How Low Can We Go in Radiation Dose for the Data-Completion Scan on a Research Whole-Body Photon-Counting Computed Tomography System

Development of a separability index for task specific characterization of spectral computed tomography

PURPOSE

In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions.

METHOD

Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated.

RESULTS

Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability.

CONCLUSION

The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.

Quantification of cartilage and subchondral bone cysts on knee specimens based on a spectral photon-counting computed tomography

Spectral photon-counting computed tomography (SPCCT) is a new technique with the capability to provide mono-energetic (monoE) images with high signal to noise ratio. We demonstrate the feasibility of SPCCT to characterize at the same time cartilage and subchondral bone cysts (SBCs) without contrast agent in osteoarthritis (OA). To achieve this goal, 10 human knee specimens (6 normal and 4 with OA) were imaged with a clinical prototype SPCCT. The monoE images at 60 keV with isotropic voxels of 250 × 250 × 250 µm3 were compared with monoE synchrotron radiation CT (SR micro-CT) images at 55 keV with isotropic voxels of 45 × 45 × 45 µm3 used as benchmark for cartilage segmentation. In the two OA knees with SBCs, the volume and density of SBCs were evaluated in SPCCT images. In 25 compartments (lateral tibial (LT), medial tibial, (MT), lateral femoral (LF), medial femoral and patella), the mean bias between SPCCT and SR micro-CT analyses were 101 ± 272 mm3 for cartilage volume and 0.33 mm ± 0.18 for mean cartilage thickness. Between normal and OA knees, mean cartilage thicknesses were found statistically different (0.005 < p < 0.04) for LT, MT and LF compartments. The 2 OA knees displayed different SBCs profiles in terms of volume, density, and distribution according to size and location. SPCCT with fast acquisitions is able to characterize cartilage morphology and SBCs. SPCCT can be used potentially as a new tool in clinical studies in OA.

Exploiting the Potential of Photon-Counting CT in Abdominal Imaging

ABSTRACT

Photon-counting computed tomography (PCCT) imaging uses a new detector technology to provide added information beyond what can already be obtained with current CT and MR technologies. This review provides an overview of PCCT of the abdomen and focuses specifically on applications that benefit the most from this new imaging technique. We describe the requirements for a successful abdominal PCCT acquisition and the challenges for clinical translation. The review highlights work done within the last year with an emphasis on new protocols that have been tested in clinical practice. Applications of PCCT include imaging of cystic lesions, sources of bleeding, and cancers. Photon-counting CT is positioned to move beyond detection of disease to better quantitative staging of disease and measurement of treatment response.

Measurement of enhanced vasa vasorum density in a porcine carotid model using photon counting detector CT

Purpose

The onset of atherosclerosis is preceded by changes in blood perfusion within the arterial wall due to localized proliferation of the vasa vasorum. The purpose of this study was to quantify these changes in spatial density of the vasa vasorum using a research whole-body photon-counting detector CT (PCD-CT) scanner and a porcine model.

Approach

Vasa vasorum angiogenesis was stimulated in the left carotid artery wall of anesthetized pigs ( n = 5 ) while the right carotid served as a control. After a 6-week recovery period, the animals were scanned on the PCD-CT prior to and after injection of iodinated contrast. Annular regions of interest were used to measure wall enhancement in the injured and control arteries. The exact Wilcoxon-signed rank test was used to determine whether a significant difference in contrast enhancement existed between the injured and control arterial walls.

Results

The greatest arterial wall enhancement was observed following contrast recirculation. The wall enhancement measurements made over these time points revealed that the enhancement was greater in the injured artery for 13/16 scanned arterial regions. Using an exact Wilcoxon-signed rank test, a significantly increased enhancement ratio was found in injured arteries compared with control arteries ( p = 0.013 ). Vasa vasorum angiogenesis was confirmed in micro-CT scans of excised arteries.

Conclusions

Whole-body PCD-CT scanners can be used to detect and quantify the increased perfusion occurring within the porcine carotid arterial wall resulting from an increased density of vasa vasorum.

Quantum Iterative Reconstruction for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lung

The aim of this study was to characterize image quality and to determine the optimal strength levels of a novel iterative reconstruction algorithm (quantum iterative reconstruction, QIR) for low-dose, ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) of the lung. Images were acquired on a clinical dual-source PCD-CT in the UHR mode and reconstructed with a sharp lung reconstruction kernel at different strength levels of QIR (QIR-1 to QIR-4) and without QIR (QIR-off). Noise power spectrum (NPS) and target transfer function (TTF) were analyzed in a cylindrical phantom. 52 consecutive patients referred for low-dose UHR chest PCD-CT were included (CTDIvol: 1 ± 0.6 mGy). Quantitative image quality analysis was performed computationally which included the calculation of the global noise index (GNI) and the global signal-to-noise ratio index (GSNRI). The mean attenuation of the lung parenchyma was measured. Two readers graded images qualitatively in terms of overall image quality, image sharpness, and subjective image noise using 5-point Likert scales. In the phantom, an increase in the QIR level slightly decreased spatial resolution and considerably decreased noise amplitude without affecting the frequency content. In patients, GNI decreased from QIR-off (202 ± 34 HU) to QIR-4 (106 ± 18 HU) (p < 0.001) by 48%. GSNRI increased from QIR-off (4.4 ± 0.8) to QIR-4 (8.2 ± 1.6) (p < 0.001) by 87%. Attenuation of lung parenchyma was highly comparable among reconstructions (QIR-off: -849 ± 53 HU to QIR-4: -853 ± 52 HU, p < 0.001). Subjective noise was best in QIR-4 (p < 0.001), while QIR-3 was best for sharpness and overall image quality (p < 0.001). Thus, our phantom and patient study indicates that QIR-3 provides the optimal iterative reconstruction level for low-dose, UHR PCD-CT of the lungs.

Quantum Iterative Reconstruction for Abdominal Photon-counting Detector CT Improves Image Quality

Background An iterative reconstruction (IR) algorithm was introduced for clinical photon-counting detector (PCD) CT. Purpose To investigate the image quality and the optimal strength level of a quantum IR algorithm (QIR; Siemens Healthcare) for virtual monoenergetic images and polychromatic images (T3D) in a phantom and in patients undergoing portal venous abdominal PCD CT. Materials and Methods In this retrospective study, noise power spectrum (NPS) was measured in a water-filled phantom. Consecutive oncologic patients who underwent portal venous abdominal PCD CT between March and April 2021 were included. Virtual monoenergetic images at 60 keV and T3D were reconstructed without QIR (QIR-off; reference standard) and with QIR at four levels (QIR 1-4; index tests). Global noise index, contrast-to-noise ratio (CNR), and voxel-wise CT attenuation differences were measured. Noise and texture, artifacts, diagnostic confidence, and overall quality were assessed qualitatively. Conspicuity of hypodense liver lesions was rated by four readers. Parametric (analyses of variance, paired t tests) and nonparametric tests (Friedman, post hoc Wilcoxon signed-rank tests) were used to compare quantitative and qualitative image quality among reconstructions. Results In the phantom, NPS showed unchanged noise texture across reconstructions with maximum spatial frequency differences of 0.01 per millimeter. Fifty patients (mean age, 59 years ± 16 [standard deviation]; 31 women) were included. Global noise index was reduced from QIR-off to QIR-4 by 45% for 60 keV and by 44% for T3D (both, P < .001). CNR of the liver improved from QIR-off to QIR-4 by 74% for 60 keV and by 69% for T3D (both, P < .001). No evidence of difference was found in mean attenuation of fat and liver (P = .79-.84) and on a voxel-wise basis among reconstructions. Qualitatively, QIR-4 outperformed all reconstructions in every category for 60 keV and T3D (P value range, <.001 to .01). All four readers rated QIR-4 superior to other strengths for lesion conspicuity (P value range, <.001 to .04). Conclusion In portal venous abdominal photon-counting detector CT, an iterative reconstruction algorithm (QIR; Siemens Healthcare) at high strength levels improved image quality by reducing noise and improving contrast-to-noise ratio and lesion conspicuity without compromising image texture or CT attenuation values. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Sinitsyn in this issue.

Dual-Contrast Biphasic Liver Imaging With Iodine and Gadolinium Using Photon-Counting Detector Computed Tomography: An Exploratory Animal Study

PURPOSE

The aims of this study were to develop a single-scan dual-contrast protocol for biphasic liver imaging with 2 intravenous contrast agents (iodine and gadolinium) and to evaluate its effectiveness in an exploratory swine study using a photon-counting detector computed tomography (PCD-CT) system.

MATERIALS AND METHODS

A dual-contrast CT protocol was developed for PCD-CT to simultaneously acquire 2 phases of liver contrast enhancement, with the late arterial phase enhanced by 1 contrast agent (iodine-based) and the portal venous phase enhanced by the other (gadolinium-based). A gadolinium contrast bolus (gadobutrol: 64 mL, 8 mL/s) and an iodine contrast bolus (iohexol: 40 mL, 5 mL/s) were intravenously injected in the femoral vein of a healthy domestic swine, with the second injection initiated after 17 seconds from the beginning of the first injection; PCD-CT image acquisition was performed 12 seconds after the beginning of the iodine contrast injection. A convolutional neural network (CNN)-based denoising technique was applied to PCD-CT images to overcome the inherent noise magnification issue in iodine/gadolinium decomposition task. Iodine and gadolinium material maps were generated using a 3-material decomposition method in image space. A set of contrast samples (mixed iodine and gadolinium) was attached to the swine belly; quantitative accuracy of material decomposition in these inserts between measured and true concentrations was calculated using root mean square error. An abdominal radiologist qualitatively evaluated the delineation of arterial and venous vasculatures in the swine liver using iodine and gadolinium maps obtained using the dual-contrast PCD-CT protocol.

RESULTS

The iodine and gadolinium samples attached to the swine were quantified with root mean square error values of 0.75 mg/mL for iodine and 0.45 mg/mL for gadolinium from the contrast material maps derived from the denoised PCD-CT images. Hepatic arteries containing iodine and veins containing gadolinium in the swine liver could be clearly visualized. Compared with the original images, better distinctions between 2 liver phases were achieved using CNN denoising, with approximately 60% to 80% noise reduction in contrast material maps acquired with the denoised PCD-CT images compared with the original images.

CONCLUSIONS

Simultaneous biphasic liver imaging in a single multienergy PCD-CT acquisition using a dual-contrast (iodine and gadolinium) injection protocol and CNN denoising was demonstrated in a swine study, where the enhanced hepatic arteries (containing iodine) and the enhanced hepatic veins (containing gadolinium) could be clearly visualized and delineated in the swine liver.

Comparison of Low Dose Performance of Photon-Counting and Energy Integrating CT

RATIONALE AND OBJECTIVES

The purpose of this study was to investigate the potential of photon-counting CT (PCCT) to improve quantitative image quality for low dose imaging compared to energy-integrating detector CT (EID CT).

MATERIALS AND METHODS

An investigational scanner (Siemens, Germany) with PCCT and EID CT subsystems was used to compare image quality performance at four dose levels: 1.7, 2, 4, 6 mGy CTDIvol, all at or below current dose values used for conventional abdominal CT. A CT quality control phantom with a homogeneous section for noise measurements and a section with cylindrical inserts of air (-910 HU), polystyrene (50 HU), acrylic (205 HU), and Teflon (1000 HU) was imaged and characterized in terms of noise, resolution, contrast-to-noise ratio (CNR), and detectability index. A second phantom with a 30 cm diameter was also imaged containing iodine solutions ranging from 0.125 to 8 mg I/mL. CNR of the iodine vials was computed as a function of CT dose and iodine concentration.

RESULTS

With resolution unaffected by dose in both PCCT and EID CT, PCCT images exhibited 22.1-24.0% improvement in noise across dose levels evaluated. This noise improvement translated into a 29-41% improvement in CNR and 20-36% improvement in detectability index. For iodine contrast, PCCT images had a higher CNR for all combinations of iodine contrast and dose evaluated.

CONCLUSION

For the conditions studied, PCCT exhibited superior image quality compared to EID CT. For iodine detection, PCCT offered a notable advantage with improved CNR at all doses and iodine concentration levels.

Evaluation of Coronary Plaques and Stents with Conventional and Photon-counting CT: Benefits of High-Resolution Photon-counting CT

PURPOSE

To compare the performance of energy-integrating detector (EID) CT, photon-counting detector CT (PCCT), and high-resolution PCCT (HR-PCCT) for the visualization of coronary plaques and reduction of stent artifacts in a phantom model.

MATERIALS AND METHODS

An investigational scanner with EID and PCCT subsystems was used to image a coronary artery phantom containing cylindrical probes simulating different plaque compositions. The phantom was imaged with and without coronary stents using both subsystems. Images were reconstructed with a clinical cardiac kernel and an additional HR-PCCT kernel. Regions of interest were drawn around probes and evaluated for in-plane diameter and a qualitative comparison by expert readers. A linear mixed-effects model was used to compare the diameter results, and a Shrout-Fleiss intraclass correlation coefficient was used to assess consistency in the reader study.

RESULTS

Comparing in-plane diameter to the physical dimension for nonstented and stented phantoms, measurements of the HR-PCCT images were more accurate (nonstented: 4.4% ± 1.1 [standard deviation], stented: -9.4% ± 4.6) than EID (nonstented: 15.5% ± 4.0, stented: -19.5% ± 5.8) and PCCT (nonstented: 19.4% ± 2.5, stented: -18.3% ± 4.4). Our analysis of variance found diameter measurements to be different across image groups for both nonstented and stented cases (P < .001). HR-PCCT showed less change on average in percent stenosis due to the addition of a stent (-5.5%) than either EID (+90.5%) or PCCT (+313%). For both nonstented and stented phantoms, observers rated the HR-PCCT images as having higher plaque conspicuity and as being the image type that was least impacted by stent artifacts, with a high level of agreement (interclass correlation coefficient = 0.85).

CONCLUSION

Despite increased noise, HR-PCCT images were able to better visualize coronary plaques and reduce stent artifacts compared with EID or PCCT reconstructions.Keywords: CT-Spectral Imaging (Dual Energy), Phantom Studies, Cardiac, Physics, Technology Assessment© RSNA, 2021.

Threshold-dependent iodine imaging and spectral separation in a whole-body photon-counting CT system

OBJECTIVE

To evaluate the dual-energy (DE) performance and spectral separation with respect to iodine imaging in a photon-counting CT (PCCT) and compare it to dual-source CT (DSCT) DE imaging.

METHODS

A semi-anthropomorphic phantom extendable with fat rings equipped with iodine vials is measured in an experimental PCCT. The system comprises a PC detector with two energy bins (20 keV, T) and (T, eU) with threshold T and tube voltage U. Measurements using the PCCT are performed at all available tube voltages (80 to 140 kV) and threshold settings (50-90 keV). Further measurements are performed using a conventional energy-integrating DSCT. Spectral separation is quantified as the relative contrast media ratio R between the energy bins and low/high images. Image noise and dose-normalized contrast-to-noise ratio (CNRD) are evaluated in resulting iodine images. All results are validated in a post-mortem angiography study.

RESULTS

R of the PC detector varies between 1.2 and 2.6 and increases with higher thresholds and higher tube voltage. Reference R of the EI DSCT is found as 2.20 on average overall phantoms. Maximum CNRD in iodine images is found for T = 60/65/70/70 keV for 80/100/120/140 kV. The highest CNRD of the PCCT is obtained using 140 kV and is decreasing with decreasing tube voltage. All results could be confirmed in the post-mortem angiography study.

CONCLUSION

Intrinsically acquired DE data are able to provide iodine images similar to conventional DSCT. However, PCCT thresholds should be chosen with respect to tube voltage to maximize image quality in retrospectively derived image sets.

KEY POINTS

• Photon-counting CT allows for the computation of iodine images with similar quality compared to conventional dual-source dual-energy CT. • Thresholds should be chosen as a function of the tube voltage to maximize iodine contrast-to-noise ratio in derived image sets. • Image quality of retrospectively computed image sets can be maximized using optimized threshold settings.

High Resolution, Full Field-of-View, Whole Body Photon-Counting Detector CT: System Assessment and Initial Experience

Computed tomography (CT) using photon-counting detectors (PCD) offers dose-efficient ultra-high-resolution imaging, high iodine contrast-to-noise ratio, multi-energy and material decomposition capabilities. We have previously demonstrated the potential benefits of PCD-CT using phantoms, cadavers, and human studies on a prototype PCD-CT system. This system, however, had several limitations in terms of scan field-of-view (FOV) and longitudinal coverage. Recently, a full FOV (50 cm) PCD-CT system with wider longitudinal coverage and higher spatial resolution (0.15 mm detector pixels) has been installed in our lab capable of human scanning at clinical dose and dose rate. In this work, we share our initial experience of the new PCD-CT system and compare its performance with a state-of-the-art 3^rd generation dual-source CT scanner. Basic image quality was assessed using an ACR CT accreditation phantom, high-resolution performance using an anthropomorphic head phantom, and multi-energy and material decomposition performance using a multi-energy CT phantom containing various concentrations of iodine and hydroxyapatite. Finally, we demonstrate the feasibility of high-resolution, full FOV PCD-CT imaging for improved delineation of anatomical and pathological features in a patient with pulmonary nodules.

Potential of ultra-high-resolution photon-counting CT of bone metastases: initial experiences in breast cancer patients

Conventional CT scanners use energy-integrating detectors (EIDs). Photon-counting detector (PCD) computed tomography (CT) utilizes a CT detector technology based on smaller detector pixels capable of counting single photons and in addition discriminating their energy. Goal of this study was to explore the potential of higher spatial resolution for imaging of bone metastases. Four female patients with histologically confirmed breast cancer and bone metastases were included between July and October 2019. All patients underwent conventional EID CT scans followed by a high resolution non-contrast experimental PCD CT scan. Ultra-high resolution (UHR) reconstruction kernels were used to reconstruct axial slices with voxel sizes of 0.3 mm × 0.3 mm (inplane) × 1 mm (z-direction). Four radiologists blinded for patient identity assessed the images and compared the quality to conventional CT using a qualitative Likert scale. In this case series, we present images of bone metastases in breast cancer patients using an experimental PCD CT scanner and ultra-high-resolution kernels. A tendency to both a smaller inter-reader variability in the structural assessment of lesion sizes and in the readers' opinion to an improved visualization of lesion margins and content was observed. In conclusion, while further studies are warranted, PCD CT has a high potential for therapy monitoring in breast cancer.

Effects of Detector Sampling on Noise Reduction in Clinical Photon-Counting Whole-Body Computed Tomography

OBJECTIVES

Reconstructing images from measurements with small pixels below the system's resolution limit theoretically results in image noise reduction compared with measurements with larger pixels. We evaluate and quantify this effect using data acquired with the small pixels of a photon-counting (PC) computed tomography scanner that can be operated in different detector pixel binning modes and with a conventional energy-integrating (EI) detector.

MATERIALS AND METHODS

An anthropomorphic abdominal phantom that can be extended to 3 sizes by adding fat extension rings, equipped with iodine inserts as well as human cadavers, was measured at tube voltages ranging from 80 to 140 kV. The images were acquired with the EI detector (0.6 mm pixel size at isocenter) and the PC detector operating in Macro mode (0.5 mm pixel size at iso) and ultrahigh-resolution (UHR) mode (0.25 mm pixel size at iso). Both detectors are components of the same dual-source prototype computed tomography system. During reconstruction, the modulation transfer functions were matched to the one of the EI detector. The dose-normalized contrast-to-noise ratio (CNRD) values are evaluated as a figure of merit.

RESULTS

Images acquired in UHR mode achieve on average approximately 6% higher CNRD compared with Macro mode at the same spatial resolution for a quantitative D40f kernel. Using a sharper B70f kernel, the improvement increases to 21% on average. In addition, the better performance of PC detectors compared with EI detectors with regard to iodine imaging has been evaluated by comparing CNRD values for Macro and EI. Combining both of these effects, a CNRD improvement of up to 34%, corresponding to a potential dose reduction of up to 43%, can be achieved for D40f.

CONCLUSIONS

Reconstruction of UHR data with a modulation transfer function below the system's resolution limit reduces image noise for all patient sizes and tube voltages compared with standard acquisitions. Thus, a relevant dose reduction may be clinically possible while maintaining image quality.

Multi-energy CT imaging for large patients using dual-source photon-counting detector CT

Multi-energy CT imaging of large patients with conventional dual-energy (DE)-CT using an energy-integrating-detector (EID) is challenging due to photon starvation-induced image artifacts, especially in lower tube potential (80-100 kV) images. Here, we performed phantom experiments to investigate the performance of DECT for morbidly obese patients, using an iodine and water material decomposition task as an example, on an emulated dual-source (DS)-photon-counting-detector (PCD)-CT, and compared its performance with a clinical DS-EID-CT. An abdominal CT phantom with iodine inserts of different concentrations was wrapped with tissue-equivalent gel layers to emulate a large patient (50 cm lateral size). The phantom was scanned on a research whole-body single-source (SS)-PCD-CT (140 kV tube potential), a DS-PCD-CT (100/Sn140 kV; Sn140 indicates 140 kV with Sn filter), and a clinical DS-EID-CT (100/Sn140 kV) with the same radiation dose. Phantom scans were repeated five times on each system. The DS-PCD-CT acquisition was emulated by scanning twice on the SS-PCD-CT using different tube potentials. The multi-energy CT images acquired on each system were then reconstructed, and iodine- and water-specific images were generated using material decomposition. The root-mean-square-error (RMSE) between true and measured iodine concentrations were calculated for each system and compared. The images acquired on the DS-EID-CT showed severe artifacts, including ringing, reduced uniformity, and photon starvation artifacts, especially for low-energy images. These were largely reduced in DS-PCD-CT images. The CT number difference that was measured using regions-of-interest across field-of-view were reduced from 20.3 ± 0.9 (DS-EID-CT) to 2.5 ± 0.4 HU on DS-PCD-CT, showing improved image uniformity using DS-PCD-CT. Iodine RMSE was reduced from 3.42 ± 0.03 mg ml^-1 (SS-PCD-CT) and 2.90 ± 0.03 mg ml^-1 (DS-EID-CT) to 2.39 ± 0.05 mg ml^-1 using DS-PCD-CT. DS-PCD-CT out-performed a clinical DS-EID-CT for iodine and water-based material decomposition on phantom emulating obese patients by reducing image artifacts and improving iodine quantification (RMSE reduced by 20%). With DS-PCD-CT, multi-energy CT can be performed on large patients that cannot be accommodated with current DECT.

A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks

Photon-counting CT detectors are the next step in advancing CT system development and will replace the current energy integrating detectors (EID) in CT systems in the near future. In this context, the performance of PCCT was compared to EID CT for three clinically relevant tasks: abdominal soft tissue imaging, where differentiating low contrast features is important; vascular imaging, where iodine detectability is critical; and, high-resolution skeletal and lung imaging. A multi-tiered phantom was imaged on an investigational clinical PCCT system (Siemens Healthineers) across different doses using three imaging modes: macro and ultra-high resolution (UHR) PCCT modes and EID CT. Images were reconstructed using filtered backprojection and soft tissue (B30f), vascular (B46f), or high-resolution (B70f; U70f for UHR) kernels. Noise power spectra, task transfer functions, and detectability index were evaluated. For a soft tissue task, PCCT modes showed comparable noise and resolution with improved contrast-to-noise ratio. For a vascular task, PCCT modes showed lower noise and improved iodine detectability. For a high resolution task, macro mode showed lower noise and comparable resolution while UHR mode showed higher noise but improved spatial resolution for both air and bone. PCCT offers competitive advantages to EID CT for clinical tasks.

Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography

PURPOSE

Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is accomplished by estimating missing projection data based on the imaging chain with the full FoV and then by appropriately truncating the analytical reconstruction of the data with the smaller FoV. While this approach works well in many clinical applications, there are applications which would benefit from spectral contrast estimates over the larger FoV (spectral extrapolation)-e.g. model-based iterative reconstruction, contrast-enhanced abdominal imaging of large patients, interior tomography, and combined temporal and spectral imaging.

METHODS

To document the fidelity of spectral extrapolation and to prototype a deep learning algorithm to perform it, we assembled a data set of 50 dual-source, dual-energy abdominal x-ray CT scans (acquired at Duke University Medical Center with 5 Siemens Flash scanners; chain A: 50 cm FoV, 100 kV; chain B: 33 cm FoV, 140 kV + Sn; helical pitch: 0.8). Data sets were reconstructed using ReconCT (v14.1, Siemens Healthineers): 768 × 768 pixels per slice, 50 cm FoV, 0.75 mm slice thickness, "Dual-Energy - WFBP" reconstruction mode with dual-source data completion. A hybrid architecture consisting of a learned piecewise linear transfer function (PLTF) and a convolutional neural network (CNN) was trained using 40 scans (five scans reserved for validation, five for testing). The PLTF learned to map chain A spectral contrast to chain B spectral contrast voxel-wise, performing an image domain analog of dual-source data completion with approximate spectral reweighting. The CNN with its U-net structure then learned to improve the accuracy of chain B contrast estimates by copying chain A structural information, by encoding prior chain A, chain B contrast relationships, and by generalizing feature-contrast associations. Training was supervised, using data from within the 33-cm chain B FoV to optimize and assess network performance.

RESULTS

Extrapolation performance on the testing data confirmed our network's robustness and ability to generalize to unseen data from different patients, yielding maximum extrapolation errors of 26 HU following the PLTF and 7.5 HU following the CNN (averaged per target organ). Degradation of network performance when applied to a geometrically simple phantom confirmed our method's reliance on feature-contrast relationships in correctly inferring spectral contrast. Integrating our image domain spectral extrapolation network into a standard dual-source, dual-energy processing pipeline for Siemens Flash scanner data yielded spectral CT data with adequate fidelity for the generation of both 50 keV monochromatic images and material decomposition images over a 30-cm FoV for chain B when only 20 cm of chain B data were available for spectral extrapolation.

CONCLUSIONS

Even with a moderate amount of training data, deep learning methods are capable of robustly inferring spectral contrast from feature-contrast relationships in spectral CT data, leading to spectral extrapolation performance well beyond what may be expected at face value. Future work reconciling spectral extrapolation results with original projection data is expected to further improve results in outlying and pathological cases.

Assessment of Multienergy Interpixel Coincidence Counters (MEICC) for Charge Sharing Correction or Compensation for Photon Counting Detectors With Boxcar Signals

Recently, multi-energy inter-pixel coincidence counter (MEICC) has been proposed for charge sharing correction and compensation for photon counting detectors (PCDs), which uses energy-dependent coincidence counters to record coincident events between multiple energy windows of a pixel-of-interest and those of neighboring pixels. A Monte Carlo (MC) simulation study was performed to assess the performance of MEICC; however, the performance might have been overestimated in a previous study. The charge sharing increases the number of photons recorded at a PCD pixel at the expense of the spatial resolution, and therefore, when spatially uniform flat-field x-ray signals are used, it gives PCDs with charge sharing more signals than a PCD without charge sharing. In this paper, we propose to use spatially modulated boxcar signals for evaluating the performances for high spatial frequency tasks because they provide consistent signals regardless of the presence of absence of charge sharing. The flat-field signals must be used for low spatial frequency tasks. We assessed the performances of MEICC and other PCDs with both flat-field signals and boxcar signals, with optimal threshold energies, and with two different pixel sizes. As it is expected, normalized Cramér-Rao lower bounds (nCRLBs) measured with the boxcar signals were worse than those with flat-field signals in general. The nCRLBs of MEICC with 225-μm pixel were close to the current 450-μm PCD. We studied a combination of flat-field signals and N×N super-pixels, where the output of N×N pixels were added, using an MC simulation and a simple charge sharing counting model. The study showed that charge sharing had two opposing impacts on the conventional CT imaging-a negative impact with double-counting among N×N pixels and a positive impact with single-counting spill-in and spill-out across the super-pixel boundary-and the positive impact diminished with increasing N. A use of large N×N super-pixels such as N≥25 was suggested to approximate the zero-frequency detection quantum efficiency of PCD with charge sharing.

Multi-energy inter-pixel coincidence counters for charge sharing correction and compensation in photon counting detectors

PURPOSE

Smaller pixel sizes of x-ray photon counting detectors (PCDs) are advantageous for count rate capabilities but disadvantageous for charge sharing. With charge sharing, the energy of an x-ray photon may be split and one photon may produce two or more counts at adjacent pixels, both at lower energies than the incident energy. This "double-counting" increases noise variance and degrades the spectral response. Overall, it has a significantly negative impact on the performance of PCD-based computed tomography (CT). Charge sharing is induced by the detection physics and occurs regardless of count rates; thus, it is impossible to avoid. We propose in this paper a method that has a potential to address both noise and bias added by charge sharing.

METHODS

We propose applying a multi-energy inter-pixel coincidence counter (MEICC) technique, which uses energy-dependent coincidence counters, keeps the book of charge sharing events during data acquisition, and provides the exact number of charge sharing occurrences, which can be used to either correct or compensate for them after the acquisition is completed. MEICC does not interfere with the primary counting process; therefore, PCDs with MEICC will remain as fast as those without MEICC. MEICC can be implemented using current electronics technology because its inter-pixel coincidence counters used to handle digital data are rather simple. We evaluated Cramér-Rao lower bound (CRLB) of PCDs with and without MEICC using a Monte Carlo simulation.

RESULTS

When the number of energy windows was four or larger and eight neighboring pixels were used, the CRLBs of 225-µm PCD with MEICC normalized by those of the current PCD with the same number of windows were 0.361-0.383 for water density images of two basis functions, which was only 5.7-16.4% worse than those of a PCD without charge sharing (which were at 0.329-0.358). In contrast, the normalized CRLBs of the PCD with one coincidence counter were 0.466-0.499, which were 37.3-45.6% worse than the PCD without charge sharing. The use of eight neighboring pixels provided ~10% better CRLB values than four neighboring pixels for MEICC. With four energy windows, decreasing the number of coincidence counters from 16 to 9 only slightly increased the CRLB from 0.255 to 0.269 (which corresponded to as little as a 5.5% change). The normalized CRLBs of MEICC for K-edge imaging (gold) were 0.295-0.426, while those of the one coincidence counter were 0.926-0.959 and the ideal PCDs were 0.126-0.146.

CONCLUSIONS

The proposed MEICC provides spectral information that can be used to address charge sharing problems in PCDs and is expected to satisfy the requirements for clinical x-ray CT. MEICC is very effective, especially for K-edge imaging, which requires accurate spectral information.

Multicolor spectral photon counting CT monitors and quantifies therapeutic cells and their encapsulating scaffold in a model of brain damage

Rationale & aim: Various types of cell therapies are currently under investigation for the treatment of ischemic stroke patients. To bridge the gap between cell administration and therapeutic outcome, there is a need for non-invasive monitoring of these innovative therapeutic approaches. Spectral photon counting computed tomography (SPCCT) is a new imaging modality that may be suitable for cell tracking. SPCCT is the next generation of clinical CT that allows the selective visualization and quantification of multiple contrast agents. The aims of this study are: (i) to demonstrate the feasibility of using SPCCT to longitudinally monitor and quantify therapeutic cells, i.e. bone marrow-derived M2-polarized macrophages transplanted in rats with brain damage; and (ii) to evaluate the potential of this approach to discriminate M2-polarized macrophages from their encapsulating scaffold. Methods: Twenty one rats received an intralesional transplantation of bone marrow-derived M2-polarized macrophages. In the first set of experiments, cells were labeled with gold nanoparticles and tracked for up to two weeks post-injection in a monocolor study via gold K-edge imaging. In the second set of experiments, the same protocol was repeated for a bicolor study, in which the labeled cells are embedded in iodine nanoparticle-labeled scaffold. The amount of gold in the brain was longitudinally quantified using gold K-edge images reconstructed from SPCCT acquisition. Animals were sacrificed at different time points post-injection, and ICP-OES was used to validate the accuracy of gold quantification from SPCCT imaging. Results: The feasibility of therapeutic cell tracking was successfully demonstrated in brain-damaged rats with SPCCT imaging. The imaging modality enabled cell monitoring for up to 2 weeks post-injection, in a specific and quantitative manner. Differentiation of labeled cells and their embedding scaffold was also feasible with SPCCT imaging, with a detection limit as low as 5,000 cells in a voxel of 250 × 250 × 250 µm in dimension in vivo. Conclusion: Multicolor SPCCT is an innovative translational imaging tool that allows monitoring and quantification of therapeutic cells and their encapsulating scaffold transplanted in the damaged rat brain.

Radiation dose efficiency of multi-energy photon-counting-detector CT for dual-contrast imaging

Compared to traditional multi-scan single-energy CT (SECT), one potential advantage of single-scan multi-energy CT (MECT) proposed for simultaneous imaging of multiple contrast agents is the radiation dose reduction. This phantom study aims to rigorously evaluate whether the radiation dose can truly be reduced in a single-scan MECT protocol (MECT_1s) in biphasic liver imaging with iodine and gadolinium, and small bowel imaging with iodine and bismuth, compared to traditional two-scan SECT protocols (SECT_2s). For MECT_1s, mixed iodine/gadolinium samples were prepared corresponding to late arterial/portal-venous phase for biphasic liver imaging. Mixed iodine/bismuth samples were prepared representing the arterial/enteric enhancement for small bowel imaging. For SECT_2s, separate contrast samples were prepared to mimic separate scans in arterial/venous phase and arterial/enteric enhancement. Samples were placed in a 35 cm wide water phantom and scanned by a research whole-body photon-counting-detector-CT (PCD-CT) system ('chess' mode). MECT images were acquired with optimized kV/threshold settings for each imaging task, and SECT images were acquired at 120 kV. Total CTDIvol was matched for the two protocols. Image-based three-material decomposition was employed in MECT_1s to determine the basis material concentration values, which were converted to CT numbers at 120 kV (i.e. virtual SECT images) to compare with the SECT images directly acquired with SECT_2s. The noise difference between the SECT and the virtual SECT images was compared to evaluate the dose efficiency of MECT_1s. Compared to SECT_2s, MECT_1s was not dose efficient for both imaging tasks. The amount of noise increase is highly task dependent, with noise increased by 203%/278% and 110%/82% in virtual SECT images for iodine/gadolinium and iodine/bismuth quantifications, respectively, corresponding to dose increase by 819%/1328% and 340%/230% in MECT_1s to achieve the same image noise level. MECT with the current PCD-CT technique requires higher radiation dose than SECT to achieve the same image quality.

Photon-counting cine-cardiac CT in the mouse

The maturation of photon-counting detector (PCD) technology promises to enhance routine CT imaging applications with high-fidelity spectral information. In this paper, we demonstrate the power of this synergy and our complementary reconstruction techniques, performing 4D, cardiac PCD-CT data acquisition and reconstruction in a mouse model of atherosclerosis, including calcified plaque. Specifically, in vivo cardiac micro-CT scans were performed in four ApoE knockout mice, following their development of calcified plaques. The scans were performed with a prototype PCD (DECTRIS, Ltd.) with 4 energy thresholds. Projections were sampled every 10 ms with a 10 ms exposure, allowing the reconstruction of 10 cardiac phases at each of 4 energies (40 total 3D volumes per mouse scan). Reconstruction was performed iteratively using the split Bregman method with constraints on spectral rank and spatio-temporal gradient sparsity. The reconstructed images represent the first in vivo, 4D PCD-CT data in a mouse model of atherosclerosis. Robust regularization during iterative reconstruction yields high-fidelity results: an 8-fold reduction in noise standard deviation for the highest energy threshold (relative to unregularized algebraic reconstruction), while absolute spectral bias measurements remain below 13 Hounsfield units across all energy thresholds and scans. Qualitatively, image domain material decomposition results show clear separation of iodinated contrast and soft tissue from calcified plaque in the in vivo data. Quantitatively, spatial, spectral, and temporal fidelity are verified through a water phantom scan and a realistic MOBY phantom simulation experiment: spatial resolution is robustly preserved by iterative reconstruction (10% MTF: 2.8–3.0 lp/mm), left-ventricle, cardiac functional metrics can be measured from iodine map segmentations with ~1% error, and small calcifications (615 μm) can be detected during slow moving phases of the cardiac cycle. Given these preliminary results, we believe that PCD technology will enhance dynamic CT imaging applications with high-fidelity spectral and material information.

Volume-of-interest imaging with dynamic fluence modulation using multiple aperture devices

Volume-of-interest (VOI) imaging is a strategy in computed tomography (CT) that restricts x-ray fluence to particular anatomical targets via dynamic beam modulation. This permits dose reduction while retaining image quality within the VOI. VOI-CT implementation has been challenged, in part, by a lack of hardware solutions for tailoring the incident fluence to the patient and anatomical site, as well as difficulties involving interior tomography reconstruction of truncated projection data. We propose a general VOI-CT imaging framework using multiple aperture devices (MADs), an emerging beam filtration scheme based on two binary x-ray filters. Location of the VOI is prescribed using two scout views at anterior-posterior (AP) and lateral perspectives. Based on a calibration of achievable fluence field patterns, MAD motion trajectories were designed using an optimization objective that seeks to maximize the relative fluence in the VOI subject to minimum fluence constraints. A modified penalized-likelihood method is developed for reconstruction of heavily truncated data using the full-field scout views to help solve the interior tomography problem. Physical experiments were conducted to show the feasibility of noncentered and elliptical VOI in two applications-spine and lung imaging. Improved dose utilization and retained image quality are validated with respect to standard full-field protocols. We observe that the contrast-to-noise ratio (CNR) is 40% higher compared with low-dose full-field scans at the same dose. The total dose reduction is 50% for equivalent image quality (CNR) within the VOI.

Dual-source photon counting detector CT with a tin filter: a phantom study on iodine quantification performance

Photon counting detectors (PCD) can provide spectral information to enable iodine quantification through multi-energy imaging but performance is limited by current PCD technology. The purpose of this work is to evaluate iodine quantification in a phantom study using dual-source PCD-CT (DS-PCD-CT), and compare to single-source (SS)-PCD-CT and traditional DS energy integrating detector (EID)-based dual-energy CT. A multi-energy CT phantom with iodine inserts (0 to 15 mg ml^-1 concentration) was imaged on a research SS-PCD-CT scanner (CTDI_vol  =  18 mGy). A DS-PCD-CT was emulated by acquiring two sequential scans (CTDI_vol  =  9 mGy each) using tube potentials: 140 kVp/80 kVp, 140 kVp/100 kVp and 140 kVp/120 kVp. For each kVp, 1 or 2 energy bins were reconstructed to achieve either dual-energy or quadruple energy CT. In addition to these energy combinations, a Sn filter was used for the high tube potential (140 kVp) of each kVp pair. For comparison, the same phantom was also scanned on a commercially available DS-EID-CT with matched radiation dose (CTDI_vol  =  18 mGy). Material decomposition was performed in image space using a standard least-squares based approach to generate iodine and water-specific images. The root-mean-square-error (RMSE) measured over each insert from the iodine image was used to determine iodine accuracy. The iodine RMSE from SS-PCD (140 kVp with 2 energy bins) was 2.72 mg ml^-1. The use of a DS configuration with 1 energy bin per kVp (140 kVp/80 kVp) resulted in a RMSE of 2.29 mg ml^-1. Two energy bins per kVp further reduced iodine RMSE to 1.83 mg ml^-1. The addition of a Sn filter to the latter quadruple energy mode reduced RMSE to 1.48 mg ml^-1. RMSE for DS-PCD-CT (2 energy bins per kVp) decreased by 1.3% (Sn140 kVp/80 kVp) and 15% (Sn140 kVp/100 kVp) as compared to DS-EID-CT. DS-PCD-CT with a Sn filter improved iodine quantification as compared to both SS-PCD-CT and DS-EID-CT.

Improving iodine contrast to noise ratio using virtual monoenergetic imaging and prior-knowledge-aware iterative denoising (mono-PKAID)

Multi-energy CT acquires simultaneous multiple x-ray attenuation measurements from different energy spectra which facilitates the computation of virtual monoenergetic images (VMI) at a specific photon energy (keV). Since the contrast between iodine attenuation and the attenuation of surrounding soft tissues increases at lower x-ray energies, VMIs in the range of 40-70 keV can be used to improve iodine visualization. However, at lower energy levels, image noise in VMIs is substantially increased, which counteracts the benefits from the increased iodine contrast, resulting in a decreased iodine contrast-to-noise ratio (CNR). There exists considerable data redundancy between multi-energy CT images created from the same acquisition. Similarly, a substantial spatio-spectral data redundancy exists between multi-energy CT images and the corresponding VMIs. In this work, we develop a denoising framework that exploits this data redundancy to improve iodine CNR in the VMIs. We accomplish this by applying prior-knowledge-aware iterative denoising to low-energy VMIs; we refer to the denoised images as mono-PKAID images. The proposed framework was evaluated using phantom and in vivo data acquired on a research whole-body photon-counting-detector CT, as well as using data from a commercial dual-source dual-energy CT system. The results of phantom experiments show that the proposed framework can preserve image resolution and noise texture compared to the original VMIs, while reducing noise to improve iodine CNR. Quantitative measurements show that the iodine CNR of 50 keV VMI is improved by 1.8-fold using the proposed method, relative to the VMI produced using commercial software (Mono+). With mono-PKAID, VMIs at lower keV take full advantage of higher iodine contrast without substantially increasing image noise. These observations were confirmed using patient data sets, which demonstrated that mono-PKAID reduced image noise, improved CNR in anatomical regions with iodine perfusion by 1.8-fold, and potentially enhanced the visibility of focal liver lesions.

Computed Tomography Technology-and Dose-in the 21st Century

In the last decade or so, a number of disruptive technological advances have taken place in x-ray computed tomography, making possible new clinical applications. Changes in scanner design have included the use of two x-ray sources and two detectors or the use of large detector arrays that provide 16 cm of longitudinal coverage in one gantry rotation. These advances have allowed images of the entire heart to be acquired in just one heartbeat, lowering the effective dose from cardiac computed tomography from ~15 mSv to <1 mSv. Dual-energy computed tomography is now in widespread clinical use, enabling the assessment of material composition and concentration, as well as a range of new clinical applications. An emerging technology known as photon-counting detector computed tomography directly measures the energies of detected photons and is capable of simultaneously acquiring more than two energy data sets. Photon-counting detector computed tomography also provides advantages such as the ability to reject electronic noise, better iodine contrast-to-noise for a given dose, and spatial resolution as fine as 150 μm. Optimized x-ray tube potential selection has allowed reduction in radiation and contrast doses. Finally, wide adoption of iterative reconstruction and noise-reduction techniques has occurred. In all, body computed tomography doses have fallen dramatically, for example, by over a factor of 3 from the early 1980s. All of these advances increase the medical benefit and decrease the potential radiation risk associated with computed tomography. However, care must be taken to ensure that doses are not lowered to the level at which the clinical task is compromised.

150-μm Spatial Resolution Using Photon-Counting Detector Computed Tomography Technology: Technical Performance and First Patient Images

OBJECTIVE

The aims of this study were to quantitatively assess two new scan modes on a photon-counting detector computed tomography system, each designed to maximize spatial resolution, and to qualitatively demonstrate potential clinical impact using patient data.

MATERIALS AND METHODS

This Health Insurance Portability Act-compliant study was approved by our institutional review board. Two high-spatial-resolution scan modes (Sharp and UHR) were evaluated using phantoms to quantify spatial resolution and image noise, and results were compared with the standard mode (Macro). Patients were scanned using a conventional energy-integrating detector scanner and the photon-counting detector scanner using the same radiation dose. In first patient images, anatomic details were qualitatively evaluated to demonstrate potential clinical impact.

RESULTS

Sharp and UHR modes had a 69% and 87% improvement in in-plane spatial resolution, respectively, compared with Macro mode (10% modulation-translation-function values of 16.05, 17.69, and 9.48 lp/cm, respectively). The cutoff spatial frequency of the UHR mode (32.4 lp/cm) corresponded to a limiting spatial resolution of 150 μm. The full-width-at-half-maximum values of the section sensitivity profiles were 0.41, 0.44, and 0.67 mm for the thinnest image thickness for each mode (0.25, 0.25, and 0.5 mm, respectively). At the same in-plane spatial resolution, Sharp and UHR images had up to 15% lower noise than Macro images. Patient images acquired in Sharp mode demonstrated better delineation of fine anatomic structures compared with Macro mode images.

CONCLUSIONS

Phantom studies demonstrated superior resolution and noise properties for the Sharp and UHR modes relative to the standard Macro mode and patient images demonstrated the potential benefit of these scan modes for clinical practice.

Characterization of Urinary Stone Composition by Use of Whole-body, Photon-counting Detector CT

RATIONAL AND OBJECTIVES

This study aims to investigate the performance of a whole-body, photon-counting detector (PCD) computed tomography (CT) system in differentiating urinary stone composition.

MATERIALS AND METHODS

Eighty-seven human urinary stones with pure mineral composition were placed in four anthropomorphic water phantoms (35-50 cm lateral dimension) and scanned on a PCD-CT system at 100, 120, and 140 kV. For each phantom size, tube current was selected to match CTDI_vol (volume CT dose index) to our clinical practice. Energy thresholds at [25, 65], [25, 70], and [25, 75] keV for 100, 120, and 140 kV, respectively, were used to generate dual-energy images. Each stone was automatically segmented using in-house software; CT number ratios were calculated and used to differentiate stone types in a receiver operating characteristic (ROC) analysis. A comparison with second- and third-generation dual-source, dual-energy CT scanners with conventional energy integrating detectors (EIDs) was performed under matching conditions.

RESULTS

For all investigated settings and smaller phantoms, perfect separation between uric acid and non-uric acid stones was achieved (area under the ROC curve [AUC] = 1). For smaller phantoms, performance in differentiation of calcium oxalate and apatite stones was also similar between the three scanners: for the 35-cm phantom size, AUC values of 0.76, 0.79, and 0.80 were recorded for the second- and third-generation EID-CT and for the PCD-CT, respectively. For larger phantoms, PCD-CT and the third-generation EID-CT outperformed the second-generation EID-CT for both differentiation tasks: for a 50-cm phantom size and a uric acid/non-uric acid differentiating task, AUC values of 0.63, 0.95, and 0.99 were recorded for the second- and third-generation EID-CT and for the PCD-CT, respectively.

CONCLUSION

PCD-CT provides comparable performance to state-of-the-art EID-CT in differentiating urinary stone composition.

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

PURPOSE

Photon counting detectors (PCDs) are being introduced in advanced x-ray computed tomography (CT) scanners. From a single PCD-CT acquisition, multiple images can be reconstructed, each based on only a part of the original x-ray spectrum. In this study, we investigated whether PCD-CT can be used to estimate stopping power ratios (SPRs) for proton therapy treatment planning, both by comparing to other SPR methods proposed for single energy CT (SECT) and dual energy CT (DECT) as well as to experimental measurements.

METHODS

A previously developed DECT-based SPR estimation method was adapted to PCD-CT data, by adjusting the estimation equations to allow for more energy spectra. The method was calibrated directly on noisy data to increase the robustness toward image noise. The new PCD SPR estimation method was tested in theoretical calculations as well as in an experimental setup, using both four and two energy bin PCD-CT images, and through comparison to two other SPR methods proposed for SECT and DECT. These two methods were also evaluated on PCD-CT images, full spectrum (one-bin) or two-bin images, respectively. In a theoretical framework, we evaluated the effect of patient-specific tissue variations (density and elemental composition) and image noise on the SPR accuracy; the latter effect was assessed by applying three different noise levels (low, medium, and high noise). SPR estimates derived using real PCD-CT images were compared to experimentally measured SPRs in nine organic tissue samples, including fat, muscle, and bone tissues.

RESULTS

For the theoretical calculations, the root-mean-square error (RMSE) of the SPR estimation was 0.1% for the new PCD method using both two and four energy bins, compared to 0.2% and 0.7% for the DECT- and SECT-based method, respectively. The PCD method was found to be very robust toward CT image noise, with a RMSE of 2.7% when high noise was added to the CT numbers. Introducing tissue variations, the RMSE only increased to 0.5%; even when adding high image noise to the changed tissues, the RMSE stayed within 3.1%. In the experimental measurements, the RMSE over the nine tissue samples was 0.8% when using two energy bins, and 1.0% for the four-bin images.

CONCLUSIONS

In all tested cases, the new PCD method produced similar or better results than the SECT- and DECT-based methods, showing an overall improvement of the SPR accuracy. This study thus demonstrated that PCD-CT scans will be a qualified candidate for SPR estimations.

Detection and Characterization of Renal Stones by Using Photon-Counting-based CT

Purpose To compare a research photon-counting-detector (PCD) CT scanner to a dual-source, dual-energy CT scanner for the detection and characterization of renal stones in human participants with known stones. Materials and Methods Thirty study participants (median age, 61 years; 10 women) underwent a clinical renal stone characterization scan by using dual-energy CT and a subsequent research PCD CT scan by using the same radiation dose (as represented by volumetric CT dose index). Two radiologists were tasked with detection of stones, which were later characterized as uric acid or non-uric acid by using a commercial dual-energy CT analysis package. Stone size and contrast-to-noise ratio were additionally calculated. McNemar odds ratios and Cohen k were calculated separately for all stones and small stones (≤3 mm). Results One-hundred sixty renal stones (91 stones that were ≤ 3 mm in axial length) were visually detected. Compared with 1-mm-thick routine images from dual-energy CT, the odds of detecting a stone at PCD CT were 1.29 (95% confidence interval: 0.48, 3.45) for all stones. Stone segmentation and characterization were successful at PCD CT in 70.0% (112 of 160) of stones versus 54.4% (87 of 160) at dual-energy CT, and was superior for stones 3 mm or smaller at PCD CT (45 vs 25 stones, respectively; P = .002). Stone characterization agreement between scanners for stones of all sizes was substantial (k = 0.65). Conclusion Photon-counting-detector CT is similar to dual-energy CT for helping to detect renal stones and is better able to help characterize small renal stones. © RSNA, 2018.

Ultra-High Resolution Photon-Counting Detector CT Reconstruction using Spectral Prior Image Constrained Compressed-Sensing (UHR-SPICCS)

Photon-counting detector based CT (PCD-CT) enables dose efficient high resolution imaging, in addition to providing multi-energy information. This allows better delineation of anatomical structures crucial for several clinical applications ranging from temporal bone imaging to pulmonary nodule visualization. Due to the smaller detector pixel sizes required for high resolution imaging, the PCD-CT images suffer from higher noise levels. The image quality is further degraded in narrow energy bins as a consequence of low photon counts. This limits the potential benefits that high-resolution PCD-CT could offer. Conventional reconstruction techniques such as the filtered back projection (FBP) have poor performance when reconstructing noisy CT projection data. To enable low noise multi-energy reconstructions, we employed a spectral prior image constrained compressed sensing (SPICCS) framework that exploits the spatio-spectral redundancy in the multi-energy acquisitions. We demonstrated noise reduction in narrow energy bins without losing energy-specific attenuation information and spatial resolution. We scanned an anthropomorphic head phantom, and a euthanized pig using our whole-body prototype PCD-CT system in the ultra-high resolution mode at 120kV. Image reconstructions were performed using SPICCS and compared with conventional FBP. Noise reduction of 18 to 46% was noticed in narrow energy bins corresponding to 25 - 65 keV and 65 - 12 keV, while the mean CT number was preserved. Spatial resolution measurement showed similar modulation transfer function (MTF) values between FBP and SPICCS, demonstrating preservation of spatial resolution.

Tutorial on X-ray photon counting detector characterization

BACKGROUND

Recent advances in photon counting detection technology have led to significant research interest in X-ray imaging.

OBJECTIVE

As a tutorial level review, this paper covers a wide range of aspects related to X-ray photon counting detector characterization.

METHODS

The tutorial begins with a detailed description of the working principle and operating modes of a pixelated X-ray photon counting detector with basic architecture and detection mechanism. Currently available methods and techniques for charactering major aspects including energy response, noise floor, energy resolution, count rate performance (detector efficiency), and charge sharing effect of photon counting detectors are comprehensively reviewed. Other characterization aspects such as point spread function (PSF), line spread function (LSF), contrast transfer function (CTF), modulation transfer function (MTF), noise power spectrum (NPS), detective quantum efficiency (DQE), bias voltage, radiation damage, and polarization effect are also remarked.

RESULTS

A cadmium telluride (CdTe) pixelated photon counting detector is employed for part of the characterization demonstration and the results are presented.

CONCLUSIONS

This review can serve as a tutorial for X-ray imaging researchers and investigators to understand, operate, characterize, and optimize photon counting detectors for a variety of applications.

Measuring arterial wall perfusion using photon-counting computed tomography (CT): improving CT number accuracy of artery wall using image deconvolution

Changes in arterial wall perfusion mark the onset of atherosclerosis. A characteristic change is the increased spatial density of vasa vasorum (VV), the microvessels in the arterial walls. Measuring this increased VV (IVV) density using contrast-enhanced computed tomography (CT) has had limited success due to blooming effects from contrast media. If the system point-spread function (PSF) is known, then the blooming effect can be modeled as a convolution between the true signal and the PSF. We report the application of image deconvolution to improve the CT number accuracy in the arterial wall of a phantom and in a porcine model of IVV density, both scanned using a whole-body research photon-counting CT scanner. A 3D-printed carotid phantom filled with three concentrations of iodinated contrast material was scanned to assess blooming and its effect on wall CT number accuracy. The results showed a reduction in blooming effects following image deconvolution, and, consequently, a better delineation between lumen and wall was achieved. Results from the animal experiment showed improved CT number difference between the carotid with IVV density and the normal carotid artery after deconvolution, enabling the detection of VV proliferation, which may serve as an early indicator of atherosclerosis.

Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image sets

Photon-counting computed tomography (PCCT) uses a photon counting detector to count individual photons and allocate them to specific energy bins by comparing photon energy to preset thresholds. This enables simultaneous multi-energy CT with a single source and detector. Phantom studies were performed to assess the spectral performance of a research PCCT scanner by assessing the accuracy of derived images sets. Specifically, we assessed the accuracy of iodine quantification in iodine map images and of CT number accuracy in virtual monoenergetic images (VMI). Vials containing iodine with five known concentrations were scanned on the PCCT scanner after being placed in phantoms representing the attenuation of different size patients. For comparison, the same vials and phantoms were also scanned on 2nd and 3rd generation dual-source, dual-energy scanners. After material decomposition, iodine maps were generated, from which iodine concentration was measured for each vial and phantom size and compared with the known concentration. Additionally, VMIs were generated and CT number accuracy was compared to the reference standard, which was calculated based on known iodine concentration and attenuation coefficients at each keV obtained from the U.S. National Institute of Standards and Technology (NIST). Results showed accurate iodine quantification (root mean square error of 0.5 mgI/cc) and accurate CT number of VMIs (percentage error of 8.9%) using the PCCT scanner. The overall performance of the PCCT scanner, in terms of iodine quantification and VMI CT number accuracy, was comparable to that of EID-based dual-source, dual-energy scanners.

Multicolor spectral photon-counting computed tomography: in vivo dual contrast imaging with a high count rate scanner

A new prototype spectral photon-counting computed tomography (SPCCT) based on a modified clinical CT system has been developed. SPCCT analysis of the energy composition of the transmitted x-ray spectrum potentially allows simultaneous dual contrast agent imaging, however, this has not yet been demonstrated with such a system. We investigated the feasibility of using this system to distinguish gold nanoparticles (AuNP) and an iodinated contrast agent. The contrast agents and calcium phosphate were imaged in phantoms. Conventional CT, gold K-edge, iodine and water images were produced and demonstrated accurate discrimination and quantification of gold and iodine concentrations in a phantom containing mixtures of the contrast agents. In vivo experiments were performed using New Zealand White rabbits at several times points after injections of AuNP and iodinated contrast agents. We found that the contrast material maps clearly differentiated the distributions of gold and iodine in the tissues allowing quantification of the contrast agents’ concentrations, which matched their expected pharmacokinetics. Furthermore, rapid, repetitive scanning was done, which allowed measurement of contrast agent kinetics with high temporal resolution. In conclusion, a clinical scale, high count rate SPCCT system is able to discriminate gold and iodine contrast media in different organs in vivo.

Estimation of signal and noise for a whole-body research photon-counting CT system

Photon-counting detector CT has a large number of acquisition parameters that require optimization, particularly the energy threshold configurations. Fast and accurate estimation of both signal and noise in photon-counting CT (PCCT) images can facilitate such optimization. Using the detector response function of a research PCCT system, we derived mathematical models for both signal and noise estimation, taking into account beam spectrum and filtration, object attenuation, water beam hardening, detector response, radiation dose, energy thresholds, and the propagation of noise. To determine the absolute noise value, a noise lookup table (LUT) for all available energy thresholds was acquired using a 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 attenuations. Validation of the estimation algorithms was performed on a whole-body research PCCT system using semianthropomorphic water phantoms and solutions of calcium and iodine. Clinical feasibility of noise estimation was assessed with scans of a cadaver head and a living swine. The algorithms achieved accurate estimation of both signal and noise for a variety of scanning parameter combinations. Maximum discrepancies were below 15%, while most errors were below 5%.

Detection of increased vasa vasorum in artery walls: Improving CT number accuracy using image deconvolution

Changes in arterial wall perfusion are an indicator of early atherosclerosis. This is characterized by an increased spatial density of vasa vasorum (VV), the micro-vessels that supply oxygen and nutrients to the arterial wall. Detection of increased VV during contrast-enhanced computed tomography (CT) imaging is limited due to contamination from blooming effect from the contrast-enhanced lumen. We report the application of an image deconvolution technique using a measured system point-spread function, on CT data obtained from a photon-counting CT system to reduce blooming and to improve the CT number accuracy of arterial wall, which enhances detection of increased VV. A phantom study was performed to assess the accuracy of the deconvolution technique. A porcine model was created with enhanced VV in one carotid artery; the other carotid artery served as a control. CT images at an energy range of 25-120 keV were reconstructed. CT numbers were measured for multiple locations in the carotid walls and for multiple time points, pre and post contrast injection. The mean CT number in the carotid wall was compared between the left (increased VV) and right (control) carotid arteries. Prior to deconvolution, results showed similar mean CT numbers in the left and right carotid wall due to the contamination from blooming effect, limiting the detection of increased VV in the left carotid artery. After deconvolution, the mean CT number difference between the left and right carotid arteries was substantially increased at all the time points, enabling detection of the increased VV in the artery wall.

Noise performance of low-dose CT: comparison between an energy integrating detector and a photon counting detector using a whole-body research photon counting CT scanner

Photon counting detector (PCD)-based computed tomography (CT) is an emerging imaging technique. Compared to conventional energy integrating detector (EID)-based CT, PCD-CT is able to exclude electronic noise that may severely impair image quality at low photon counts. This work focused on comparing the noise performance at low doses between the PCD and EID subsystems of a whole-body research PCD-CT scanner, both qualitatively and quantitatively. An anthropomorphic thorax phantom was scanned, and images of the shoulder portion were reconstructed. The images were visually and quantitatively compared between the two subsystems in terms of streak artifacts, an indicator of the impact of electronic noise. Furthermore, a torso-shaped water phantom was scanned using a range of tube currents. The product of the noise and the square root of the tube current was calculated, normalized, and compared between the EID and PCD subsystems. Visual assessment of the thorax phantom showed that electronic noise had a noticeably stronger degrading impact in the EID images than in the PCD images. The quantitative results indicated that in low-dose situations, electronic noise had a noticeable impact (up to a 5.8% increase in magnitude relative to quantum noise) on the EID images, but negligible impact on the PCD images.

Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography

Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43-73%) without sacrificing CT number accuracy or spatial resolution.