Cone-beam CT (CBCT) is a prevalent tool for image-guided radiation therapy (IGRT). It can be used for patient positioning and dose calculation, which are needed at the early and later stages of each fraction of treatment, respectively. The requirement of image quality on patient positioning is less demanding than that on dose calculation. This work introduces a two-pass data acquisition approach for CBCT imaging in IGRT, with the first pass being left-handed helix and the second pass being right-handed helix. The first scan alone produces images for patient positioning, whereas the two scans together are used to produce quality improved images for dose calculation. We refer to this two-pass data acquisition geometry as the double-helix trajectory. We propose a dedicated image reconstruction algorithm for the double-helix trajectory and demonstrate the algorithm via computer simulations.
Multiple CT vendors have released clinical multi-detector (MD) computed tomography (CT) systems with 16cm coverage. Axial CT for voxels outside the acquisition plane does not satisfy a fundamental completeness condition, which leads to so called cone-beam artifacts. This paper revisits the iterative filtered back-projection (FBP) algorithm from 2008 and analyzes it in the context of Brerman iterations. Also, we propose a one application of this algorithm along with a recently published filtering orthogonal to the acquisition plane as a pragmatic way to considerably reduce the cone-beam artifacts in axial CT scans with high coverage.
Photon-counting CT (PCCT) is an emerging technique that may bring new possibilities to clinical practice. Compared to
conventional CT, PCCT is able to exclude electronic noise that may severely impair image quality at low photon counts.
This work focused on assessing the low-dose performance of a whole-body research PCCT scanner consisting of two
subsystems, one equipped with an energy-integrating detector, and the other with a photon-counting detector. Evaluation
of the low-dose performance of the research PCCT scanner was achieved by comparing the noise performance of the
two subsystems, with an emphasis on examining the impact of electronic noise on image quality in low-dose situations.
Photon-counting CT (PCCT) may yield potential value for many clinical applications due to its relative immunity to
electronic noise, increased geometric efficiency relative to current scintillating detectors, and the ability to resolve energy
information about the detected photons. However, there are a large number of parameters that require optimization,
particularly the energy thresholds configuration. Fast and accurate estimation of signal and noise in PCCT can benefit
the optimization of acquisition parameters for specific diagnostic tasks. Based on the acquisition parameters and detector
response of our research PCCT system, we derived mathematical models for both signal and noise. The signal model
took the tube spectrum, beam filtration, object attenuation, water beam hardening, and detector response into account.
The noise model considered the relationship between noise and radiation dose, as well as the propagation of noise as
threshold data are subtracted to yield energy bin data. To determine the absolute noise value, a noise look-up table
(LUT) was acquired using a limited number of calibration scans. The noise estimation algorithm then used the noise
LUT to estimate noise for scans with a variety of combination of energy thresholds, dose levels, and object attenuation.
Validation of the estimation algorithms was performed on our whole-body research PCCT system using semianthropomorphic
water phantoms and solutions of calcium and iodine. The algorithms achieved accurate estimation of
signal and noise for a variety of scanning parameter combinations. The proposed method can be used to optimize energy
thresholds configuration for many clinical applications of PCCT.
A high-resolution (HR) data collection mode has been introduced to a whole-body, research photon-counting-detector
CT system installed in our laboratory. In this mode, 64 rows of 0.45 mm x 0.45 mm detector pixels were used, which
corresponded to a pixel size of 0.25 mm x 0.25 mm at the iso-center. Spatial resolution of this HR mode was quantified
by measuring the MTF from a scan of a 50 micron wire phantom. An anthropomorphic lung phantom, cadaveric swine
lung, temporal bone and heart specimens were scanned using the HR mode, and image quality was subjectively assessed
by two experienced radiologists. High spatial resolution of the HR mode was evidenced by the MTF measurement, with
15 lp/cm and 20 lp/cm at 10% and 2% modulation. Images from anthropomorphic phantom and cadaveric specimens
showed clear delineation of small structures, such as lung vessels, lung nodules, temporal bone structures, and coronary
arteries. Temporal bone images showed critical anatomy (i.e. stapes superstructure) that was clearly visible in the PCD
system. These results demonstrated the potential application of this imaging mode in lung, temporal bone, and vascular
imaging. Other clinical applications that require high spatial resolution, such as musculoskeletal imaging, may also
benefit from this high resolution mode.
Photon-counting CT (PCCT) potentially offers both improved dose efficiency and material decomposition capabilities relative to CT systems using energy integrating detectors. With respect to material decomposition, both projection-based and image-based methods have been proposed, most of which require accurate a priori information regarding the shape of the x-ray spectra and the response of the detectors. Additionally, projection-based methods require access to projection data. These data can be difficult to obtain, since spectra, detector response, and projection data formats are proprietary information. Further, some published image-based, 3-material decomposition methods require a volume conservation assumption, which is often violated in solutions. We have developed an image-based material decomposition method that can overcome those limitations. We introduced a general condition on volume constraint that does not require the volume to be conserved in a mixture. An empirical calibration can be performed with various concentrations of basis materials. The material decomposition method was applied to images acquired from a prototype whole-body PCCT scanner. The results showed good agreement between the estimation and known mass concentration values. Factors affecting the performance of material decomposition, such as energy threshold configuration and volume conservation constraint, were also investigated. Changes in accuracy of the mass concentration estimates were demonstrated for four different energy configurations and when volume conservation was assumed.
To perform task-based image quality assessment in CT, it is desirable to have a large number of realistic patient images with known diagnostic truth. One effective way to achieve this objective is to create hybrid images that combine patient images with simulated lesions. Because conventional hybrid images generated in the image-domain fails to reflect the impact of scan and reconstruction parameters on lesion appearance, this study explored a projection-domain approach. Liver lesion models were forward projected according to the geometry of a commercial CT scanner to acquire lesion projections. The lesion projections were then inserted into patient projections (decoded from commercial CT raw data with the assistance of the vendor) and reconstructed to acquire hybrid images. To validate the accuracy of the forward projection geometry, simulated images reconstructed from the forward projections of a digital ACR phantom were compared to physically acquired ACR phantom images. To validate the hybrid images, lesion models were inserted into patient images and visually assessed. Results showed that the simulated phantom images and the physically acquired phantom images had great similarity in terms of HU accuracy and high-contrast resolution. The lesions in the hybrid image had a realistic appearance and merged naturally into the liver background. In addition, the inserted lesion demonstrated reconstruction-parameter-dependent appearance. Compared to conventional image-domain approach, our method enables more realistic hybrid images for image quality assessment.
X-ray computed tomography (CT) with energy-discriminating capabilities presents exciting opportunities for increased dose efficiency and improved material decomposition analyses. However, due to constraints imposed by the inability of photon-counting detectors (PCD) to respond accurately at high photon flux, to date there has been no clinical application of PCD-CT. Recently, our lab installed a research prototype system consisting of two x-ray sources and two corresponding detectors, one using an energy-integrating detector (EID) and the other using a PCD. In this work, we report the first third-party evaluation of this prototype CT system using both phantoms and a cadaver head. The phantom studies demonstrated several promising characteristics of the PCD sub-system, including improved longitudinal spatial resolution and reduced beam hardening artifacts, relative to the EID sub-system. More importantly, we found that the PCD sub-system offers excellent pulse pileup control in cases of x-ray flux up to 550 mA at 140 kV, which corresponds to approximately 2.5×10<sup>11</sup> photons per cm<sup>2</sup> per second. In an anthropomorphic phantom and a cadaver head, the PCD sub-system provided image quality comparable to the EID sub-system for the same dose level. Our results demonstrate the potential of the prototype system to produce clinically-acceptable images<i> in vivo</i>.
Task-based image quality assessment is a valuable methodology for development, optimization and evaluation of new image formation processes in x-ray computed tomography (CT), as well as in other imaging modalities. A simple way to perform such an assessment is through the use of two (or more) alternative forced choice (AFC) experiments. In this paper, we are interested in drawing statistical inference from outcomes of multiple AFC experiments that are obtained using multiple readers as well as multiple cases. We present a non-parametric covariance estimator for this problem. Then, we illustrate its usefulness with a practical example involving x-ray CT simulations. The task for this example is classification between presence or absence of one lesion with unknown location within a given object. This task is used for comparison of three standard image reconstruction algorithms in x-ray CT using four human observers.
Over the last decade, significant progress has been made in terms of treatment of diseases using minimallyinvasive
procedures. This progress was facilitated through multiple refinements of the imaging capabilities of
C-arm systems in the interventional room, and more sophisticated procedures may become feasible by further
refining the performance of these systems. Our primary focus is to eliminate two strong limitations of the
current circular cone-beam imaging approach: cone-beam artifacts and limited extent of the volume covered in
the direction of the patient bed. To solve this problem, we seek a source trajectory that (i) is complete in terms
of Tuy's condition, (ii) can be periodically-repeated without discontinuities to allow long-object imaging, (iii)
is practical, and (iv) offers full R-line coverage (an R-line is a line that connects any two source positions). A
trajectory that satisfies all of our constraint is the
Arc-Extended-Line-Arc(AELA) trajectory. Unfortunately,
this trajectory does not allow smooth, continuous scanning at reasonable dose. In this work, we propose a new
data acquisition geometry: the Ellipse-Line-Ellipse (ELE) trajectory. This geometry satisfies all of our constraints
along with the attractive feature that smooth, continuous scanning at reasonable dose is enabled.