Dual-source photon-counting CT combines the high temporal resolution and high pitch of dual-source CT with the material quantification capabilities of photon-counting CT. It, however, results in cross-scatter that increases in severity with increased patient size and collimation. This cross-scatter must be corrected to ensure the removal of scatter artifacts and improve quantitative accuracy. To evaluate residual cross-scatter of a first-generation dual-source photon-counting CT and the effect of phantom size, collimation, and radiation dose, a phantom was scanned in single- and dual-source modes with and without its extension ring at three collimations and three radiation doses. Virtual monoenergetic images (VMI) at 50 keV, VMI 150 keV, and iodine density maps were reconstructed to determine variation between acquisition parameters in single- and dual-source modes. Additionally, differences relative to single-source acquisitions and to singlesource and small collimation acquisitions were calculated to reflect residual cross-scatter with and without matched collimation. At VMI 50 keV, inserts exhibited accuracy and similar variation between single- and dual-source modes, averaging 5.4 ± 2.6 and 6.2 ± 2.5 HU, respectively, across phantom size, collimation, and radiation dose. Differences relative to single-source measured 5.1 ± 8.5 and 0.4 ± 4.2 HU while differences relative to single-source and small collimation acquisitions were 6.4 ± 10.8 HU and -0.5 ± 3.9 HU for VMI 50 and 150 keV, respectively. This minimal residual cross-scatter increases confidence in the quantitative accuracy of spectral results necessary for clinical applications of dual-source photon-counting CT with motion, such as cardiac imaging.
Cardiac CT is a useful tool for cardiovascular diagnostics that offers different acquisition modes, each with its advantages. The development of direct converting detector technology has resulted in the clinical translation of dual-source photon-counting CT. This takes advantage of the improved image quality at high heart rates from dual-source CT while making available spectral results for more precise material characterization and quantification. To evaluate the stability of spectral results among different acquisition modes and heart rates, a cardiac motion phantom with a rod mimicking a 50% coronary stenosis was scanned with a dual-source photon-counting CT in three different acquisition modes (retrospective dual-source spiral, prospective dual-source step-and-shoot, dual-source flash spiral) and at different heart rates (60, 80, 100 bpm). Dice scores of stenosed regions relative to a static scan, eccentricity of non-stenosed regions, full width half max, and normalized area under the curve of line profiles were calculated for iodine density maps, and virtual mono-energetic images at 40 and 70 keV. Dice scores and eccentricity were consistent and not significantly affected by acquisition mode or heart rate for spectral results. Full width half max and normalized area under the curve similarly illustrated minor differences between acquisition modes and heart rates. The consistency in these metrics demonstrate preserved image structure and allows for the use of spectral results with high confidence. Dual-source photon-counting CT will enable cardiovascular diagnostics with better material characterization and differentiation.
Signal separability is an important factor in the differentiation of materials in spectral computed tomography. In this work, we evaluated the separability of two such materials, iodine and gadolinium with k-edges of 33.1 keV and 50.2 keV, respectively, with an investigational photon-counting CT scanner (Siemens, Germany). A 20 cm water equivalent phantom containing vials of iodine and gadolinium was imaged. Two datasets were generated by either varying the amount of contrast (iodine – 0.125-10 mg/mL, gadolinium 0.125-12 mg/mL) or by varying the tube current (50-300 mAs). Regions of interest were drawn within vials and then used to construct multivariate Gaussian models of signal. We evaluated three separation metrics using the Gaussian models: the area under the curve (AUC) of the receiver operating characteristic curve, the mean Mahalanobis distance, and the Jaccard index. For the dataset with varying contrast, all three metrics showed similar trends by indicating a higher separability when there was a large difference in signal magnitude between iodine and gadolinium. For the dataset with varying tube current, AUC showed the least variation due to change in noise condition and had a higher coefficient of determination (0.99, 0.97) than either mean Mahalanobis distance (0.69, 0.62) or Jaccard index (0.80, 0.75) when compared to material decomposition results for iodine or gadolinium respectively.
In this work, we define a theoretical approach to characterizing the signal-to-noise ratio (SNR) of multi-channeled systems such as spectral computed tomography image series. Spectral image datasets encompass multiple near-simultaneous acquisitions that share information. The conventional definition of SNR is applicable to a single image and thus does not account for the interaction of information between images in a series. We propose an extension of the conventional SNR definition into a multivariate space where each image in the series is treated as a separate information channel thus defining a spectral SNR matrix. We apply this to the specific case of contrast-to-noise ratio (CNR). This matrix is able to account for the conventional CNR of each image in the series as well as a covariance weighted CNR (Cov-CNR), which accounts for the covariance between two images in the series. We evaluate this experimentally with data from an investigational photon-counting CT scanner (Siemens).
The purpose of this study was to examine the effect of energy threshold selection on the quantification of contrast agents in photon-counting CT (PCCT). A phantom was devised consisting of vials of iodine (4, 8, 16 mg/mL), gadolinium (4, 8, 16 mg/mL), and bismuth (5, 10, 15 mg/mL) within a cylindrical water container. The phantom was scanned on a prototype photon-counting CT scanner. The detected photons were binned into two energy bins using a fixed lower threshold of 20 keV and an upper threshold that varied between 50 to 90 keV. An image containing all the spectral information (threshold 1) was examined along with both binned images. Images were evaluated for the mean and standard deviation of CT number in each vial and contrast-to-noise ratio (CNR) for each concentration. CT number values in the threshold 1 image remained mostly unchanged as energy threshold was increased. Vials of iodine and gadolinium had slightly higher CT numbers in lower energy bin images than the threshold 1 images, but the percentage difference varied slightly (6-37% for iodine and 5-22% for gadolinium) with energy threshold. In higher energy bin images, CT numbers were lower (20-68% for iodine and 10-59% for gadolinium) than threshold 1 and the difference decreased with increasing energy threshold. For bismuth, the percentage difference in the lower bin decreased (by 11-19%) with energy level while it increased (by 18-23%) in the upper bin. CNR varied only slightly in the lower energy bins and decreased with increasing energy threshold for all materials.
This study aimed to develop and compare two methods of inserting computerized virtual lesions into CT datasets. 24
physical (synthetic) nodules of three sizes and four morphologies were inserted into an anthropomorphic chest phantom
(LUNGMAN, KYOTO KAGAKU). The phantom was scanned (Somatom Definition Flash, Siemens Healthcare) with and
without nodules present, and images were reconstructed with filtered back projection and iterative reconstruction
(SAFIRE) at 0.6 mm slice thickness using a standard thoracic CT protocol at multiple dose settings. Virtual 3D CAD
models based on the physical nodules were virtually inserted (accounting for the system MTF) into the nodule-free CT
data using two techniques. These techniques include projection-based and image-based insertion. Nodule volumes were
estimated using a commercial segmentation tool (iNtuition, TeraRecon, Inc.). Differences were tested using paired t-tests
and R2 goodness of fit between the virtually and physically inserted nodules. Both insertion techniques resulted in nodule
volumes very similar to the real nodules (<3% difference) and in most cases the differences were not statistically
significant. Also, R2 values were all <0.97 for both insertion techniques. These data imply that these techniques can
confidently be used as a means of inserting virtual nodules in CT datasets. These techniques can be instrumental in building
hybrid CT datasets composed of patient images with virtually inserted nodules.
The purpose of this study was to develop a 3D quantification technique to assess the impact of imaging system on depiction of lesion morphology. Regional Hausdorff Distance (RHD) was computed from two 3D volumes: virtual mesh models of synthetic nodules or “virtual nodules” and CT images of physical nodules or “physical nodules”. The method can be described in following steps. First, the synthetic nodule was inserted into anthropomorphic Kyoto thorax phantom and scanned in a Siemens scanner (Flash). Then, nodule was segmented from the image. Second, in order to match the orientation of the nodule, the digital models of the “virtual” and “physical” nodules were both geometrically translated to the origin. Then, the “physical” was gradually rotated at incremental 10 degrees. Third, the Hausdorff Distance was calculated from each pair of “virtual” and “physical” nodules. The minimum HD value represented the most matching pair. Finally, the 3D RHD map and the distribution of RHD were computed for the matched pair. The technique was scalarized using the FWHM of the RHD distribution. The analysis was conducted for various shapes (spherical, lobular, elliptical, and speculated) of nodules. The calculated FWHM values of RHD distribution for the 8-mm spherical, lobular, elliptical, and speculated “virtual” and “physical” nodules were 0.23, 0.42, 0.33, and 0.49, respectively.
The purpose of this study was to substantiate the interdependency of image quality, radiation dose, and contrast
material dose in CT towards the patient-specific optimization of the imaging protocols. The study deployed two
phantom platforms. First, a variable sized phantom containing an iodinated insert was imaged on a representative CT
scanner at multiple CTDI values. The contrast and noise were measured from the reconstructed images for each
phantom diameter. Linearly related to iodine-concentration, contrast to noise ratio (CNR), was calculated for
different iodine-concentration levels. Second, the analysis was extended to a recently developed suit of 58 virtual
human models (5D-XCAT) with added contrast dynamics. Emulating a contrast-enhanced abdominal image
procedure and targeting a peak-enhancement in aorta, each XCAT phantom was “imaged” using a CT simulation
platform. 3D surfaces for each patient/size established the relationship between iodine-concentration, dose, and CNR.
The Sensitivity of Ratio (SR), defined as ratio of change in iodine-concentration versus dose to yield a constant
change in CNR was calculated and compared at high and low radiation dose for both phantom platforms. The results
show that sensitivity of CNR to iodine concentration is larger at high radiation dose (up to 73%). The SR results were
highly affected by radiation dose metric; CTDI or organ dose. Furthermore, results showed that the presence of
contrast material could have a profound impact on optimization results (up to 45%).
In computed tomography (CT), patient-specific organ dose can be estimated using pre-calculated organ dose conversion coefficients (organ dose normalized by CTDIvol, h factor) database, taking into account patient size and scan coverage. The conversion coefficients have been previously estimated for routine body protocol classes, grouped by scan coverage, across an adult population for fixed tube current modulated CT. The coefficients, however, do not include the widely utilized tube current (mA) modulation scheme, which significantly impacts organ dose. This study aims to extend the h factors and the corresponding dose length product (DLP) to create effective dose conversion coefficients (k factor) database incorporating various tube current modulation strengths. Fifty-eight extended cardiac-torso (XCAT) phantoms were included in this study representing population anatomy variation in clinical practice. Four mA profiles, representing weak to strong mA dependency on body attenuation, were generated for each phantom and protocol class. A validated Monte Carlo program was used to simulate the organ dose. The organ dose and effective dose was further normalized by CTDIvol and DLP to derive the h factors and k factors, respectively. The h factors and k factors were summarized in an exponential regression model as a function of body size. Such a population-based mathematical model can provide a comprehensive organ dose estimation given body size and CTDIvol. The model was integrated into an iPhone app XCATdose version 2, enhancing the 1st version based upon fixed tube current modulation. With the organ dose calculator, physicists, physicians, and patients can conveniently estimate organ dose.
Contrast enhancement is a key component of computed tomography (CT) imaging and offers opportunities for optimization. The design and optimization of techniques, however, require orchestration with the scan parameters and, further, a methodology to relate contrast enhancement and injection function. We used such a methodology to develop a method, the analytical inverse method, to predict the required injection function to achieve a desired contrast enhancement in a given organ by incorporation of a physiologically based compartmental model. The method was evaluated across 32 different target contrast enhancement functions for aorta, kidney, stomach, small intestine, and liver. The results exhibited that the analytical inverse method offers accurate performance with error in the range of 10% deviation between the predicted and desired organ enhancement curves. However, this method is incapable of predicting the injection function based on the liver enhancement. The findings of this study can be useful in optimizing contrast medium injection function as well as scan timing to provide more consistency in the way contrast-enhanced CT examinations are performed. To our knowledge, this work is one of the first attempts to predict the contrast material injection function for a desired organ enhancement curve.
Contrast enhancement is a key component of CT imaging and offer opportunities for optimization. The design and optimization of new techniques however requires orchestration with the scan parameters and further a methodology to relate contrast enhancement and injection function. In this study, we used such a methodology to develop a method, analytical inverse method, to predict the required injection function to achieve a desired contrast enhancement in a given organ by incorporation of a physiologically based compartmental model. The method was evaluated across 32 different target contrast enhancement functions for aorta, kidney, stomach, small intestine, and liver. The results exhibited that the analytical inverse method offers accurate performance with error in the range of 10% deviation between the predicted and desired organ enhancement curves. However, this method is incapable of predicting the injection function based on the liver enhancement. The findings of this study can be useful in optimizing contrast medium injection function as well as the scan timing to provide more consistency in the way that the contrast enhanced CT examinations are performed. To our knowledge, this work is one of the first attempts to predict the contrast material injection function for a desired organ enhancement curve.
In this work, we demonstrate the ability to determine the material composition of a sample by measuring coherent scatter
diffraction patterns generated using a coded-aperture x-ray scatter imaging (CAXSI) system. Most materials are known
to exhibit unique diffraction patterns through coherent scattering of low-energy x-rays. However, clinical x-ray imagers
typically discard scatter radiation as noise that degrades image quality. Through the addition of a coded aperture, the
system can be sensitized to coherent scattered photons that carry information about the identity and location of the
scattering material. In this work, we demonstrate this process using a Monte-Carlo simulation of a CAXSI system. A
simulation of a CAXSI system was developed in GEANT4 with modified physics libraries to model coherent scatter
diffraction patterns in materials. Simulated images were generated from 10 materials including plastics, hydrocarbons,
and biological tissue. The materials were irradiated using collimated pencil- and fan-beams with energies of 160 kVp.
The diffraction patterns were imaged using a simulated 2D detector and mathematically deconstructed using an
analytical projection model that accounted for the known x-ray source spectrum. The deconstructed diffraction patterns
were then matched with a library of known coherent scatter form-factors of different materials to determine the identity
of the scatterer at different locations in the object. The results showed good agreement between the measured and known
scatter patterns from the materials, demonstrating the ability to image and identify materials at different 3D locations
within an object using a projection-based CAXSI system.
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