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.
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.
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.
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
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.
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.
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.
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.
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
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.
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.
Proc. SPIE. 1954, Signal and Data Processing of Small Targets 1993
KEYWORDS: Target detection, Infrared search and track, Signal to noise ratio, Detection and tracking algorithms, Computer simulations, Monte Carlo methods, Palladium, Electronic filtering, Filtering (signal processing), Algorithms
As detection processing becomes increasingly advanced, for example, in infrared search and track (IRST) systems, the detection threshold becomes the bottleneck to overall system performance. Significantly reducing this threshold requires the capability to track targets in a high clutter environment. In theory, the multiple hypothesis tracking (MHT) algorithm is a solution to this problem. However, in practice, MHT in its basic form becomes computationally prohibitive for all but low to moderate false alarm densities. In this paper, we evaluate a computationally feasible alternate form, which we call a bi-level MHT algorithm. The basic form of this algorithm has been previously proposed, but results on its performance have been lacking. In addition to describing an implementation of a bi-level MHT algorithm, this paper present Monte Carlo simulation results characterizing the performance of the algorithm, and demonstrates the tradeoff between track acquisition range and false track rate for a simple IRST fly-by scenario.