In 3rd generation CT systems projection data, generated by X-rays emitted from a single source and passing
through the imaged object, are acquired by a single detector covering the entire field of view (FOV). Novel
CT system architectures employing distributed sources [1,2] could extend the axial coverage, while
removing cone-beam artifacts and improving spatial resolution and dose. The sources can be distributed in
plane and/or in the longitudinal direction. We investigate statistical iterative reconstruction of multi-axial
data, acquired with simulated CT systems with multiple sources distributed along the in-plane and
longitudinal directions. The current study explores the feasibility of 3D iterative Full and Half Scan
reconstruction methods for CT systems with two different architectures. In the first architecture the sources
are distributed in the longitudinal direction, and in the second architecture the sources are distributed both
longitudinally and trans-axially. We used Penalized Weighted Least Squares Transmission Reconstruction
(PWLSTR) and incorporated a projector-backprojector model matching the simulated architectures. The
proposed approaches minimize artifacts related to the proposed geometries. The reconstructed images show
that the investigated architectures can achieve good image quality for very large coverage without severe
cone-beam artifacts.
KEYWORDS: Monte Carlo methods, Sensors, Computer simulations, Signal detection, Computed tomography, X-rays, X-ray computed tomography, Scanners, 3D modeling, Aluminum
We present a new simulation environment for X-ray computed tomography, called CatSim. CatSim provides a research platform for GE researchers and collaborators to explore new reconstruction algorithms, CT architectures, and X-ray source or detector technologies. The main requirements for this simulator are accurate physics modeling, low computation times, and geometrical flexibility. CatSim allows simulating complex analytic phantoms, such as the FORBILD phantoms, including boxes, ellipsoids, elliptical cylinders, cones, and cut planes. CatSim incorporates polychromaticity, realistic quantum and electronic noise models, finite focal spot size and shape, finite detector cell size, detector cross-talk, detector lag or afterglow, bowtie filtration, finite detector efficiency, non-linear partial volume, scatter (variance-reduced Monte Carlo), and absorbed dose. We present an overview of CatSim along with a number of validation experiments.
In a 3rd generation CT system, a single source projects the entire field of view (FOV) onto a large detector opposite to
the source. In multi-source inverse geometry CT imaging, a multitude of sources sequentially project complementary
parts of the FOV on a much smaller detector. These sources may be distributed in both the trans-axial and axial
directions and jointly cover the entire FOV. Multi-source CT has several important advantages, including large axial
coverage, improved dose-efficiency, and improved spatial resolution. One of the challenges of this concept is to ensure
that no artifacts emerge in the reconstructed images where the sampling switches from one source to the next. This
work studies iterative reconstruction for multi-source imaging and focuses on the appearance of such artifacts. For that
purpose, phantom data are simulated using a realistic multi-source CT geometry, iteratively reconstructed and inspected
for artifact content. More realistic experiments using rebinned clinical datasets (emulating a multi-source CT system)
have also been performed. The results confirm the feasibility of artifact-free multi-source CT imaging in both full-scan
and half-scan situations.
Third-generation CT architectures are approaching fundamental limits. Spatial resolution is limited by the focal spot size and the detector cell size. Temporal resolution is limited by mechanical constraints on gantry rotation speed, and alternative geometries such as electron-beam CT and two-tube-two-detector CT come with severe tradeoffs in terms of image quality, dose-efficiency and complexity. Image noise is fundamentally linked to patient dose, and dose-efficiency is limited by finite detector efficiency and by limited spatio-temporal control over the X-ray flux. Finally, volumetric coverage is limited by detector size, scattered radiation, conebeam artifacts, Heel effect, and helical over-scan. We propose a new concept, multi-source inverse geometry CT, which allows CT to break through several of the above limitations. The proposed architecture has several advantages compared to third-generation CT: the detector is small and can have a high detection efficiency, the optical spot size is more consistent throughout the field-of-view, scatter is minimized even when eliminating the anti-scatter grid, the X-ray flux from each source can be modulated independently to achieve an optimal noise-dose tradeoff, and the geometry offers unlimited coverage without cone-beam artifacts. In this work we demonstrate the advantages of multi-source inverse geometry CT using computer simulations.
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.
Multi-slice CT scanners use EKG gating to predict the cardiac phase during slice reconstruction from projection data. Cardiac phase is generally defined with respect to the RR interval. The implicit assumption made is that the duration of events in a RR interval scales linearly when the heart rate changes. Using a more detailed EKG analysis, we evaluate the impact of relaxing this assumption on image quality. We developed a reconstruction algorithm that analyzes the associated EKG waveform to extract the natural cardiac states. A wavelet transform was used to decompose each RR-interval into P, QRS, and T waves. Subsequently, cardiac phase was defined with respect to these waves instead of a percentage or time delay from the beginning or the end of RR intervals. The projection data was then tagged with the cardiac phase and processed using temporal weights that are function of their cardiac phases. Finally, the tagged projection data were combined from multiple cardiac cycles using a multi-sector algorithm to reconstruct images. The new algorithm was applied to clinical data, collected on a 4-slice (GE LightSpeed Qx/i) and 8-slice CT scanner (GE LightSpeed Plus), with heart rates of 40 to 80 bpm. The quality of reconstruction is assessed by the visualization of the major arteries, e.g. RCA, LAD, LC in the reformat 3D images. Preliminary results indicate that Cardiac State Driven reconstruction algorithm offers better image quality than their RR-based counterparts.
Using helical, multi-detector computed tomography (CT) imaging technology operating at sub-second scanning speeds, clinicians are investigating the capabilities of CT for cardiac imaging. In this paper, we describe the application of novel modeling tools to assess CT system capability. These tools allow us to quantify the capabilities of both hardware and software algorithms for cardiac imaging. The model consists of a human thorax, a dynamic model of a human heart, and a complete physics-based, CT system model. The use of the model to predict image quality is demonstrated by varying both the reconstruction algorithm (half-scan, sector-based) and CT system parameters (axial detector resolution). The mathematical tools described provide a means to rapidly evaluate new reconstruction algorithms and CT system designs for cardiac imaging.
Cardiac imaging is still a challenge to CT reconstruction algorithms due to the dynamic nature of the heart. We have developed a new reconstruction technique, called the Flexible Algorithm, which achieves high temporal resolution while it is robust to heart-rate variations. The Flexible Algorithm, first, retrospectively tags helical CT views with corresponding cardiac phases obtained from associated EKG. Next, it determines a set of views for each slice, a stack of which covers the entire heart. Subsequently, the algorithm selects an optimum subset of views to achieve the highest temporal resolution for the desired cardiac phase. Finally, it spatiotemporally filters the views in the selected subsets to reconstruct slices. We tested the performance of our algorithm using both a dynamic analytical phantom and clinical data. Preliminary results indicate that the Flexible Algorithm obtains improved spatiotemporal resolution for a large range of heart rates and variations than standard algorithms do. By providing improved image quality at any desired cardiac phase, and robustness to heart rate variations, the Flexible Algorithm enables cardiac applications in CT, including those that benefit from multiphase information.
With the introduction of helical, multi-detector computed tomography (CT) scanners having sub-second scanning speeds, clinicians are currently investigating the role of CT in cardiac imaging. In this paper, we describe a four-dimensional (4D) x-ray attenuation model of a human heart and the use of this model to assess the capabilities of both hardware and software algorithms for cardiac imaging. We developed a model of the human thorax, composed of several analytical structures, and a model of the human heart, constructed from several elliptical surfaces. A model for each coronary vessel consists of a torus placed at a suitable location on the heart's surface. The motion of the heart during the cardiac cycle was implemented by applying transformational operators to each surface composing the heart. We used the 4D model of the heart to generate forward projection data, which then became input into a model of a CT imaging system. The use of the model to predict image quality is demonstrated by varying both the reconstruction algorithm (sector-based, half-scan) and CT system parameters (gantry speed, spatial resolution). The mathematical model of the human heart, while having limitations, provides a means to rapidly evaluate new reconstruction algorithms and CT system designs for cardiac imaging.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.