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BACKGROUND: The first commercially available photon-counting-detector CT (PCD-CT) has been introduced for clinical use. However, its spectral performance on single- and dual-contrast imaging tasks has not been comprehensively assessed. PURPOSE: To evaluate the spectral imaging performance of a clinical PCD-CT system for single-contrast material [iodine (I) or gadolinium (Gd)] and dual-contrast materials (I and Gd) in comparison with a dual-source dual-energy CT (DS-DECT). METHODS: Iodine (5, 10, and 15 mg/mL) and gadolinium (3.3, 6.6, and 9.9 mg/mL) samples, and their mixtures (I/Gd: 5/3.3 and 10/6.6 mg/mL) were prepared and placed in two torso-shaped water phantoms (lateral dimensions: 30 and 40 cm). These phantoms were scanned on a PCD-CT (NAEOTOM Alpha, Siemens) at 90, 120, and 140 kV. The same phantoms were scanned on a DS-DECT (SOMATOM Force, Siemens) with 70/Sn150, 80/Sn150, 90/Sn150, and 100/Sn150 kV. The radiation dose levels were matched [volume CT dose index (CTDIvol): 10 mGy for the 30 cm phantom and 20 mGy for the 40 cm phantom] across all tube voltage settings and between scanners. Two-material decomposition (I/water or Gd/water) was performed on iodine or gadolinium samples, and three-material decomposition (I/Gd/water) on both individual samples and mixtures. On each decomposed image, mean mass concentration (± standard deviation) was measured in circular region-of-interests placed on the contrast samples. Root-mean-square-error (RMSE) values of iodine and gadolinium concentrations were reported based on the measurements across all contrast samples and repeated on 10 consecutive slices. RESULTS: For all material decomposition tasks on the DS-DECT, the kV pairs with greater spectral separation (70/Sn150 kV and 80/Sn150 kV) yielded lower RMSE values than other DS-DECT and PCD-CT alternatives. Specifically, for the optimal 70/Sn150 kV, RMSE values were 1.2 ± 0.1 mg/mL (I) for I/water material decomposition, 1.0 ± 0.1 mg/mL (Gd) for Gd/water material decomposition, and 4.5 ± 0.2 mg/mL (I) and 3.7 ± 0.2 mg/mL (Gd), respectively, for I/Gd/water material decomposition. On the PCD-CT, the optimal tube voltages were 120 or 140 kV for I/water decomposition with RMSE values of 2.0 ± 0.1 mg/mL (I). For Gd/water decomposition on PCD-CT, the optimal tube voltage was 140 kV with gadolinium RMSE values of 1.5 ± 0.1 mg/mL (Gd), with the 90 kV setting on PCD-CT generating higher RMSE values for gadolinium concentration compared to all DS-DECT and PCD-CT alternatives. For three material decomposition, both imaging modalities demonstrated substantially higher RMSE values for iodine and gadolinium, with 90 kV being the optimal tube potential for Gd/I quantitation on PCD-CT [5.4 ± 0.3 mg/mL (I) and 3.9 ± 0.2 mg/mL (Gd)], and DS-DECT at 100/Sn150 kV having larger RMSE values for both materials compared to the alternatives for either modality. CONCLUSION: Optimal tube voltage for material decomposition on the clinical PCD-CT is task-dependent but inferior to DS-DECT using 70/Sn150 kV or 80/Sn150 kV in two-material decomposition for single-contrast imaging (iodine/water or gadolinium/water). Three material decomposition (iodine/gadolinium/water) in dual-contrast imaging yields substantially higher RMSE for both imaging platforms.

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BACKGROUND AND PURPOSE: Photon-counting detector CT (PCD-CT) is now clinically available and offers ultra-high-resolution (UHR) imaging. Our purpose was to prospectively evaluate the relative image quality and impact on diagnostic confidence of head CTA images acquired by using a PCD-CT compared with an energy-integrating detector CT (EID-CT). MATERIALS AND METHODS: Adult patients undergoing head CTA on EID-CT also underwent a PCD-CT research examination. For both CT examinations, images were reconstructed at 0.6 mm by using a matched standard resolution (SR) kernel. Additionally, PCD-CT images were reconstructed at the thinnest section thickness of 0.2 mm (UHR) with the sharpest kernel, and denoised with a deep convolutional neural network (CNN) algorithm (PCD-UHR-CNN). Two readers (R1, R2) independently evaluated image quality in randomized, blinded fashion in 2 sessions, PCD-SR versus EID-SR and PCD-UHR-CNN versus EID-SR. The readers rated overall image quality (1 [worst] to 5 [best]) and provided a Likert comparison score (-2 [significantly inferior] to 2 [significantly superior]) for the 2 series when compared side-by-side for several image quality features, including visualization of specific arterial segments. Diagnostic confidence (0-100) was rated for PCD versus EID for specific arterial findings, if present. RESULTS: Twenty-eight adult patients were enrolled. The volume CT dose index was similar (EID: 37.1 ± 4.7 mGy; PCD: 36.1 ± 4.0 mGy). Overall image quality for PCD-SR and PCD-UHR-CNN was higher than EID-SR (eg, PCD-UHR-CNN versus EID-SR: 4.0 ± 0.0 versus 3.0 ± 0.0 (R1), 4.9 ± 0.3 versus 3.0 ± 0.0 (R2); all P values < .001). For depiction of arterial segments, PCD-SR was preferred over EID-SR (R1: 1.0-1.3; R2: 1.0-1.8), and PCD-UHR-CNN over EID-SR (R1: 0.9-1.4; R2: 1.9-2.0). Diagnostic confidence of arterial findings for PCD-SR and PCD-UHR-CNN was significantly higher than EID-SR: eg, PCD-UHR-CNN versus EID-SR: 93.0 ± 5.8 versus 78.2 ± 9.3 (R1), 88.6 ± 5.9 versus 70.4 ± 5.0 (R2); all P values < .001. CONCLUSIONS: PCD-CT provides improved image quality for head CTA images compared with EID-CT, both when PCD and EID reconstructions are matched, and to an even greater extent when PCD-UHR reconstruction is combined with a CNN denoising algorithm.

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PURPOSE: Subtle liver metastases may be missed in contrast enhanced CT imaging. We determined the impact of lesion location and conspicuity on metastasis detection using data from a prior reader study. METHODS: In the prior reader study, 25 radiologists examined 40 CT exams each and circumscribed all suspected hepatic metastases. CT exams were chosen to include a total of 91 visually challenging metastases. The detectability of a metastasis was defined as the fraction of radiologists that circumscribed it. A conspicuity index was calculated for each metastasis by multiplying metastasis diameter with its contrast, defined as the difference between the average of a circular region within the metastasis and the average of the surrounding circular region of liver parenchyma. The effects of distance from liver edge and of conspicuity index on metastasis detectability were measured using multivariable linear regression. RESULTS: The median metastasis was 1.4 cm from the edge (interquartile range [IQR], 0.9-2.1 cm). Its diameter was 1.2 cm (IQR, 0.9-1.8 cm), and its contrast was 38 HU (IQR, 23-68 HU). An increase of one standard deviation in conspicuity index was associated with a 6.9% increase in detectability (p = 0.008), whereas an increase of one standard deviation in distance from the liver edge was associated with a 5.5% increase in detectability (p = 0.03). CONCLUSION: Peripheral liver metastases were missed more frequently than central liver metastases, with this effect depending on metastasis size and contrast.

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OBJECTIVE: To determine the accuracy of photon-counting-detector CT (PCD-CT) at deriving bone morphometric indices and demonstrate utility in vivo in the distal radius. METHODS: Ten cadaver wrists were scanned using PCD-CT and high-resolution peripheral quantitative CT (HRpQCT). Correlation between PCD-CT and HRpQCT morphometric indices was determined. Agreement was assessed by Lin's concordance correlation coefficient (Lin's CCC). Wrist PCD-CTs of patients between 02/2022 and 08/2023 were also evaluated for clinical utility. Morphometric indices of the in vivo distal radii were extracted and compared between patients with or without osteoporosis. RESULTS: In cadavers, strong correlation between PCD-CT and HRpQCT was observed for cortical thickness (Spearman correlation, ρ, 0.85), trabecular spacing (ρ = 0.98), and trabecular bone volume fraction (ρ = 0.68). Moderate negative correlation (ρ = - 0.49) was observed for trabecular thickness. PCD-CT shows good agreement to HRpQCT for cortical thickness, trabecular spacing, and trabecular bone volume fraction (Lin's CCC = 0.80, 0.94, and 0.86, respectively) but poor agreement (Lin's CCC = - 0.1) for trabecular thickness. In forty participants (31 adults and 9 pediatric), bone morphometrics indices for cortical thickness, trabecular thickness, trabecular spacing, and trabecular bone volume fraction were 0.99 mm (IQR, 0.89-1.06), 0.38 mm (IQR, 0.25-0.40), 0.82 mm (IQR, 0.72-1.05), and 0.28 (IQR, 0.25-0.33), respectively. Patients with osteoporosis had statistically significantly larger trabecular spacing (p = 0.025) and lower trabecular volumetric bone mineral density (p = 0.042). CONCLUSION: This study demonstrates the agreement of PCD-CT to HRpQCT in cadavers of most cortical and bone morphometrics examined and provide in vivo quantitative metrics of bone microarchitecture from routine clinical PCD-CT images of the distal radius.

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OBJECTIVE: Pulmonary CT angiography (CTA) to detect pulmonary emboli can be performed using conventional dual-source CT with single-energy acquisition at high-pitch (high-pitch conventional CT), which minimizes motion artifacts, or routine-pitch, dual-energy acquisitions (routine-pitch conventional DECT), which maximize iodine signal. We compared iodine signal, radiation dose, and motion artifacts of pulmonary CTA between these conventional CT modalities and dual-source photon-counting detector CT with high-pitch, multienergy acquisitions (high-pitch photon-counting CT). METHODS: Consecutive clinically indicated pulmonary CTA exams were collected. CT number/noise was measured from the main to right lower lobe segmental pulmonary arteries using 120 kV threshold low, 120 kV, and mixed kV (0.6 linear blend) images. Three radiologists reviewed anonymized, randomized exams, rating them using a 4- or 5-point Likert scale (1 = worst, and 4/5 = best) for contrast enhancement in pulmonary arteries, motion artifacts in aortic root to subsegmental pulmonary arteries, lung image quality; pulmonary blood volume (PBV) map image quality (for multienergy or dual-energy exams), and contribution to reader confidence. RESULTS: One hundred fifty patients underwent high-pitch photon-counting CT (n = 50), high-pitch conventional CT (n = 50), and routine-pitch conventional DECT (n = 50). High-pitch photon-counting CT had lower radiation dose (CTDIvol: 8.1 ± 2.5 vs 9.6 ± 6.8 and 16.2 ± 8.5 mGy, respectively; P < 0.001), and routine-pitch conventional DECT had significantly less contrast (P < 0.009). CT number and CNR measurements were significantly greater at high-pitch photon-counting CT (P < 0.001). Across readers, high-pitch photon-counting CT demonstrated significantly higher subjective contrast enhancement in the pulmonary arteries compared to the other modalities (4.7 ± 0.6 vs 4.4 ± 0.7 vs 4.3 ± 0.7; P = 0.011) and lung image quality (3.4 ± 0.5 vs 3.1 ± 0.5 vs 3.1 ± 0.5; P = 0.013). High-pitch photon-counting CT and high-pitch conventional CT had fewer motion artifacts at all levels compared to DECT (P < 0.001). High-pitch photon-counting CT PBV maps had superior image quality (P < 0.001) and contribution to reader confidence (P < 0.001) compared to routine-pitch conventional DECT. CONCLUSION: High-pitch photon-counting pulmonary CTA demonstrated higher contrast in pulmonary arteries at lower radiation doses with improved lung image quality and fewer motion artifacts compared to high-pitch conventional CT and routine-pitch conventional dual-energy CT.

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PURPOSE: A dual-source CT system can be operated in a high-pitch helical mode to provide a temporal resolution of 66 ms, which reduces motion artifacts in CT pulmonary angiography (CTPA). It can also be operated in a multi-energy (ME) mode to provide iodine maps, beneficial in the evaluation of pulmonary embolism (PE). No energy-integrating detector (EID) CT can perform simultaneous ME and high-pitch acquisition. This phantom study aimed to evaluate the ability of a photon-counting-detector (PCD) CT to perform simultaneous high-pitch and ME imaging for CTPA. METHODS: A motion phantom was used to mimic the respiratory motion. Two tubes filled with iodine with intravascular thrombus mimicked by injecting glue within the tubes were placed along with 5, 10, and 15 mg/mL iodine samples, on a motion phantom at 20 and 30 revolutions per minute. Separate high-pitch and ME EID-CT scans and a single high-pitch ME PCD scan were acquired and virtual monoenergetic images and iodine maps reconstructed. Percent thrombus occlusion was measured and compared between static and moving images. RESULTS: When there was motion, EID-CT ME suffered from significant motion artifacts. The measured iodine concentrations with PCD-CT in high-pitch ME were more stable when there was a motion, with a lower standard deviation than EID-CT in ME mode. The estimated percent thrombus occlusion dropped significantly with applied motion on EID-CT, while PCD-CT high-pitch ME mode showed good agreement between measurements on static or moving images. CONCLUSION: PCD-CT with combined ME and high-pitch mode facilitates simultaneous accurate iodine quantification and assessment of intravascular occlusion.

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BACKGROUND: Inserting noise into existing patient projection data to simulate lower-radiation-dose exams has been frequently used in traditional energy-integrating-detector (EID)-CT to optimize radiation dose in clinical protocols and to generate paired images for training deep-learning-based reconstruction and noise reduction methods. Recent introduction of photon counting detector CT (PCD-CT) also requires such a method to accomplish these tasks. However, clinical PCD-CT scanners often restrict the users access to the raw count data, exporting only the preprocessed, log-normalized sinogram. Therefore, it remains a challenge to employ projection domain noise insertion algorithms on PCD-CT. PURPOSE: To develop and validate a projection domain noise insertion algorithm for PCD-CT that does not require access to the raw count data. MATERIALS AND METHODS: A projection-domain noise model developed originally for EID-CT was adapted for PCD-CT. This model requires, as input, a map of the incident number of photons at each detector pixel when no object is in the beam. To obtain the map of incident number of photons, air scans were acquired on a PCD-CT scanner, then the noise equivalent photon number (NEPN) was calculated from the variance in the log normalized projection data of each scan. Additional air scans were acquired at various mA settings to investigate the impact of pulse pileup on the linearity of NEPN measurement. To validate the noise insertion algorithm, Noise Power Spectra (NPS) were generated from a 30 cm water tank scan and used to compare the noise texture and noise level of measured and simulated half dose and quarter dose images. An anthropomorphic thorax phantom was scanned with automatic exposure control, and noise levels at different slice locations were compared between simulated and measured half dose and quarter dose images. Spectral correlation between energy thresholds T1 and T2, and energy bins, B1 and B2, was compared between simulated and measured data across a wide range of tube current. Additionally, noise insertion was performed on a clinical patient case for qualitative assessment. RESULTS: The NPS generated from simulated low dose water tank images showed similar shape and amplitude to that generated from the measured low dose images, differing by a maximum of 5.0% for half dose (HD) T1 images, 6.3% for HD T2 images, 4.1% for quarter dose (QD) T1 images, and 6.1% for QD T2 images. Noise versus slice measurements of the lung phantom showed comparable results between measured and simulated low dose images, with root mean square percent errors of 5.9%, 5.4%, 5.0%, and 4.6% for QD T1, HD T1, QD T2, and HD T2, respectively. NEPN measurements in air were linear up until 112 mA, after which pulse pileup effects significantly distort the air scan NEPN profile. Spectral correlation between T1 and T2 in simulation agreed well with that in the measured data in typical dose ranges. CONCLUSIONS: A projection-domain noise insertion algorithm was developed and validated for PCD-CT to synthesize low-dose images from existing scans. It can be used for optimizing scanning protocols and generating paired images for training deep-learning-based methods.

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Performing CT in children comes with unique challenges such as greater degrees of patient motion, smaller and densely packed anatomy, and potential risks of radiation exposure. The technical advancements of photon-counting detector (PCD) CT enable decreased radiation dose and noise, as well as increased spatial and contrast resolution across all ages, compared with conventional energy-integrating detector CT. It is therefore valuable to review the relevant technical aspects and principles specific to protocol development on the new PCD CT platform to realize the potential benefits for this population. The purpose of this article, based on multi-institutional clinical and research experience from pediatric radiologists and medical physicists, is to provide protocol guidance for use of PCD CT in the imaging of pediatric patients.

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Purpose: We aim to reduce image noise in high-resolution (HR) virtual monoenergetic images (VMIs) from photon-counting detector (PCD) CT scans by developing a prior knowledge-aware iterative denoising neural network (PKAID-Net) that efficiently exploits the unique noise characteristics of VMIs at different energy (keV) levels. Approach: PKAID-Net offers two major features: first, it utilizes a lower-noise VMI (e.g., 70 keV) as a prior input; second, it iteratively constructs a refined training dataset to improve the neural network's denoising performance. In each iteration, the denoised image from the previous module serves as an updated target image, which is included in the dataset for the subsequent training iteration. Our study includes 10 patient coronary CT angiography exams acquired on a clinical dual-source PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50, 70, and 100 keV, using a sharp vascular kernel (Bv68) and thin (0.6 mm) slice thickness (0.3 mm increment). PKAID-Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy. Results: PKAID-Net achieved a noise reduction of 96% compared to filtered back projection and 65% relative to iterative reconstruction, all while preserving spatial and spectral fidelity and maintaining a natural noise texture. The iterative refinement of PCD-CT data during the training process substantially enhanced the robustness of deep learning-based denoising compared to the original method, which resulted in some spatial detail loss. Conclusions: The PKAID-Net provides substantial noise reduction while maintaining spatial and spectral fidelity of the HR VMIs from PCD-CT.

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Purpose: We aim to compare the low-contrast detectability of a clinical whole-body photon-counting-detector (PCD)-CT at different scan modes and image types with an energy-integrating-detector (EID)-CT. Approach: We used a channelized Hotelling observer (CHO) previously optimized for quality control purposes. An American College of Radiology CT accreditation phantom was scanned on both PCD-CT and EID-CT with 10 phantom positionings. For PCD-CT, images were generated using two scan modes, standard resolution (SR) and ultra-high-resolution (UHR); two image types, virtual monochromatic images at 70 keV and low-energy threshold (T3D); both filtered-back-projection (FBP) and iterative reconstruction (IR) reconstruction methods; and three reconstruction kernels. For each positioning, three repeated scans were acquired for each scan mode, image type, and CTDIvol of 6, 12, and 24 mGy. For EID-CT, images acquired from scans (10 positionings × 3 repeats × 3 doses) were reconstructed using the closest counterpart FBP and IR kernels. CHO was applied to calculate the index of detectability (d') on both scanners. Results: With the smooth Br44 kernel, the d' of UHR was mostly comparable with that of the SR mode (difference: -11.4% to 8.3%, p=0.020 to 0.956), and the T3D images had a higher d' (difference: 0.7% to 25.6%) than 70 keV images on PCD-CT. Compared with the EID-CT, UHR-T3D of PCD-CT had non-inferior d' (difference: -2.7% to 12.9%) with IR and non-superior d' (difference: 0.8% to 11.2%) with FBP using the Br44 kernel. PCD-CT produced higher d' than EID-CT by 61.8% to 247.1% with the sharper reconstruction kernels. Conclusions: The comparison between PCD-CT and EID-CT was significantly influenced by the reconstruction method and kernel. With a smooth kernel that is typically used in low-contrast detection tasks, the PCD-CT demonstrated low-contrast detectability that was comparable to EID-CT with IR and showed no superiority when using FBP. With the use of sharper kernels, the PCD-CT significantly outperformed EID-CT in low-contrast detectability.

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Objective. Photon-counting detector (PCD) CT enables routine virtual-monoenergetic image (VMI) reconstruction. We evaluated the performance of an automatic VMI energy level (keV) selection tool on a clinical PCD-CT system in comparison to an automatic tube potential (kV) selection tool from an energy-integrating-detector (EID) CT system from the same manufacturer.Approach.Four torso-shaped phantoms (20-50 cm width) containing iodine (2, 5, and 10 mg cc-1) and calcium (100 mg cc-1) were scanned on PCD-CT and EID-CT. Dose optimization techniques, task-based VMI energy level and tube-potential selection on PCD-CT (CARE keV) and task-based tube potential selection on EID-CT (CARE kV), were enabled. CT numbers, image noise, and dose-normalized contrast-to-noise ratio (CNRd) were compared.Main results. PCD-CT produced task-specific VMIs at 70, 65, 60, and 55 keV for non-contrast, bone, soft tissue with contrast, and vascular settings, respectively. A 120 kV tube potential was automatically selected on PCD-CT for all scans. In comparison, EID-CT used x-ray tube potentials from 80 to 150 kV based on imaging task and phantom size. PCD-CT achieved consistent dose reduction at 9%, 21% and 39% for bone, soft tissue with contrast, and vascular tasks relative to the non-contrast task, independent of phantom size. On EID-CT, dose reduction factor for contrast tasks relative to the non-contrast task ranged from a 65% decrease (vascular task, 70 kV, 20 cm phantom) to a 21% increase (soft tissue with contrast task, 150 kV, 50 cm phantom) due to size-specific tube potential adaptation. PCD-CT CNRdwas equivalent to or higher than those of EID-CT for all tasks and phantom sizes, except for the vascular task with 20 cm phantom, where 70 kV EID-CT CNRdoutperformed 55 keV PCD-CT images.Significance. PCD-CT produced more consistent CT numbers compared to EID-CT due to standardized VMI output, which greatly benefits standardization efforts and facilitates radiation dose reduction.

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Deep learning-based image reconstruction and noise reduction (DLIR) methods have been increasingly deployed in clinical CT. Accurate assessment of their data uncertainty properties is essential to understand the stability of DLIR in response to noise. In this work, we aim to evaluate the data uncertainty of a DLIR method using real patient data and a virtual imaging trial framework and compare it with filtered-backprojection (FBP) and iterative reconstruction (IR). The ensemble of noise realizations was generated by using a realistic projection domain noise insertion technique. The impact of varying dose levels and denoising strengths were investigated for a ResNet-based deep convolutional neural network (DCNN) model trained using patient images. On the uncertainty maps, DCNN shows more detailed structures than IR although its bias map has less structural dependency, which implies that DCNN is more sensitive to small changes in the input. Both visual examples and histogram analysis demonstrated that hotspots of uncertainty in DCNN may be associated with a higher chance of distortion from the truth than IR, but it may also correspond to a better detection performance for some of the small structures.

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Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

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The Channelized Hotelling observer (CHO) is well correlated with human observer performance in many CT detection/classification tasks but has not been widely adopted in routine CT quality control and performance evaluation, mainly because of the lack of an easily available, efficient, and validated software tool. We developed a highly automated solution - CT image quality evaluation and Protocol Optimization (CTPro), a web-based software platform that includes CHO and other traditional image quality assessment tools such as modulation transfer function and noise power spectrum. This tool can allow easy access to the CHO for both the research and clinical community and enable efficient, accurate image quality evaluation without the need of installing additional software. Its application was demonstrated by comparing the low-contrast detectability on a clinical photon-counting-detector (PCD)-CT with a traditional energy-integrating-detector (EID)-CT, which showed UHR-T3D had 6.2% higher d' than EID-CT with IR (p = 0.047) and 4.1% lower d' without IR (p = 0.122).

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Coronary CT angiography (cCTA) is a fast non-invasive imaging exam for coronary artery disease (CAD) but struggles with dense calcifications and stents due to blooming artifacts, potentially causing stenosis overestimation. Virtual monoenergetic images (VMIs) at higher keV (e.g., 100 keV) from photon counting detector (PCD) CT have shown promise in reducing blooming artifacts and improving lumen visibility through its simultaneous high-resolution and multi-energy imaging capability. However, most cCTA exams are performed with single-energy CT (SECT) using conventional energy-integrating detectors (EID). Generating VMIs through EID-CT requires advanced multi-energy CT (MECT) scanners and potentially sacrifices temporal resolution. Given these limitations, MECT cCTA exams are not commonly performed on EID-CT and VMIs are not routinely generated. To tackle this, we aim to enhance the multi-energy imaging capability of EID-CT through the utilization of a convolutional neural network to LEarn MONoenergetic imAging from VMIs at Different Energies (LEMONADE). The neural network was trained using ten patient cCTA exams acquired on a clinical PCD-CT (NAEOTOM Alpha, Siemens Healthineers), with 70 keV VMIs as input (which is nominally equivalent to the SECT from EID-CT scanned at 120 kV) and 100 keV VMIs as the target. Subsequently, we evaluated the performance of EID-CT equipped with LEMONADE on both phantom and patient cases (n=10) for stenosis assessment. Results indicated that LEMONADE accurately quantified stenosis in three phantoms, aligning closely with ground truth and demonstrating stenosis percentage area reductions of 13%, 8%, and 9%. In patient cases, it led to a 12.9% reduction in average diameter luminal stenosis when compared to the original SECT without LEMONADE. These outcomes highlight LEMONADE's capacity to enable multi-energy CT imaging, mitigate blooming artifacts, and improve stenosis assessment for the widely available EID-CT. This has a high potential impact as most cCTA exams are performed on EID-CT.

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BACKGROUND: The detectability performance of a CT scanner is difficult to precisely quantify when nonlinearities are present in reconstruction. An efficient detectability assessment method that is sensitive to small effects of dose and scanner settings is desirable. We previously proposed a method using a search challenge instrument: a phantom is embedded with hundreds of lesions at random locations, and a model observer is used to detect lesions. Preliminary tests in simulation and a prototype showed promising results. PURPOSE: In this work, we fabricated a full-size search challenge phantom with design updates, including changes to lesion size, contrast, and number, and studied our implementation by comparing the lesion detectability from a nonprewhitening (NPW) model observer between different reconstructions at different exposure levels, and by estimating the instrument sensitivity to detect changes in dose. METHODS: Designed to fit into QRM anthropomorphic phantoms, our search challenge phantom is a cylindrical insert 10 cm wide and 4 cm thick, embedded with 12 000 lesions (nominal width of 0.6 mm, height of 0.8 mm, and contrast of -350 HU), and was fabricated using PixelPrint, a 3D printing technique. The insert was scanned alone at a high dose to assess printing accuracy. To evaluate lesion detectability, the insert was placed in a QRM thorax phantom and scanned from 50 to 625 mAs with increments of 25 mAs, once per exposure level, and the average of all exposure levels was used as high-dose reference. Scans were reconstructed with three different settings: filtered-backprojection (FBP) with Br40 and Br59, and Sinogram Affirmed Iterative Reconstruction (SAFIRE) with strength level 5 and Br59 kernel. An NPW model observer was used to search for lesions, and detection performance of different settings were compared using area under the exponential transform of free response ROC curve (AUC). Using propagation of uncertainty, the sensitivity to changes in dose was estimated by the percent change in exposure due to one standard deviation of AUC, measured from 5 repeat scans at 100, 200, 300, and 400 mAs. RESULTS: The printed insert lesions had an average position error of 0.20 mm compared to printing reference. As the exposure level increases from 50 mAs to 625 mAs, the lesion detectability AUCs increase from 0.38 to 0.92, 0.42 to 0.98, and 0.41 to 0.97 for FBP Br40, FBP Br59, and SAFIRE Br59, respectively, with a lower rate of increase at higher exposure level. FBP Br59 performed best with AUC 0.01 higher than SAFIRE Br59 on average and 0.07 higher than FBP Br40 (all P < 0.001). The standard deviation of AUC was less than 0.006, and the sensitivity to detect changes in mAs was within 2% for FBP Br59. CONCLUSIONS: Our 3D-printed search challenge phantom with 12 000 submillimeter lesions, together with an NPW model observer, provide an efficient CT detectability assessment method that is sensitive to subtle effects in reconstruction and is sensitive to small changes in dose.

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Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical studies examining the benefit of PCCT relative to conventional CT and integration of PCCT systems into multivendor, multispecialty radiology practices are discussed.

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BACKGROUND: Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. PURPOSE: In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. METHODS: The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. RESULTS: The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. CONCLUSIONS: A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

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