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We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g., data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.

In practical CBCT imaging such as that in interventional procedures, data collected are truncated due to the limited detector size that cannot cover the imaged subject and especially the involved interventional devices. We study optimization-based image reconstruction directly from truncated data, with an aim to reduce truncation artifacts observed in practical or clinical reconstructions. In the study, the reconstruction problem is formulated as a constrained optimization program in which a data-derivative fidelity term was introduced for effective suppression of the truncation artifacts. In this work, we carry specifically out a quantitative investigation of impact of the data-derivative term on the artifact reduction. In the investigation, the contribution of the data-derivative term is characterized by a weighting parameter, and the investigation is enabled subsequently by tailoring the Chambolle-Pock (CP) algorithm to solve the optimization program containing the data-derivative fidelity term. The results of the study suggest that an appropriately designed data-derivative fidelity can be exploited for yielding image reconstruction with reduced artifacts directly from truncated data.

As percutaneous endovascular procedures address more complex and broader disease states, there is an increasing need for intra-procedure 3D vascular imaging. In this paper, we investigate C-Arm 2-axis tomosynthesis (“Tomo”) as an alternative to C-Arm Cone Beam Computed Tomography (CBCT) for workflow situations in which the CBCT acquisition may be inconvenient or prohibited. We report on our experience in performing tomosynthesis acquisitions with a digital angiographic imaging system (GE Healthcare Innova 4100 Angiographic Imaging System, Milwaukee, WI). During a tomo acquisition the detector and tube each orbit on a plane above and below the table respectively. The tomo orbit may be circular or elliptical, and the tomographic half-angle in our studies varied from approximately 16 to 28 degrees as a function of orbit period. The trajectory, geometric calibration, and gantry performance are presented. We overview a multi-resolution iterative reconstruction employing compressed sensing techniques to mitigate artifacts associated with incomplete data reconstructions. In this work, we focus on the reconstruction of small high contrast objects such as iodinated vasculature and interventional devices. We evaluate the overall performance of the acquisition and reconstruction through phantom acquisitions and a swine study. Both tomo and comparable CBCT acquisitions were performed during the swine study thereby enabling the use of CBCT as a reference in the evaluation of tomo vascular imaging. We close with a discussion of potential clinical applications for tomo, reflecting on the imaging and workflow results achieved.

There is a clinical need to improve cerebral perfusion assessment during the treatment of ischemic stroke in the interventional suite. The clinician is able to determine whether the arterial blockage was successfully opened but is unable to sufficiently assess blood flow through the parenchyma. C-arm spin acquisitions can image the cerebral blood volume (CBV) but are challenged to capture the temporal dynamics of the iodinated contrast bolus, which is required to derive, e.g., cerebral blood flow (CBF) and mean transit time (MTT). Here we propose to utilize a circular tomosynthesis acquisition on the C-arm to achieve the necessary temporal sampling of the volume at the cost of incomplete data. We address the incomplete data problem by using tools from compressed sensing and incorporate temporal interpolation to improve our temporal resolution. A CT neuro perfusion data set is utilized for generating a dynamic (4D) volumetric model from which simulated tomo projections are generated. The 4D model is also used as a ground truth reference for performance evaluation. The performance that may be achieved with the tomo acquisition and 4D reconstruction (under simulation conditions, i.e., without considering data fidelity limitations due to imaging physics and imaging chain) is evaluated. In the considered scenario, good agreement between the ground truth and the tomo reconstruction in the parenchyma was achieved.

An unsupervised stochastic model-based approach to image segmentation is described, and some of its properties investigated. In this approach, the problem of model parameter estimation is formulated as a problem of parameter estimation from incomplete data, and the expectation-maximization (EM) algorithm is used to determine a maximum-likelihood (ML) estimate. Previously, the use of the EM algorithm in this application has encountered difficulties since an analytical expression for the conditional expectations required in the EM procedure is generally unavailable, except for the simplest models. In this paper, two solutions are proposed to solve this problem: a Monte Carlo scheme and a scheme related to Besag's (1986) iterated conditional mode (ICM) method. Both schemes make use of Markov random-field modeling assumptions. Examples are provided to illustrate the implementation of the EM algorithm for several general classes of image models. Experimental results on both synthetic and real images are provided. >

This chapter provides an overview of the GE Discovery CT750HD dual energy imaging capability known as gemstone spectral imaging (GSI). The CT750HD is a single X-ray source system that employs fast kVp switching for dual energy acquisitions. This acquisition method enables precise temporal registration of the dual-energy sinograms, projection-based material decomposition, and delivers a full 50 cm material decomposition scan field of view. The subsystem technologies employed to achieve the dual energy acquisitions are detailed in the discussion of system design. Calibration of fast kVp switching data, material decomposition, and visualization of the resulting images are covered in the image reconstruction and post processing sections. The chapter closes with GSI implementation in the context of challenging diagnostic applications.

Coronary CT Angiography (CTA) is limited in patients with calcified plaque and stents. CTA is unable to confidently differentiate fibrous from lipid plaque. Fast switched dual energy CTA offers certain advantages. Dual energy CTA removes calcium thereby improving visualization of the lumen and potentially providing a more accurate measure of stenosis. Dual energy CTA directly measures calcium burden (calcium hydroxyapatite) thereby eliminating a separate non-contrast series for Agatston Scoring. Using material basis pairs, the differentiation of fibrous and lipid plaques is also possible. Patency of a previously stented coronary artery is difficult to visualize with CTA due to resolution constraints and localized beam hardening artifacts. Monochromatic 70 keV or Iodine images coupled with Virtual Non-stent images lessen beam hardening artifact and blooming. Virtual removal of stainless steel stents improves assessment of in-stent re-stenosis. A beating heart phantom with 'cholesterol' and 'fibrous' phantom coronary plaques were imaged with dual energy CTA. Statistical classification methods (SVM, kNN, and LDA) distinguished 'cholesterol' from 'fibrous' phantom plaque tissue. Applying this classification method to 16 human soft plaques, a lipid 'burden' may be useful for characterizing risk of coronary disease. We also found that dual energy CTA is more sensitive to iodine contrast than conventional CTA which could improve the differentiation of myocardial infarct and ischemia on delayed acquisitions. These phantom and patient acquisitions show advantages with using fast switched dual energy CTA for coronary imaging and potentially extends the use of CT for addressing problem areas of non-invasive evaluation of coronary artery disease.

Hypodense metastases are not always completely distinguishable from benign cysts in the liver using conventional Computed Tomography (CT) imaging, since the two lesion types present with overlapping intensity distributions due to similar composition as well as other factors including beam hardening and patient motion. This problem is extremely challenging for small lesions with diameter less than 1 cm. To accurately characterize such lesions, multiple follow-up CT scans or additional Positron Emission Tomography or Magnetic Resonance Imaging exam are often conducted, and in some cases a biopsy may be required after the initial CT finding. Gemstone Spectral Imaging (GSI) with fast kVp switching enables projection-based material decomposition, offering the opportunity to discriminate tissue types based on their energy-sensitive material attenuation and density. GSI can be used to obtain monochromatic images where beam hardening is reduced or eliminated and the images come inherently pre-registered due to the fast kVp switching acquisition. We present a supervised learning method for discriminating between cysts and hypodense liver metastases using these monochromatic images. Intensity-based statistical features extracted from voxels inside the lesion are used to train optimal linear and nonlinear classifiers. Our algorithm only requires a region of interest within the lesion in order to compute relevant features and perform classification, thus eliminating the need for an accurate segmentation of the lesion. We report classifier performance using M-fold cross-validation on a large lesion database with radiologist-provided lesion location and labels as the reference standard. Our results demonstrate that (a) classification using a single projection-based spectral CT image, i.e., a monochromatic image at a specified keV, outperforms classification using an image-based dual energy CT pair, i.e., low and high kVp images derived from the same fast kVp acquisition and (b) classification using monochromatic images can achieve very high accuracy in separating benign liver cysts and metastases, especially for small lesions.

The clinical application of Gemstone Spectral ImagingTM, a fast kV switching dual energy acquisition, is explored in the context of noninvasive kidney stone characterization. Utilizing projection-based material decomposition, effective atomic number and monochromatic images are generated for kidney stone characterization. Analytical and experimental measurements are reported and contrasted. Phantoms were constructed using stone specimens extracted from patients. This allowed for imaging of the different stone types under similar conditions. The stone specimens comprised of Uric Acid, Cystine, Struvite and Calcium-based compositions. Collectively, these stone types span an effective atomic number range of approximately 7 to 14. While Uric Acid and Calcium based stones are generally distinguishable in conventional CT, stone compositions like Cystine and Struvite are difficult to distinguish resulting in treatment uncertainty. Experimental phantom measurements, made under increasingly complex imaging conditions, illustrate the impact of various factors on measurement accuracy. Preliminary clinical studies are reported.

Dual energy CT cardiac imaging is challenging due to cardiac motion and the resolution requirements of clinical applications. In this paper we investigate dual energy CT imaging via fast kVp switching acquisitions of a novel dynamic cardiac phantom. The described cardiac phantom is realistic in appearance with pneumatic motion control driven by an ECG waveform. In the reported experiments the phantom is driven off a 60 beats per minute simulated ECG waveform. The cardiac phantom is inserted into a phantom torso cavity. A fast kVp switching axial step and shoot acquisition is detailed. The axial scan time at each table position exceeds one heart cycle so as to enable retrospective gating. Gating is performed as a mechanism to mitigate the resolution impact of heart motion. Processing of fast kVp data is overviewed and the resulting kVp, material decomposed density, and monochromatic reconstructions are presented. Imaging results are described in the context of potential clinical cardiac applications.

Linear discriminate analysis (LDA) is applied to dual kVp CT and used for tissue characterization. The potential to quantitatively model both malignant and benign, hypo-intense liver lesions is evaluated by analysis of portal-phase, intravenous CT scan data obtained on human patients. Masses with an a priori classification are mapped to a distribution of points in basis material space. The degree of localization of tissue types in the material basis space is related to both quantum noise and real compositional differences. The density maps are analyzed with LDA and studied with system simulations to differentiate these factors. The discriminant analysis is formulated so as to incorporate the known statistical properties of the data. Effective kVp separation and mAs relates to precision of tissue localization. Bias in the material position is related to the degree of X-ray scatter and partial-volume effect. Experimental data and simulations demonstrate that for single energy (HU) imaging or image-based decomposition pixel values of water-like tissues depend on proximity to other iodine-filled bodies. Beam-hardening errors cause a shift in image value on the scale of that difference sought between in cancerous and cystic lessons. In contrast, projection-based decomposition or its equivalent when implemented on a carefully calibrated system can provide accurate data. On such a system, LDA may provide novel quantitative capabilities for tissue characterization in dual energy CT.

Recently there has been significant interest in dual energy CT imaging with several acquisition methods being actively pursued. Here we investigate fast kVp switching where the kVp alternates between low and high kVp every view. Fast kVp switching enables fine temporal registration, helical and axial acquisitions, and full field of view. It also presents several processing challenges. The rise and fall of the kVp, which occurs during the view integration period, is not instantaneous and complicates the measurement of the effective spectrum for low and high kVp views. Further, if the detector digital acquisition system (DAS) and generator clocks are not fully synchronous, jitter is introduced in the kVp waveform relative to the view period. In this paper we develop a method for estimation of the resulting spectrum for low and high kVp views. The method utilizes static kVp acquisitions of air with a small bowtie filter as a basis set. A fast kVp acquisition of air with a small bowtie filter is performed and the effective kVp is estimated as a linear combination of the basis vectors. The effectiveness of this method is demonstrated through the reconstruction of a water phantom acquired with a fast kVp acquisition. The impact of jitter due to the generator and detector DAS clocks is explored via simulation. The error is measured relative to spectrum variation and material decomposition accuracy.

In a conventional X-ray CT system, where an object is scanned with a selected incident x-ray spectrum, or kVp, the reconstructed images only approximate the linear X-ray attenuation coefficients of the imaged object at an effective energy of the incident X-ray beam. The errors are primarily the result of beam hardening due to the polychromatic nature of the X-ray spectrum. Modem clinical CT scanners can reduce this error by a process commonly referred to as spectral calibration. Spectral calibration linearizes the measured projection value to the thickness of water. However, beam hardening from bone and contrast agents can still induce shading and streaking artifacts and cause CT number inaccuracies in the image. In this paper, we present a dual kVp scanning method, where during the scan, the kVp is alternately switching between target low and high preset values, typically 80kVp and 140 kVp, with a period less than 1ms. The measured projection pairs are decomposed into the density integrals of two basis materials in projection space. The reconstructed density images are further processed to obtain monochromatic attenuation coefficients of the object at any desired energy. Energy levels yielding optimized monochromatic images are explored, and their analytical representations are derived.

The capabilities of flat panel interventional x-ray systems continue to expand, enabling a broader array of medical applications to be performed in a minimally invasive manner. Although CT is providing pre-operative 3D information, there is a need for 3D imaging of low contrast soft tissue during interventions in a number of areas including neurology, cardiac electro-physiology, and oncology. Unlike CT systems, interventional angiographic x-ray systems provide real-time large field of view 2D imaging, patient access, and flexible gantry positioning enabling interventional procedures. However, relative to CT, these C-arm flat panel systems have additional technical challenges in 3D soft tissue imaging including slower rotation speed, gantry vibration, reduced lateral patient field of view (FOV), and increased scatter. The reduced patient FOV often results in significant data truncation. Reconstruction of truncated (incomplete) data is known an "interior problem", and it is mathematically impossible to obtain an exact reconstruction. Nevertheless, it is an important problem in 3D imaging on a C-arm to address the need to generate a 3D reconstruction representative of the object being imaged with minimal artifacts. In this work we investigate the application of an iterative Maximum Likelihood Transmission (MLTR) algorithm to truncated data. We also consider truncated data with limited views for cardiac imaging where the views are gated by the electrocardiogram(ECG) to combat motion artifacts.

We investigated a method, motion compensated integration (MCI), for enhancing stent Contrast-to-Noise Ratio (CNR) such that stent deployment may be more easily assessed. MCI registers fluoroscopic frames on the basis of stent motion and performs pixel-wise integration to reduce noise. Registration is based on marker balls, high contrast interventional devices which guide the clinician in stent placement. It is assumed that stent motion is identical to that of the marker balls. Detecting marker balls and identifying their centroids with a high degree of accuracy is a non-trivial task. To address the required registration accuracy, in this work we examine MCI’s visualization benefit as a function of registration error. We employ adaptive forced choice experiments to quantify human discrimination fidelity. Perception results are contrasted with CNR measurements. For each level of registration inaccuracy investigated, MCI conferred a benefit (p

Image segmentation is an important and early processing stage in many image analysis problems. Often, this must be done in an unsupervised fashion in that training data is not available and the class-conditioned feature vectors must be estimated directly from the data. A major problem in such applications is the determination of the number of classes actually present in an image. This problem, called the cluster validation problem, remains essentially unsolved. We investigate the cluster validation problem associated with the use of a previously developed unsupervised segmentation algorithm based upon the expectation-maximization (EM) algorithm. More specifically, we consider several well-known information-theoretic criteria (ICs) as candidate solutions to the validation problem when used in conjunction with this EM-based segmentation scheme. We show that these criteria generally provide inappropriate solutions due to the domination of the penalty term by the associated log-likelihood function. As an alternative we propose a model-fitting technique in which the complete data log-likelihood functional is modeled as an exponential function in the number of classes acting. The estimated number of classes are then determined in a manner similar to finding the rise time of the exponential function. This new validation technique is shown to be robust and outperform the ICs in our experiments. Experimental results for both synthetic and real world imagery are detailed.

The future need to detect and track low-observable targets against clutter backgrounds means that the track processor will be required to cope with much higher false detection rates than can be handled by traditional `zero-scan' tracking algorithms. We have developed a Bi-Level MHT algorithm which is capable of tracking in such a demanding environment. This paper provides an update to this previously reported algorithm, and extends previously reported single-target performance results to the case of two crossing targets. Specifically, we present Monte Carlo simulation results characterizing the ability of the algorithm to hold onto two target tracks as they cross, under a range of false detection densities. We will also assess the loss in performance due to target interaction, as well as gain in performance obtained from propagating multiple hypothesis. Finally, we will give an indication of computational complexity by measuring various operation counts as a function of false detection density.

The detection of small targets in infrared (IR) clutter is a problem of critical importance to Infrared Search and Track (IRST) systems. This paper presents techniques for analyzing and improving the detection performance of IRST systems. Only spatial, or single-frame, processing will be addressed. For clutter with spatially slowly varying statistics, the approach is based on linear filtering. Models of target and cluttter are developed and used to analyze matched filter performance and sensitivity. This sensitivity analysis is used to improve filter bank design. A clutter classification scheme which can separate clutter of different types is presented. Finally, to improve system performance in the presence of large intensity gradients, such as cloud edges, an improved adaptive threshold scheme is presented.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An Infrared Search and Track (IRST) system may consist of a target detection pre-processor and a higher-level processor which evaluates candidate detections and forms tracking hypotheses. The detection signal processing must evaluate large numbers (tens of thousands) of pixels at the sensor frame rate and determine a small number (tens) of candidate detections. To be effective, detection processing must be able to detect targets at long ranges and extract targets from background clutter. While the higher-level process will be able to reject some false alarms and occasionally fill in missed targets, system performance will be critically degraded by poor detection capabilities. For most applications, such as airborne reconnaissance, size and weight considerations impose significant limitations on allowable computational complexity. Thus, the detection processing must be as accurate as possible while remaining fast and simple.