Photon-Counting CT in the Head and Neck: Current Applications and Future Prospects.

Photon-counting detectors (PCDs) represent a major milestone in the evolution of CT imaging. CT scanners using PCD systems have already been shown to generate images with substantially greater spatial resolution, superior iodine contrast-to-noise ratio, and reduced artifact compared with conventional energy-integrating detector-based systems. These benefits can be achieved with considerably decreased radiation dose. Recent studies have focused on the advantages of PCD-CT scanners in numerous anatomic regions, particularly the coronary and cerebral vasculature, pulmonary structures, and musculoskeletal imaging. However, PCD-CT imaging is also anticipated to be a major advantage for head and neck imaging. In this paper, we review current clinical applications of PCD-CT in head and neck imaging, with a focus on the temporal bone, facial bones, and paranasal sinuses; minor arterial vasculature; and the spectral capabilities of PCD systems.

Morphology of the scaphotrapeziotrapezoid joint: A multi-domain statistical shape modeling approach.

The scaphotrapeziotrapezoid (STT) joint is involved in load transmission between the wrist and thumb. A quantitative description of baseline STT joint morphometrics is needed to capture the variation of normal anatomy as well as to guide staging of osteoarthritis. Statistical shape modeling (SSM) techniques quantify variations in three-dimensional shapes and relative positions. The objectives of this study are to describe the morphology of the STT joint using a multi-domain SSM. We asked: (1) What are the dominant modes of variation that impact bone and articulation morphology at the STT joint, and (2) what are the morphometrics of SSM-generated STT joints? Thirty adult participants were recruited to a computed tomography study of normal wrist imaging and biomechanics. Segmentations of the carpus were converted to three-dimensional triangular surface meshes. A multi-domain, particle-based entropy system SSM was used to quantify variation in carpal bone shape and position as well as articulation morphology. Articular surface areas and interosseous proximity distributions were calculated between mesh vertex pairs on adjacent bones within distance (2.0 mm) and surface-normal angular (35°) thresholds. In the SSM, the first five modes of variation captured 76.2% of shape variation and contributed to factors such as bone scale, articular geometries, and carpal tilt. Median interosseous proximities-a proxy for joint space-were 1.39 mm (scaphotrapezium), 1.42 mm (scaphotrapezoid), and 0.61 mm (trapeziotrapezoid). This study quantifies morphological and articular variations at the STT joint, presenting a range of normative anatomy. The range of estimated interosseous proximities may guide interpretation of imaging-derived STT joint space.

The PRC2 complex epigenetically silences GATA4 to suppress cellular senescence and promote the progression of breast cancer.

BACKGROUND: The transcription factor GATA4 is pivotal in cancer development but is often silenced through mechanisms like DNA methylation and histone modifications. This silencing suppresses the transcriptional activity of GATA4, disrupting its normal functions and promoting cancer progression. However, the precise molecular mechanisms and implications of GATA4 silencing in tumorigenesis remain unclear. Here, we aim to elucidate the mechanisms underlying GATA4 silencing and explore its role in breast cancer progression and its potential as a therapeutic target. METHODS: The GATA4-breast cancer prognosis link was explored via bioinformatics analyses, with GATA4 expression measured in breast tissues. Functional gain/loss experiments were performed to gauge GATA4's impact on breast cancer cell malignancy. GATA4-PRC2 complex interaction was analyzed using silver staining and mass spectrometry. Chromatin immunoprecipitation, coupled with high-throughput sequencing, was used to identify GATA4-regulated downstream target genes. The in vitro findings were validated in an in situ breast cancer xenograft mouse model. RESULTS: GATA4 mutation and different breast cancer subtypes were correlated, suggesting its involvement in disease progression. GATA4 suppressed cell proliferation, invasion, and migration while inducing apoptosis and senescence in breast cancer cells. The GATA4-PRC2 complex interaction silenced GATA4 expression, which altered the regulation of FAS, a GATA4 downstream gene. In vivo experiments verified that GATA4 inhibits tumor growth, suggesting its regulatory function in tumorigenesis. CONCLUSIONS: This comprehensive study highlights the epigenetic regulation of GATA4 and its impact on breast cancer development, highlighting the PRC2-GATA4-FAS pathway as a potential target for therapeutic interventions in breast cancers.

Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network.

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.

Standardizing technical parameters and terms for abdominopelvic photon-counting CT: laying the groundwork for innovation and evidence sharing.

Photon-counting detector CT (PCD-CT) is a new technology that has multiple diagnostic benefits including increased spatial resolution, iodine signal, and radiation dose efficiency, as well as multi-energy imaging capability, but which also has unique challenges in abdominal imaging. The purpose of this work is to summarize key features, technical parameters, and terms, which are common amongst current abdominopelvic PCD-CT systems and to propose standardized terminology (where none exists). In addition, user-selectable protocol parameters are highlighted to facilitate both scientific evaluation and early clinical adoption. Unique features of PCD-CT systems include photon-counting detectors themselves, energy thresholds and bins, and tube potential considerations for preserved spectral separation. Key parameters for describing different PCD-CT systems are reviewed and explained. While PCD-CT can generate multi-energy images like dual-energy CT, there are new types of images such as threshold images, energy bin images, and special spectral images. The standardized terms and concepts herein build upon prior interdisciplinary consensus and have been endorsed by the newly created Society of Abdominal Radiology Photon-counting CT Emerging Technology Commission.

Detection of scapholunate interosseous ligament injury using dynamic computed tomography-derived arthrokinematics: A prospective clinical trial.

Scapholunate interosseous ligament injuries are a major cause of wrist instability and can be difficult to diagnose radiographically. To improve early diagnosis of scapholunate ligament injuries, we compared injury detection between bilateral routine clinical radiographs, static CT, and dynamic four-dimensional CT (4DCT) during wrist flexion-extension and radioulnar deviation. Participants with unilateral scapholunate ligament injuries were recruited to a prospective clinical trial investigating the diagnostic utility of 4DCT imaging for ligamentous wrist injury. Twenty-one participants underwent arthroscopic surgery to confirm scapholunate ligament injury. Arthrokinematics, defined as distributions of interosseous proximities across radioscaphoid and scapholunate articular surfaces at different positions within the motion cycle, were used as CT-derived biomarkers. Preoperative radiographs, static CT, and extrema of 4DCT were compared between uninjured and injured wrists using Wilcoxon signed rank or Kolmogorov-Smirnov tests. Median interosseous proximities at the scapholunate interval were significantly greater in the injured versus the uninjured wrists at static-neutral and maximum flexion, extension, radial deviation, and ulnar deviation. Mean cumulative distribution functions at the radioscaphoid joint were not significantly different between wrists but were significantly shifted at the scapholunate interval towards increased interosseous proximities in injured versus uninjured wrists in all positions. Median and cumulative distribution scapholunate proximities from static-neutral and 4DCT-derived extrema reflect injury status.

Task-based automatic keV selection: leveraging routine virtual monoenergetic imaging for dose reduction on clinical photon-counting detector CT.

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.

Improving Stenosis Assessment in Energy Integrating Detector CT via Learned Monoenergetic Imaging Capability.

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.

3D printed phantom with 12 000 submillimeter lesions to improve efficiency in CT detectability assessment.

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.

Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

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.

Enhancer of zeste homolog 2 facilitates phenotypic transition of vascular smooth muscle cells leading to aortic aneurysm/dissection.

Thoracic aortic aneurysms (TAAs) are a major cause of death owing to weaker blood vessel walls and higher rupture rates in affected individuals. Vascular smooth muscle cells (VSMCs) are the predominant cell type within the aortic wall and their dysregulation may contribute to TAA progression. Enhancer of zeste homolog 2 (EZH2), a histone methyltransferase, is involved in several pathological processes; however, the biological functions and mechanisms underlying VSMC phenotype transition and vascular intimal hyperplasia remain unclear. The present study aimed to determine the involvement of EZH2 in mediating VSMC function in the development of TAAs. The expression of EZH2 was revealed to be elevated in patients with thoracic aortic dissection and TAA mouse model through western blotting and reverse transcription-quantitative PCR experiments. Subsequently, a mouse model was established using ß-aminopropionitrile. In vitro, EdU labeling, Transwell assay, wound healing assay and hematoxylin-eosin staining revealed that knocking down the Ezh2 gene could reduce the proliferation, invasion, migration, and calcification of mouse primary aortic smooth muscle cells. Flow cytometry analysis found that EZH2 deficiency increased cell apoptosis. Depletion of Ezh2 in mouse primary aortic VSMCs promoted the transformation of VSMCs from a synthetic to a contractile phenotype. Using RNA-sequencing analysis, it was demonstrated that Ezh2 regulated a group of genes, including integrin ß3 (Itgb3), which are critically involved in the extracellular matrix signaling pathway. qChIP found Ezh2 occupies the Itgb3 promoter, thereby suppressing the expression of Itgb3. Ezh2 promotes the invasion and calcification of VSMCs, and this promoting effect is partially reversed by co-knocking down Itgb3. In conclusion, the present study identified a previously unrecognized EZH2-ITGB3 regulatory axis and thus provides novel mechanistic insights into the pathophysiological function of EZH2. EZH2 may thus serve as a potential target for the management of TAAs.

Histone deacetylase 9-mediated phenotypic transformation of vascular smooth muscle cells is a potential target for treating aortic aneurysm/dissection.

Aortic aneurysm/dissection (AAD) is a serious cardiovascular condition characterized by rapid onset and high mortality rates. Currently, no effective drug treatment options are known for AAD. AAD pathogenesis is associated with the phenotypic transformation and abnormal proliferation of vascular smooth muscle cells (VSMCs). However, endogenous factors that contribute to AAD progression remain unclear. We aimed to investigate the role of histone deacetylase 9 (HDAC9) in AAD pathogenesis. HDAC9 expression was considerably increased in human thoracic aortic dissection specimens. Using RNA-sequencing (RNA-seq) and chromatin immunoprecipitation, we demonstrated that HDAC9 transcriptionally inhibited the expression of superoxide dismutase 2 and insulin-like growth factor-binding protein-3, which are critically involved in various signaling pathways. Furthermore, HDAC9 triggered the transformation of VSMCs from a systolic to synthetic phenotype, increasing their proliferation and migration abilities and suppressing their apoptosis. Consistent with these results, in vivo experiments revealed that TMP195, a pharmacological inhibitor of HDAC9, suppressed the formation of the ß-aminopropionitrile-induced AAD phenotype in mice. Our findings indicate that HDAC9 may be a novel endogenous risk factor that promotes the onset of AAD by mediating the phenotypic transformation of VSMCs. Therefore, HDAC9 may serve as a potential therapeutic target for drug-based AAD treatment. Furthermore, TMP195 holds potential as a therapeutic agent for AAD treatment.

Multiple Kernel Synthesis of Head CT Using a Task-Based Loss Function.

In CT imaging of the head, multiple image series are routinely reconstructed with different kernels and slice thicknesses. Reviewing the redundant information is an inefficient process for radiologists. We address this issue with a convolutional neural network (CNN)-based technique, synthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON), that creates a single, thin, low-noise series that combines the favorable features from smooth and sharp head kernels. ZIRCON uses a CNN model with an autoencoder U-Net architecture that accepts two input channels (smooth- and sharp-kernel CT images) and combines their salient features to produce a single CT image. Image quality requirements are built into a task-based loss function with a smooth and sharp loss terms specific to anatomical regions. The model is trained using supervised learning with paired routine-dose clinical non-contrast head CT images as training targets and simulated low-dose (25%) images as training inputs. One hundred unique de-identified clinical exams were used for training, ten for validation, and ten for testing. Visual comparisons and contrast measurements of ZIRCON revealed that thinner slices and the smooth-kernel loss function improved gray-white matter contrast. Combined with lower noise, this increased visibility of small soft-tissue features that would be otherwise impaired by partial volume averaging or noise. Line profile analysis showed that ZIRCON images largely retained sharpness compared to the sharp-kernel input images. ZIRCON combined desirable image quality properties of both smooth and sharp input kernels into a single, thin, low-noise series suitable for both brain and skull imaging.

Motion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks.

Objectives.To improve quality of coronary CT angiography (CCTA) images using a generalizable motion-correction algorithm.Approach. A neural network with attention gate and spatial transformer (ATOM) was developed to correct coronary motion. Phantom and patient CCTA images (39 males, 32 females, age range 19-92, scan date 02/2020 to 10/2021) retrospectively collected from dual-source CT were used to create training, development, and testing sets corresponding to 140- and 75 ms temporal resolution, with 75 ms images as labels. To test generalizability, ATOM was deployed for locally adaptive motion-correction in both 140- and 75 ms patient images. Objective metrics were used to assess motion-corrupted and corrected phantom and patient images, including structural-similarity-index (SSIM), dice-similarity-coefficient (DSC), peak-signal-noise-ratio (PSNR), and normalized root-mean-square-error (NRMSE). In objective quality assessment, ATOM was compared with several baseline networks, including U-net, U-net plus attention gate, U-net plus spatial transformer, VDSR, and ResNet. Two cardiac radiologists independently interpreted motion-corrupted and -corrected images at 75 and 140 ms in a blinded fashion and ranked diagnostic image quality (worst to best: 1-4, no ties).Main results. ATOM improved quality metrics (p< 0.05) before/after correction: in phantom, SSIM 0.87/0.95, DSC 0.85/0.93, PSNR 19.4/22.5, NRMSE 0.38/0.27; in patient images, SSIM 0.82/0.88, DSC 0.88/0.90, PSNR 30.0/32.0, NRMSE 0.16/0.12. ATOM provided more consistent improvement of objective image quality, compared to the presented baseline networks. The motion-corrected images received better ranks than un-corrected at the same temporal resolution (p< 0.05): 140 ms images 1.65/2.25, and 75 ms images 3.1/3.2. The motion-corrected 75 ms images received the best rank in 65% of testing cases. A fair-to-good inter-reader agreement was observed (Kappa score 0.58).Significance. ATOM reduces motion artifacts, improving visualization of coronary arteries. This algorithm can be used to virtually improve temporal resolution in both single- and dual-source CT.

Technical Note: Assessment of Pulse Pileup on Single-Energy and Multienergy Images From a Clinical Photon-Counting Detector Computed Tomography.

OBJECTIVE: Pulse pileup effects occur when pulses occur so close together that they fall on top of one another, resulting in count loss and errors in energy thresholding. To date, there has been little work systematically detailing the quantitative effects of pulse pileup on material decomposition accuracy for photon-counting detector (PCD) computed tomography (CT). Our aim in this work was to quantify the effects of pulse pileup on single-energy and multienergy CT images, including low-energy bin (BL), high-energy bin (BH), iodine map, and virtual noncontrast images from a commercial PCD-CT. METHODS: Scans of a 20-cm diameter multienergy CT phantom with 10 solid inserts were acquired at a fixed tube potential as the tube current was varied across the available range. Four types of images (BL, BH, iodine map, and virtual noncontrast) were reconstructed using an iterative reconstruction algorithm at strength 2, a quantitative reconstruction kernel (Qr40), 2-/1-mm slice thickness/increment, and a 210-mm field-of-view. The mean and standard deviation of CT numbers were recorded and the ratios of CT number between BL and BH images were calculated and plotted, along with noise versus tube current and noise × versus tube current. RESULTS: As tube current was increased, the range of variations in CT numbers was less than 13.4 HU for all inserts and image types evaluated. Noise × versus tube current showed a small positive slope equal to a noise increase from 100 mA of 10% at 500 mA and 15% at 900 mA compared with what would be expected if the slope was zero. CONCLUSIONS: Minimal impact on single-energy and multienergy CT numbers and noise performance was observed for the evaluated clinical PCD-CT system.

Targeted Training Reduces Search Errors but Not Classification Errors for Hepatic Metastasis Detection at Contrast-Enhanced CT.

RATIONALE AND OBJECTIVES: Methods are needed to improve the detection of hepatic metastases. Errors occur in both lesion detection (search) and decisions of benign versus malignant (classification). Our purpose was to evaluate a training program to reduce search errors and classification errors in the detection of hepatic metastases in contrast-enhanced abdominal computed tomography (CT). MATERIALS AND METHODS: After Institutional Review Board approval, we conducted a single-group prospective pretest-posttest study. Pretest and posttest were identical and consisted of interpreting 40 contrast-enhanced abdominal CT exams containing 91 liver metastases under eye tracking. Between pretest and posttest, readers completed search training with eye-tracker feedback and coaching to increase interpretation time, use liver windows, and use coronal reformations. They also completed classification training with part-task practice, rating lesions as benign or malignant. The primary outcome was metastases missed due to search errors (<2 seconds gaze under eye tracker) and classification errors (>2 seconds). Jackknife free-response receiver operator characteristic (JAFROC) analysis was also conducted. RESULTS: A total of 31 radiologist readers (8 abdominal subspecialists, 8 nonabdominal subspecialists, 15 senior residents/fellows) participated. Search errors were reduced (pretest 11%, posttest 8%, difference 3% [95% confidence interval, 0.3%-5.1%], P = .01), but there was no difference in classification errors (difference 0%, P = .97) or in JAFROC figure of merit (difference -0.01, P = .36). In subgroup analysis, abdominal subspecialists demonstrated no evidence of change. CONCLUSION: Targeted training reduced search errors but not classification errors for the detection of hepatic metastases at contrast-enhanced abdominal CT. Improvements were not seen in all subgroups.

Photon Counting Versus Energy-integrated Detector CT in Detection of Superior Semicircular Canal Dehiscence.

BACKGROUND: Superior semicircular canal dehiscence (SSCD), an osseous defect overlying the SSC, is associated with a constellation of audiovestibular symptoms. This study sought to compare conventional energy-integrated detector (EID) computed tomography (CT) to photon-counting detector (PCD)-CT in the detection of SSCD. MATERIAL AND METHODS: Included patients were prospectively recruited to undergo a temporal bone CT on both EID-CT and PCD-CT scanners. Two blinded neuroradiologists reviewed both sets of images for 1) the presence or absence of SSCD (graded as present, absent, or indeterminate), and 2) the width of the bone overlying the SSC (if present). Any discrepancies in the presence or absence of SSCD were agreed upon by consensus. RESULTS: In the study 31 patients were evaluated, for a total of 60 individual temporal bones (2 were excluded). Regarding SSCD presence or absence, there was substantial agreement between EID-CT and PCD-CT (k = 0.76; 95% confidence interval, CI 0.54-0.97); however, SSCD was present in only 9 (15.0%) temporal bones on PCD-CT, while EID-CT examinations were interpreted as being positive in 14 (23.3%) temporal bones. This yielded a false positive rate of 8.3% on EID-CT. The bone overlying the SSC was thinner on EID-CT images (0.66 mm; SD = 0.64) than on PCD-CT images (0.72 mm; SD = 0.66) (p < 0.001). CONCLUSION: The EID-CT examinations tend to overcall the presence of SSCD compared to PCD-CT and also underestimate the thickness of bone overlying the SSC.

Coronary artery stenosis quantification in patients with dense calcifications using ultra-high-resolution photon-counting-detector computed tomography.

BACKGROUND: To quantify differences in coronary artery stenosis severity in patients with calcified lesions between conventional energy-integrating detector (EID) CT and ultra-high-resolution (UHR) photon-counting-detector (PCD) CT. METHODS: Patients undergoing clinically indicated coronary CT angiography were prospectively recruited and scanned first on an EID-CT (SOMATOM Force, Siemens Healthineers) and then a PCD-CT (NAEOTOM Alpha, Siemens Healthineers) on the same day. EID-CT was performed with standard mode (192 â€‹× â€‹0.6 â€‹mm detector collimation) following our clinical protocol. PCD-CT scans were performed under UHR mode (120 â€‹× â€‹0.2 â€‹mm detector collimation). For each patient, left main, left anterior descending, right coronary artery, and circumflex were reviewed and the most severe stenosis from dense calcification for each coronary was quantified using commercial software. Additionally, each measured stenosis was assigned a severity category based on percent diameter stenosis, and changes in severity category across EID-CT and PCD-CT were assessed. RESULTS: A total of 23 patients were enrolled, with 34 coronary artery stenoses analyzed. Stenosis was significantly reduced in PCD-CT compared to EID-CT (p â€‹< â€‹0.001), resulting in an average of 11% (SD â€‹= â€‹11%) reduction in percent diameter stenosis. Among the 34 lesions, 15 changed in stenosis severity category: 3 went from moderate to minimal, 1 from moderate to mild, 9 from mild to minimal, and 2 from minimal to mild with the use of PCD-CT compared to EID-CT. CONCLUSION: Use of UHR PCD-CT decreased percent diameter stenosis by an average of 11% relative to EID-CT, resulting in 13 of 34 stenoses being downgraded in stenosis severity category, potentially sparing patients from unnecessary intervention.

Photon-Counting Detector Computed Tomography Versus Energy-Integrating Detector Computed Tomography for Coronary Artery Calcium Quantitation.

OBJECTIVES: Photon-counting detector (PCD) computed tomography (CT) offers improved spatial and contrast resolution, which can impact quantitative measurements. This work aims to determine in human subjects the effect of dual-source PCD-CT on the quantitation of coronary artery calcification (CAC) compared with dual-source energy-integrating detector (EID) CT in both 1- and 3-mm images. METHODS: This prospective study enrolled patients receiving a clinical EID-CT CAC examination to undergo a research PCD-CT CAC examination. Axial images were reconstructed with a 512 × 512 matrix, 200-mm field of view, 3-mm section thickness/1.5-mm interval using a quantitative kernel (Qr36). Sharper kernels (Qr56/QIR strength 4 for PCD and Qr49/ADMIRE strength 5 for EID) were used to reconstruct images with 1-mm section thickness/0.5-mm interval. Pooled analysis was performed for all calcifications with nonzero values, and volume and Agatston scores were compared between EID-CT and PCD-CT. A Wilcoxon signed-rank test was performed with P < 0.05 considered statistically significant. RESULTS: In 21 subjects (median age, 58 years; range, 50-75 years; 13 male [62%]) with a total of 42 calcified arteries detected at 3 mm and 46 calcified arteries at 1-mm images, EID-CT CAC volume and Agatston scores were significantly lower than those of PCD-CT ( P ≤ 0.001). At 3-mm thickness, the mean (standard deviation) volume and Agatston score for EID-CT were 55.5 (63.4) mm 3 and 63.8 (76.9), respectively, and 61.5 (69.4) mm 3 and 70.4 (85.3) for PCD-CT ( P = 0.0001 and P = 0.0013). At 1-mm thickness, the mean (standard deviation) volume and score for EID-CT were 50.0 (56.3) mm 3 and 61.1 (69.3), respectively, and 59.5 (63.9) mm 3 and 72.5 (79.9) for PCD-CT ( P < 0.0001 for both). The applied radiation dose (volume CT dose index) for the PCD-CT scan was 2.1 ± 0.6 mGy, which was 13% lower than for the EID-CT scan (2.4 ± 0.7 mGy, P < 0.001). CONCLUSIONS: Relative to EID-CT, PCD-CT demonstrated a small but significant increase in coronary artery calcium volume and Agatston score.