Purpose To develop and evaluate a Deep learnIng-bAsed MONoenergetic imaging at Different energies (DIAMOND) framework for generating virtual monoenergetic images (VMIs) from conventional energy-integrating detector (EID) CT, aiming to reduce blooming artifacts and improve stenosis assessment in coronary CT angiography (CCTA) with heavily calcified plaques. Materials and Methods This study (August 2022-September 2023) used a combination of retrospective and prospective imaging data. DIAMOND was trained using a simplified U-Net architecture on a retrospective dataset of 10 CCTA examinations performed with ultrahigh-resolution (UHR) photon-counting detector (PCD) CT; 70-keV PCD VMIs (energy equivalent to 120-kV single-energy EID CT) served as inputs and 100-keV PCD VMIs as targets. The trained model was then applied to a prospective dataset of participants with heavily calcified plaques who underwent EID CT at 120 kV followed by same-day PCD CT. Percent diameter stenosis (PDS) was quantified for a phantom and participants by using commercial software and compared across EID CT, DIAMOND, and PCD CT using Bland-Altman analysis. Changes in stenosis severity categorization based on PDS were evaluated. Results DIAMOND reduced blooming artifacts and improved lumen visualization, with image quality resembling PCD CT. In 23 participants (mean age, 69 years ± 8 [SD]; 18 male), average PDS decreased from 35.65% (EID CT) to 25.19% (DIAMOND, P < .05), approaching 24.27% with UHR PCD CT (P < .05). Relative to EID CT, DIAMOND led to Coronary Artery Disease Reporting and Data System reclassification in 11 of 26 (42%) lesions, mainly in mild to moderate stenosis ranges. Processing time was approximately 0.21 second per axial section on a standard graphics processing unit. Conclusion This study demonstrated the feasibility of using DIAMOND to generate high-kiloelectron volt VMIs from single-energy EID CT, providing artifact-reduced coronary imaging and improved stenosis quantification for heavily calcified plaques comparable to PCD CT without hardware upgrades. Keywords: Coronary CT Angiography, Coronary Artery Stenosis, Energy-Integrating Detector CT, Photon-Counting Detector CT, Deep Learning, CT-Photon Counting, Angiography, Coronary Arteries Supplemental material is available for this article. © RSNA, 2026.

BACKGROUND AND PURPOSE:Ultra-high-resolution (UHR) photon-counting-detector (PCD) CT improves image resolution but increases noise, necessitating the use of smoother reconstruction kernels that reduce resolution below the 0.125-mm maximum spatial resolution. A denoising convolutional neural network (CNN) was developed to reduce noise in images reconstructed with the available sharpest reconstruction kernel while preserving resolution for enhanced temporal bone visualization to address this issue. MATERIALS AND METHODS:With institutional review board approval, the CNN was trained on 6 patient cases of clinical temporal bone imaging (1885 images) and tested on 20 independent cases using a dual-source PCD-CT (NAEOTOM Alpha). Images were reconstructed using quantum iterative reconstruction at strength 3 (QIR3) with both a clinical routine kernel (Hr84) and the sharpest available head kernel (Hr96). The CNN was applied to images reconstructed with Hr96 and QIR1 kernel. For each case, three series of images (Hr84-QIR3, Hr96-QIR3, and Hr96-CNN) were randomized for review by 2 neuroradiologists assessing the overall quality and delineating the modiolus, stapes footplate, and incudomallear joint. RESULTS:The CNN reduced noise by 80% compared with Hr96-QIR3 and by 50% relative to Hr84-QIR3, while maintaining high resolution. Compared with the conventional method at the same kernel (Hr96-QIR3), Hr96-CNN significantly decreased image noise (from 204.63 to 47.35 HU) and improved its structural similarity index (from 0.72 to 0.99). Hr96-CNN images ranked higher than Hr84-QIR3 and Hr96-QIR3 in overall quality (P < .001). Readers preferred Hr96-CNN for all 3 structures. CONCLUSIONS:The proposed CNN significantly reduced image noise in UHR PCD-CT, enabling the use of the sharpest kernel. This combination greatly enhanced diagnostic image quality and anatomic visualization.

OBJECTIVE:To prospectively estimate the ability of photon-counting detector (PCD) CT to improve radiologist confidence for peritoneal disease compared with conventional energy-integrating detector (EID) CT. METHODS:Patients with suspected abdominopelvic peritoneal disease underwent clinically indicated EID-CT followed by a same-day research PCD-CT after informed consent. Both scans used IV contrast (up to 200 mL total across both scans). In separate evaluation sessions, 4 radiologists independently reviewed each subject's images once per session, randomly evaluating EID-CT images (1 or 2 mm) or PCD-CT images (120 kV threshold low 1 mm or 50 keV 2 mm). Readers evaluated right versus left supracolic, and inframesocolic spaces, rating confidence in peritoneal malignancy (0 to 100). Overall tumor burden in these regions and on serosal surfaces of the liver, spleen, small bowel, and anterior peritoneal reflection was also graded (0 to 3 scale). In side-by-side comparisons (blinded to CT modality and reconstruction configuration), radiologists ranked preference for each modality/configuration from greatest to least, with ties permitted and with differences ≥ of 2 indicating increased confidence in diagnosis, with separate comparisons for the upper abdomen, mid-abdomen, and pelvis. Linear regression with mixed effects was used to compare confidence and tumor burden scores for each modality/configuration while logistic regression with mixed effects was used for side-by-side comparisons. RESULTS:Twenty-one patients underwent same-day EID-CT and PCD-CT (mean CTDI vol EID-CT-11.6 versus PCD-CT-11.2 mGy). Despite significantly less iodinated contrast used at PCD-CT (63.8±11.7 versus 136.2±11.7 mL, P <0.0001), confidence scores were similar between modalities. Tumor burden was estimated to be greater for serosal metastases to the liver ( P =0.043) when using 1 mm PCD-CT images. In blinded side-by-side comparisons, the odds of 120 kV 1-mm or 50 keV 2-mm PCD-CT images having a preference rank reflecting increased confidence compared with EID-CT 1 mm in the mid-abdomen was significant [odds ratios (OR): 5.19 and 8.54, respectively, P <0.001] with similar findings in the pelvis (OR for 50 keV PCD-CT: 5.26, P =0.007). CONCLUSION:Despite using substantially less IV contrast, radiologist confidence in malignancy was similar at PCD-CT performed after EID-CT. In side-by-side comparisons, PCD-CT images had a significantly increased odds of increasing radiologist confidence compared with EID-CT in the mid-abdomen and pelvis.

OBJECTIVES:To determine the feasibility of reduced-dose chest computed tomographic angiography (CTA) with convolutional neural network (CNN) denoising for detecting pulmonary arteriovenous malformations (pAVMs) in children with hereditary hemorrhagic telangiectasia (cwHHT). METHODS:Fifteen cwHHT underwent a chest CTA (ie, a controlled "study" dose). Noise was inserted to simulate a quarter dose (QD) exam. Images were reconstructed using iterative reconstruction (IR) and our self-trained CNN denoising model. For each case, 3 sets of images were created: study dose (SD)+IR, QD+IR, and QD+CNN. Two thoracic radiologists independently scored each set to assess quality, spatial resolution, artifacts, and the presence of pAVMs using 4-level ordinal scales. Quantitative assessments of image quality were performed using contrast-to-noise ratios (CNRs) with comparisons made between the experimental conditions. RESULTS:Thirteen of the 15 patients recruited with hereditary hemorrhagic telangiectasia (mean age: 9.3±4.5 y) were positive for pAVM by transthoracic contrast echocardiography. The sensitivities using QD+CNN were 0.85 and 1.00 for readers 1 and 2, respectively. This was compared with 0.69 and 0.84 using QD+IR versus 0.85 and 0.92 for SD+IR. Inter-reader agreement for pAVM detection utilizing QD+CNN was moderate and resulted in kappa=0.59 ( P =0.012). The subjective assessments for QD+CNN were comparable to the SD technique. Regression analysis of reader scores revealed improved quality in QD+CNN versus QD+IR ( P =0.001). Similarly, the QD+CNN condition demonstrated the highest CNRs. CONCLUSIONS:Reduced-dose chest CTA with CNN denoising provides a level of sensitivity comparable to standard dose CTA and high CNRs for the detection of pAVMs in cwHHT.

Photon-counting detector (PCD) CT has been introduced for clinical use, and several studies have showcased the benefits of ultra-high-resolution imaging using PCD-CT. Traditionally, spatial resolution measurements for CT imaging systems including clinical PCD-CT are reported near scan isocenter where the source-detector alignment and beam geometry are the most optimal. The spatial resolution performance away from isocenter where x-ray beam diverges has not been investigated on the clinical PCD-CT system to the best of our knowledge. To maximize the benefits of UHR PCD-CT and guide the design of optimal imaging protocols, we investigated the spatial resolution variability in the clinical PCD-CT as a function of table height (0, 5 and 10 cm), reconstruction field of view (50, 100 and 150 mm) and x-ray focal spots (sUHR: 0.4 mm x 0.5 mm, and UHR: 0.6 mm x 0.7 mm focal spot sizes). Modulation transfer function (MTF) showed a percent decrease (10% MTF) up to 11.8% off-isocenter (10 cm table height offset relative to scan isocenter). Further, a 12.6% decrease in 10% MTF value was observed between sUHR and UHR focal spots. Our results provide key insights into scan configurations such as anatomic positioning, focal spot selection and reconstruction parameters specific to ultra-high resolution PCD-CT.

BACKGROUND AND PURPOSE:Photon-counting detector CT myelography (PCD-CTM) is a recently described technique used for detecting spinal CSF leaks, including CSF-venous fistulas. Various image reconstruction techniques, including smoother-versus-sharper kernels and virtual monoenergetic images, are available with photon-counting CT. Moreover, denoising algorithms have shown promise in improving sharp kernel images. No prior studies have compared image quality of these different reconstructions on photon-counting CT myelography. Here, we sought to compare several image reconstructions using various parameters important for the detection of CSF-venous fistulas. MATERIALS AND METHODS:We performed a retrospective review of all consecutive decubitus PCD-CTM between February 1, 2022, and August 1, 2024, at 1 institution. We included patients whose studies had the following reconstructions: Br48-40 keV virtual monoenergetic reconstruction, Br56 low-energy threshold (T3D), Qr89-T3D denoised with quantum iterative reconstruction, and Qr89-T3D denoised with a convolutional neural network algorithm. We excluded patients who had extradural CSF on preprocedural imaging or a technically unsatisfactory myelogram-. All 4 reconstructions were independently reviewed by 2 neuroradiologists. Each reviewer rated spatial resolution, noise, the presence of artifacts, image quality, and diagnostic confidence (whether positive or negative) on a 1-5 scale. These metrics were compared using the Friedman test. Additionally, noise and contrast were quantitatively assessed by a third reviewer and compared. RESULTS:The Qr89 reconstructions demonstrated higher spatial resolution than their Br56 or Br48-40keV counterparts. Qr89 with convolutional neural network denoising had less noise, better image quality, and improved diagnostic confidence compared with Qr89 with quantum iterative reconstruction denoising. The Br48-40keV reconstruction had the highest contrast-to-noise ratio quantitatively. CONCLUSIONS:In our study, the sharpest quantitative kernel (Qr89-T3D) with convolutional neural network denoising demonstrated the best performance regarding spatial resolution, noise level, image quality, and diagnostic confidence for detecting or excluding the presence of a CSF-venous fistula.

To compare same-day photon-counting detector CT (PCD-CT) to conventional energy-integrating detector CT (EID-CT) for detection of small renal stones (≤ 3 mm). Patients undergoing clinical dual-energy EID-CT for known or suspected stone disease underwent same-day research PCD-CT. Patients with greater than 10 stones and no visible stones under 3 mm were excluded. Three radiologists selected the optimal reconstruction configuration for each CT modality and created the reference standard for renal stone presence. Two other radiologists, blinded to imaging modality, independently reviewed anonymized images to detect renal stones, rating confidence in potential stones using a Likert scale (1 = Definitely present, 2 = Probably present, 3 = Questionably present, 4 = Not seen). Sensitivity and false positive detections for PCD and EID-CT were calculated. Twenty-one patients underwent clinical EID-CT followed by same-day PCD-CT, with the reference standard identifying 121 renal stones (mean size 2.8 ± 2.6 mm). 0.4-mm PCD-CT images were more likely to display a stone as definitely present compared to 1- or 2-mm EID-CT images (p < 0.0001). Overall sensitivity for detection of all stones was greater at PCD-CT (0.75 vs. 0.55, p < 0.05). Pooled sensitivity of stones ≤ 3 mm was also significantly higher at PCD-CT (0.67 vs. 0.41, p < 0.05), with false positive detections differing between readers and modalities (PCD-CT vs. EID-CT: R1—7 v. 5; R2 – 7 v. 1). Sensitivity for renal stones was significantly higher using high spatial resolution PCD-CT vs. EID-CT, especially for stones 3 mm or less in size, which may be important for at-risk patient populations. Prospective evaluation in larger patient populations that will benefit from detection of small stones is warranted.

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 (Improved LUMEN visualization through Artificial super-resoluTion imagEs (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.

BACKGROUND:SynthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON) is a multi-kernel synthesis method that creates a single series of thin-slice computed tomography (CT) images displaying low noise and high spatial resolution, increasing reader efficiency and minimizing partial volume averaging. PURPOSE:To compare the diagnostic performance of a single set of ZIRCON images to two routine clinical image series using conventional CT head and bone reconstruction kernels for diagnosing intracranial findings and fractures in patients with trauma or suspected acute neurologic deficit. MATERIAL AND METHODS:In total, 50 patients underwent clinically indicated head CT in the ER (15 normal, 35 abnormal cases). A non-reader neuroradiologist established the reference standard. Three neuroradiologists reviewed two routine clinical series (head and bone kernels) and a single ZIRCON series, detecting intracranial findings or fractures and rating confidence (0-100). Sensitivity, specificity, and jackknife free-response receiver operating characteristic (JAFROC) figure of merit (FOM) were compared (limit of non-inferiority: -0.10). RESULTS:ZIRCON and conventional images demonstrated comparable performance for fractures (sensitivity: 51.5% vs. 54.5%; specificity: 40.2% vs. 34.2%) and intracranial findings (sensitivity: 88.2% vs. 91.4%; specificity: 77.2% vs. 73.7%).The estimated difference of JAFROC FOM demonstrated ZIRCON non-inferiority for acute pathologies overall (0.003 [95% CI=-0.051-0.057]) and fractures (0.048 [95% CI=-0.050-0.145]) but not for intracranial findings alone (-0.024 [95% CI=-0.100-0.052]). CONCLUSION:Thin-slice, low noise, and high spatial resolution images can be created to display intracranial findings and fractures replacing multiple images series in head CT with similar performance. Future studies in more patients and further algorithmic development are warranted.

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.

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 x 0.6 mm detector collimation) following our clinical protocol. PCD-CT scans were performed under UHR mode (120 x 0.2 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 < 0.001), resulting in an average of 11% (SD = 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.

BACKGROUND. The higher spatial resolution and image contrast for iodine-containing tissues of photon-counting detector (PCD) CT may address challenges in evaluating small calcified vessels when performing lower extremity CTA by energy-integrating detector (EID) CTA. OBJECTIVE. The purpose of the study was to compare the evaluation of infrapopliteal vasculature between lower extremity CTA performed using EID CT and PCD CT. METHODS. This prospective study included 32 patients (mean age, 69.7 +/- 11.3 [SD] years; 27 men, five women) who underwent clinically indicated lower extremity EID CTA between April 2021 and March 2022; participants underwent investigational lower extremity PCD CTA later the same day as EID CTA using a reduced IV contrast media dose. Two radiologists independently reviewed examinations in two sessions, each containing a random combination of EID CTA and PCD CTA examinations; the readers assessed the number of visualized fibular perforators, characteristics of stenoses at 11 infrapopliteal segmental levels, and subjective arterial sharpness. RESULTS. Mean IV contrast media dose was 60.0 +/- 11.0 (SD) mL for PCD CTA versus 139.6 +/- 11.8 mL for EID CTA ( p <.001). The number of identified fibular perforators per lower extremity was significantly higher for PCD CTA than for EID CTA for reader 1 (R1) (mean +/- SD, 6.4 +/- 3.2 vs 4.2 +/- 2.4; p <.001) and reader 2 (R2) (8.8 +/- 3.4 vs 7.6 +/- 3.3; p =.04). Reader confidence for assessing stenosis was significantly higher for PCD CTA than for EID CTA for R1 (mean +/- SD, 82.3 +/- 20.3 vs 78.0 +/- 20.2; p <.001) but not R2 (89.8 +/- 16.7 vs 90.6 +/- 7.1; p =.24). The number of segments per lower extremity with total occlusion was significantly lower for PCD CTA than for EID CTA for R2 (mean +/- SD, 0.5 +/- 1.3 vs 0.9 +/- 1.7; p =.04) but not R1 (0.6 +/- 1.3 vs 1.0 +/- 1.5; p =.07). The number of segments per lower extremity with clinically significant nonocclusive stenosis was significantly higher for PCD CTA than for EID CTA for R1 (mean +/- SD, 2.2 +/- 2.2 vs 1.6 +/- 1.7; p =.01) but not R2 (1.1 +/- 2.0 vs 1.1 +/- 1.4; p =.89). Arterial sharpness was significantly greater for PCD CTA than for EID CTA for R1 (mean +/- SD, 3.2 +/- 0.5 vs 1.8 +/- 0.5; p <.001) and R2 (3.2 +/- 0.4 vs 1.7 +/- 0.8; p <.001). CONCLUSION. PCD CTA yielded multiple advantages relative to EID CTA for visualizing small infrapopliteal vessels and characterizing associated plaque. CLINICAL IMPACT. The use of PCD CTA may improve vascular evaluation in patients with peripheral arterial disease.

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.

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.

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.

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.

The authors regret that there is an error in Figure 2 of the manuscript. We sincerely apologize for the error and any confusion this has caused. The error is regarding percent diameter stenosis vs percent area stenosis associated with the sample cases in Figure 2. These were meant to be percent diameter stenosis, unfortunately, some are percent area stenosis. This mistake happened during the revision of our submission. Correct values were included in the original submission, but during revisions, Figure 2 was modified to include more cases based on reviewer comments (panel E). We mistakenly put in the percent area stenosis values from our original submission (the software used in our study, syngo.via, reports both percent diameter and area stenosis) for some of the images during this process (panels A-D). Current Figure 2 The corrected percent diameter values should be as follows. Correct Figure 2 The images in Figure 2 are correct. This error only impacted Figure 2, and does not impact data analysis or results, in which correct values of percent diameter stenosis were used. Therefore this error does not impact the conclusion of our study. The authors would like to apologize for any inconvenience caused. Coronary artery stenosis quantification in patients with dense calcifications using ultra-high-resolution photon-counting-detector computed tomographyJournal of Cardiovascular Computed TomographyVol. 18Issue 1PreviewTo 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. Full-Text PDF

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 CNRd was 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 CNRd outperformed 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.

Photon counting detector (PCD) CT represents the newest advance in CT technology, with improved radiation dose efficiency, increased spatial resolution, inherent spectral imaging capabilities, and the ability to eliminate electronic noise. Its design fundamentally differs from conventional energy integrating detector CT because photons are directly converted to electrical signal in a single step. Rather than converting X-rays to visible light and having an output signal that is a summation of energies, PCD directly counts each photon and records its individual energy information. The current commercially available PCD-CT utilizes a dual-source CT geometry, which allows 66 ms cardiac temporal resolution and high-pitch (up to 3.2) scanning. This can greatly benefit pediatric patients by facilitating high quality fast scanning to allow sedation-free imaging. The energy-resolving nature of the utilized PCDs allows "always-on" dual-energy imaging capabilities, such as the creation of virtual monoenergetic, virtual non-contrast, virtual non-calcium, and other material-specific images. These features may be combined with high-resolution imaging, made possible by the decreased size of individual detector elements and the absence of interelement septa. This work reviews the foundational concepts associated with PCD-CT and presents examples to highlight the benefits of PCD-CT in the pediatric population.

The feasibility of low-dose photon-counting detector (PCD) CT to measure alpha and acetabular version angles of femoroacetabular impingement (FAI). FAI patients undergoing an energy-integrating detector (EID) CT underwent an IRB-approved prospective ultra-high-resolution (UHR) PCD-CT between 5/2021 and 12/2021. PCD-CT was dose-matched to the EID-CT or acquired at 50