Segmentation and quantification of materials with energy discriminating computed tomography: a phantom study

Photon-Counting Computed Tomography (PCCT): Technical Background and Cardio-Vascular Applications

Photon-counting computed tomography (PCCT) is a new advanced imaging technique that is going to transform the standard clinical use of computed tomography (CT) imaging. Photon-counting detectors resolve the number of photons and the incident X-ray energy spectrum into multiple energy bins. Compared with conventional CT technology, PCCT offers the advantages of improved spatial and contrast resolution, reduction of image noise and artifacts, reduced radiation exposure, and multi-energy/multi-parametric imaging based on the atomic properties of tissues, with the consequent possibility to use different contrast agents and improve quantitative imaging. This narrative review first briefly describes the technical principles and the benefits of photon-counting CT and then provides a synthetic outline of the current literature on its use for vascular imaging.

Framework for Photon Counting Quantitative Material Decomposition

In this paper, the accuracy of material decomposition (MD) using an energy discriminating photon counting detector was studied. An MD framework was established and validated using calcium hydroxyapatite (CaHA) inserts of known densities (50 mg/cm³, 100 mg/cm³, 250 mg/cm³, 400 mg/cm³), and diameters (1.2, 3.0, and 5.0 mm). These inserts were placed in a cardiac rod phantom that mimics a tissue equivalent heart and measured using an experimental photon counting detector cone beam computed tomography (PCD-CBCT) setup. The quantitative coronary calcium scores (density, mass, and volume) obtained from the MD framework were compared with the nominal values. In addition, three different calibration techniques, signal-to-equivalent thickness calibration (STC), polynomial correction (PC), and projected equivalent thickness calibration (PETC) were compared to investigate the effect of the calibration method on the quantitative values. The obtained MD estimates agreed well with the nominal values for density (mass) with mean absolute percent errors (MAPEs) 8 ± 11% (9 ± 15%) and 4 ± 6% (9 ± 14%) for STC and PETC calibration methods, respectively. PC displayed large MAPEs for density (27 ± 9%), and mass (25 ± 12%). Volume estimation resulted in large deviations between true and measured values with notable MAPEs for STC (40 ± 90%), PC (40 ± 80%), and PETC (40 ± 90%). The framework demonstrated the feasibility of quantitative CaHA mass and density scoring using PCD-CBCT.

Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm

Spectral information in CT may be used for material decomposition to produce accurate reconstructions of material density and to separate materials with similar overall attenuation. Traditional methods separate the reconstruction and decomposition steps, often resulting in undesirable trade-offs (e.g. sampling constraints, a simplified spectral model). In this work, we present a model-based material decomposition algorithm which performs the reconstruction and decomposition simultaneously using a multienergy forward model. In a kV-switching simulation study, the presented method is capable of reconstructing iodine at 0.5 mg ml^-1 with a contrast-to-noise ratio greater than two, as compared to 3.0 mg ml^-1 for image domain decomposition. The presented method also enables novel acquisition methods, which was demonstrated in this work with a combined kV-switching/split-filter acquisition explored in simulation and physical test bench studies. This novel design used four spectral channels to decompose three materials: water, iodine, and gadolinium. In simulation, the presented method accurately reconstructed concentration value estimates with RMSE values of 4.86 mg ml^-1 for water, 0.108 mg ml^-1 for iodine and 0.170 mg ml^-1 for gadolinium. In test-bench data, the RMSE values were 134 mg ml^-1, 5.26 mg ml^-1 and 1.85 mg ml^-1, respectively. These studies demonstrate the ability of model-based material decomposition to produce accurate concentration estimates in challenging spatial/spectral sampling acquisitions.

Experimental study to optimize configurations of PCD Spectral CT

BACKGROUND

High dose efficiency of photon counting detector based spectral CT (PCD-SCT) and its value in some clinical diagnosis have been well acknowledged. However, it has not been widely adopted in practical use for medical diagnosis and security inspection.

OBJECTIVE

To evaluate the influence on PCD-SCT from multiple aspects including the number of energy channels, k-edge materials, energy thresholding, basis functions in spectral information decomposition, and the combined optimal setting for these parameters and configurations.

METHODS

Basis material decomposition after spatial reconstruction is applied for PCD-SCT. A "one-step" synthesis method, merging decomposition with synthesis, is proposed to obtain virtual monochromatic images. An I-RMSE is computed using the bias part of I-RMSE to describe the difference of a synthesized signal from ground truth and the standard deviation part of I-RMSE to express the noise level. In addition, virtual monochromatic images commonly used in the medical area are also synthesized. Both numerical simulations and practical experiments are conducted for validation.

RESULTS

Results indicated that the I-RMSE for matters significantly reduced with an increased number of energy channels compared with dual-energy channel. The maximum reduction is 6% for triple-, 18% for quadruple-and 24% for quintuple-energy, respectively. However, the improvement is not linear, and also slows down after the number of energy channels reaches a certain number. Contrast agents of high concentration can introduce up to 50% error to surrounding matters. Moreover, different energy partitions influence the total error, which demonstrates the necessity of energy threshold optimization. Last, the optimal basis-material combination varies according to targeted imaging matters and the interested monochromatic energies.

CONCLUSIONS

Gain from more energy channels could be significant with the increase of energy channel number. Introduction of contrast agents in scanned objects will increase overall error in spectral CT imaging. Energy thresholding optimization is beneficial for information recovery. Moreover, the choice of basis materials could also be important to obtain low noise results. With these studies of the effect from various configurations for PCD-SCT, one may optimize the configuration of PCD-SCT accordingly.

Microcalcification detectability using a bench-top prototype photon-counting breast CT based on a Si strip detector

PURPOSE

To investigate the feasibility of detecting breast microcalcification (μCa) with a dedicated breast computed tomography (CT) system based on energy-resolved photon-counting silicon (Si) strip detectors.

METHODS

The proposed photon-counting breast CT system and a bench-top prototype photon-counting breast CT system were simulated using a simulation package written in matlab to determine the smallest detectable μCa. A 14 cm diameter cylindrical phantom made of breast tissue with 20% glandularity was used to simulate an average-sized breast. Five different size groups of calcium carbonate grains, from 100 to 180 μm in diameter, were simulated inside of the cylindrical phantom. The images were acquired with a mean glandular dose (MGD) in the range of 0.7-8 mGy. A total of 400 images was used to perform a reader study. Another simulation study was performed using a 1.6 cm diameter cylindrical phantom to validate the experimental results from a bench-top prototype breast CT system. In the experimental study, a bench-top prototype CT system was constructed using a tungsten anode x-ray source and a single line 256-pixels Si strip photon-counting detector with a pixel pitch of 100 μm. Calcium carbonate grains, with diameter in the range of 105-215 μm, were embedded in a cylindrical plastic resin phantom to simulate μCas. The physical phantoms were imaged at 65 kVp with an entrance exposure in the range of 0.6-8 mGy. A total of 500 images was used to perform another reader study. The images were displayed in random order to three blinded observers, who were asked to give a 4-point confidence rating on each image regarding the presence of μCa. The μCa detectability for each image was evaluated by using the average area under the receiver operating characteristic curve (AUC) across the readers.

RESULTS

The simulation results using a 14 cm diameter breast phantom showed that the proposed photon-counting breast CT system can achieve high detection accuracy with an average AUC greater than 0.89 ± 0.07 for μCas larger than 120 μm in diameter at a MGD of 3 mGy. The experimental results using a 1.6 cm diameter breast phantom showed that the prototype system can achieve an average AUC greater than 0.98 ± 0.01 for μCas larger than 140 μm in diameter using an entrance exposure of 1.2 mGy.

CONCLUSIONS

The proposed photon-counting breast CT system based on a Si strip detector can potentially offer superior image quality to detect μCa with a lower dose level than a standard two-view mammography.

Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications

In x-ray computed tomography (CT), materials having different elemental compositions can be represented by identical pixel values on a CT image (ie, CT numbers), depending on the mass density of the material. Thus, the differentiation and classification of different tissue types and contrast agents can be extremely challenging. In dual-energy CT, an additional attenuation measurement is obtained with a second x-ray spectrum (ie, a second "energy"), allowing the differentiation of multiple materials. Alternatively, this allows quantification of the mass density of two or three materials in a mixture with known elemental composition. Recent advances in the use of energy-resolving, photon-counting detectors for CT imaging suggest the ability to acquire data in multiple energy bins, which is expected to further improve the signal-to-noise ratio for material-specific imaging. In this review, the underlying motivation and physical principles of dual- or multi-energy CT are reviewed and each of the current technical approaches is described. In addition, current and evolving clinical applications are introduced.

Characterization of energy response for photon-counting detectors using x-ray fluorescence

PURPOSE

To investigate the feasibility of characterizing a Si strip photon-counting detector using x-ray fluorescence.

METHODS

X-ray fluorescence was generated by using a pencil beam from a tungsten anode x-ray tube with 2 mm Al filtration. Spectra were acquired at 90° from the primary beam direction with an energy-resolved photon-counting detector based on an edge illuminated Si strip detector. The distances from the source to target and the target to detector were approximately 19 and 11 cm, respectively. Four different materials, containing silver (Ag), iodine (I), barium (Ba), and gadolinium (Gd), were placed in small plastic containers with a diameter of approximately 0.7 cm for x-ray fluorescence measurements. Linear regression analysis was performed to derive the gain and offset values for the correlation between the measured fluorescence peak center and the known fluorescence energies. The energy resolutions and charge-sharing fractions were also obtained from analytical fittings of the recorded fluorescence spectra. An analytical model, which employed four parameters that can be determined from the fluorescence calibration, was used to estimate the detector response function.

RESULTS

Strong fluorescence signals of all four target materials were recorded with the investigated geometry for the Si strip detector. The average gain and offset of all pixels for detector energy calibration were determined to be 6.95 mV/keV and -66.33 mV, respectively. The detector's energy resolution remained at approximately 2.7 keV for low energies, and increased slightly at 45 keV. The average charge-sharing fraction was estimated to be 36% within the investigated energy range of 20-45 keV. The simulated detector output based on the proposed response function agreed well with the experimental measurement.

CONCLUSIONS

The performance of a spectral imaging system using energy-resolved photon-counting detectors is very dependent on the energy calibration of the detector. The proposed x-ray fluorescence technique offers an accurate and efficient way to calibrate the energy response of a photon-counting detector.

Breast tissue decomposition with spectral distortion correction: a postmortem study

PURPOSE

To investigate the feasibility of an accurate measurement of water, lipid, and protein composition of breast tissue using a photon-counting spectral computed tomography (CT) with spectral distortion corrections.

METHODS

Thirty-eight postmortem breasts were imaged with a cadmium-zinc-telluride-based photon-counting spectral CT system at 100 kV. The energy-resolving capability of the photon-counting detector was used to separate photons into low and high energy bins with a splitting energy of 42 keV. The estimated mean glandular dose for each breast ranged from 1.8 to 2.2 mGy. Two spectral distortion correction techniques were implemented, respectively, on the raw images to correct the nonlinear detector response due to pulse pileup and charge-sharing artifacts. Dual energy decomposition was then used to characterize each breast in terms of water, lipid, and protein content. In the meantime, the breasts were chemically decomposed into their respective water, lipid, and protein components to provide a gold standard for comparison with dual energy decomposition results.

RESULTS

The accuracy of the tissue compositional measurement with spectral CT was determined by comparing to the reference standard from chemical analysis. The averaged root-mean-square error in percentage composition was reduced from 15.5% to 2.8% after spectral distortion corrections.

CONCLUSIONS

The results indicate that spectral CT can be used to quantify the water, lipid, and protein content in breast tissue. The accuracy of the compositional analysis depends on the applied spectral distortion correction technique.

Energy dispersive CdTe and CdZnTe detectors for spectral clinical CT and NDT applications

We are developing room temperature compound semiconductor detectors for applications in energy-resolved high-flux single x-ray photon-counting spectral computed tomography (CT), including functional imaging with nanoparticle contrast agents for medical applications and non destructive testing (NDT) for security applications. Energy-resolved photon-counting can provide reduced patient dose through optimal energy weighting for a particular imaging task in CT, functional contrast enhancement through spectroscopic imaging of metal nanoparticles in CT, and compositional analysis through multiple basis function material decomposition in CT and NDT. These applications produce high input count rates from an x-ray generator delivered to the detector. Therefore, in order to achieve energy-resolved single photon counting in these applications, a high output count rate (OCR) for an energy-dispersive detector must be achieved at the required spatial resolution and across the required dynamic range for the application. The required performance in terms of the OCR, spatial resolution, and dynamic range must be obtained with sufficient field of view (FOV) for the application thus requiring the tiling of pixel arrays and scanning techniques. Room temperature cadmium telluride (CdTe) and cadmium zinc telluride (CdZnTe) compound semiconductors, operating as direct conversion x-ray sensors, can provide the required speed when connected to application specific integrated circuits (ASICs) operating at fast peaking times with multiple fixed thresholds per pixel provided the sensors are designed for rapid signal formation across the x-ray energy ranges of the application at the required energy and spatial resolutions, and at a sufficiently high detective quantum efficiency (DQE). We have developed high-flux energy-resolved photon-counting x-ray imaging array sensors using pixellated CdTe and CdZnTe semiconductors optimized for clinical CT and security NDT. We have also fabricated high-flux ASICs with a two dimensional (2D) array of inputs for readout from the sensors. The sensors are guard ring free and have a 2D array of pixels and can be tiled in 2D while preserving pixel pitch. The 2D ASICs have four energy bins with a linear energy response across sufficient dynamic range for clinical CT and some NDT applications. The ASICs can also be tiled in 2D and are designed to fit within the active area of the sensors. We have measured several important performance parameters including; the output count rate (OCR) in excess of 20 million counts per second per square mm with a minimum loss of counts due to pulse pile-up, an energy resolution of 7 keV full width at half maximum (FWHM) across the entire dynamic range, and a noise floor about 20keV. This is achieved by directly interconnecting the ASIC inputs to the pixels of the CdZnTe sensors incurring very little input capacitance to the ASICs. We present measurements of the performance of the CdTe and CdZnTe sensors including the OCR, FWHM energy resolution, noise floor, as well as the temporal stability and uniformity under the rapidly varying high flux expected in CT and NDT applications.

Theoretical comparison of a dual energy system and photon counting silicon detector used for material quantification in spectral CT

Any method using dual energy computed tomography (CT) has to make prior assumptions in order to quantify k-edge contrast agents. This work estimates the mean square error (MSE) in contrast agent quantification employing a method based on assigning each reconstructed voxel a ratio of soft tissue and fat using dual energy CT. The results are compared to the MSE using a photon counting silicon detector with multiple bins. The square root of the MSEs of the quantifications of iodine and gadolinium for an object consisting of soft tissue and fat using the silicon detector and dual energy CT range from below 2% and 1% of the contrast agent content for 100 mg/cm(3) of iodine and gadolinium, up to approximately 10% and 13%, and 6% and 4%, for 5 mg/cm(3) of iodine and gadolinium, respectively. When adding bone with a voxel volume fraction of 2.2%, the square root of the MSEs of the quantifications of iodine and gadolinium using dual energy CT increases to 25% and 6%, respectively, for 5 mg/cm(3) of contrast agent. In conclusion, results indicate that the noise levels of the material quantification using the silicon detector are higher than the noise levels using a dual energy CT when the composition of the object is known. However, using a dual energy CT increases the risk of model specification error and subsequently a large bias in contrast agent quantification, a problem which does not exist when using a multi-bin CT where the number of energy bins is larger than two.

Stratification of patients with liver fibrosis using dual-energy CT

Assessing the severity of liver fibrosis has direct clinical implications for patient diagnosis and treatment. Liver biopsy, typically considered the gold standard, has limited clinical utility due to its invasiveness. Therefore, several imaging-based techniques for staging liver fibrosis have emerged, such as magnetic resonance elastography (MRE) and ultrasound elastography (USE), but they face challenges that include limited availability, high cost, poor patient compliance, low repeatability, and inaccuracy. Computed tomography (CT) can address many of these limitations, but is still hampered by inaccuracy in the presence of confounding factors, such as liver fat. Dual-energy CT (DECT), with its ability to discriminate between different tissue types, may offer a viable alternative to these methods. By combining the "multi-material decomposition" (MMD) algorithm with a biologically driven hypothesis we developed a method for assessing liver fibrosis from DECT images. On a twelve-patient cohort the method produced quantitative maps showing the spatial distribution of liver fibrosis, as well as a fibrosis score for each patient with statistically significant correlation with the severity of fibrosis across a wide range of disease severities. A preliminary comparison of the proposed algorithm against MRE showed good agreement between the two methods. Finally, the application of the algorithm to longitudinal DECT scans of the cohort produced highly repeatable results. We conclude that our algorithm can successfully stratify patients with liver fibrosis and can serve to supplement and augment current clinical practice and the role of DECT imaging in staging liver fibrosis.

Energy response calibration of photon-counting detectors using x-ray fluorescence: a feasibility study

Accurate energy calibration is critical for the application of energy-resolved photon-counting detectors in spectral imaging. The aim of this study is to investigate the feasibility of energy response calibration and characterization of a photon-counting detector using x-ray fluorescence. A comprehensive Monte Carlo simulation study was performed using Geant4 Application for Tomographic Emission (GATE) to investigate the optimal technique for x-ray fluorescence calibration. Simulations were conducted using a 100 kVp tungsten-anode spectra with 2.7 mm Al filter for a single pixel cadmium telluride (CdTe) detector with 3 × 3 mm(2) in detection area. The angular dependence of x-ray fluorescence and scatter background was investigated by varying the detection angle from 20° to 170° with respect to the beam direction. The effects of the detector material, shape, and size on the recorded x-ray fluorescence were investigated. The fluorescent material size effect was considered with and without the container for the fluorescent material. In order to provide validation for the simulation result, the angular dependence of x-ray fluorescence from five fluorescent materials was experimentally measured using a spectrometer. Finally, eleven of the fluorescent materials were used for energy calibration of a CZT-based photon-counting detector. The optimal detection angle was determined to be approximately at 120° with respect to the beam direction, which showed the highest fluorescence to scatter ratio (FSR) with a weak dependence on the fluorescent material size. The feasibility of x-ray fluorescence for energy calibration of photon-counting detectors in the diagnostic x-ray energy range was verified by successfully calibrating the energy response of a CZT-based photon-counting detector. The results of this study can be used as a guideline to implement the x-ray fluorescence calibration method for photon-counting detectors in a typical imaging laboratory.

Dual-energy cone-beam CT with a flat-panel detector: effect of reconstruction algorithm on material classification

PURPOSE

Cone-beam CT (CBCT) with a flat-panel detector (FPD) is finding application in areas such as breast and musculoskeletal imaging, where dual-energy (DE) capabilities offer potential benefit. The authors investigate the accuracy of material classification in DE CBCT using filtered backprojection (FBP) and penalized likelihood (PL) reconstruction and optimize contrast-enhanced DE CBCT of the joints as a function of dose, material concentration, and detail size.

METHODS

Phantoms consisting of a 15 cm diameter water cylinder with solid calcium inserts (50-200 mg/ml, 3-28.4 mm diameter) and solid iodine inserts (2-10 mg/ml, 3-28.4 mm diameter), as well as a cadaveric knee with intra-articular injection of iodine were imaged on a CBCT bench with a Varian 4343 FPD. The low energy (LE) beam was 70 kVp (+0.2 mm Cu), and the high energy (HE) beam was 120 kVp (+0.2 mm Cu, +0.5 mm Ag). Total dose (LE+HE) was varied from 3.1 to 15.6 mGy with equal dose allocation. Image-based DE classification involved a nearest distance classifier in the space of LE versus HE attenuation values. Recognizing the differences in noise between LE and HE beams, the LE and HE data were differentially filtered (in FBP) or regularized (in PL). Both a quadratic (PLQ) and a total-variation penalty (PLTV) were investigated for PL. The performance of DE CBCT material discrimination was quantified in terms of voxelwise specificity, sensitivity, and accuracy.

RESULTS

Noise in the HE image was primarily responsible for classification errors within the contrast inserts, whereas noise in the LE image mainly influenced classification in the surrounding water. For inserts of diameter 28.4 mm, DE CBCT reconstructions were optimized to maximize the total combined accuracy across the range of calcium and iodine concentrations, yielding values of ∼ 88% for FBP and PLQ, and ∼ 95% for PLTV at 3.1 mGy total dose, increasing to ∼ 95% for FBP and PLQ, and ∼ 98% for PLTV at 15.6 mGy total dose. For a fixed iodine concentration of 5 mg/ml and reconstructions maximizing overall accuracy across the range of insert diameters, the minimum diameter classified with accuracy >80% was ∼ 15 mm for FBP and PLQ and ∼ 10 mm for PLTV, improving to ∼ 7 mm for FBP and PLQ and ∼ 3 mm for PLTV at 15.6 mGy. The results indicate similar performance for FBP and PLQ and showed improved classification accuracy with edge-preserving PLTV. A slight preference for increased smoothing of the HE data was found. DE CBCT discrimination of iodine and bone in the knee was demonstrated with FBP and PLTV at 6.2 mGy total dose.

CONCLUSIONS

For iodine concentrations >5 mg/ml and detail size ∼ 20 mm, material classification accuracy of >90% was achieved in DE CBCT with both FBP and PL at total doses <10 mGy. Optimal performance was attained by selection of reconstruction parameters based on the differences in noise between HE and LE data, typically favoring stronger smoothing of the HE data, and by using penalties matched to the imaging task (e.g., edge-preserving PLTV in areas of uniform enhancement).

Clinical applications of spectral molecular imaging: potential and challenges

Spectral molecular imaging is a new X-ray-based imaging technology providing highly specific 3D imaging at high spatial resolution that has the potential to measure disease activity and response to treatment noninvasively. The ability to identify and quantify components of tissue and biomarkers of disease activity derive from the properties of the photon-processing detector. Multiple narrow sections of the energy spectrum are sampled simultaneously, providing a range of energy dependent Hounsfield units. As each material has a specific measurable X-ray spectrum, spectroscopic imaging allows for multiple materials to be quantified and differentiated from each other simultaneously. The technology, currently in its infancy, is set to grow rapidly, much as magnetic resonance did. The critical clinical applications have not yet been established, but it is likely to play a major role in identifying and directing treatment for unstable atherosclerotic plaque, assessing activity and response to treatment of a range of inflammatory diseases, and monitoring biomarkers of cancer and its treatment. If combined with Positron-emission tomography (PET), spectral molecular imaging could have a far greater effective role in cancer diagnosis and treatment monitoring than PET-CT does at present. It is currently used for small animal and specimen imaging. There are many challenges to be overcome before spectral imaging can be introduced into clinical medicine - these include technological improvements to detector design, bonding to the semiconductor layer, image reconstruction and display software, identifying which biomarkers are of most relevance to the disease in question, and accelerating drug discovery enabled by the new capabilities provided by spectral imaging.

Hybrid spectral micro-CT: system design, implementation, and preliminary results

Spectral CT has proven an important development in biomedical imaging, and there have been several publications in the past years demonstrating its merits in pre-clinical and clinical applications. In 2012, Xu reported that near-term implementation of spectral micro-CT could be enhanced by a hybrid architecture: a narrow-beam spectral “interior” imaging chain integrated with a traditional wide-beam “global” imaging chain. This hybrid integration coupled with compressive sensing (CS)-based interior tomography demonstrated promising results for improved contrast resolution, and decreased system cost and radiation dose. The motivation for the current study is implementation and evaluation of the hybrid architecture with a first-of-its-kind hybrid spectral micro-CT system. Preliminary results confirm improvements in both contrast and spatial resolution. This technology is shown to merit further investigation and potential application in future spectral CT scanner design.

A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images

The ability of dual-energy computed-tomographic (CT) systems to determine the concentration of constituent materials in a mixture, known as material decomposition, is the basis for many of dual-energy CT's clinical applications. However, the complex composition of tissues and organs in the human body poses a challenge for many material decomposition methods, which assume the presence of only two, or at most three, materials in the mixture. We developed a flexible, model-based method that extends dual-energy CT's core material decomposition capability to handle more complex situations, in which it is necessary to disambiguate among and quantify the concentration of a larger number of materials. The proposed method, named multi-material decomposition (MMD), was used to develop two image analysis algorithms. The first was virtual unenhancement (VUE), which digitally removes the effect of contrast agents from contrast-enhanced dual-energy CT exams. VUE has the ability to reduce patient dose and improve clinical workflow, and can be used in a number of clinical applications such as CT urography and CT angiography. The second algorithm developed was liver-fat quantification (LFQ), which accurately quantifies the fat concentration in the liver from dual-energy CT exams. LFQ can form the basis of a clinical application targeting the diagnosis and treatment of fatty liver disease. Using image data collected from a cohort consisting of 50 patients and from phantoms, the application of MMD to VUE and LFQ yielded quantitatively accurate results when compared against gold standards. Furthermore, consistent results were obtained across all phases of imaging (contrast-free and contrast-enhanced). This is of particular importance since most clinical protocols for abdominal imaging with CT call for multi-phase imaging. We conclude that MMD can successfully form the basis of a number of dual-energy CT image analysis algorithms, and has the potential to improve the clinical utility of dual-energy CT in disease management.

Quantitative material characterization from multi-energy photon counting CT

PURPOSE

To quantify the concentration of soft-tissue components of water, fat, and calcium through the decomposition of the x-ray spectral signatures in multi-energy CT images.

METHODS

Decomposition of dual-energy and multi-energy x-ray data into basis materials can be performed in the projection domain, image domain, or during image reconstruction. In this work, the authors present methodology for the decomposition of multi-energy x-ray data in the image domain for the application of soft-tissue characterization. To demonstrate proof-of-principle, the authors apply several previously proposed methods and a novel content-aware method to multi-energy images acquired with a prototype photon counting CT system. Data from phantom and ex vivo specimens are evaluated.

RESULTS

The number and type of materials in a region can be limited based on a priori knowledge or classification strategies. The proposed difference classifier successfully classified the image into air only, water+fat, water+fat+iodine, and water+calcium regions. Then, the content-aware material decomposition based on weighted least-square optimization generated quantitative maps of concentration. Bias in the estimation of the concentration of water and oil components in a phantom study was <0.10 ± 0.15 g/cc on average. Decomposition of ex vivo carotid endarterectomy specimens suggests the presence of water, lipid, and calcium deposits in the plaque walls.

CONCLUSIONS

Initial application of the proposed methodology suggests that it can decompose multi-energy CT images into quantitative maps of water, adipose, iodine, and calcium concentrations.

K-edge ratio method for identification of multiple nanoparticulate contrast agents by spectral CT imaging

OBJECTIVE

Recently introduced energy-sensitive X-ray CT makes it feasible to discriminate different nanoparticulate contrast materials. The purpose of this work is to present a K-edge ratio method for differentiating multiple simultaneous contrast agents using spectral CT.

METHODS

The ratio of two images relevant to energy bins straddling the K-edge of the materials is calculated using an analytic CT simulator. In the resulting parametric map, the selected contrast agent regions can be identified using a thresholding algorithm. The K-edge ratio algorithm is applied to spectral images of simulated phantoms to identify and differentiate up to four simultaneous and targeted CT contrast agents.

RESULTS

We show that different combinations of simultaneous CT contrast agents can be identified by the proposed K-edge ratio method when energy-sensitive CT is used. In the K-edge parametric maps, the pixel values for biological tissues and contrast agents reach a maximum of 0.95, whereas for the selected contrast agents, the pixel values are larger than 1.10. The number of contrast agents that can be discriminated is limited owing to photon starvation. For reliable material discrimination, minimum photon counts corresponding to 140 kVp, 100 mAs and 5-mm slice thickness must be used.

CONCLUSION

The proposed K-edge ratio method is a straightforward and fast method for identification and discrimination of multiple simultaneous CT contrast agents.

ADVANCES IN KNOWLEDGE

A new spectral CT-based algorithm is proposed which provides a new concept of molecular CT imaging by non-iteratively identifying multiple contrast agents when they are simultaneously targeting different organs.

Breast composition measurement with a cadmium-zinc-telluride based spectral computed tomography system

PURPOSE

To investigate the feasibility of breast tissue composition in terms of water, lipid, and protein with a cadmium-zinc-telluride (CZT) based computed tomography (CT) system to help better characterize suspicious lesions.

METHODS

Simulations and experimental studies were performed using a spectral CT system equipped with a CZT-based photon-counting detector with energy resolution. Simulations of the figure-of-merit (FOM), the signal-to-noise ratio (SNR) of the dual energy image with respect to the square root of mean glandular dose (MGD), were performed to find the optimal configuration of the experimental acquisition parameters. A calibration phantom 3.175 cm in diameter was constructed from polyoxymethylene plastic with cylindrical holes that were filled with water and oil. Similarly, sized samples of pure adipose and pure lean bovine tissues were used for the three-material decomposition. Tissue composition results computed from the images were compared to the chemical analysis data of the tissue samples.

RESULTS

The beam energy was selected to be 100 kVp with a splitting energy of 40 keV. The tissue samples were successfully decomposed into water, lipid, and protein contents. The RMS error of the volumetric percentage for the three-material decomposition, as compared to data from the chemical analysis, was estimated to be approximately 5.7%.

CONCLUSIONS

The results of this study suggest that the CZT-based photon-counting detector may be employed in the CT system to quantify the water, lipid, and protein mass densities in tissue with a relatively good agreement.

Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study

A simple and accurate measurement of breast density is crucial for the understanding of its impact in breast cancer risk models. The feasibility to quantify volumetric breast density with a photon-counting spectral mammography system has been investigated using both computer simulations and physical phantom studies. A computer simulation model involved polyenergetic spectra from a tungsten anode x-ray tube and a Si-based photon-counting detector has been evaluated for breast density quantification. The figure-of-merit (FOM), which was defined as the signal-to-noise ratio of the dual energy image with respect to the square root of mean glandular dose, was chosen to optimize the imaging protocols, in terms of tube voltage and splitting energy. A scanning multi-slit photon-counting spectral mammography system has been employed in the experimental study to quantitatively measure breast density using dual energy decomposition with glandular and adipose equivalent phantoms of uniform thickness. Four different phantom studies were designed to evaluate the accuracy of the technique, each of which addressed one specific variable in the phantom configurations, including thickness, density, area and shape. In addition to the standard calibration fitting function used for dual energy decomposition, a modified fitting function has been proposed, which brought the tube voltages used in the imaging tasks as the third variable in dual energy decomposition. For an average sized 4.5 cm thick breast, the FOM was maximized with a tube voltage of 46 kVp and a splitting energy of 24 keV. To be consistent with the tube voltage used in current clinical screening exam (∼32 kVp), the optimal splitting energy was proposed to be 22 keV, which offered a FOM greater than 90% of the optimal value. In the experimental investigation, the root-mean-square (RMS) error in breast density quantification for all four phantom studies was estimated to be approximately 1.54% using standard calibration function. The results from the modified fitting function, which integrated the tube voltage as a variable in the calibration, indicated a RMS error of approximately 1.35% for all four studies. The results of the current study suggest that photon-counting spectral mammography systems may potentially be implemented for an accurate quantification of volumetric breast density, with an RMS error of less than 2%, using the proposed dual energy imaging technique.

Image-based spectral distortion correction for photon-counting x-ray detectors

PURPOSE

To investigate the feasibility of using an image-based method to correct for distortions induced by various artifacts in the x-ray spectrum recorded with photon-counting detectors for their application in breast computed tomography (CT).

METHODS

The polyenergetic incident spectrum was simulated with the tungsten anode spectral model using the interpolating polynomials (TASMIP) code and carefully calibrated to match the x-ray tube in this study. Experiments were performed on a Cadmium-Zinc-Telluride (CZT) photon-counting detector with five energy thresholds. Energy bins were adjusted to evenly distribute the recorded counts above the noise floor. BR12 phantoms of various thicknesses were used for calibration. A nonlinear function was selected to fit the count correlation between the simulated and the measured spectra in the calibration process. To evaluate the proposed spectral distortion correction method, an empirical fitting derived from the calibration process was applied on the raw images recorded for polymethyl methacrylate (PMMA) phantoms of 8.7, 48.8, and 100.0 mm. Both the corrected counts and the effective attenuation coefficient were compared to the simulated values for each of the five energy bins. The feasibility of applying the proposed method to quantitative material decomposition was tested using a dual-energy imaging technique with a three-material phantom that consisted of water, lipid, and protein. The performance of the spectral distortion correction method was quantified using the relative root-mean-square (RMS) error with respect to the expected values from simulations or areal analysis of the decomposition phantom.

RESULTS

The implementation of the proposed method reduced the relative RMS error of the output counts in the five energy bins with respect to the simulated incident counts from 23.0%, 33.0%, and 54.0% to 1.2%, 1.8%, and 7.7% for 8.7, 48.8, and 100.0 mm PMMA phantoms, respectively. The accuracy of the effective attenuation coefficient of PMMA estimate was also improved with the proposed spectral distortion correction. Finally, the relative RMS error of water, lipid, and protein decompositions in dual-energy imaging was significantly reduced from 53.4% to 6.8% after correction was applied.

CONCLUSIONS

The study demonstrated that dramatic distortions in the recorded raw image yielded from a photon-counting detector could be expected, which presents great challenges for applying the quantitative material decomposition method in spectral CT. The proposed semi-empirical correction method can effectively reduce these errors caused by various artifacts, including pulse pileup and charge sharing effects. Furthermore, rather than detector-specific simulation packages, the method requires a relatively simple calibration process and knowledge about the incident spectrum. Therefore, it may be used as a generalized procedure for the spectral distortion correction of different photon-counting detectors in clinical breast CT systems.