The use of cone-beam computed tomography (CBCT) to inspect objects containing aluminum, Inconel and other metals is a challenge due to high amounts of scattered radiation in the projection data. Following in the footsteps of VSHARP, our kernel-based two-dimensional (2D) software scatter correction algorithm that operates on x-ray projection data, we introduce three-dimensional (3D) VSHARP designed to offer improved accuracy and flexibility. The core of 3D VSHARP is fast finite element Linear Boltzmann Transport Equation (LBTE) solver that models photon transport within the object-of-interest and computes the scatter component of the projection data with Monte Carlo-like accuracy but greatly reduced computation times. 3D VSHARP utilizes an object model consisting of 3D material density maps based on a first pass reconstruction, and an imaging chain model describing the system geometry, the x-ray source components (polychromatic spectrum, beam filtration, and collimation hardware), and the x-ray detector components (flat-panel imager in this case). The 3D material density maps making up the object model may be further informed by Computer Aided Design (CAD) files. Validation against Monte Carlo (MC) simulations of overlapping metal blocks showed that 3D VSHARP- and MC-computed scatter fractions agreed to within 3% RMS. The 3D VSHARP execution time was less than 1 sec per projection using a single NVidia TitanXp GPU (NVidia Inc., Santa Clara CA) while the MC simulations took 1566 CPU-hours using two Intel-Silver-Xenon, 2.2 GHz, 10x core CPU (Intel Inc., Santa Clara CA), in a Dell 7920 workstation (Dell Inc., Austin TX). Experimental CBCT scans were taken of an aluminum motorcycle cylinder head and Inconel turbine blade at 450kVp. Results showed that high quality reconstructions and volume renderings were obtained which may open up the possibility of in-line CBCT applications.

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.

Digital detector arrays are used for industrial radiography and cone beam CT. Scattering and beam hardening artifacts reduce the achievable contrast sensitivity and can severely limit the size of the part to be scanned or require large amounts of shielding to block scatter outside the inspection area of interest. Varian’s image processing toolkit contains a set of dll’s that can easily be deployed to correct radiographs and enhance CT system performance. While this image processing package has been established in the medical area it is not starting to be applied to industrial imaging. Recently, a study was performed on a selection of aluminum industrial parts at 225 kV where computed scatter and beam hardening corrections were shown to improve DR contrast sensitivity and CT number uniformity. In this study, new results are presented based on studies of steel and aluminum parts with 225 kV and 950 kV x-ray sources. Additionally, the effect of a resolution enhancement algorithm (REA) that corrects the light spread from thicker scintillator screens and allows the user to take advantage of higher efficiency screens without sacrificing spatial resolution is evaluated.

Digital tomosynthesis is a three-dimensional imaging technique with a lower radiation dose than computed tomography (CT). Due to the missing data in tomosynthesis systems, out-of-plane structures in the depth direction cannot be completely removed by the reconstruction algorithms. In this work, we analyzed the impulse responses of common tomosynthesis systems on a plane-to-plane basis and proposed a fast and accurate convolution-based blur-and-add (BAA) model to simulate the backprojected images. In addition, the analysis formalism describing the impulse response of out-of-plane structures can be generalized to both rotating and parallel gantries. We implemented a ray tracing forward projection and backprojection (ray-based model) algorithm and the convolution-based BAA model to simulate the shift-and-add (backproject) tomosynthesis reconstructions. The convolution-based BAA model with proper geometry distortion correction provides reasonably accurate estimates of the tomosynthesis reconstruction. A numerical comparison indicates that the simulated images using the two models differ by less than 6% in terms of the root-mean-squared error. This convolution-based BAA model can be used in efficient system geometry analysis, reconstruction algorithm design, out-of-plane artifacts suppression, and CT-tomosynthesis registration.

PURPOSE An improved method of image guidance for lung tumor biopsies could help reduce the high rate of false negatives. The aim of this work is to optimize the geometry of the scanning-beam digital tomography system (SBDX) for providing real-time 3D tomographic reconstructions for target verification. The unique geometry of the system requires trade-offs between patient dose, imaging field of view (FOV), and tomographic angle. METHODS Tomosynthetic angle as a function of tumor-to-detector distance was calculated. Monte Carlo Software (PCXMC) was used to calculate organ doses and effective dose for source-to-detector distances (SDDs) from 90 to 150 cm, patient locations with the tumor at 20 cm from the source to 20 cm from the detector, and FOVs centered on left lung and right lung as well as medial and distal peripheries of the lungs. These calculations were done for two systems, a SBDX system and a GE OEC-9800 C-arm fluoroscopic unit. To evaluate the dose effect of the system geometry, results from PCXMC were calculated using a scan of 300 mAs for both SBDX and fluoroscopy. The Rose Criterion was used to find the fluence required for a tumor SNR of 5, factoring in scatter, air-gap, system geometry, and patient position for all models generated with PCXMC. Using the calculated fluence for constant tumor SNR, the results from PCXMC were used to compare the patient dose for a given SNR between SBDX and fluoroscopy. RESULTS Tomographic angle changes with SDD only in the region near the detector. Due to their geometry, the source array and detector have a peak tomographic angle for any given SDD at a source to tumor distance that is 69.7% of the SDD assuming constant source and detector size. Changing the patient location in order to increase tomographic angle has a significant effect on organ dose distribution due to geometrical considerations. With SBDX and fluoroscopy geometries, the dose to organs typically changes in an opposing manner with changing patient location. When tumor SNR is held constant (i.e., x-ray fluence is scaled appropriately), SBDX gives 2-10 times less dose than fluoroscopy for the same conditions within the typical range of patient locations. The relative position of the patient (as a percent of SDD) has a much more significant impact on dose than either SDD or patient position. The patient position providing the minimum dose for a given tumor SNR and SDD is approximately the same as the position of maximum tomographic angle. CONCLUSIONS SBDX offers a significant dose advantage over currently used C-arm fluoroscopy. The patient location with lowest dose coincides with the location of maximum tomographic angle. In order to provide adequate space for the patient and for the pulmonologists' equipment, a SDD of 100 cm is recommended.

We investigate a real-time digital tomosynthesis (DTS) imaging modality, based on the scanning beam digital x-ray (SBDX) hardware, used in conjunction with an electromagnetic navigation bronchoscopy (ENB) system to provide improved image guidance for minimally invasive transbronchial needle biopsy (TBNbx). Because the SBDX system source uses electron beams, steered by electromagnets, to generate x-rays, and the ENB system generates an electromagnetic field to localize and track steerable navigation catheters, the two systems will affect each other when operated in proximity. We first investigate the compatibility of the systems by measuring the ENB system localization error as a function of distance between the two systems. The SBDX system reconstructs DTS images, which provide depth information, and so we investigate the improvement in lung nodule visualization using SBDX system DTS images and compare them to fluoroscopic images currently used for biopsy verification. Target localization error remains below 2mm (or virtually error free) if the volume-of-interest (VOI) is at least 50cm away from the SBDX system source and detector. Inside this region, tomographic angle ranges from 3 degrees to 10 degrees depending on the VOI location. Improved lung nodule (<= 20mm diameter) contrast is achieved by imaging the VOI near the SBDX system detector, where the tomographic angle is maximized. The combination of the SBDX image guidance with an ENB system would provide real-time visualization during biopsy with improved localization of the target and needle/biopsy instruments, thereby increasing the average and lowering the variance of the yield for TBNbx.

PURPOSEAn iterative tomographic reconstruction algorithm that simultaneously segments and reconstructs the reconstruction domain is proposed and applied to tomographic reconstructions from a sparse number of projection images.METHODSThe proposed algorithm uses a two-phase level set method segmentation in conjunction with an iterative tomographic reconstruction to achieve simultaneous segmentation and reconstruction. The simultaneous segmentation and reconstruction is achieved by alternating between level set function evolutions and per-region intensity value updates. To deal with the limited number of projections, a priori information about the reconstruction is enforced via penalized likelihood function. Specifically, smooth function within each region (piecewise smooth function) and bounded function intensity values for each region are assumed. Such a priori information is formulated into a quadratic objective function with linear bound constraints. The level set function evolutions are achieved by artificially time evolving the level set function in the negative gradient direction; the intensity value updates are achieved by using the gradient projection conjugate gradient algorithm.RESULTSThe proposed simultaneous segmentation and reconstruction results were compared to "conventional" iterative reconstruction (with no segmentation), iterative reconstruction followed by segmentation, and filtered backprojection. Improvements of 6%-13% in the normalized root mean square error were observed when the proposed algorithm was applied to simulated projections of a numerical phantom and to real fan-beam projections of the Catphan phantom, both of which did not satisfy the a priori assumptions.CONCLUSIONSThe proposed simultaneous segmentation and reconstruction resulted in improved reconstruction image quality. The algorithm correctly segments the reconstruction space into regions, preserves sharp edges between different regions, and smoothes the noise within each region. The proposed algorithm framework has the flexibility to be adapted to different a priori constraints while maintaining the benefits achieved by the simultaneous segmentation and reconstruction.

PURPOSE:The authors had previously published measurements of the detectability of disk-shaped contrast objects in images obtained from a C-arm CT system. A simple approach based on Rose's criterion was used to scale the date, assuming the threshold for the smallest diameter detected should be inversely proportional to (dose)1/2. A more detailed analysis based on recent theoretical modeling of C-arm CT images is presented in this work.METHODS:The signal and noise propagations in a C-arm based CT system have been formulated by other authors using cascaded systems analysis. They established a relationship between detectability and the noise equivalent quanta. Based on this model, the authors obtained a relation between x-ray dose and the diameter of the smallest disks detected. A closed form solution was established by assuming no rebinning and no resampling of data, with low additive noise and using a ramp filter. For the case when no such assumptions were made, a numerically calculated solution using previously reported imaging and reconstruction parameters was obtained. The detection probabilities for a range of dose and kVp values had been measured previously. These probabilities were normalized to a single dose of 56.6 mGy using the Rose-criteria-based relation to obtain a universal curve. Normalizations based on the new numerically calculated relationship were compared to the measured results.RESULTS:The theoretical and numerical calculations have similar results and predict the detected diameter size to be inversely proportional to (dose)1/3 and (dose)1/2.8, respectively. The normalized experimental curves and the associated universal plot using the new relation were not significantly different from those obtained using the Rose-criterion-based normalization.CONCLUSIONS:From numerical simulations, the authors found that the diameter of detected disks depends inversely on the cube root of the dose. For observer studies for disks larger than 4 mm, the cube root as well as square root relations appear to give similar results when used for normalization.

Tomosynthesis is an imaging technique that has gained renewed interest with recent advancements of flat-panel digital detectors. Because of the wide range of potential applications, a systematic analysis of 3D tomosynthesis imaging systems would contribute to the understanding and development. This paper extends a systematic evaluation of thoracic tomosynthetic imaging performance as a function of imaging parameters, such as the number of projections, tomosynthesis orbital extent, and reconstruction filters. We evaluate lung nodule detectability and anatomical clutter as a function of tomosynthesis orbital extent using anthropomorphic phantoms and a table-top acquisition system. Tomosynthesis coronal slices were reconstructed using the FDK algorithm for cone-beam geometry from 91 projections uniformly distributed over acquisition orbital extents (θ) ranging from 10° to 180°. Visual comparisons of different tomosynthesis reconstructions of a lung nodule show the progressive decrease of anatomical clutter as θ increases. Additionally, three quantitative figures of merit were computed and compared: signal-difference-to-noise ratio (SDNR), anatomical clutter power spectrum (PS), and theoretical detectability index (DI). Lung nodule SDNR increases as θ increases from 0° to 120°. Anatomical clutter PS shows that the clutter magnitude and correlation decrease as θ increases, increasing detectability. Similarly, 2D and 3D DI increase as θ increases in the anatomical dominated exposure ranges. On the other hand, 2D slice DI is lower than the 3D DI for larger θ (e.g. 120°), because of the information loss in the depth direction for 2D slices. In other words, inspecting 3D is better for larger acquisition orbital extents, because the extra information acquired at larger angles cannot be fully recovered from 2D tomosynthesis reconstruction slices. In summary, detectability in tomosynthesis reconstructions for thoracic imaging increases as fixed dose is distributed over a larger acquisition orbital extent (up to 120°).

Phantoms are widely used during the development of new imaging systems and algorithms. For development and optimization of new imaging systems such as tomosynthesis, where conventional image quality metrics may not be applicable, a realistic phantom that can be used across imaging systems is desirable. A novel anthropomorphic lung phantom was developed by plastination of an actual pig lung. The plastinated phantom is characterized and compared with reference to in vivo images of the same tissue prior to plastination using high resolution 3D CT. The phantom is stable over time and preserves the anatomical features and relative locations of the in vivo sample. The volumes for different tissue types in the phantom are comparable to the in vivo counterparts, and CT numbers for different tissue types fall within a clinically useful range. Based on the measured CT numbers, the phantom cardiac tissue experienced a 92% decrease in bulk density and the phantom pulmonary tissue experienced a 78% decrease in bulk density compared to their in vivo counterparts. By-products in the phantom from the room temperature vulcanizing silicone and plastination process are also identified. A second generation phantom, which eliminates most of the by-products, is presented. Such anthropomorphic phantoms can be used to evaluate a wide range of novel imaging systems.

This study examines the dynamic properties extracted from a suite of reinforced concrete buildings in South Korea, with emphasis on the performance of system identification approaches applied to short duration ambient records. The resulting extracted properties are then analyzed to identify trends relating damping and frequency to parameters characterizing the structural system and building geometry to highlight underlying behaviors and establish predictive tools suitable for use in the design stage. Comparisons between finite element models and in-situ values are provided in select cases to demonstrate challenges associated with modeling reinforced concrete structures.

A 3D reconstruction formula has been derived for a circular cone-beam (CB) short scan using 1D shift-invariant filtering, CB backprojection, and equal weighting. By first converting the divergent projections to parallel projections, we analyze the circular CB data using the classic central slice theorem. The sampling density in Fourier space is investigated and 1D shift-invariant filtering before backprojection can be used to compensate for the nonuniformity. The final formula consists of a conventional FDK reconstruction and a correction term using differential backprojection and the 1D Hilbert transform in the image domain. On a full scan, the approach reduces to the FDK algorithm, while for a short scan, the CB artifacts are suppressed by the second term. This algorithm outperforms the modified FDK algorithm with Parker's weighting, as illustrated by computer simulations and experimental results. Due to its shift-invariant filtered-backprojection structure, the proposed algorithm is implemented efficiently, and requires a simple adaptation of the FDK algorithm. (c) 2007 American Association of Physicists in Medicine.

We propose an iterative tomographic reconstruction algorithm from sparse angularly sampled projections for applications where the underlying data is well approximated as a piecewise constant function. We impose this a priori constraint of the underlying data by using the multiphase level set framework introduced by Vese et al. As a result, level set method is incorporated into the updates of the proposed iterative reconstruction algorithm. Using our proposed algorithm, we reconstruct from 13 projections of a numerical chest phantom uniformly sampled over 180deg and compare it with reconstructions by unfiltered backprojection, filtered backprojection, and maximum likelihood expectation maximization (MLEM) algorithm. Results show that there is no loss of reconstruction quality for the noise-free case and improved image quality for the noisy case. Our results are promising for a broad spectrum of applications where the number of projections are inherently limited.

Mathematical observers that track human performance can be used to reduce the number of human observer studies needed to optimize imaging systems. The performance of human observers for the detection of a 3.6 mm lung nodule in anatomical backgrounds was measured as a function of varying tomosynthetic angle and compared with mathematical observers. The human observer results showed a dramatic increase in the percent of correct responses, from 80% in the projection images to 96% in the projection images with a tomosynthetic angle of just 3 degrees. This result suggests the potential usefulness of the scanned beam digital x-ray system for this application. Given the small number of images (40) used per tomosynthetic angle and the highly nonstationary statistical nature of the backgrounds, the nonprewhitening eye observer achieved a higher performance than the channelized Hotelling observer using a Laguerre-Gauss basis. The channelized Hotelling observer with internal noise and the eye filter matched to the projection data were shown to track human performance as the tomosynthetic angle changed. The validation of these mathematical observers extends their applicability to the optimization of tomosynthesis systems.

We are currently investigating the application of tomosynthesis to lung nodule detection using technology developed for the Scanning-Beam Digital X-ray (SBDX) system[1]. For system understanding and optimization, the interplay of various parameters must be investigated via simulations. We present a fast image-based SBDX system simulation model that produces equivalent tomosynthesis reconstructions to those from a physics-based model. Comparison between the two models were made using the central 75% of the reconstructed images. After applying geometric corrections arising from the SBDX system geometry, image-based model results were different by less than 3% and computed more than 10 times faster than physics-based model with comparable quality results. This work provides groundwork for SBDX system optimization for lung nodule detection. Furthermore, such analysis can be generalized to any tomosynthesis system for which the acquisition geometry is well known.