Filtered Backprojection Algorithm Research Articles

Comparison between OSEM and FBP reconstruction algorithms for the qualitative and quantitative interpretation of brain DAT-SPECT using an anthropomorphic striatal phantom: implications for the practice

The aim of this study was to evaluate the effect of ordered subsets maximization expectation (OSEM) and filtered backprojection (FBP) reconstruction algorithms for the qualitative interpretation of DAT-SPECT. Data were acquired using an anthropomorphic striatal phantom and a gamma camera (SPECT/CT). Images were reconstructed using different values of the covariates applied in the OSEM and FBP algorithms (iteration, subsets, order, and cut-off frequency). Image quality was compared according to each set of covariates used in the reconstruction and extracted quantitative parameters of the image quality, such as C, CV RCR, and RSR, as well as the angular coefficients of the edges in regions of interest. Variations in the order from 0 to 10 do not influence the image if a trustworthy cut-off frequency range for FBP (0.9 to 1.6) and OSEM (1.2 to 1.5) are used. For OSEM, the iteration value and the number of subsets that guarantee better quality were those suggested by the developer of the algorithm used (3i8s). For a quality image, it is necessary to use the range of values found for the covariables order and cut-off frequency of the Butterworth filter in the FBP and OSEM reconstruction. OSEM reconstruction shows superiority in the images compared with FBP when the recommended three iterations and eight subsets are used. In the quantitative analyses, the evaluation showed that increasing the number of updates while allowing a better distinction of certain areas, culminates with a degradation image, making it inappropriate for clinical.

GPU acceleration of a model-based iterative method for Digital Breast Tomosynthesis

Digital Breast Tomosynthesis (DBT) is a modern 3D Computed Tomography X-ray technique for the early detection of breast tumors, which is receiving growing interest in the medical and scientific community. Since DBT performs incomplete sampling of data, the image reconstruction approaches based on iterative methods are preferable to the classical analytic techniques, such as the Filtered Back Projection algorithm, providing fewer artifacts. In this work, we consider a Model-Based Iterative Reconstruction (MBIR) method well suited to describe the DBT data acquisition process and to include prior information on the reconstructed image. We propose a gradient-based solver named Scaled Gradient Projection (SGP) for the solution of the constrained optimization problem arising in the considered MBIR method. Even if the SGP algorithm exhibits fast convergence, the time required on a serial computer for the reconstruction of a real DBT data set is too long for the clinical needs. In this paper we propose a parallel SGP version designed to perform the most expensive computations of each iteration on Graphics Processing Unit (GPU). We apply the proposed parallel approach on three different GPU boards, with computational performance comparable with that of the boards usually installed in commercial DBT systems. The numerical results show that the proposed GPU-based MBIR method provides accurate reconstructions in a time suitable for clinical trials.

Neutron tomography study of a lithium-ion coin battery

Neutron imaging of lithium-ion coin cell battery was obtained using tomography technique at Neutron Scattering Laboratory in Multi-Purpose Research Reactor G. A. Siwabessy (RSG-GAS) Serpong facility. The coin cell battery was CR2032 (20d x 3.2t mm) type consisted of positive and negative cases made of stainless steel, a cathode layer from lithium Ferro phosphate coated on aluminum foil, polyethylene film as separator, lithium hexafluorophosphate solution in ethylene carbonate and diethyl carbonate as electrolyte, an anode layer from graphite coated on copper foil, and spacer as well as spring made of stainless steel. The neutron tomography was used to observe the inside structure of the full charged coin cell. The observation was carried out with a neutron flux of 107 n/s.cm2 at 15 MWatt of reactor power. The image of the object is obtained by a Charge Coupled Detector (CCD) and the reconstruction software based on a filtered back-projection algorithm. The neutron imaging clearly shows the cell structure inside the casing. Even though the cell is not at the center of the casing, but it appears that the cell component consists of a cathode, separator and anode neatly arranged. The cross-cut image shows the important role of spring and spacer in improving the contact between cell and casing. The results showed the ability of neutron tomography techniques at RSG-GAS to investigate in detail the inside structure of a coin battery without disassembly (non-destructive test).

An End-to-End Deep Network for Reconstructing CT Images Directly From Sparse Sinograms

Recently, deep-learning based methods have been widely used for computed tomography (CT) reconstruction. However, most of these methods need extra steps to convert the sinogrmas into CT images and so their networks are not end-to-end. In this paper, we propose an end-to-end deep network for CT image reconstruction, which directly maps sparse sinogramss to CT images. Our network has three cascaded blocks, where the first block is used to denoise and interpolate the sinograms, the second to map the sinograms to CT images and the last to denoise the CT images. The second block of our network implements the filter backprojection (FBP) algorithm or the Feldkamp-Davis-Kress (FDK) algorithm, where the filter step is implemented by a one-dimensional convolution layer and the backprojection is implemented by a sparse matrix multiplication. By incorporating the FBP/FDK algorithm into our network, training a fully connected layer to convert the sinograms to CT images is avoided and the number of weights of our network is decreased. Our network is trained with two labels, the sinograms and CT images, and can reconstruct good CT images even if the input sinograms are very sparse. Experimental results show that our network outperforms the state-of-the-art approaches on test datasets for the sparse CT reconstruction under fan beam and circular cone beam scanning geometry.

Image-Domain Based Material Decomposition by Multi-Constraint Optimization for Spectral CT

As a new generation computed tomography (CT) technology, spectral CT has great potential in many aspects, especially in the identification and decomposition of materials. To achieve higher accuracy of materials decomposition, we propose a multi-constraint based nonlocal total variation (NLTV) method, named as MCNLTV. Because image-domain based material decomposition belongs to the two-step material decomposition method, the Filter Back-Projection (FBP) algorithm or SART algorithm is used to reconstruct spectral CT images in the first step. Then the material attenuation coefficient matrix is obtained from the reconstruction results. In the second step, MCNLTV regularization is utilized to obtain the material decomposition image. Both simulation experiments and real data experiments are carried out. Experiment results show that the proposed method can obtain higher accuracy of material decomposition than traditional total variation based material decomposition (TVMD), ROF-LLT regularization and direct inverse transformation (DI) for spectral CT.

Dynamically-Collimated Digital Tomosynthesis Reconstruction by Using a Compressed-Sensing Based Algorithm

Conventional digital tomosynthesis (DTS) reconstruction by using the filtered-backprojection algorithm requires a full field-of-view scan and relatively dense projections to obtain high-quality images, which results in a high radiation dose to patients. Interior DTS (iDTS) with a proper collimator offers a possible imaging modality for reducing the dose of radiation delivered because the X-ray beam is able to target a small region-of-interest (ROI) containing the target area. Collimators for iDTS often have a fixed rectangular shape, but focusing the X-ray beam on an arbitrarily shaped ROI would be preferable because it further reduces the excessive radiation dose. In this study, we propose a new iDTS scan method to create an ROI with an arbitrary shape to minimize the radiation dose at each angle of view. We used a compressed-sensing-based algorithm for accurate iDTS reconstruction. To validate the proposed method, we performed a systematic simulation and an experiment, and we investigated the image characteristics. Our results indicate that the proposed method may effectively reduce radiation dose in iDTS in real imaging systems.

Muon scattering tomography of spent fuel dry storage casks

In recent years, the cosmic-ray muon imaging technique has been widely used in industrial practical applications, such as the nuclear reactor monitoring and the internal container scanning. However, it is restricted by the lagging imaging algorithm technology, resulting in poor image quality and time-consuming. In this study, a cosmic-ray imaging system of Gas Electron Multiplier (GEM) has been constructed with the help of the Filter Back Projection (FBP) algorithm. Aiming at the dry spent fuel storage casks of Westinghouse MC-10, two different imaging algorithms are carried out. Two reasons are proposed by comparing their effects on the constructed image quality. The results demonstrate that the scattered reconstructed image is more sharply resolved than the transmitted one, and the sharpness of the edge is increased by 20%. The outline and missing part of the fuel assembly in the cask can be displayed clearly by using this method, even if the position resolution of the gas detector is only 300 μm.

Method to calculate frequency characteristics of reconstruction filter kernel in X-ray computed tomography.

A computed tomography (CT) image is generally reconstructed by a filtered back projection (FBP) algorithm. In an FBP algorithm, the image quality primarily depends on a reconstruction filter kernel. Although the details of the filter kernel are not disclosed to users, the frequency response of the filter kernel can theoretically be calculated using the relational formula of the filter kernel and the modulation transfer function (MTF) of the reconstruction algorithm (MTFA). In this study, we proposed a method to determine the frequency response of a filter kernel and verify its validity. Two clinical CT scanners were used to derive the filter kernel. The MTF was obtained and subsequently separated to the MTF of the scanner system and MTFA. Using the relational formula of the filter kernel and MTFA, we calculated the frequency response of the filter kernel. To verify the calculated result, we measured the noise power spectrum (NPS). Additionally, the filter kernel was calculated using the relational formula of the filter kernel and NPS. In both CT scanners, the filter kernels calculated by the two methods showed good agreement, and we confirmed the validity of the results and the effectiveness of the proposed method. Furthermore, the inherent image quality performance of the CT scanner could be clarified by the reconstruction filter kernel.

Twente Photoacoustic Mammoscope 2: system overview and three-dimensional vascular network images in healthy breasts.

.We present the Twente Photoacoustic Mammoscope 2, a photoacoustic breast imaging system employing a tomographic configuration. It images one breast pendant inside an imaging tank filled with water while a woman lies prone on a bed. A dual-head laser (755 and 1064 nm) illuminates the breast with one beam directed at the nipple and nine beams directed at the sides. Ultrasound signals are detected using 12 arc-shaped arrays, each curving along the pendant breast. Each array comprises 32 piezocomposite elements each with a center frequency of 1 MHz. The imaging tank and the ultrasound arrays rotate around the breast in steps to obtain additional multiple projections. Three-dimensional images are reconstructed using a filtered backprojection algorithm. The system is described in detail, and measurements on a test object are presented. As part of a preliminary study to assess the system’s in vivo performance, the breasts of two healthy volunteers were imaged. These images show the breast contour, the nipple, and the vascular anatomy within the breast. In the nipple of one case, multiple high-intensity “hot spots” are observed, which we suspect are associated with the lactiferous ducts terminating in the nipple.

A theoretical approach for the reduction of X-ray radiation dose within the optimization of the tomographic scanning process

X-ray tomography is one of the widespread methods of medical diagnostics. Modern medical tomographic setups have many advantages among them high resolution, high level of reproducibility, the simplicity of utilization, and many others. However, one of the most significant challenges of this method is a reducing of the radiation dose. We propose here an alternative scanning approach and an optimized filtered backprojection algorithm as a tool for the dose reduction by lower illumination area during the scan. Further, we demonstrate the power of this approach for dental application, where tomographic visualization is used as the main diagnostics instrument, employing a 3D model of a skull with teeth. From the results of our reconstruction, different defects in the teeth are successfully detected with an acceptable resolution. We hope that the proposed approach will be used as a fundamental idea to low the radiation dose in medical computer topology.

Gaussian Light Model in Brightfield Optical Projection Tomography

This study focuses on improving the reconstruction process of the brightfield optical projection tomography (OPT). OPT is often described as the optical equivalent of X-ray computed tomography, but based on visible light. The detection optics used to collect light in OPT focus on a certain distance and induce blurring in those features out of focus. However, the conventionally used inverse Radon transform assumes an absolute focus throughout the propagation axis. In this study, we model the focusing properties of the detection by coupling Gaussian beam model (GBM) with the Radon transform. The GBM enables the construction of a projection operator that includes modeling of the blurring caused by the light beam. We also introduce the concept of a stretched GBM (SGBM) in which the Gaussian beam is scaled in order to avoid the modeling errors related to the determination of the focal plane. Furthermore, a thresholding approach is used to compress memory usage. We tested the GBM and SGBM approaches using simulated and experimental data in mono- and multifocal modes. When compared with the traditionally used filtered backprojection algorithm, the iteratively computed reconstructions, including the Gaussian models GBM and SGBM, provided smoother images with higher contrast.

VVBP-Tensor in the FBP Algorithm: Its Properties and Application in Low-Dose CT Reconstruction

For decades, commercial X-ray computed tomography (CT) scanners have been using the filtered backprojection (FBP) algorithm for image reconstruction. However, the desire for lower radiation doses has pushed the FBP algorithm to its limit. Previous studies have made significant efforts to improve the results of FBP through preprocessing the sinogram, modifying the ramp filter, or postprocessing the reconstructed images. In this paper, we focus on analyzing and processing the stacked view-by-view backprojections (named VVBP-Tensor) in the FBP algorithm. A key challenge for our analysis lies in the radial structures in each backprojection slice. To overcome this difficulty, a sorting operation was introduced to the VVBP-Tensor in its z direction (the direction of the projection views). The results show that, after sorting, the tensor contains structures that are similar to those of the object, and structures in different slices of the tensor are correlated. We then analyzed the properties of the VVBP-Tensor, including structural self-similarity, tensor sparsity, and noise statistics. Considering these properties, we have developed an algorithm using the tensor singular value decomposition (named VVBP-tSVD) to denoise the VVBP-Tensor for low-mAs CT imaging. Experiments were conducted using a physical phantom and clinical patient data with different mAs levels. The results demonstrate that the VVBP-tSVD is superior to all competing methods under different reconstruction schemes, including sinogram preprocessing, image postprocessing, and iterative reconstruction. We conclude that the VVBP-Tensor is a suitable processing target for improving the quality of FBP reconstruction, and the proposed VVBP-tSVD is an effective algorithm for noise reduction in low-mAs CT imaging. This preliminary work might provide a heuristic perspective for reviewing and rethinking the FBP algorithm.

A proton imaging system using a volumetric liquid scintillator: a preliminary study

With the expansion of proton radiotherapy for cancer treatments, it has become important to explore proton-based imaging technologies to increase the accuracy of proton treatment planning, alignment, and verification. The purpose of this study is to demonstrate the feasibility of using a volumetric liquid scintillator to generate proton radiographs at a clinically relevant energy (180 MeV) using an integrating detector approach. The volumetric scintillator detector is capable of capturing a wide distribution of residual proton beam energies from a single beam irradiation. It has the potential to reduce acquisition time and imaging dose compared to other proton radiography methods. The imaging system design is comprised of a volumetric (20 × 20 × 20 cm3) organic liquid scintillator working as a residual-range detector and a charge-coupled device (CCD) placed along the beams’-eye-view for capturing radiographic projections. The scintillation light produced within the scintillator volume in response to a 3-dimensional distribution of residual proton beam energies is captured by the CCD as a 2-dimensional grayscale image. A light intensity-to-water equivalent thickness (WET) curve provided WET values based on measured light intensities. The imaging properties of the system, including its contrast, signal-to-noise ratio, and spatial resolution (0.19 line-pairs/mm) were determined. WET values for selected Gammex phantom inserts including solid water, acrylic, and cortical bone were calculated from the radiographs with a relative accuracy of −0.82%, 0.91%, and −2.43%, respectively. Image blurring introduced by system optics was accounted for, resulting in sharper image features. Finally, the system’s ability to reconstruct proton CT images from radiographic projections was demonstrated using a filtered back-projection algorithm. The WET retrieved from the reconstructed CT slice was within 0.3% of the WET obtained from MC. In this work, the viability of a cumulative approach to proton imaging using a volumetric liquid scintillator detector and at a clinically-relevant energy was demonstrated.

NEMA NU 4-2008 and in vivo imaging performance of RAYCAN trans-PET/CT X5 small animal imaging system

The RAYCAN Trans-PET/CT X5 is a preclinical positron emission tomography and computed tomography (PET/CT) system intended for in vivo imaging of rats and mice, featuring all-digital readout electronics for PET data acquisition.The National Electrical Manufacturers Association (NEMA) NU 4-2008 performance evaluation was conducted on the RAYCAN Trans-PET/CT X5 in addition to assessing in vivo imaging performance of the system on live animals. The performance characteristics of the system were evaluated, including system spatial resolution, count rate performance, sensitivity and image quality. The system imaging performance is assessed in dynamic in vivo PET imaging.The system resolution defined as full width half maximum (FWHM) was 2.07 mm, 2.11 mm and 1.31 mm for the tangential, radial and axial resolution, respectively, at the center of the field of view. The peak noise equivalent count rate (NECR) values measured were 61 kcps at 0.19 MBq ml−1 for the rat size phantom and 126 kcps at 1.53 MBq ml−1 for the mouse size phantom. Scatter fractions were 24% and 14% for the rat and mouse phantom. The measured peak sensitivity of the system was 1.70%. Image quality in static imaging was deemed sufficient based on the image quality phantom study, with average activity concentration of 155 ± 8.6 kBq ml−1 and image uniformity of 5.57% when using two-dimensional filtered backprojection algorithm (2D-FBP). Rods in the image quality phantom were visualized easily up to 2 mm in size. In dynamic in vivo PET imaging, time-activity-curves from several regions were successfully measured, characterizing the radioactivity distribution in myocardial blood pool, liver, left ventricle and the lung.In conclusion, the RAYCAN Trans-PET/CT X5 system can be considered a suitable option for basic imaging needs in preclinical imaging.

Radon single-pixel imaging with projective sampling

A novel technique for Radon single-pixel imaging with projective sampling, which is based on the theorem of the Radon transform, is proposed. In contrast to current patterns in conventional single-pixel imaging systems, candy-striped patterns called Radon basis patterns, which are produced by projecting the 1D Hadamard functions along different angles, are employed in our proposed technique. Here, the patterns are loaded into a projection system and then illuminated onto an object. The light reflected from the object is detected by a single-pixel detector. An iterative reconstruction method is used to restore the object's 1D projection functions by summing the 1D Hadamard functions and detected intensities. Next, the Radon spectrum of the object is recovered by arranging the 1D projection functions along the projection angle. Finally, the image of the object can be recovered using a filtered back-projection algorithm with the Radon spectrum. Experiments demonstrate that the proposed technique can obtain the information of the Radon spectrum and image of the object. Recognition directly in the Radon spectrum domain, rather than in the image domain, is fast and yields robust and high classification rates. A recognition experiment is performed by detecting the lines in one scene by searching the singular peaks in the Radon spectrum domain. According to the results, the lines in the scene can be easily detected in the Radon spectrum domain. Other shapes can also be detected by the characteristics of those shapes in the Radon spectrum domain.

Optimization of Radiation Dose and Image Quality using ADMIRE Algorithm and Quantitative Evaluation

목적: 최신 반복적 재구성법인 ADMIRE 알고리즘을 물질의 종류, 검사 조건, 적용 단계에 관하여 정량적으로 평가하고 이를 활용하여 환자 피폭선량과 영상의 질을 최적화 하고자 한다. 대상 및 방법: 증류수와 조영제의 혼합용액으로 밀도가 다른 네 종류의 물질 A(30~40 HU), B(60~70 HU), C(100~110 HU), D(200~210 HU)를 제작하여 AAPM phantom의 저 대조도 측정 부위에 주입하였다. 100, 120 kVp 두 종류의 관전압과 250~ 50 mAs 관전류로 선량을 50 mAs 단위로 감소시켜 영상을 획득하고 FBP와 1~5단계의 ADMIRE를 적용하여 CT number, SNR, CNR을 정량적으로 평가하였다. 결과: CT number(HU)값은 FBP와 ADMIRE의 적용 단계에 따라서는 차이가 없었고(P>0.05), 120 kVp와 비교하여 100 kVp를 사용하였을 경우 23±2%(P<0.05) 증가하였다. SNR은 ADMIRE를 1~5단계까지 순차적으로 적용하였을 경우 각각 10%, 23%, 38%, 60%, 98% 증가하였고, CNR은 10%, 22%, 36%, 58%, 97% 증가하였다. 120 kVp, 200 mAs, FBP영상을 기준으로 선량이 150 mAs(75%), 100 mAs(50%), 50 mAs(25%)로 감소하였을 때, 영상의 질을 유지하기 위해서는 1, 3, 5단계 이상의 ADMIRE 적용을 필요로 하였다. 결론: ADMIRE를 적용할 경우 영상의 질 저하 없이 최대 75%의 선량감소 효과를 기대할 수 있으며, 50%이상의 선량을 감소시켰을 경우에는 3단계 이상의 ADMIRE적용을 필요로 한다.Purpose: The ADMIRE algorithm which is the latest iterative reconstruction (IR) was quantitatively evaluated depending on the type of substances, scan parameter, and the stages of application to optimize patient exposure dose and image quality. Material and method: The four different types of substances; A(30~40 HU), B(60~70 HU), C(100~110 HU), and D(200~210 HU), were manufactured by using mixture of water and contrast medium and injected into the hole of the low contrast measuring part of AAPM phantom. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) were quantitatively evaluated with CT numbers depending on the two types of tube voltage(100, 120 kVp), the five types of tube current by reducing the dose to 50 mAs(250, 200, 150, 100, 50 mAs), and the five types of algorithms (FBP, 1 to 5 steps in ADMIRE). Result: There were no significant differences of CT number (HU) among images depending on the application phase of FBP and ADMIRE algorithms(p>0.05); however, HUs with 100 kVp were increased up to 23±2% compared to 120 kVp(p<0.05). SNR was increased by 10%, 23%, 38%, 60%, and 98% when ADMIRE algorithms were applied sequentially to steps 1 to 5. Also, CNR was increased by 10%, 22%, 36%, 58%, and 97% in the same ADMIRE algorithm condition. Equal quality of filtered back projection (FBP) image taken with 120 kVp and 200 mAs, was maintained by applying more than 1, 3, and 5 strengths of ADMIRE in case of 150 mAs(75%), 100 mAs(50%), and 50 mAs(25%) patient dose reduction. Conclusion: ADMIRE can reduce patient dose up to 75% without degradation of image quality. In addition, the application of ADMIRE more than 3 strengths are required if the patient dose reduced by more than 50%.

Three-dimensional refractive index measurement of special optical fiber based on optical vortex phase-shifting digital holographic microscopy

We propose an approach for three-dimensional (3-D) refractive index (RI) measurement of special optical fiber by phase-shifting digital holographic microscopy based on optical vortex. The experimental system consists of a modified Mach–Zehnder interferometer and related lab-developed software. The phase shifts are obtained by loading different computer-generated spiral phase holograms on a liquid-crystal spatial light modulator with digital blazed grating, which is more accurate than the traditional phase-shift methods. The optical vortex generated in +1 diffraction order of the diffraction field is utilized as the reference beam. Eight digital holograms with the phase-shift increments of 0, π / 2, π, and 3π / 2 are recorded by a CCD before and after the tested optical fiber is placed in the experimental system. Then, a four-step phase-shifting algorithm and phase unwrapping algorithm are adopted to extract the phase distribution from the holograms. Based on a filtered backprojection algorithm, the 3-D RI distribution of the tested fiber is obtained at high accuracy. Experimental results demonstrate the feasibility of the proposed method for 3-D RI measurement of special optical fiber.

A novel total variation based ring artifact suppression method for CBCT imaging with two-dimensional antiscatter grids.

Two-dimensional antiscatter grids (2DASG) for cone beam computed tomography (CBCT) is a new area of research to reduce scatter intensity, and consequently improve CBCT image quality. One of the challenges in implementation of 2DASGs is their septa shadows that are impinged on the projections. If these artifacts are not corrected, they may lead to ring artifacts in CBCT images. In this work, we present a novel method to suppress ring artifacts in FPD-based CBCT images. Briefly, our method first detects the locations of 2DASG's septa shadows in projections and then, reduces projection pixel values in septa shadows iteratively until a residual-based convergence criterion is met. To suppress the 2DASG's septa shadows, we developed a total variation minimization (TVM) formulation, referred to as adaptive-diffusive total variation minimization (adTVM), where the diffusivity regularization parameter was adaptively adjusted during each iteration based on the magnitude of the local pixel gradients. To test our method, we have acquired CBCT scans of phantoms using three 2DASG prototypes with different grid geometries. Projections were acquired with a linac mounted CBCT system, operated in offset detector geometry. These projections were then corrected in the following steps: first, projections were corrected using a gantry angle-specific gain correction map; next, projections were corrected by applying our adTVM method. CBCT images were reconstructed using FDK filtered backprojection algorithm. To evaluate adTVM's performance, pixel value statistics and contrast-to-noise ratio (CNR) were compared in CBCT images corrected with and without our adTVM method. Without our adTVM method, all three 2DASG prototypes introduced ring artifacts with varying intensities in CBCT images. With our method, significant reduction in ring artifacts was observed in all test cases. Standard deviation of CT numbers was reduced by 7-74% in uniform density phantom CBCT images, CNR was increased by 8-67%, and CT number accuracy of contrast objects embedded in the phantom was preserved. We propose a new method to suppress ring artifacts caused by the 2DASG's septa shadows in CBCT images. Our initial investigations indicated that adTVM method could substantially reduce such ring artifacts while preserving CT number accuracy and maintaining good spatial resolution. Therefore, our method may potentially play an important role in enabling the implementation of 2DASGs in flat panel detector based CBCT systems.

Back-projection Filtration Image Reconstruction Approach for Reducing Out-of-plane Artifacts in Laminography

Because of incomplete depth information in computed laminography, 3D reconstructed images are contaminated by out-of-plane artifacts, or ripple artifacts along the depth direction. Furthermore, these issues become more severe whenever printed circuit boards (PCB) are imaged due to the presence of high density materials, such as ball grid array, chips, etc. In this reasearch, in order to remove these artifacts, we proposed the use of the voting strategy. This method is a weighted back-projection which gives less weight to outliers, or odd values. However, standard analytical reconstruction methods, such as filtered back-projection (FBP), cannot fully judge and remove the outliers. Thus, we proposed to combine this strategy with the back-projection filtration (BPF) reconstruction method, which first reconstructs the derivative of the projection image, and then filters the reconstructed image by the Hilbert transform. Ripple artifacts were shown to have been dramatically reduced by using the voting strategy in the BPF algorithm.

Evaluation of denoising digital breast tomosynthesis data in both projection and image domains and a study of noise model on digital breast tomosynthesis image domain.

Digital breast tomosynthesis (DBT) is an imaging technique created to visualize 3-D mammary structures for the purpose of diagnosing breast cancer. This imaging technique is based on the principle of computed tomography. Due to the use of a dangerous ionizing radiation, the "as low as reasonably achievable" (ALARA) principle should be respected, aiming at minimizing the radiation dose to obtain an adequate examination. Thus, a noise filtering method is a fundamental step to achieve the ALARA principle, as the noise level of the image increases as the radiation dose is reduced, making it difficult to analyze the image. In our work, a double denoising approach for DBT is proposed, filtering in both projection (prereconstruction) and image (postreconstruction) domains. First, in the prefiltering step, methods were used for filtering the Poisson noise. To reconstruct the DBT projections, we used the filtered backprojection algorithm. Then, in the postfiltering step, methods were used for filtering Gaussian noise. Experiments were performed on simulated data generated by open virtual clinical trials (OpenVCT) software and on a physical phantom, using several combinations of methods in each domain. Our results showed that double filtering (i.e., in both domains) is not superior to filtering in projection domain only. By investigating the possible reason to explain these results, it was found that the noise model in DBT image domain could be better modeled by a Burr distribution than a Gaussian distribution. Finally, this important contribution can open a research direction in the DBT denoising problem.