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PurposeTo 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.MethodsThirty-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.ResultsThe 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.ConclusionsThe 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.

PurposeTo 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.MethodsThirty-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.ResultsThe 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.ConclusionsThe 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.

We investigated the effect of different imaging parameters, such as dose, beam energy, energy resolution and the number of energy bins, on the image quality of K-edge spectral computed tomography (CT) of gold nanoparticles (GNP) accumulated in an atherosclerotic plaque. A maximum likelihood technique was employed to estimate the concentration of GNP, which served as a targeted intravenous contrast material intended to detect the degree of the plaques inflammation. The simulation studies used a single-slice parallel beam CT geometry with an x-ray beam energy ranging between 50 and 140 kVp. The synthetic phantoms included small (3 cm in diameter) cylinder and chest (33 × 24 cm(2)) phantoms, where both phantoms contained tissue, calcium and gold. In the simulation studies, GNP quantification and background (calcium and tissue) suppression tasks were pursued. The x-ray detection sensor was represented by an energy resolved photon counting detector (e.g., CdZnTe) with adjustable energy bins. Both ideal and more realistic (12% full width at half maximum (FWHM) energy resolution) implementations of the photon counting detector were simulated. The simulations were performed for the CdZnTe detector with a pixel pitch of 0.5-1 mm, which corresponds to a performance without significant charge sharing and cross-talk effects. The Rose model was employed to estimate the minimum detectable concentration of GNPs. A figure of merit (FOM) was used to optimize the x-ray beam energy (kVp) to achieve the highest signal-to-noise ratio with respect to the patient dose. As a result, the successful identification of gold and background suppression was demonstrated. The highest FOM was observed at the 125 kVp x-ray beam energy. The minimum detectable GNP concentration was determined to be approximately 1.06 µmol mL(-1) (0.21 mg mL(-1)) for an ideal detector and about 2.5 µmol mL(-1) (0.49 mg mL(-1)) for a more realistic (12% FWHM) detector. The studies show the optimal imaging parameters at the lowest patient dose using an energy resolved photon counting detector to image GNP in an atherosclerotic plaque.

We investigated the effect of different imaging parameters, such as dose, beam energy, energy resolution and the number of energy bins, on the image quality of K-edge spectral computed tomography (CT) of gold nanoparticles (GNP) accumulated in an atherosclerotic plaque. A maximum likelihood technique was employed to estimate the concentration of GNP, which served as a targeted intravenous contrast material intended to detect the degree of the plaque's inflammation. The simulation studies used a single-slice parallel beam CT geometry with an x-ray beam energy ranging between 50 and 140 kVp. The synthetic phantoms included small (3 cm in diameter) cylinder and chest (33 × 24 cm(2)) phantoms, where both phantoms contained tissue, calcium and gold. In the simulation studies, GNP quantification and background (calcium and tissue) suppression tasks were pursued. The x-ray detection sensor was represented by an energy resolved photon counting detector (e.g., CdZnTe) with adjustable energy bins. Both ideal and more realistic (12% full width at half maximum (FWHM) energy resolution) implementations of the photon counting detector were simulated. The simulations were performed for the CdZnTe detector with a pixel pitch of 0.5-1 mm, which corresponds to a performance without significant charge sharing and cross-talk effects. The Rose model was employed to estimate the minimum detectable concentration of GNPs. A figure of merit (FOM) was used to optimize the x-ray beam energy (kVp) to achieve the highest signal-to-noise ratio with respect to the patient dose. As a result, the successful identification of gold and background suppression was demonstrated. The highest FOM was observed at the 125 kVp x-ray beam energy. The minimum detectable GNP concentration was determined to be approximately 1.06 µmol mL(-1) (0.21 mg mL(-1)) for an ideal detector and about 2.5 µmol mL(-1) (0.49 mg mL(-1)) for a more realistic (12% FWHM) detector. The studies show the optimal imaging parameters at the lowest patient dose using an energy resolved photon counting detector to image GNP in an atherosclerotic plaque.

We investigated the effect of different imaging parameters, such as dose, beam energy, energy resolution and the number of energy bins, on the image quality of K-edge spectral computed tomography (CT) of gold nanoparticles (GNP) accumulated in an atherosclerotic plaque. A maximum likelihood technique was employed to estimate the concentration of GNP, which served as a targeted intravenous contrast material intended to detect the degree of the plaque's inflammation. The simulation studies used a single-slice parallel beam CT geometry with an x-ray beam energy ranging between 50 and 140 kVp. The synthetic phantoms included small (3 cm in diameter) cylinder and chest (33 × 24 cm(2)) phantoms, where both phantoms contained tissue, calcium and gold. In the simulation studies, GNP quantification and background (calcium and tissue) suppression tasks were pursued. The x-ray detection sensor was represented by an energy resolved photon counting detector (e.g., CdZnTe) with adjustable energy bins. Both ideal and more realistic (12% full width at half maximum (FWHM) energy resolution) implementations of the photon counting detector were simulated. The simulations were performed for the CdZnTe detector with a pixel pitch of 0.5-1 mm, which corresponds to a performance without significant charge sharing and cross-talk effects. The Rose model was employed to estimate the minimum detectable concentration of GNPs. A figure of merit (FOM) was used to optimize the x-ray beam energy (kVp) to achieve the highest signal-to-noise ratio with respect to the patient dose. As a result, the successful identification of gold and background suppression was demonstrated. The highest FOM was observed at the 125 kVp x-ray beam energy. The minimum detectable GNP concentration was determined to be approximately 1.06 µmol mL(-1) (0.21 mg mL(-1)) for an ideal detector and about 2.5 µmol mL(-1) (0.49 mg mL(-1)) for a more realistic (12% FWHM) detector. The studies show the optimal imaging parameters at the lowest patient dose using an energy resolved photon counting detector to image GNP in an atherosclerotic plaque.