon-Bates (MKB) algorithm provides an
efficient means of reducing streaks from static tissue but can suffer from low SNR and other artifacts due to data
truncation and noise. We have added truncation correction and bilateral nonlinear filtering to the MKB algorithm to
reduce streaking and improve image quality. The modified MKB algorithm was implemented on a graphical processing
unit (GPU) to maximize efficiency. Results show that a nearly 4x improvement in SNR is obtained compared to the
conventional FDK phase-correlated reconstruction and that high quality 4D images with 0.4 second temporal resolution
and 1 mm3 isotropic spatial resolution can be reconstructed in less than 20 seconds after data acquisition completes.
KEYWORDS: Convolution, Monte Carlo methods, Sensors, Point spread functions, Atrial fibrillation, X-rays, Data modeling, Superposition, Scatter measurement, Imaging systems
X-ray cone-beam (CB) projection data often contain high amounts of scattered radiation, which must be properly
modeled in order to produce accurate computed tomography (CT) reconstructions. A well known correction technique is
the scatter kernel superposition (SKS) method that involves deconvolving projection data with kernels derived from
pencil beam-generated scatter point-spread functions. The method has the advantages of being practical and
computationally efficient but can suffer from inaccuracies. We show that the accuracy of the SKS algorithm can be
significantly improved by replacing the symmetric kernels that traditionally have been used with nonstationary
asymmetric kernels. We also show these kernels can be well approximated by combinations of stationary kernels thus
allowing for efficient implementation of convolution via FFT. To test the new algorithm, Monte Carlo simulations and
phantom experiments were performed using a table-top system with geometry and components matching those of the
Varian On-Board Imager (OBI). The results show that asymmetric kernels produced substantially improved scatter
estimates. For large objects with scatter-to-primary ratios up to 2.0, scatter profiles were estimated to within 10% of
measured values. With all corrections applied, including beam hardening and lag, the resulting accuracies of the CBCT
reconstructions were within ±25 Hounsfield Units (±2.5%).
Digital flat panel a-Si x-ray detectors can exhibit image lag of several percent. The image lag can limit the temporal
resolution of the detector, and introduce artifacts into CT reconstructions. It is believed that the majority of image lag is
due to defect states, or traps, in the a-Si layer. Software methods to characterize and correct for the image lag exist, but
they may make assumptions such as the system behaves in a linear time-invariant manner. The proposed method of
reducing lag is a hardware solution that makes few additional hardware changes. For pulsed irradiation, the proposed
method inserts a new stage in between the readout of the detector and the data collection stages. During this stage the
photodiode is operated in a forward bias mode, which fills the defect states with charge. Parameters of importance are
current per diode and current duration, which were investigated under light illumination by the following design
parameters: 1.) forward bias voltage across the photodiode and TFT switch, 2.) number of rows simultaneously forward
biased, and 3.) duration of the forward bias current. From measurements, it appears that good design criteria for the
particular imager used are 8 or fewer active rows, 2.9V (or greater) forward bias voltage, and a row frequency of 100
kHz or less. Overall, the forward bias method has been found to reduce first frame lag by as much as 95%. The panel
was also tested under x-ray irradiation. Image lag improved (94% reduction), but the temporal response of the
scintillator became evident in the turn-on step response.
Recently, we proposed a scatter correction method for x-ray imaging using primary modulation. A primary
modulator with spatially variant attenuating materials is inserted between the x-ray source and the object to
make the scatter and part of the primary distributions strongly separate in the Fourier domain. Linear filtering
and demodulation techniques suffice to extract and correct the scatter for this modified system. The method has
been verified by computer simulations and preliminary experimental results on a simple object. In this work, we
improve performance by using a new primary modulator with a higher modulation frequency and by refining the
algorithm. The improved method is evaluated experimentally using a pelvis phantom. The imaging parameters
are chosen to match the Varian Acuity CT simulator, where scatter correction has been shown to be challenging
due to complicated artifact patterns. The results using our approach are compared with those without scatter
correction, and with scatter estimated and corrected using a slit measurement as a pre-scan. The comparison
shows that the primary modulation method greatly reduces the scatter artifacts and improves image contrast.
Using only one single scan, this method achieves CT HU accuracy comparable to that obtained using a slit measurement as a pre-scan.
A unique 64-row flat panel (FP) detector has been developed for sub-second multidetector-row CT (MDCT). The intent
was to explore the image quality achievable with relatively inexpensive amorphous silicon (a-Si) compared to existing
diagnostic scanners with discrete crystalline diode detectors. The FP MDCT system is a bench-top design that consists
of three FP modules. Each module uses a 30 cm x 3.3 cm a-Si array with 576 x 64 photodiodes. The photodiodes are
0.52 mm x 0.52 mm, which allows for about twice the spatial resolution of most commercial MDCT scanners. The
modules are arranged in an overlapping geometry, which is sufficient to provide a full-fan 48 cm diameter scan. Scans
were obtained with various detachable scintillators, e.g. ceramic Gd2O2S, particle-in-binder Gd2O2S:Tb and columnar
CsI:Tl. Scan quality was evaluated with a Catphan-500 performance phantom and anthropomorphic phantoms. The FP
MDCT scans demonstrate nearly equivalent performance scans to a commercial 16-slice MDCT scanner at comparable
10 - 20 mGy/100mAs doses. Thus far, a high contrast resolution of 15 lp/cm and a low contrast resolution of 5 mm @
0.3 % have been achieved on 1 second scans. Sub-second scans have been achieved with partial rotations. Since the
future direction of MDCT appears to be in acquiring single organ coverage per scan, future efforts are planned for
increasing the number of detector rows beyond the current 64- rows.
Accurate prediction of reconstructed noise in computed tomography (CT) images is important for purposes of
system design, optimization and evaluation. A large body of work describes noise prediction methods for CT,
the vast majority of which assume stationarity of both noise and signal processes. Consequently, these methods
are usually applied to and evaluated using simple phantoms, and only a portion of the image is scrutinized.
In this work, we derive a practical method for reconstructing CT noise variance maps for arbitrary objects.
Photon Poisson noise and system electronic noise are considered. The final formula has the same structure as
that of the filtered backprojection (FBP) formula, but with different weighting factors and convolution kernels.
The algorithm is verified using computer simulations of the Shepp-Logan phantom, and a good match is found
between the predicted noise map from one single noisy scan and the measured noise using 128 noisy scans.
As compared to other proposed noise models, our complementary work provides a method of noise prediction
by simple adaptation of FBP reconstruction algorithms. The result is a tool that can be useful for system
optimization and evaluation tasks as well as the design of reconstruction filters.
KEYWORDS: Modulation transfer functions, Sensors, Prototyping, Reconstruction algorithms, Detector arrays, X-rays, Calibration, Point spread functions, 3D metrology, Data acquisition
This work investigates the modulation transfer function (MTF) of a prototype table-top inverse-geometry volumetric CT (IGCT) system. The IGCT system has been proposed to acquire sufficient volumetric data in one circular rotation using a large-area scanned source and a narrower array of fast detectors. The source and detector arrays have the same axial, or slice, extent, thus providing sufficient volumetric coverage. A prototype system has been built using a NexRay Scanning-Beam Digital X-ray system (NexRay, Inc., Los Gatos, CA) with the C-arm gantry in the horizontal position and a stage placed between the source and detector to rotate the scanned object. The resulting system has a 16-cm in-plane field of view (FOV) and 5-cm axial FOV. Two phantoms were constructed for measuring the MTF. A 76 micron tungsten wire placed axially in a plastic frame was used to measure the in-plane MTF, and the same wire slanted at 45 degrees was used to test the isotropy of the MTF. The data were calibrated for flat-field intensity and geometric misalignment and reconstructed using a modified 3D PET algorithm. For both phantoms, slices perpendicular to the wires were reconstructed. Simulations which model the IGCT system were used to verify the MTF measurement, along with analytical predictions. The measured MTF curve was similar in shape to the predicted curve with a 10% point at 20 lp/cm compared to a predicted 18 lp/cm. Future work will also study the uniformity of the MTF across the FOV and further characterize the IGCT system.
The performance of an inverse geometry volumetric CT (IGCT) system with multiple detector arrays is being
investigated. The system is capable of a complete acquisition of a volume free from cone-beam artifacts with
only a single rotation of the gantry. The IGCT system is composed of a large source array opposite three small
detector arrays with a field-of-view (FOV) large enough for clinical imaging (45cm). Simulations were conducted to estimate the MTF at different points in the FOV. The simulations involved generating 2D projection data of a 100um circular object followed by a reconstruction algorithm that uses gridding and filtered backprojection. The simulations also modeled finite source spot and detector element sizes. The estimated MTF’s were compared with theoretical MTF’s at 0 cm, 10 cm, and 20 cm away from the isocenter. The simulated MTF’s closely matched the theoretical MTF’s. The MTF in the radial direction was over 10% at 16 lp/cm across the entire FOV while the azimuthal MTF 10% point degraded to 10.4 lp/cm at the edge of the FOV. This degradation in azimuth, which can be corrected for, is due to gridding in the angular direction which is magnified at large distances away from the isocenter. The simulations show promising results for the in-plane resolution of the multiple detector array IGCT system. Noise properties and other factors impacting performance are currently being investigated.
The Scanning-Beam Digital X-ray (SBDX) system utilizes a scanning x-ray pencil beam and a small-area detector array for low-dose cardiac angiography with tomographic imaging capabilities. For the system to provide adequate signal-to-noise ratios, the multi-element detector must be highly efficient and capable of high photon count rates. Cadmium telluride (CdTe) is well suited to these purposes. The CdTe SBDX detector is a direct-conversion photon-counting device consisting of 2304 elements. The efficiency of the detector is a function of several factors including the incident photon energy, the fluorescence properties of CdTe, and the discriminator threshold that determines whether sufficient energy was deposited in an element to register a count. For maximum efficiency, the discriminator threshold must be set low enough to detect CdTe k-fluorescence photons (23-31 keV), but not so low as to register false counts from electronic noise. The purpose of this investigation was to evaluate the energy-dependent quantum detective efficiency (QDE) of a new lower-noise SBDX detector design and to determine whether adequately low thresholds can be achieved. Experiments were performed using metal fluorescer foils to generate quasi-monochromatic x-ray beams with energies of 17.5, 25.3, and 46.0 keV. The resulting spectral purities were high, although fluence rates were low. The measured QDE values at 17.5, 25.3, and 46.0 keV were 60%, 76%, and 86% repsectively.
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