Advanced Image Reconstruction Algorithms Research Articles

ACR benchmark testing of a novel high-speed ring-gantry linac kV-CBCT system.

A new generation cone-beam computed tomography (CBCT) system with new hardware design and advanced image reconstruction algorithms is available for radiation treatment simulation or adaptive radiotherapy (HyperSight CBCT imaging solution, Varian Medical Systems-a Siemens Healthineers company). This study assesses the CBCT image quality metrics using the criteria routinely used for diagnostic CT scanner accreditation as a first step towards the future use of HyperSight CBCT images for treatment planning and target/organ delineations. Image performance was evaluated using American College of Radiology (ACR) Program accreditation phantom tests for diagnostic computed tomography systems (CTs) and compared HyperSight images with a standard treatment planning diagnostic CT scanner (Siemens SOMATOM Edge) and with existing CBCT systems (Varian TrueBeam version 2.7 and Varian Halcyon version 2.0). Image quality performance for all Varian HyperSight CBCT vendor-provided imaging protocols were assessed using ACR head and body ring CT phantoms, then compared to existing imaging modalities. Image quality analysis metrics included contrast-to-noise (CNR), spatial resolution, Hounsfield number (HU) accuracy, image scaling, and uniformity. All image quality assessments were made following the recommendations and passing criteria provided by the ACR. The Varian HyperSight CBCT imaging system demonstrated excellent image quality, with the majority of vendor-provided imaging protocols capable of passing all ACR CT accreditation standards. Nearly all (8/11) vendor-provided protocols passed ACR criteria using the ACR head phantom, with the Abdomen Large, Pelvis Large, and H&N vendor-provided protocols produced HU uniformity values slightly exceeding passing criteria but remained within the allowable minor deviation levels (5-7 HU maximum differences). Compared to other existing CT and CBCT imaging modalities, both HyperSight Head and Pelvis imaging protocols matched the performance of the SOMATOM CT scanner, and both the HyperSight and SOMATOM CT substantially surpassed the performance of the Halcyon 2.0 and TrueBeam version 2.7 systems. Varian HyperSight CBCT imaging system could pass almost all tests for all vendor-provided protocols using ACR accreditation criteria, with image quality similar to those produced by diagnostic CT scanners and significantly better than existing linac-based CBCT imaging systems.

Compressed SVD-based L + S model to reconstruct undersampled dynamic MRI data using parallel architecture.

Magnetic Resonance Imaging (MRI) is a highly demanded medical imaging system due to high resolution, large volumetric coverage, and ability to capture the dynamic and functional information of body organs e.g. cardiac MRI is employed to assess cardiac structure and evaluate blood flow dynamics through the cardiac valves. Long scan time is the main drawback of MRI, which makes it difficult for the patients to remain still during the scanning process. By collecting fewer measurements, MRI scan time can be shortened, but this undersampling causes aliasing artifacts in the reconstructed images. Advanced image reconstruction algorithms have been used in literature to overcome these undersampling artifacts. These algorithms are computationally expensive and require a long time for reconstruction which makes them infeasible for real-time clinical applications e.g. cardiac MRI. However, exploiting the inherent parallelism in these algorithms can help to reduce their computation time. Low-rank plus sparse (L+S) matrix decomposition model is a technique used in literature to reconstruct the highly undersampled dynamic MRI (dMRI) data at the expense of long reconstruction time. In this paper, Compressed Singular Value Decomposition (cSVD) model is used in L+S decomposition model (instead of conventional SVD) to reduce the reconstruction time. The results provide improved quality of the reconstructed images. Furthermore, it has been observed that cSVD and other parts of the L+S model possess highly parallel operations; therefore, a customized GPU based parallel architecture of the modified L+S model has been presented to further reduce the reconstruction time. Four cardiac MRI datasets (three different cardiac perfusion acquired from different patients and one cardiac cine data), each with different acceleration factors of 2, 6 and 8 are used for experiments in this paper. Experimental results demonstrate that using the proposed parallel architecture for the reconstruction of cardiac perfusion data provides a speed-up factor up to 19.15× (with memory latency) and 70.55× (without memory latency) in comparison to the conventional CPU reconstruction with no compromise on image quality. The proposed method is well-suited for real-time clinical applications, offering a substantial reduction in reconstruction time.

Dedicated Cone-Beam Breast CT: Reproducibility of Volumetric Glandular Fraction with Advanced Image Reconstruction Methods.

Dedicated cone-beam breast computed tomography (CBBCT) is an emerging modality and provides fully three-dimensional (3D) images of the uncompressed breast at an isotropic voxel resolution. In an effort to translate this modality to breast cancer screening, advanced image reconstruction methods are being pursued. Since radiographic breast density is an established risk factor for breast cancer and CBBCT provides volumetric data, this study investigates the reproducibility of the volumetric glandular fraction (VGF), defined as the proportion of fibroglandular tissue volume relative to the total breast volume excluding the skin. Four image reconstruction methods were investigated: the analytical Feldkamp-Davis-Kress (FDK), a compressed sensing-based fast, regularized, iterative statistical technique (FRIST), a fully supervised deep learning approach using a multi-scale residual dense network (MS-RDN), and a self-supervised approach based on Noise-to-Noise (N2N) learning. Projection datasets from 106 women who participated in a prior clinical trial were reconstructed using each of these algorithms at a fixed isotropic voxel size of (0.273 mm3). Each reconstructed breast volume was segmented into skin, adipose, and fibroglandular tissues, and the VGF was computed. The VGF did not differ among the four reconstruction methods (p = 0.167), and none of the three advanced image reconstruction algorithms differed from the standard FDK reconstruction (p > 0.862). Advanced reconstruction algorithms developed for low-dose CBBCT reproduce the VGF to provide quantitative breast density, which can be used for risk estimation.

Comparative study of advanced image reconstruction algorithms for complex arbitrary components

Accurate ultrasonic imaging and inspection of specimens with non-planar complex surfaces are often challenging from both transducer coupling and imaging perspectives. Several inspection strategies have been developed to inspect such complex interfaced components such as flat arrays with flexible wedges or water-filled chambers with a flexible membrane. In these methods, firstly surface profiles are estimated and then Fermat’s principle or other algorithms are applied to calculate delay laws. The main drawback of these methods is the longer computation time and therefore real time inspection is limited, also a coupling problem due to the complex geometries. Alternative approaches have been developed to compute the ultrasonic beam paths such as the Fast-Marching Method (FMM), Dijkstra’s algorithms, numerical analysis, bisection and root-finding algorithms for the inspection of complex surface components. These algorithms can be applied in two and three-dimensional domains resulting in high computation time. This work presents an experimental comparison of an iterative beamforming method for arbitrary shaped surface profile imaging against both Full Matrix Capture-Total Focusing Method (FMC-TFM) and CIVA simulation on an aluminium calibration block with Side drilled holes (SDHs) at various depths in the range of 15-80 mm. The performance of the iterative beamforming method is evaluated in terms of sensitivity, Signal to Noise Ratio (SNR) and Array Performance Indicators (API). The method provides a 2 dB improved SNR when compared to FMC-TFM.

The 100[formula omitted]PET project: A small-animal PET scanner for ultra-high resolution molecular imaging with monolithic silicon pixel detectors

Recent developments in semiconductor pixel detectors allow a new generation of positron-emission tomography (PET) scanners that, combined with advanced image reconstruction algorithms, will allow for a few hundred microns spatial resolutions. Such novel scanners will pioneer ultra-high resolution molecular imaging, a field that is expected to have an enormous impact in several medical domains, neurology among others.The University of Geneva, the École Polytechnique Fédérale de Lausanne, and the University of Lucerne, have launched the 100μPET project that aims to produce a small-animal PET scanner with ultra-high resolution. The scanner will be composed of 4 ”towers”, each containing a stack of 60 monolithic silicon pixel sensors for the direct measurement of the annihilation photons. The sensors are 270 μm thick, providing unprecedented depth-of-interaction measurement. Monte Carlo simulations were done simulating different scanner conditions, resulting in a spatial resolution down to 0.2 mm FWHM and a sensitivity of 3.2%.

Optical breast atlas as a testbed for image reconstruction in optical mammography

We present two optical breast atlases for optical mammography, aiming to advance the image reconstruction research by providing a common platform to test advanced image reconstruction algorithms. Each atlas consists of five individual breast models. The first atlas provides breast vasculature surface models, which are derived from human breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using image segmentation. A finite element-based method is used to deform the breast vasculature models from their natural shapes to generate the second atlas, compressed breast models. Breast compression is typically done in X-ray mammography but also necessary for some optical mammography systems. Technical validation is presented to demonstrate how the atlases can be used to study the image reconstruction algorithms. Optical measurements are generated numerically with compressed breast models and a predefined configuration of light sources and photodetectors. The simulated data is fed into three standard image reconstruction algorithms to reconstruct optical images of the vasculature, which can then be compared with the ground truth to evaluate their performance.

Parallel implementation of L + S signal recovery in dynamic MRI.

Dynamic MRI is useful to diagnose different diseases, e.g. cardiac ailments, by monitoring the structure and function of the heart and blood flow through the valves. Faster data acquisition is highly desirable in dynamic MRI, but this may lead to aliasing artifacts due to under-sampling. Advanced image reconstruction algorithms are required to obtain aliasing-free MR images from the acquired under-sampled data. One major limitation of using the advanced reconstruction algorithms is their computationally expensive and time-consuming nature, which make them infeasible for clinical use, especially for applications like cardiac MRI. L + S decomposition model is an approach provided in literature which separates the sparse and low-rank information in dynamic MRI. However, L + S decomposition model is a computationally complex process demanding significant computation time. In this paper, a parallel framework is proposed to accelerate the image reconstruction process of L + S decomposition model using GPU. Experiments are performed on cardiac perfusion dataset ([Formula: see text]) and cardiac cine dataset ([Formula: see text]) using NVIDIA's GeForce GTX780 GPU and Core-i7 CPU. The results show that the proposed method provides up to 18 × speed-up including the memory transfer time (i.e. data transfer between the CPU and GPU) and ~ 46 × speed-up without memory transfer for the cardiac perfusion dataset in our experiments. This level of improvement in the reconstruction time will increase the usefulness of L + S reconstruction by making it feasible for clinical applications.

DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network

Positron emission tomography (PET) is an advanced medical imaging technique widely used in various clinical applications, such as tumor detection and neurologic disorders. Reducing the radiotracer dose is desirable in PET imaging because it decreases the patient’s radiation exposure. However, reducing the dose can also increase noise, affecting the image quality. Therefore, an advanced image reconstruction algorithm based on low-dose PET data is needed to improve the quality of the reconstructed image. In this article, we propose the use of a direct PET image reconstruction network (DPIR-Net) using an improved Wasserstein generative adversarial network (WGAN) framework to enhance image quality. This article provides two main findings. First, our network uses sinogram data as input and outputs high-quality PET images direct, resulting in shorter reconstruction times compared with traditional model-based reconstruction networks. Second, we combine perceptual loss, mean square error, and the Wasserstein distance as the loss function, which effectively solves the problem of excessive smoothness and loss of detailed information in traditional network image reconstruction. We performed a comparative study using maximum-likelihood expectation maximization (MLEM) with a post-Gaussian filter, a total variation (TV)-norm regularization, a nonlocal means (NLMs) denoising method, a neural network denoising method, a traditional deep learning PET reconstruction network, and our proposed DPIR-Net method and evaluated the proposed method using both human and mouse data. The mouse data were obtained from a small animal PET prototype system developed by our laboratory. The quantitative and qualitative results show that our proposed method outperformed the conventional methods.

DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.

The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimization of advanced image reconstruction algorithms, and the computational expense associated with these state-of-the art methods. We thus present a novel end-to-end PET image reconstruction technique, called DeepPET, based on a deep convolutional encoder-decoder network, which takes PET sinogram data as input and directly and quickly outputs high quality, quantitative PET images. Using simulated data derived from a whole-body digital phantom, we randomly sampled the configurable parameters to generate realistic images, which were each augmented to a total of more than 291,000 reference images. Realistic PET acquisitions of these images were simulated, resulting in noisy sinogram data, used for training, validation, and testing the DeepPET network. We demonstrated that DeepPET generates higher quality images compared to conventional techniques, in terms of relative root mean squared error (11%/53% lower than ordered subset expectation maximization (OSEM)/filtered back-projection (FBP), structural similarity index (1%/11% higher than OSEM/FBP), and peak signal-to-noise ratio (1.1/3.8dB higher than OSEM/FBP). In addition, we show that DeepPET reconstructs images 108 and 3 times faster than OSEM and FBP, respectively. Finally, DeepPET was successfully applied to real clinical data. This study shows that an end-to-end encoder-decoder network can produce high quality PET images at a fraction of the time compared to conventional methods.

Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction

Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in CT community. Since analytic image reconstruction algorithms would lead to severe image artifacts, various iterative algorithms have been developed for reconstructing images from sparsely view-sampled projection data. However, iterative algorithms take much longer computation time than the analytic algorithms, and images are usually prone to different types of image artifacts that heavily depend on the reconstruction parameters. Interpolation methods have also been utilized to fill the missing data in the sinogram of sparse-view CT thus providing synthetically full data for analytic image reconstruction. In this work, we introduce a deep-neural-network-enabled sinogram synthesis method for sparse-view CT, and show its outperformance to the existing interpolation methods and also to the iterative image reconstruction approach.

MODEL-BASED ITERATIVE RECONSTRUCTION ENABLES THE EVALUATION OF THIN-SLICE COMPUTED TOMOGRAPHY IMAGES WITHOUT DEGRADING IMAGE QUALITY OR INCREASING RADIATION DOSE.

Computed tomography (CT) is one of the most important modalities in a radiological department. This technique not only produces images that enable radiological reports with high diagnostic confidence, but it may also provide an elevated radiation dose to the patient. The radiation dose can be reduced by using advanced image reconstruction algorithms. This study was performed on a Brilliance iCT, equipped with iDose(4) iterative reconstruction and an iterative model-based reconstruction (IMR) method. The purpose was to investigate the effect of reduced slice thickness combined with an IMR method on image quality compared with standard slice thickness with iDose(4) reconstruction. The results of objective and subjective image quality evaluations showed that a thinner slice combined with IMR can improve the image quality and reduce partial volume artefacts compared with the standard slice thickness with iDose(4) In conclusion, IMR enables reduction of the slice thickness while maintaining or even improving image quality versus iDose(4).

Preliminary clinical application of an adaptive iterative statistical reconstruction algorithm inhead and neck computed tomography angiography with low tube voltage and a low concentration of contrast medium

Abstract Objective To evaluate the feasibility of using a low concentration of contrast medium (Visipaque 270 mgI/mL), low tube voltage, and an advanced image reconstruction algorithm in head and neck computed tomography angiography (CTA). Methods Forty patients (22 men and 18 women; average age 48.7 ± 14.25 years; average body mass index 23.9 ± 3.7 kg/m2) undergoing CTA for suspected vascular diseases were randomly assigned into two groups. Group A (n = 20) was administered 370 mgI/mL contrast medium, and group B (n = 20) was administered 270 mgI/mL contrast medium. Both groups were administered at a rate of 4.8 mL/s and an injection volume of 0.8 mL/kg. Images of group A were obtained with 120 kVp and filtered back projection (FBP) reconstruction, whereas images of group B were obtained with 80 kVp and 80% adaptive iterative statistical reconstruction algorithm (ASiR). The CT values and standard deviations of intracranial arteries and image noise on the corona radiata were measured to calculate the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). The beam-hardening artifacts (BHAs) around the skull base were calculated. Two readers evaluated the image quality with volume rendered images using scores from 1 to 5. The values between the two groups were statistically compared. Results The mean CT value of the intracranial arteries in group B was significantly higher than that in group A (P < 0.001). The CNR and SNR values in group B were also statistically higher than those in group A (P < 0.001). Image noise and BHAs were not significantly different between the two groups. The image quality score of VR images of in group B was significantly higher than that in group A (P = 0.001). However, the quality scores of axial enhancement images in group B became significantly smaller than those in group A (P< 0.001). The CT dose index volume and dose-length product were decreased by 63.8% and 64%, respectively, in group B (P < 0.001 for both). Conclusion Visipaque combined with 80 kVp and 80% ASiR provided similar image quality in intracranial CTA with 64% radiation dose reduction compared with the use of Iopamidol, 120 kVp, and FBP reconstruction.

Contrast agent and radiation dose reduction in abdominal CT by a combination of low tube voltage and advanced image reconstruction algorithms.

ObjectivesTo assess image quality in abdominal CT at low tube voltage combined with two types of iterative reconstruction (IR) at four reduced contrast agent dose levels.MethodsMinipigs were scanned with standard 320 mg I/mL contrast concentration at 120 kVp, and with reduced formulations of 120, 170, 220 and 270 mg I/mL at 80 kVp with IR. Image quality was assessed by CT value, dose normalized contrast and signal to noise ratio (CNRD and SNRD) in the arterial and venous phases. Qualitative analysis was included by expert reading.ResultsProtocols with 170 mg I/mL or higher showed equal or superior CT values: aorta (278–468 HU versus 314 HU); portal vein (205–273 HU versus 208 HU); liver parenchyma (122–146 HU versus 115 HU). In the aorta, all 170 mg I/mL protocols or higher yielded equal or superior CNRD (15.0–28.0 versus 13.7). In liver parenchyma, all study protocols resulted in higher SNRDs. Radiation dose could be reduced from standard CTDIvol = 7.8 mGy (6.2 mSv) to 7.6 mGy (5.2 mSv) with 170 mg I/mL.ConclusionCombining 80 kVp with IR allows at least a 47 % contrast agent dose reduction and 16 % radiation dose reduction for images of comparable quality.Key Points• There is a balance between image quality, contrast dose and radiation dose. • Iterative reconstruction has a major, positive impact on this balance. • Both contrast dose and radiation dose can be reduced in abdominal CT. • The trade-off can be quantitatively described by a 3D model. • Contrast and radiation dose can be tailored according to specific safety concerns.

SU‐E‐J‐02: 4D Digital Tomosynthesis Based On Algebraic Image Reconstruction and Total‐Variation Minimization for the Improvement of Image Quality

Purpose:In this paper, we implemented the four‐dimensional (4D) digital tomosynthesis (DTS) imaging based on algebraic image reconstruction technique and total‐variation minimization method in order to compensate the undersampled projection data and improve the image quality.Methods:The projection data were acquired as supposed the cone‐beam computed tomography system in linear accelerator by the Monte Carlo simulation and the in‐house 4D digital phantom generation program. We performed 4D DTS based upon simultaneous algebraic reconstruction technique (SART) among the iterative image reconstruction technique and total‐variation minimization method (TVMM). To verify the effectiveness of this reconstruction algorithm, we performed systematic simulation studies to investigate the imaging performance.Results:The 4D DTS algorithm based upon the SART and TVMM seems to give better results than that based upon the existing method, or filtered‐backprojection.Conclusion:The advanced image reconstruction algorithm for the 4D DTS would be useful to validate each intra‐fraction motion during radiation therapy. In addition, it will be possible to give advantage to real‐time imaging for the adaptive radiation therapy.This research was supported by Leading Foreign Research Institute Recruitment Program (Grant No.2009‐00420) and Basic Atomic Energy Research Institute (BAERI); (Grant No. 2009‐0078390) through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP).

Temporal resolution and motion artifacts in single‐source and dual‐source cardiac CT

The temporal resolution of a given image in cardiac computed tomography (CT) has so far mostly been determined from the amount of CT data employed for the reconstruction of that image. The purpose of this paper is to examine the applicability of such measures to the newly introduced modality of dual-source CT as well as to methods aiming to provide improved temporal resolution by means of an advanced image reconstruction algorithm. To provide a solid base for the examinations described in this paper, an extensive review of temporal resolution in conventional single-source CT is given first. Two different measures for assessing temporal resolution with respect to the amount of data involved are introduced, namely, either taking the full width at half maximum of the respective data weighting function (FWHM-TR) or the total width of the weighting function (total TR) as a base of the assessment. Image reconstruction using both a direct fan-beam filtered backprojection with Parker weighting as well as using a parallel-beam rebinning step are considered. The theory of assessing temporal resolution by means of the data involved is then extended to dual-source CT. Finally, three different advanced iterative reconstruction methods that all use the same input data are compared with respect to the resulting motion artifact level. For brevity and simplicity, the examinations are limited to two-dimensional data acquisition and reconstruction. However, all results and conclusions presented in this paper are also directly applicable to both circular and helical cone-beam CT. While the concept of total TR can directly be applied to dual-source CT, the definition of the FWHM of a weighting function needs to be slightly extended to be applicable to this modality. The three different advanced iterative reconstruction methods examined in this paper result in significantly different images with respect to their motion artifact level, despite exactly the same amount of data being used in the reconstruction process. The concept of assessing temporal resolution by means of the data employed for reconstruction can nicely be extended from single-source to dual-source CT. However, for advanced (possibly nonlinear iterative) reconstruction algorithms the examined approach fails to deliver accurate results. New methods and measures to assess the temporal resolution of CT images need to be developed to be able to accurately compare the performance of such algorithms.

A system-level simulator for indoor mmW SAR imaging and its applications

Recently, the research interest in indoor active millimeter wave (mmW) imaging by applying the synthetic aperture radar (SAR) technique is increasing. However, there is a lack of proper computer-aided design (CAD) tools at the system level, and almost all the R&D activities rely on experiments solely. The high cost of such a system stops many researchers from investigating such a fascinating research topic. Moreover, the experiment-oriented studies may blind the researchers to some details during the imaging process, since in most cases they are only interested in the readout from the receivers and do not know how the waves perform in reality. To bridge such a gap, we propose a modeling approach at mmW frequencies, which is able to simulate the physical process during SAR imaging. We are not going to discuss about advanced image reconstruction algorithms, since they have already been investigated intensively for decades. To distinguish from previous work, for the first time, we model the data acquisition process in a SAR imaging system successfully at mmW frequencies. We show how to perform some system-level studies based on such a simulator via a common PC, including the influence of reflectivity contrast between object and background, sampling step, and antenna's directivity on image quality. The simulator can serve system design purposes and it can be easily extended to THz frequencies.

Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography

Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image reconstruction algorithms for improving image quality in three-dimensional (3D) OAT. In this work, we implement and investigate two iterative image reconstruction methods for use with a 3D OAT small animal imager: namely a penalized least-squares (PLS) method employing a quadratic smoothness penalty and a PLS method employing a total variation norm penalty. The reconstruction algorithms employ accurate models of the ultrasonic transducer impulse responses. Experimental data sets are employed to compare the performances of the iterative reconstruction algorithms to that of a 3D filtered backprojection (FBP) algorithm. By the use of quantitative measures of image quality, we demonstrate that the iterative reconstruction algorithms can mitigate image artifacts and preserve spatial resolution more effectively than FBP algorithms. These features suggest that the use of advanced image reconstruction algorithms can improve the effectiveness of 3D OAT while reducing the amount of data required for biomedical applications.

Compressive ghost imaging

We describe an advanced image reconstruction algorithm for pseudothermal ghost imaging, reducing the number of measurements required for image recovery by an order of magnitude. The algorithm is based on compressed sensing, a technique that enables the reconstruction of an N-pixel image from much less than N measurements. We demonstrate the algorithm using experimental data from a pseudothermal ghost-imaging setup. The algorithm can be applied to data taken from past pseudothermal ghost-imaging experiments, improving the reconstruction’s quality.

TH-D-342-04: Advanced CT Image Reconstruction Algorithms Via Compressed Sensing

The key components of an x-ray computed tomography system are the source, detector, and image reconstruction algorithms. The introduction of multi-row detectors has enabled larger scanning coverage, higher spatial and faster complete organ scanning in the general purpose CT scanners. These improvements have enabled a variety of advanced clinical applications. The introduction of flat-panel detectors has enabled dedicated cone-beam CT in breast imaging, image-guided interventions, and image-guided radiation therapy. There is a constant interplay between the development of new and improved CT systems and advanced CT image reconstruction algorithms. New CT systems require advanced image reconstruction algorithms, and breakthroughs in image reconstruction algorithms may also enable future innovations in the future design of CT systems. The Shannon sampling theory is the foundation of conventional linear image reconstruction algorithms such as the Fourier transform and filtered backprojection methods. In these conventional image reconstruction frameworks, the violation of the Shannon sampling criterion often results in aliasing artifacts such as streaks in the reconstructed images. Recently, a new sampling theory, i.e., compressive sampling, has been formulated. It has been proven that accurate image reconstruction is possible using highly undersampled data sets provided that a nonlinear iterative image reconstruction method is utilized. This nonlinear iterative image reconstruction method is widely referred to as compressed sensing (CS). Based on the CS theory, some other generalized CS image reconstruction algorithms such as prior image constrained compressed sensing (PICCS) were recently proposed to reconstruct CT images. In this talk, we will discuss the potential impact of CS-based CT image reconstruction algorithms and their impact on current and future CT development. Educational Objectives: 1. Understand violation of sampling criterion may cause streaking artifacts in conventional linear image reconstruction algorithm. 2. Understand Compressed Sensing based image reconstruction algorithms can generate streak-free image using a highly undersampled data set. 3. Understand the potential benefit of PICCS algorithm in radiation dose reduction and improve temporal resolution using a slow gantry.

Visualization of Asymmetric Solids Distribution in Horizontal Swirling Flows Using Electrical Resistance Tomography

Efficient slurry transportation is vital to many industries. It has been proposed that helically formed pipes, which can be used to keep particulate solids in suspension, should be applied to enhance the distribution of solids in piped slurries. The use of a new in-situ measurement method based on electrical impedance tomography is proposed to assist in understanding the effect of particle suspension and as well as the effect on the wear of pipes by solid particle impingement due to the application of such a swirl-inducing pipe. In the absence of accurate predictive models for such complex flows, it is demonstrated that this method enables direct visualization of the solids concentration profiles in the pipes. Through the application of an advanced impedance image reconstruction algorithm and other analysis software, the asymmetric solids concentration distribution in horizontal swirling flows can be quantified. Particle concentration regimes are reported as a function of the water axial flow velocity and the downstream distance from the swirl-inducing pipe section. The nature of the solids distribution at a typical cross-section is reproducible and takes the form of a crescent region of a higher solids concentration, re-orientated in the direction of swirl by approximately π/2 from the normal settling position. Low concentration areas in a crescent shape appear on the opposite semi-circumference to the particle burden. These appear to develop into a ring structure as the water flow increases to a high axial velocity. At a high flow velocity, the mode of particle dispersion along the length of the pipeline appears to take the form of a group of ellipses. Larger ellipses were observed near the swirl-inducing pipe.