McCollough, C

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 Background Coronary computed tomography angiography (cCTA) is a widely used noninvasive diagnostic exam to assess patients for coronary artery disease (CAD). However, the spatial resolution of most CT scanners is limited due to the use of energy‐integrating detectors (EIDs). Purpose To develop a convolutional neural network ( I mproved LUMEN visualization through A rtificial super‐resolu T ion imag E s (ILUMENATE)) informed by photon‐counting‐detector (PCD)‐CT to improve EID‐CT image resolution and determine its impact on cCTA. Materials and methods With IRB approval, 30 patients undergoing clinically indicated cCTA were scanned with EID‐CT (SOMATOM Force, Siemens Healthineers, Forchheim, Germany) and subsequently with ultra‐high‐resolution (UHR) PCD‐CT (NAEOTOM Alpha, Siemens Healthineers) on the same day. ILUMENATE was trained on eight patient PCD‐CT datasets (67,890 patch pairs with 90% for training (61,101), 10% reserved for validation (6,789)) and applied to 22 unseen EID‐CT cases. Spatial resolution was evaluated using line profiles and percent diameter stenosis quantified with a severity score assigned. Two experienced radiologists, blinded to image type, selected preferred series and scored images for overall quality, sharpness, and noise comparing original EID‐CT and ILUMENATE output. Results Visual assessment and line profiles showed substantial resolution improvement with ILUMENATE. Percent diameter stenosis was significantly reduced (mean ± standard deviation: 4.42% ± 4.82%) using ILUMENATE ( p < 0.001) with nine lesions shifting down in severity score. Readers preferred ILUMENATE images in 22/22 cases and scored ILUMENATE superiorly for overall quality, sharpness, and noise ( p < 0.05). Conclusions ILUMENATE enhanced image resolution, resulting in improved overall image quality, reduced calcium blooming artifacts, and improved lumen visibility in cCTA exams performed using EID‐CT. This could potentially allow for improved accessibility to UHR image quality, allowing for more accurate assessment of CAD.

AbstractBackgroundPhoton counting X‐ray detectors (PCDs) provide better spatial resolution than energy integrating X‐ray detectors, but even higher resolution is desired in some applications. Certain charge sharing compensation techniques such as coincidence counting preferentially detect photons that arrive at the boundary between pixels, and this could be used for subpixel localization of incident photons and enhanced spatial resolution.PurposeTo estimate improvements to spatial resolution and detective quantum efficiency that are possible when using coincidence counting for high‐resolution, non‐spectral imaging.MethodsThe modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE) were estimated using numerical simulations of a two‐dimensional parallel‐beam CT system. Coincidence counters were modeled either geometrically or with Monte Carlo simulations. The geometric model consisted of narrow coincidence counters that interlaced with wider ordinary pixels, and showed that using standard filtered backprojection decreases low‐frequency DQE, but that a frequency weighting technique could be used to restore DQE. The Monte Carlo simulations were used to estimate the possible improvements that could be expected from real systems. The pixel pitch was 0.25 mm and the source apertures considered were 0, 0.125, and 0.5 mm. A numerical stent phantom was also used to illustrate possible improvements.ResultsAssuming a 0.125 mm source aperture and the Monte Carlo model, the limiting MTF (at 10%) increased from 20 to 40 lp/cm using coincidence counters. This can be explained by the increased sampling (and higher Nyquist limit) possible from coincidence counters. In the geometric model, coincidence counters are compared to conventional double sampling techniques such as in‐plane flying focal spot, and the MTF at 38 lp/cm increased from 5% to 53%. Without the frequency weighting technique, low‐frequency DQE was reduced by about 20%, but these losses are recovered with frequency weighting. Improvements are much more modest with the 0.25 mm source aperture because the system becomes source‐limited.ConclusionsCoincidence counting could be used to increase spatial resolution in PCDs. The increases in system resolution could be large if a high‐resolution X‐ray source were available.

AbstractBackgroundObjective and quantitative evaluation for low‐contrast detectability that correlates with human observer performance is lacking for routine CT quality control testing. Channelized Hotelling observer (CHO) is considered a strong candidate to fill the need but has long been deemed impractical to implement due to its requirement of a large number of repeated scans in order to provide accurate and precise estimates of index of detectability (d’). In our previous work, we optimized a CHO model observer on the American College of Radiology (ACR) CT accreditation phantom and achieved accurate measurement of d’ with only 1–3 repeat scans.PurposeIn this work, we aim to validate the repeatability of the proposed CHO‐based low‐contrast evaluation on four scanner models using the ACR CT accreditation phantom.MethodsThe repeatability test was performed on four different scanners from two major CT manufacturers: Siemens Force and Alpha; Canon Prism and Prime SP. An ACR CT phantom was scanned 10 times, each time after repositioning of the phantom. For each repositioning, 3 repeated scans were acquired at 24, 12, and 6 mGy on all four scanner models. CHO was applied at the measured dose levels for different low‐contrast object sizes (4–6 mm). The CHO was also applied to images created using deep learning‐based reconstructions on Canon Prism and to four different scan/reconstruction modes on the Siemens Alpha, a photon‐counting‐detector (PCD)‐CT. The repeatability was evaluated by the probability that a measurement would fall within the ±15% tolerance (P<15%).ResultsWith the CHO setting optimized for the ACR phantom and the use of 3 repeated scans and 9 non‐overlapping slices per scan, the CHO measurement could provide high repeatability with P<15% of 98.8%–99.9% at 12 mGy with IR reconstruction on all four scanners. On scanner A, P<15% were 91.5%–99.9% at the three dose levels and for all three object sizes while the numbers were 93.6%–99.998% on scanner B. P<15% were 96.5%–97.2% for the two deep learning reconstructions and 97.0%–99.97% for the four scan/reconstruction modes on the PCD‐CT.ConclusionThe CHO provided highly repeatable measurements with over 95% probability that a CHO measurement would lie within the ±15% tolerance for most of the dose levels and object sizes on the ACR phantom. The repeatability was maintained when the CHO was applied to images created with a commercial deep learning‐based reconstruction and various scan/reconstruction modes on a PCD‐CT. This study demonstrates that practical implementation of CHO for routine quality control and performance evaluation is feasible.

AbstractBackgroundCurrent CT quality control (QC) for low‐contrast detectability relies on visual inspection and measurement of contrast‐to‐noise ratio (CNR). However, CNR numbers become unreliable when it comes to nonlinear methods, such as iterative reconstruction (IR) and deep‐learning‐based techniques. Image quality metrics using channelized Hotelling observer (CHO) have been validated to be well correlated with human observer performance on phantom‐based and patient‐based tasks, but it has not been widely used in routine CT QC mainly because the CHO calculation typically requires a large number of repeated scans in order to provide accurate and precise estimate of index of detectability (d′).PurposeThe main goal of this work is to optimize channel filters and other CHO parameters and accurately estimate the low‐contrast detectability with minimum number of repeated scans for the widely used American College of Radiology (ACR) CT accreditation phantom so that it can become practically feasible for routine CT QC tests.MethodsTo provide a converged d′ value, an ACR phantom was repeatedly scanned 100 times at three dose levels (24, 12, and 6 mGy). Images were reconstructed with two kernels (FBP Br44 and IR Br44‐3). d′ as a function of number of repeated scans was determined for different number of background regions of interest (ROIs), different number of low‐contrast objects, different number of slices per each object, and different channel filter options. A reference d′ was established using the optimized CHO setting, and the bias of d′ was quantified using the d′ calculated from all 100 repeated scans. The variation of d′ at each condition was estimated using a resampling method combining random subsampling among 100 repeated scans and bootstrapping of the ensembles of signal and background ROIs.ResultsOptimized parameters in CHO calculation were determined: two background ROIs per object, four objects per low‐contrast object size, nine non‐overlapping slices per object, and a 4‐channel Gabor filter. The bias and uncertainty were estimated at different numbers of repeated scans using these parameters. When only one single scan was used in the CHO calculation, the bias of d′ was below 6.2% and the uncertainty 15.6‐19.6% for the 6, 5, and 4 mm objects, while with three repeated scans the bias was below 2.0% and uncertainty 8.7‐10.9% for the three object sizes.ConclusionWith optimized parameter settings in CHO, efficient and accurate measurement of low‐contrast detectability on the commonly used ACR phantom becomes feasible, which could potentially lead to adoption of CHO‐based low‐contrast evaluation in routine QC tests.

AbstractPurposeThe purpose of this work is to evaluate the scaled computed tomography (CT) number accuracy of an artificial 120 kV reconstruction technique based on phantom experiments in the context of radiation therapy planning.MethodsAn abdomen‐shaped electron density phantom was scanned on a clinical CT scanner capable of artificial 120 kV reconstruction using different tube potentials from 70 to 150 kV. A series of tissue‐equivalent phantom inserts (lung, adipose, breast, solid water, liver, inner bone, 30%/50% CaCO3, cortical bone) were placed inside the phantom. Images were reconstructed using a conventional quantitative reconstruction kernel as well as the artificial 120 kV reconstruction kernel. Scaled CT numbers of inserts were measured from images acquired at different kVs and compared with those acquired at 120 kV, which were deemed as the ground truth. The relative error was quantified as the percentage deviation of scaled CT numbers acquired at different tube potentials from their ground truth values acquired at 120 kV.ResultsScaled CT numbers measured from images reconstructed using the conventional reconstruction demonstrated a strong kV‐dependence. The relative error in scaled CT numbers ranged from 0.6% (liver insert) to 31.1% (cortical bone insert). The artificial 120 kV reconstruction reduced the kV dependence, especially for bone tissues. The relative error in scaled CT number was reduced to 0.4% (liver insert) and 2.6% (30% CaCO3 insert) using this technique. When tube potential selection was limited to the range of 90 to 150 kV, the relative error was further restrained to <1.2% for all tissue types.ConclusionPhantom results demonstrated that using the artificial 120 kV technique, it was feasible to acquire raw projection data at the desired tube potential and then reconstruct images with scaled CT numbers comparable to those obtained directly at 120 kV. In radiotherapy applications, this technique may allow optimization of tube potential without complicating clinical workflow by eliminating the necessity of maintaining multiple sets of CT calibration curves.

Purpose To determine the accuracy of CT number and calcium score of a kV‐independent technique based on an artificial 120 kV reconstruction, and its potential to reduce radiation dose. Methods Anthropomorphic chest phantoms were scanned on a third‐generation dual‐source CT system equipped with the artificial 120 kV reconstruction. First, a phantom module containing a 20‐mm diameter hydroxyapatite (HA) insert was scanned inside the chest phantoms at different tube potentials (70–140 kV) to evaluate calcium CT number accuracy. Next, three small HA inserts (diameter/length = 5 mm) were inserted into a pork steak and scanned inside the phantoms to evaluate calcium score accuracy at different kVs. Finally, the same setup was scanned using automatic exposure control (AEC) at 120 kV, and then with automatic kV selection (auto‐kV). Phantoms were also scanned at 120 kV using a size‐dependent mA chart. CT numbers of soft tissue and calcium were measured from different kV images. Calcium score of each small HA insert was measured using commercial software. Results The CT number difference from 120 kV was small with tube potentials from 90 to 140 kV for both soft tissue and calcium (maximal difference of 4/5 HU, respectively). Consistent calcium scores were obtained from images of different kVs compared to 120 kV, with a relative difference <8%. Auto‐kV provided a 25–34% dose reduction compared to AEC alone. Conclusion A kV‐independent calcium scoring technique can produce artificial 120 kV images with consistent soft tissue and calcium CT numbers compared to standard 120 kV examinations. When coupled with auto‐kV, this technique can reduce radiation by 25–34% compared to that with AEC alone, while providing consistent calcium scores as that of standard 120 kV examinations.

Abstract 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.