Ramp Filter Research Articles

Iterative metal artifact reduction for x-ray computed tomography using unmatched projector/backprojector pairs.

Metal artifact reduction (MAR) is a major problem and a challenging issue in x-ray computed tomography (CT) examinations. Iterative reconstruction from sinograms unaffected by metals shows promising potential in detail recovery. This reconstruction has been the subject of much research in recent years. However, conventional iterative reconstruction methods easily introduce new artifacts around metal implants because of incomplete data reconstruction and inconsistencies in practical data acquisition. Hence, this work aims at developing a method to suppress newly introduced artifacts and improve the image quality around metal implants for the iterative MAR scheme. The proposed method consists of two steps based on the general iterative MAR framework. An uncorrected image is initially reconstructed, and the corresponding metal trace is obtained. The iterative reconstruction method is then used to reconstruct images from the unaffected sinogram. In the reconstruction step of this work, an iterative strategy utilizing unmatched projector/backprojector pairs is used. A ramp filter is introduced into the back-projection procedure to restrain the inconsistency components in low frequencies and generate more reliable images of the regions around metals. Furthermore, a constrained total variation (TV) minimization model is also incorporated to enhance efficiency. The proposed strategy is implemented based on an iterative FBP and an alternating direction minimization (ADM) scheme, respectively. The developed algorithms are referred to as "iFBP-TV" and "TV-FADM," respectively. Two projection-completion-based MAR methods and three iterative MAR methods are performed simultaneously for comparison. The proposed method performs reasonably on both simulation and real CT-scanned datasets. This approach could reduce streak metal artifacts effectively and avoid the mentioned effects in the vicinity of the metals. The improvements are evaluated by inspecting regions of interest and by comparing the root-mean-square errors, normalized mean absolute distance, and universal quality index metrics of the images. Both iFBP-TV and TV-FADM methods outperform other counterparts in all cases. Unlike the conventional iterative methods, the proposed strategy utilizing unmatched projector/backprojector pairs shows excellent performance in detail preservation and prevention of the introduction of new artifacts. Qualitative and quantitative evaluations of experimental results indicate that the developed method outperforms classical MAR algorithms in suppressing streak artifacts and preserving the edge structural information of the object. In particular, structures lying close to metals can be gradually recovered because of the reduction of artifacts caused by inconsistency effects.

Total Variation-Based Reduction of Streak Artifacts, Ring Artifacts and Noise in 3D Reconstruction from Optical Projection Tomography.

Optical projection tomography (OPT) is a computed tomography technique at optical frequencies for samples of 0.5-15 mm in size, which fills an important "imaging gap" between confocal microscopy (for smaller samples) and large-sample methods such as fluorescence molecular tomography or micro magnetic resonance imaging. OPT operates in either fluorescence or transmission mode. Two-dimensional (2D) projections are taken over 360° with a fixed rotational increment around the vertical axis. Standard 3D reconstruction from 2D OPT uses the filtered backprojection (FBP) algorithm based on the Radon transform. FBP approximates the inverse Radon transform using a ramp filter that spreads reconstructed pixels to neighbor pixels thus producing streak and other types of artifacts, as well as noise. Artifacts increase the variation of grayscale values in the reconstructed images. We present an algorithm that improves the quality of reconstruction even for a low number of projections by simultaneously minimizing the sum of absolute brightness changes in the reconstructed volume (the total variation) and the error between measured and reconstructed data. We demonstrate the efficiency of the method on real biological data acquired on a dedicated OPT device.

Effect of filters and reconstruction algorithms on I-124 PET in Siemens Inveon PET scanner

Purpose: To assess the effects of filtering and reconstruction on Siemens I-124 PET data.Methods: A Siemens Inveon PET was used. Spatial resolution of I-124 was measured to a transverse offset of 50 mm from the center FBP, 2D ordered subset expectation maximization (OSEM2D), 3D re-projection algorithm (3DRP), and maximum a posteriori (MAP) methods were tested. Non-uniformity (NU), recovery coefficient (RC), and spillover ratio (SOR) parameterized image quality. Mini deluxe phantom data of I-124 was also assessed. Results: Volumetric resolution was 7.3 mm3 from the transverse FOV center when FBP reconstruction algorithms with ramp filter was used. MAP yielded minimal NU with β =1.5. OSEM2D yielded maximal RC. SOR was below 4% for FBP with ramp, Hamming, Hanning, or Shepp-Logan filters. Based on the mini deluxe phantom results, an FBP with Hanning or Parzen filters, or a 3DRP with Hanning filter yielded feasible I-124 PET data.Conclusions: Reconstruction algorithms and filters were compared. FBP with Hanning or Parzen filters, or 3DRP with Hanning filter yielded feasible data for quantifying I-124 PET.

Backprojection regularization with weighted ramp filter for tomographic reconstruction.

Although filtered backprojection (FBP) is popular, backprojection then filtering (BPF) still receives a few attentions. Usually, BPF is inferior to FBP in terms of reconstruction quality. There are two main causes. First, BPF has to use a 2-dimensional discrete ramp filter formed by sampling the continuous ramp filter, resulting in DC shift and aliasing artefacts. Second, the common ramp filter amplifies high frequency noise much. To address such two issues, a weighted ramp filter is investigated to reduce the amplification of high frequency noise, and then a total-variation based backprojection regularization (BPR) method is developed to mitigate the DC shift and improve the robustness to noise. The experimental results show that BPR outperforms FBP for low-dose CT imaging reconstruction.

SU‐E‐I‐10: Investigation On Detectability of a Small Target for Different Slice Direction of a Volumetric Cone Beam CT Image

Purpose:To investigate the detectability of a small target for different slice direction of a volumetric cone beam CT image and its impact on dose reduction.Methods:Analytic projection data of a sphere object (1 mm diameter, 0.2/cm attenuation coefficient) were generated and reconstructed by FDK algorithm. In this work, we compared the detectability of the small target from four different backprojectionMethods:hanning weighted ramp filter with linear interpolation (RECON 1), hanning weighted ramp filter with Fourier interpolation (RECON2), ramp filter with linear interpolation (RECON 3), and ramp filter with Fourier interpolation (RECON4), respectively. For noise simulation, 200 photons per measurement were used, and the noise only data were reconstructed using FDK algorithm. For each reconstructed volume, axial and coronal slice were extracted and detection‐SNR was calculated using channelized Hotelling observer (CHO) with dense difference‐of‐Gaussian (D‐DOG) channels.Results:Detection‐SNR of coronal images varies for different backprojection methods, while axial images have a similar detection‐SNR. Detection‐SNR2 ratios of coronal and axial images in RECON1 and RECON2 are 1.33 and 1.15, implying that the coronal image has a better detectability than axial image. In other words, using coronal slices for the small target detection can reduce the patient dose about 33% and 15% compared to using axial slices in RECON 1 and RECON 2.Conclusion:In this work, we investigated slice direction dependent detectability of a volumetric cone beam CT image. RECON 1 and RECON 2 produced the highest detection‐SNR, with better detectability in coronal slices. These results indicate that it is more beneficial to use coronal slice to improve detectability of a small target in a volumetric cone beam CT image.This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the IT Consilience Creative Program (NIPA‐2014‐H0201‐14‐1002) supervised by the NIPA (National IT Industry Promotion Agency). Authors declares that s/he has no conflict of Interest in relation to the work in this abstract.

WE‐EF‐207‐10: Striped Ratio Grids: A New Concept for Scatter Estimation

Purpose:To propose a new method for estimating scatter in x‐ray imaging. We propose the “striped ratio grid,” an anti‐scatter grid with alternating stripes of high scatter rejection (attained, for example, by high grid ratio) and low scatter rejection. To minimize artifacts, stripes are oriented parallel to the direction of the ramp filter. Signal discontinuities at the boundaries between stripes provide information on local scatter content, although these discontinuities are contaminated by variation in primary radiation.Methods:We emulated a striped ratio grid by imaging phantoms with two sequential CT scans, one with and one without a conventional grid, and processed them together to mimic a striped ratio grid. Two phantoms were scanned with the emulated striped ratio grid and compared with a conventional anti‐scatter grid and a fan‐beam acquisition, which served as ground truth. A nonlinear image processing algorithm was developed to mitigate the problem of primary variation.Results:The emulated striped ratio grid reduced scatter more effectively than the conventional grid alone. Contrast is thereby improved in projection imaging. In CT imaging, cupping is markedly reduced. Artifacts introduced by the striped ratio grid appear to be minimal.Conclusion:Striped ratio grids could be a simple and effective evolution of conventional anti‐scatter grids. Unlike several other approaches currently under investigation for scatter management, striped ratio grids require minimal computation, little new hardware (at least for systems which already use removable grids) and impose few assumptions on the nature of the object being scanned.

An adaptive filtered back-projection for photoacoustic image reconstruction.

The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors' new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal's contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing the correct signal strength of the absorbers. The reconstructed image of the second phantom further demonstrates the capability to form clear images of the spheres with sharp borders in the overlapping geometry. The smallest sphere is clearly visible and distinguishable, even though it is surrounded by two big spheres. In addition, image reconstructions were conducted with randomized noise added to the observed signals to mimic realistic experimental conditions. The authors have developed a new FBP algorithm that is capable for reconstructing high quality images with correct relative intensities and sharp borders for PAT. The results demonstrate that the weighting function serves as a precise ramp filter for processing the observed signals in the Fourier domain. In addition, this algorithm allows an adaptive determination of the cutoff frequency for the applied low pass filter.

Revisit of the Ramp Filter.

An important part of the filtered backprojection (FBP) algorithm is the ramp filter. This paper derives the discrete version of the ramp filter in the Fourier domain and studies the windowing effects. When a window function is used to control the noise, the image amplitude will be affected and reduced. A simple remedy is proposed to improve the image accuracy when a window function must be used.

Development of simple processing for deleting undershooting artifact using the FBP method ─evaluation of simulation data─

The undershooting artifact occurs using the filtered back projection (FBP) method. This artifact is influenced by a ramp filter. Thereby, the fall of the target accumulation and a deficit arise and it becomes a clinical problem. We developed a new image reconstruction method based on the FBP method to delete the undershooting artifact of FBP. The image quality of the FBP method is equivalent to that obtained by an evaluation using a digital phantom. The two segmentation and ordinary FBP methods were evaluated in terms of hot contrast, cold contrast, coefficient of variation (%CV), and root mean square uncertainty (%RSMU). The two segmentation FBP method showed equivalent values of hot contrast, % CV, and% RSMU compared with those of the ordinary FBP method. With a threshold level value, cold contrast sharply changed. However, when the threshold level of the two segmentation FBP method was set as the proper value, 90% contrast was obtained. It is necessary to set a threshold level as a proper value using the two segmentation FBP methods. I thought that it can delete an artifact in a simple way, without impairing the image quality. However, it is an examination of only a digital phantom this time. Before using it clinically, one has to use and verify a real phantom.

The Effect of Milk, Water and Lemon Juice on Various Subdiaphragmatic Activity-Related Artifacts in Myocardial Perfusion Imaging.

Background:Subdiaphragmatic activity can produce subdiaphragmatic-related artifacts, which can degrade the quality of myocardial perfusion imaging (MPI).Objectives:We examined the impact of drinking milk, water, and lemon juice on different subdiaphragmatic-related artifacts by using 99mTc-sestamibi myocardial single-photon emission computed tomography (SPECT) in order to determine a feasible method for improving the image quality.Patients and Methods:A total of 179 patients (age 58 ± 9.6 years) were enrolled in this study. The patients were randomly divided into five groups. Ten minutes after injection of 740 MBq 99mTc-sestamibi in both pharmacologic stress and rest phases, the individuals in group 1 were given water and milk (125 mL of each); those in group 2 were given lemon juice (250 mL); group 3 was given milk (250 mL); and group 4 was given water (250 mL), whereas no intervention was performed in group 5. The study was double-blind for both subjects and data collectors. MPI was performed for all patients and image quality was controlled by 2 experienced nuclear physicians. Interfering activity was determined visually on reconstructed images and categorized as extracardiac normalization artifact, overlapping of activity, scattering of activity, and ramp filter artifact.Results:There were significant differences in terms of interfering activity among the five groups; group 3 (milk) had significantly lower interfering activity than other groups had, as defined by overlapping of activity (on both stress and rest images), ramp filter artifact (stress images), and scatter artifact (rest images) (P < 0.05). Furthermore, there was a significant difference in the incidence of good-quality images, with no interfering activity in group 3 in the resting state compared with the other groups in the study (P < 0.05).Conclusions:Drinking 250 mL of milk in either the stress phase or the rest phase of imaging diminishes interfering subdiaphragmatic-related artifacts, particularly overlapping of activity in MPI SPECT, resulting in better-quality images.

Covariance Matrix Estimation for the Cryo-EM Heterogeneity Problem.

In cryo-electron microscopy (cryo-EM), a microscope generates a top view of a sample of randomly oriented copies of a molecule. The problem of single particle reconstruction (SPR) from cryo-EM is to use the resulting set of noisy two-dimensional projection images taken at unknown directions to reconstruct the three-dimensional (3D) structure of the molecule. In some situations, the molecule under examination exhibits structural variability, which poses a fundamental challenge in SPR. The heterogeneity problem is the task of mapping the space of conformational states of a molecule. It has been previously suggested that the leading eigenvectors of the covariance matrix of the 3D molecules can be used to solve the heterogeneity problem. Estimating the covariance matrix is challenging, since only projections of the molecules are observed, but not the molecules themselves. In this paper, we formulate a general problem of covariance estimation from noisy projections of samples. This problem has intimate connections with matrix completion problems and high-dimensional principal component analysis. We propose an estimator and prove its consistency. When there are finitely many heterogeneity classes, the spectrum of the estimated covariance matrix reveals the number of classes. The estimator can be found as the solution to a certain linear system. In the cryo-EM case, the linear operator to be inverted, which we term the projection covariance transform, is an important object in covariance estimation for tomographic problems involving structural variation. Inverting it involves applying a filter akin to the ramp filter in tomography. We design a basis in which this linear operator is sparse and thus can be tractably inverted despite its large size. We demonstrate via numerical experiments on synthetic datasets the robustness of our algorithm to high levels of noise.

FLUCTUATION SPECTROSCOPY: A NEW PROBE OF OLD STELLAR POPULATIONS

We introduce a new method to determine the relative contributions of different types of stars to the integrated light of nearby early-type galaxies. As is well known, the surface brightness of these galaxies shows pixel-to-pixel fluctuations due to Poisson variations in the number of giant stars. Differential spectroscopy of pixels as a function of fluctuation strength ("fluctuation spectroscopy") effectively measures the spectral variation of stars as a function of their luminosity, information that is otherwise difficult to obtain for individual stars outside of the Local Group. We apply this technique to the elliptical galaxy NGC 4472, using HST/ACS imaging in six narrow-band ramp filters tuned to spectral features in the range 0.8-1.0 micron. Pixels with +- 5% broad-band variations show differential color variations of 0.1% - 1.0% in the narrow-band filters. These variations are primarily due to the systematic increase in TiO absorption strength with increasing luminosity on the upper giant branch. The data are very well reproduced by the same Conroy & van Dokkum (2012) stellar population synthesis model that is the best fit to the integrated light, with residuals in the range 0.03% - 0.09%. Models with ages or metallicities that are significantly different from the integrated-light values do not yield good fits. We can also rule out several modifications to the underlying model, including the presence of a significant (>3% of the light) population of late M giants. The current observations constitute a powerful test of the expected luminosities and temperatures of metal-rich giants in massive early-type galaxies. Studies of pixels with much larger (negative) fluctuations will provide unique information on main sequence stars and the stellar initial mass function.

Image reconstruction for digital breast tomosynthesis (DBT) by using projection-angle-dependent filter functions

Digital breast tomosynthesis (DBT) is considered in clinics as a standard three-dimensional imaging modality, allowing the earlier detection of cancer. It typically acquires only 10–30 projections over a limited angle range of 15–60° with a stationary detector and typically uses a computationally-efficient filtered-backprojection (FBP) algorithm for image reconstruction. However, a common FBP algorithm yields poor image quality resulting from the loss of average image value and the presence of severe image artifacts due to the elimination of the dc component of the image by the ramp filter and to the incomplete data, respectively. As an alternative, iterative reconstruction methods are often used in DBT to overcome these difficulties, even though they are still computationally expensive. In this study, as a compromise, we considered a projection-angle-dependent filtering method in which one-dimensional geometry-adapted filter kernels are computed with the aid of a conjugate-gradient method and are incorporated into the standard FBP framework. We implemented the proposed algorithm and performed systematic simulation works to investigate the imaging characteristics. Our results indicate that the proposed method is superior to a conventional FBP method for DBT imaging and has a comparable computational cost, while preserving good image homogeneity and edge sharpening with no serious image artifacts.

TU-F-18C-08: Micro-Calcification Detectability Using Spectral Breast CT Based On a Si Strip Detector

Purpose: To investigate the feasibility of micro-calcification (μCa) detectability by using an energy-resolved photon-counting Si strip detector for spectral breast computed tomography (CT). Methods: A bench-top CT system was constructed using a tungsten anode x-ray source with a focal spot size of 0.8 mm and a single line 256-pixel Si strip photon counting detector with a pixel pitch of 100 μm. The slice thickness was 0.5 mm. Five different size groups of calcium carbonate grains, from 105 to 215 μm in diameter, were embedded in a cylindrical resin phantom with a diameter of 16 mm to simulate μCas. The phantoms were imaged at 65 kVp with an Entrance Skin Air Kerma (ESAK) of 1.2, 3, 6, and 8 mGy. The images were reconstructed using a standard filtered back projection (FBP) with a ramp filter. A total of 200 μCa images (5 different sizes of μCas × 4 different doses × 10 images for each setting) were combined with another 200 control images without μCas, to ultimately form 400 images for the reader study. The images were displayed in random order to three blinded observers, who were asked to give a binary score on each image regarding the presence of μCas.more » The μCa detectability for each image was evaluated in terms of binary decision theory metrics. The sensitivity, specificity, and accuracy were calculated to study the size and dose-dependence for μCa detectability. Additionally, the influence of the partial volume effect on the μCa detectability was investigated by simulation. Results: For a μCa larger than 140 μm in diameter, detection accuracy of above 90 % was achieved with the investigated prototype spectral CT system at ESAK of 1.2 mGy. Conclusion: The proposed Si strip detector is expected to offer superior image quality with the capability to detect μCas for low dose breast imaging.« less

Noise-weighted spatial domain FBP algorithm.

The purpose of this paper is to implement a noise-weighted filtered backprojection (FBP) algorithm in the form of "convolution" backprojection, but this "convolution" has a spatially variant integration kernel. Noise-weighted FBP algorithms have been developed in recent years, with filtering being performed in the Fourier domain. The noise weighting makes the ramp filter in the FBP algorithm shift-varying. It is not efficient to implement shift-varying filtration in the Fourier domain. It is known that Fourier-domain multiplication is equivalent to spatial-domain convolution. An expansion method is suggested in this paper to obtain a closed-form integration kernel. The noise weighted FBP algorithm can now be implemented in the spatial domain efficiently. The total computation cost is less than that of the Fourier domain implementation. Computer simulations are used to show the three-term expansion method to approximate the filter kernel. A clinical study is used to verify the feasibility of the proposed algorithm.

Towards Clinical Application of a Laplace Operator-Based Region of Interest Reconstruction Algorithm in C-Arm CT

It is known that a reduction of the field-of-view in 3-D X-ray imaging is proportional to a reduction in radiation dose. The resulting truncation, however, is incompatible with conventional reconstruction algorithms. Recently, a novel method for region of interest reconstruction that uses neither prior knowledge nor extrapolation has been published, named approximated truncation robust algorithm for computed tomography (ATRACT). It is based on a decomposition of the standard ramp filter into a 2-D Laplace filtering and a 2-D Radon-based residual filtering step. In this paper, we present two variants of the original ATRACT. One is based on expressing the residual filter as an efficient 2-D convolution with an analytically derived kernel. The second variant is to apply ATRACT in 1-D to further reduce computational complexity. The proposed algorithms were evaluated by using a reconstruction benchmark, as well as two clinical data sets. The results are encouraging since the proposed algorithms achieve a speed-up factor of up to 245 compared to the 2-D Radon-based ATRACT. Reconstructions of high accuracy are obtained, e.g., even real-data reconstruction in the presence of severe truncation achieve a relative root mean square error of as little as 0.92% with respect to nontruncated data.

Fan beam image reconstruction with generalized fourier slice theorem

For parallel beam geometry the Fourier reconstruction works via the Fourier slice theorem (or central slice theorem, projection slice theorem). For fan beam situation, Fourier slice can be extended to a generalized Fourier slice theorem (GFST) for fan-beam image reconstruction. We have briefly introduced this method in a conference. This paper reintroduces the GFST method for fan beam geometry in details. The GFST method can be described as following: the Fourier plane is filled by adding up the contributions from all fanbeam projections individually; thereby the values in the Fourier plane are directly calculated for Cartesian coordinates such avoiding the interpolation from polar to Cartesian coordinates in the Fourier domain; inverse fast Fourier transform is applied to the image in Fourier plane and leads to a reconstructed image in spacial domain. The reconstructed image is compared between the result of the GFST method and the result from the filtered backprojection (FBP) method. The major differences of the GFST and the FBP methods are: (1) The interpolation process are at different data sets. The interpolation of the GFST method is at projection data. The interpolation of the FBP method is at filtered projection data. (2) The filtering process are done in different places. The filtering process of the GFST is at Fourier domain. The filtering process of the FBP method is the ramp filter which is done at projections. The resolution of ramp filter is variable with different location but the filter in the Fourier domain lead to resolution invariable with location. One advantage of the GFST method over the FBP method is in short scan situation, an exact solution can be obtained with the GFST method, but it can not be obtained with the FBP method. The calculation of both the GFST and the FBP methods are at O(N<formula>^3</formula>), where N is the number of pixel in one dimension.

Direct Reconstruction of CT-based Attenuation Correction Images for PET with Cluster-Based Penalties.

Extremely low-dose CT acquisitions for the purpose of PET attenuation correction will have a high level of noise and biasing artifacts due to factors such as photon starvation. This work explores a priori knowledge appropriate for CT iterative image reconstruction for PET attenuation correction. We investigate the maximum a posteriori (MAP) framework with cluster-based, multinomial priors for the direct reconstruction of the PET attenuation map. The objective function for direct iterative attenuation map reconstruction was modeled as a Poisson log-likelihood with prior terms consisting of quadratic (Q) and mixture (M) distributions. The attenuation map is assumed to have values in 4 clusters: air+background, lung, soft tissue, and bone. Under this assumption, the MP was a mixture probability density function consisting of one exponential and three Gaussian distributions. The relative proportion of each cluster was jointly estimated during each voxel update of direct iterative coordinate decent (dICD) method. Noise-free data were generated from NCAT phantom and Poisson noise was added. Reconstruction with FBP (ramp filter) was performed on the noise-free (ground truth) and noisy data. For the noisy data, dICD reconstruction was performed with the combination of different prior strength parameters (β and γ) of Q- and M-penalties. The combined quadratic and mixture penalties reduces the RMSE by 18.7% compared to post-smoothed iterative reconstruction and only 0.7% compared to quadratic alone. For direct PET attenuation map reconstruction from ultra-low dose CT acquisitions, the combination of quadratic and mixture priors offers regularization of both variance and bias and is a potential method to derive attenuation maps with negligible patient dose. However, the small improvement in quantitative accuracy relative to the substantial increase in algorithm complexity does not currently justify the use of mixture-based PET attenuation priors for reconstruction of CT images for PET attenuation correction.

SU-E-I-02: Effects of Projection View Sampling On CT Numbers and Noise Level in Cone Beam Breast CT

Purpose: To investigate and optimize the effects of the number of projection views for minimal noise level in cone bream breast CT. Methods: An Anrad flat panel detector was used on a bench top experimental CBCT system with fixed exposure. A wax phantom simulating a breast was used to study the reconstruction artifacts. A Lucite phantom with uniform structure was used to study the variation of the noise level with the number of views. The phantoms were scanned with 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200 and 1400 evenly spaced projection views over 360° while keeping the total exposure at a level corresponding to a mean glandular dose (MGD) of 7.2 mGys. Each image set was reconstructed with Feldkamp's backprojection algorithm using a ramp filter. Two regions of interest (ROIs) were selected in the reconstructed images, one at the center and the other near the edge. Mean values and standard variations were measured in ROIs and plotted as the function of the number of projection views. Results: The mean values for the wax phantom were found to peak at 300 projection views. The standard variation was lowest with 400 projection views for both Lucite and wax phantoms. Conclusion: For the detector and exposure level used, 400 projection views resulted in lowest noise level, indicating optimal combination of reconstruction artifacts from projection view sampling and image noises, which tend to decrease and increase with the number of projection views, respectively. This work was supported in part by grants CA139830, CA138502 and CA124585 from the NIH‐NCI.

SU-D-116-06: Improved Visualization with High Resolution Breast CT Images

Purpose: To investigate the visualization that could be improved with high resolution breast CT (BCT) images acquired with a high resolution BCT scanner. Methods: A high resolution BCT scanner employing a CMOS flat panel detector (2923, Dexela Limited, London, UK) with a pixel size of 0.075 mm and 0.5 mm thick CsI scintillator was used to image mastectomy specimens with IRB approval. The flat panel digital detector was operated with non‐binning mode for image acquisition to acquire high resolution projection views. 300 projection views were acquired over 360° for each scan. The radiation dose per scan was comparable to that of a two‐view mammography (about 4 mGy). The high resolution projection views were reconstructed with Feldkamp‐Davis‐Kress (FDK) filtered backprojection algorithm and a ramp filter for image reconstruction to generate high resolution BCT image sets with voxel size of 0.06 mm. Following, the projection views were processed with 2×2 and 4×4 binning to simulate low resolution projection views and then reconstructed to obtain two BCT image sets with voxel sizes of 0.12 and 0.24 mm, respectively. The BCT image sets with all different voxel sizes were displayed on a LCD monitor and then visually compared the findings based on mammograms and the diagnostic reports. Results: Based on visual comparison, significantly improved visualization for small structures (either calcifications or irregular masses) in the 0.06 mm‐voxel BCT images was observed compared to the 0.12 or 0.24 mm‐voxel BCT images. Conclusion: We have successfully demonstrated improved visualization for small structures with the use of the high resolution BCT images, which may have a potential for better detection sensitivity and characterization accuracy, leading to reduced recall rate in screening and reduced biopsy rate in diagnosis of breast cancers. This work was supported in part by grants CA139830, CA138502 and CA124585 from the NIH‐NCI.