Shanblatt, Elisabeth R.

Abstract Background Photon counting detector CT (PCD‐CT) has the potential to sort detected x‐ray photons into more than two energy bins to perform multi‐material decomposition using K‐edge materials without additional mathematical constraints/assumptions. The availability of multiple energy thresholds in PCD‐CT opens the possibility for unique clinical applications such as simultaneous multi‐contrast‐agent imaging. Purpose To evaluate the technical performance of a PCD‐CT system with four energy thresholds for conducting multi‐material decomposition of iodine, gadolinium, and water after a comprehensive system calibration procedure. Methods Experiments were performed on a commercial PCD‐CT scanner (NAEOTOM Alpha, Siemens Healthineers) using a research mode with four energy thresholds. Material calibration was performed by scanning contrast materials with known concentration placed in various sizes of cylindrical water‐equivalent phantoms at various tube currents and positions. From these data, per‐voxel contrast models were derived for each material by multiple linear regressions up to second order, where the predictor variables were combinations of tube current, phantom diameter, and distance to isocenter. After calibration, small (20 cm diameter) and large (30 cm × 40 cm) uniform multi‐energy CT phantoms and an anthropomorphic thorax phantom (31 cm × 39 cm), each containing bone, soft tissue, and several quantitative inserts of iodine (0.2–5.0 mg/ml), gadolinium (0.3–5.0 mg/ml), and a mixture of both (5.0 mg/ml each) were scanned using the four‐energy threshold mode at 140 kV with energy thresholds set at 20, 52, 75, and 82 keV. Images for each energy threshold were reconstructed with a filtered back‐projection algorithm and a quantitative kernel (Qr44). Subsequent image‐based material decomposition was performed on individual voxels of the energy threshold images using the models derived from the calibration process to generate water, iodine, and gadolinium basis images. Quantitative material concentrations and image noise magnitudes, and noise power spectra (NPS) were measured. Results Iodine and gadolinium concentrations above 1.25 mg/ml were visible in the material basis images in all phantoms. Averaged quantitative accuracy for iodine and gadolinium in the small, large, and thorax phantoms were 0.39 ± 0.30 mg/ml, 0.96 ± 0.53 mg/ml, and 0.53 ± 0.35 mg/ml, respectively. In the water basis images, accuracy was within 24 HU in all phantoms. Noise magnitudes in iodine and gadolinium basis images were 2.91 and 3.52 times that of the lowest energy threshold image for the small phantom, and were 6.92 and 7.17 times that of the lowest threshold image for the large phantom. Noise texture did not change between the energy threshold and material basis images for either size phantom as measured by the NPS. Conclusions After a comprehensive system calibration, a commercial PCD‐CT with four energy thresholds performed multiple material decomposition of iodine, gadolinium, and water for concentrations above 1.25 mg/ml in human‐sized phantoms. Noise amplification and quantitative accuracy from the decomposition greatly depended on object size.

Abstract Objectives To assess quantitative and qualitative image characteristics in phantoms and patients using a simultaneous ultra-high resolution (UHR) and multi-energy (ME) dual-source cardiac scan mode in a photon counting detector (PCD) CT scanner. Methods UHR-ME cardiac scans were performed to determine the spatial resolution and multi-energy performance. UHR reconstructions were completed to measure transverse and axial spatial resolution in terms of modulation transfer function (MTF) and slice sensitivity profile (SSP). Virtual monoenergetic images (VMIs), virtual non-contrast (VNC) images, and iodine concentration images were created using moderately sharp reconstruction kernels to assess ME performance. With IRB approval, coronary CT angiograms were acquired in 19 patients using the UHR-ME mode, from which iodine contrast-to-noise ratio (CNR) was measured. Various ME post-processing tasks were performed to show the utility of the spectral capabilities. Results VMIs showed a mean CT number error of 6.0%, while iodine accuracy was 0.71 mg/cc (root-mean-square-error). Spatial resolution matched well with a standard UHR mode. Patient images showed a trend of higher CNR at lower keV, while higher keV images showed decreased calcium blooming. ME techniques to separate iodine and calcium were used successfully. Conclusions The combination of UHR-ME cardiac mode retains UHR image quality while providing flexibility for ME post-processing. Advances in knowledge Cardiac UHR-ME PCD-CT scanning offers simultaneous UHR and ME imaging without loss of spatial or temporal resolution or quantitative accuracy.

Objective The aim of this study was to evaluate the clinical impact of a higher spatial resolution, full field-of-view investigational photon-counting detector computed tomography (PCD-CT) on radiologist confidence in imaging findings and diagnosis of usual interstitial pneumonia (UIP) compared with conventional energy-integrating detector CT (EID-CT). Materials and Methods Patients suspected of interstitial lung disease were scanned on a PCD-CT system after informed consent and a clinically indicated EID-CT. In 2 sessions, 3 thoracic radiologists blinded to clinical history and scanner type evaluated CT images of the right and left lungs separately on EID- or PCD-CT, reviewing each lung once/session, rating confidence in imaging findings of reticulation, traction bronchiectasis, honeycombing, ground-glass opacities (GGOs), mosaic pattern, and lower lobe predominance (100-point scale: 0–33, likely absent; 34–66, indeterminate; 67–100, likely present). Radiologists also rated confidence for the probability of UIP (0–20, normal; 21–40, inconsistent with UIP; 41–60, indeterminate UIP; 61–81; probable UIP; 81–100, definite UIP) and graded image quality. Because a confidence scale of 50 represented completely equivocal findings, magnitude score (the absolute value of confidence scores from 50) was used for analysis (higher scores were more confident). Image noise was measured for each modality. The magnitude score was compared using linear mixed effects regression. The consistency of findings and diagnosis between 2 scanners were evaluated using McNemar test and weighted κ statistics, respectively. Results A total of 30 patients (mean age, 68.8 ± 11.0 years; M:F = 18:12) underwent conventional EID-CT (median CTDIvol, 7.88 mGy) and research PCD-CT (median CTDIvol, 6.49 mGy). The magnitude scores in PCD-CT were significantly higher than EID-CT for imaging findings of reticulation (40.7 vs 38.3; P = 0.023), GGO (34.4 vs 31.7; P = 0.019), and mosaic pattern (38.6 vs 35.9; P = 0.013), but not for other imaging findings (P ≥ 0.130) or confidence in UIP (34.1 vs 22.2; P < 0.059). Magnitude score of probability of UIP in PCD-CT was significantly higher than EID-CT in one reader (26.0 vs 21.5; P = 0.009). Photon-counting detector CT demonstrated a decreased number of indeterminate GGO (17 vs 26), an increased number of unlikely GGO (74 vs 50), and an increased number of likely reticulations (140 vs 130) relative to EID-CT. Interobserver agreements among 3 readers for imaging findings and probability of UIP were similar between PCD-CT and EID-CT (intraclass coefficient: 0.507–0.818 vs 0.601–0.848). Photon-counting detector CT had higher scores in overall image quality (4.84 ± 0.38) than those in EID-CT (4.02 ± 0.40; P < 0.001) despite increased image noise (mean 85.5 vs 36.1 HU). Conclusions Photon-counting detector CT provided better image quality and improved the reader confidence for presence or absence of imaging findings of reticulation, GGO, and mosaic pattern with idiosyncratic improvement in confidence in UIP presence.

Abstract We report a comprehensive evaluation of a full field-of-view (FOV) photon-counting detector (PCD) computed tomography (CT) system using phantoms, and qualitatively assess image quality in patient examples. A whole-body PCD-CT system with 50 cm FOV, 5.76 cm z-detector coverage and two acquisition modes (standard: 144 × 0.4 mm collimation and ultra-high resolution (UHR): 120 × 0.2 mm collimation) was used in this study. Phantoms were scanned to assess image uniformity, CT number accuracy, noise power spectrum, spatial resolution, material decomposition and virtual monoenergetic imaging (VMI) performance. Four patients were scanned on the PCD-CT system with matched or lower radiation dose than their prior clinical CT scans performed using energy-integrating detector (EID) CT, and the potential clinical impact of PCD-CT was qualitatively evaluated. Phantom results showed water CT numbers within ±5 HU, and image uniformity measured between peripheral and central regions-of-interests to be within ±5 HU. For the UHR mode using a dedicated sharp kernel, the cut-off spatial frequency was 40 line-pairs cm −1 , which corresponds to a 125 μ m limiting in-plane spatial resolution. The full-width-at-half-maximum for the section sensitivity profile was 0.33 mm for the smallest slice thickness (0.2 mm) using the UHR mode. Material decomposition in a multi-energy CT phantom showed accurate material classification, with a root-mean-squared-error of 0.3 mg cc −1 for iodine concentrations (2–15 mg cc −1 ) and 14.2 mg cc −1 for hydroxyapatite concentrations (200 and 400 mg cc −1 ). The average percent error for CT numbers corresponding to the iodine concentrations in VMI (40–70 keV) was 2.75%. Patient PCD-CT images demonstrated better delineation of anatomy for chest and temporal bone exams performed with the UHR mode, which allowed the use of very sharp kernels not possible with EID-CT. VMI and virtual non-contrast images generated from a patient head CT angiography exam using the standard acquisition mode demonstrated the multi-energy capability of the PCD-CT system.

PurposeTalbot‐Lau grating interferometry enables the use of polychromatic x‐ray sources, extending the range of potential applications amenable to phase contrast imaging. However, these sources introduce beam hardening effects not only from the samples but also from the gratings. As a result, grating inhomogeneities due to manufacturing imperfections can cause spectral nonuniformity artifacts when used with polychromatic sources. Consequently, the different energy dependencies of absorption, phase, and visibility contrasts impose challenges that so far have limited the achievable image quality. The purpose of this work was to develop and validate a correction strategy for grating‐based x‐ray imaging that accounts for beam hardening generated from both the imaged object and the gratings.MethodsThe proposed two‐variable polynomial expansion strategy was inspired by work performed to address beam hardening from a primary modulator. To account for the multicontrast nature of grating interferometry, this approach was extended to each contrast to obtain three sets of correction coefficients, which were determined empirically from a calibration scan. The method’s feasibility was demonstrated using a tabletop Talbot‐Lau grating interferometer micro‐computed tomography (CT) system using CT acquisitions of a water sample and a silicon sample, representing low and high atomic number materials. Spectral artifacts such as cupping and ring artifacts were quantified using mean squared error (MSE) from the beam‐hardening‐free target image and standard deviation within a reconstructed image of the sample. Finally, the model developed using the water sample was applied to a fixated murine lung sample to demonstrate robustness for similar materials.ResultsThe water sample’s absorption CT image was most impacted by spectral artifacts, but following correction to decrease ring artifacts, an 80% reduction in MSE and 57% reduction in standard deviation was observed. The silicon sample created severe artifacts in all contrasts, but following correction, MSE was reduced by 94% in absorption, 96% in phase, and 90% in visibility images. These improvements were due to the removal of ring artifacts for all contrasts and reduced cupping in absorption and phase images and reduced capping in visibility images. When the water calibration coefficients were applied to the lung sample, ring artifacts most prominent in the absorption contrast were eliminated.ConclusionsThe described method, which was developed to remove artifacts in absorption, phase, and normalized visibility micro‐CT images due to beam hardening in the system gratings and imaged object, reduced the MSE by up to 96%. The method depends on calibrations that can be performed on any system and does not require detailed knowledge of the x‐ray spectrum, detector energy response, grating attenuation properties and imperfections, or the geometry and composition of the imaged object.

PurposeDemonstrate realistic simulation of grating‐based x‐ray phase‐contrast imaging (GB‐XPCI) using wave optics and the four‐dimensional Mouse Whole Body (MOBY) phantom defined with non‐uniform rational B‐splines (NURBS).MethodsWe use a full‐wave approach, which uses wave optics for x‐ray wave propagation from the source to the detector. This forward imaging model can be directly applied to NURBS‐defined numerical phantoms such as MOBY. We assign the material properties (attenuation coefficient and electron density) of each model part using the data for adult human tissues. The Poisson noise is added to the simulated images based on the calculated photon flux at each pixel.ResultsWe simulate the intensity images of the MOBY phantom for eight different grating positions. From the simulated images, we calculate the absorption, differential phase, and normalized visibility contrast images. We also predict how the image quality is affected by different exposure times.ConclusionsGB‐XPCI can be simulated with the full‐wave approach and a realistic numerical phantom defined with NURBS.