Improved Detection of Small and Low-Density Plaques in Virtual Noncontrast Imaging-based Calcium Scoring at Photon-Counting Detector CT.

Purpose To investigate the impact of plaque size and density on virtual noncontrast (VNC)-based coronary artery calcium scoring (CACS) using photon-counting detector CT and to provide safety net reconstructions for improved detection of subtle plaques in patients whose VNC-based CACS would otherwise be erroneously zero when compared with true noncontrast (TNC)-based CACS. Materials and Methods In this prospective study, CACS was evaluated in a phantom containing calcifications with different diameters (5, 3, and 1 mm) and densities (800, 400, and 200 mg/cm3) and in participants who underwent TNC and contrast-enhanced cardiac photon-counting detector CT (July 2021-March 2022). VNC images were reconstructed at different virtual monoenergetic imaging (55-80 keV) and quantum iterative reconstruction (QIR) levels (QIR,1-4). TNC scans at 70 keV with QIR off served as the reference standard. In vitro CACS was analyzed using standard settings (3.0-mm sections, kernel Qr36, 130-HU threshold). Calcification detectability and CACS of small and low-density plaques were also evaluated using 1.0-mm sections, kernel Qr44, and 120- or 110-HU thresholds. Safety net reconstructions were defined based on background Agatston scores and evaluated in vivo in TNC plaques initially nondetectable using standard VNC reconstructions. Results The in vivo cohort included 63 participants (57.8 years ± 15.5 [SD]; 37 [59%] male, 26 [41%] female). Correlation and agreement between standard CACSVNC and CACSTNC were higher in large- and medium-sized and high- and medium-density than in low-density plaques (in vitro: intraclass correlation coefficient [ICC] ≥ 0.90; r > 0.9 vs ICC = 0.20-0.48; r = 0.5-0.6). Small plaques were not detectable using standard VNC reconstructions. Calcification detectability was highest using 1.0-mm sections, kernel Qr44, 120- and 110-HU thresholds, and QIR level of 2 or less VNC reconstructions. Compared with standard VNC, using safety net reconstructions (55 keV, QIR 2, 110-HU threshold) for in vivo subtle plaque detection led to higher detection (increased by 89% [50 of 56]) and improved correlation and agreement of CACSVNC with CACSTNC (in vivo: ICC = 0.51-0.61; r = 0.6). Conclusion Compared with TNC-based calcium scoring, VNC-based calcium scoring was limited for small and low-density plaques but improved using safety net reconstructions, which may be particularly useful in patients with low calcium scores who would otherwise be treated based on potentially false-negative results. Keywords: Coronary Artery Calcium CT, Photon-Counting Detector CT, Virtual Noncontrast, Plaque Size, Plaque Density Supplemental material is available for this article. © RSNA, 2024.

Impact of reconstruction parameters on the accuracy of myocardial extracellular volume quantification on a first-generation, photon-counting detector CT.

BACKGROUND: The potential role of cardiac computed tomography (CT) has increasingly been demonstrated for the assessment of diffuse myocardial fibrosis through the quantification of extracellular volume (ECV). Photon-counting detector (PCD)-CT technology may deliver more accurate ECV quantification compared to energy-integrating detector CT. We evaluated the impact of reconstruction settings on the accuracy of ECV quantification using PCD-CT, with magnetic resonance imaging (MRI)-based ECV as reference. METHODS: In this post hoc analysis, 27 patients (aged 53.1 ± 17.2 years (mean ± standard deviation); 14 women) underwent same-day cardiac PCD-CT and MRI. Late iodine CT scans were reconstructed with different quantum iterative reconstruction levels (QIR 1-4), slice thicknesses (0.4-8 mm), and virtual monoenergetic imaging levels (VMI, 40-90 keV); ECV was quantified for each reconstruction setting. Repeated measures ANOVA and t-test for pairwise comparisons, Bland-Altman plots, and Lin's concordance correlation coefficient (CCC) were used. RESULTS: ECV values did not differ significantly among QIR levels (p = 1.000). A significant difference was observed throughout different slice thicknesses, with 0.4 mm yielding the highest agreement with MRI-based ECV (CCC = 0.944); 45-keV VMI reconstructions showed the lowest mean bias (0.6, 95% confidence interval 0.1-1.4) compared to MRI. Using the most optimal reconstruction settings (QIR4. slice thickness 0.4 mm, VMI 45 keV), a 63% reduction in mean bias and a 6% increase in concordance with MRI-based ECV were achieved compared to standard settings (QIR3, slice thickness 1.5 mm; VMI 65 keV). CONCLUSIONS: The selection of appropriate reconstruction parameters improved the agreement between PCD-CT and MRI-based ECV. RELEVANCE STATEMENT: Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility. KEY POINTS: • CT is increasingly promising for myocardial tissue characterization, assessing focal and diffuse fibrosis via late iodine enhancement and ECV quantification, respectively. • PCD-CT offers superior performance over conventional CT, potentially improving ECV quantification and its agreement with MRI-based ECV. • Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility.

Ultra-high resolution coronary CT angiography on photon-counting detector CT: bi-centre study on the impact of quantum iterative reconstruction on image quality and accuracy of stenosis measurements.

PURPOSE: To assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT. METHOD: A dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1-4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans. RESULTS: Increasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15). CONCLUSIONS: The QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level.

Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.

This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p < 0.05), similar in obese but significantly different in non-obese patients (p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.

Ultrahigh-Spatial-Resolution Photon-counting Detector CT Angiography of Coronary Artery Disease for Stenosis Assessment.

Background Coronary CT angiography is a first-line test in coronary artery disease but is limited by severe calcifications. Photon-counting-detector (PCD) CT improves spatial resolution. Purpose To investigate the effect of improved spatial resolution on coronary stenosis assessment and reclassification. Materials and Methods Coronary stenoses were evaluated prospectively in a vessel phantom (in vitro) containing two stenoses (25%, 50%), and retrospectively in patients (in vivo) who underwent ultrahigh-spatial-resolution cardiac PCD CT (from July 2022 to April 2023). Images were reconstructed at standard resolution (section thickness, 0.6 mm; increment, 0.4 mm; Bv44 kernel), high spatial resolution (section thickness, 0.4 mm; increment, 0.2 mm; Bv44 kernel), and ultrahigh spatial resolution (section thickness, 0.2; increment, 0.1 mm; Bv64 kernel). Percentages of diameter stenosis (DS) were compared between reconstructions. In vitro values were compared with the manufacturer specifications of the phantom and patient results were assessed regarding effects on Coronary Artery Disease Reporting and Data System (CAD-RADS) reclassification. Results The in vivo sample included 114 patients (mean age, 68 years ± 9 [SD]; 71 male patients). In vitro percentage DS measurements were more accurate with increasing spatial resolution for both 25% and 50% stenoses (mean bias for standard resolution, high spatial resolution, and ultrahigh spatial resolution, respectively: 10.1%, 8.0%, and 2.3%; P < .001). In vivo results confirmed decreasing median percentage DS with increasing spatial resolution for calcified stenoses (n = 161) (standard resolution, high spatial resolution, and ultrahigh spatial resolution, respectively: 41.5% [IQR, 27.3%-58.2%], 34.8% [IQR, 23.7%-55.1%], and 26.7% [IQR, 18.6%-44.3%]; P < .001), whereas noncalcified (n = 13) and mixed plaques (n = 19) did not show evidence of a difference (P ≥ .88). Ultrahigh-spatial-resolution reconstructions led to reclassification of 62 of 114 (54.4%) patients to lower CAD-RADS category than that assigned using standard resolution. Conclusion In vivo and in vitro coronary stenosis assessment improved for calcified stenoses by using ultrahigh-spatial-resolution PCD CT reconstructions, leading to lower percentage DS compared with standard resolution and clinically relevant rates of reclassification. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by McCollough in this issue.

Nonradiology Health Care Professionals Significantly Benefit From AI Assistance in Emergency-Related Chest Radiography Interpretation.

BACKGROUND: Chest radiographs (CXRs) are still of crucial importance in primary diagnostics, but their interpretation poses difficulties at times. RESEARCH QUESTION: Can a convolutional neural network-based artificial intelligence (AI) system that interprets CXRs add value in an emergency unit setting? STUDY DESIGN AND METHODS: A total of 563 CXRs acquired in the emergency unit of a major university hospital were retrospectively assessed twice by three board-certified radiologists, three radiology residents, and three emergency unit-experienced nonradiology residents (NRRs). They used a two-step reading process: (1) without AI support; and (2) with AI support providing additional images with AI overlays. Suspicion of four suspected pathologies (pleural effusion, pneumothorax, consolidations suspicious for pneumonia, and nodules) was reported on a five-point confidence scale. Confidence scores of the board-certified radiologists were converted into four binary reference standards of different sensitivities. Performance by radiology residents and NRRs without AI support/with AI support were statistically compared by using receiver-operating characteristics (ROCs), Youden statistics, and operating point metrics derived from fitted ROC curves. RESULTS: NRRs could significantly improve performance, sensitivity, and accuracy with AI support in all four pathologies tested. In the most sensitive reference standard (reference standard IV), NRR consensus improved the area under the ROC curve (mean, 95% CI) in the detection of the time-critical pathology pneumothorax from 0.846 (0.785-0.907) without AI support to 0.974 (0.947-1.000) with AI support (P < .001), which represented a gain of 30% in sensitivity and 2% in accuracy (while maintaining an optimized specificity). The most pronounced effect was observed in nodule detection, with NRR with AI support improving sensitivity by 53% and accuracy by 7% (area under the ROC curve without AI support, 0.723 [0.661-0.785]; with AI support, 0.890 [0.848-0.931]; P < .001). Radiology residents had smaller, mostly nonsignificant gains in performance, sensitivity, and accuracy with AI support. INTERPRETATION: We found that in an emergency unit setting without 24/7 radiology coverage, the presented AI solution features an excellent clinical support tool to nonradiologists, similar to a second reader, and allows for a more accurate primary diagnosis and thus earlier therapy initiation.

Photon-Counting Detector CT Virtual Monoenergetic Images for Coronary Artery Stenosis Quantification: Phantom and In Vivo Evaluation.

BACKGROUND. Calcium blooming causes stenosis overestimation on coronary CTA. OBJECTIVE. The purpose of this article was to evaluate the impact of virtual monoenergetic imaging (VMI) reconstruction level on coronary artery stenosis quantification using photon-counting detector (PCD) CT. METHODS. A phantom containing two custom-made vessels (representing 25% and 50% stenosis) underwent PCD CT acquisitions without and with simulated cardiac motion. A retrospective analysis was performed of 33 patients (seven women, 26 men; mean age, 71.3 ± 9.0 [SD] years; 64 coronary artery stenoses) who underwent coronary CTA by PCD CT followed by invasive coronary angiography (ICA). Scans were reconstructed at nine VMI energy levels (40-140 keV). Percentage diameter stenosis (PDS) was measured, and bias was determined from the ground-truth stenosis percentage in the phantom and ICA-derived quantitative coronary angiography measurements in patients. Extent of blooming artifact was measured in the phantom and in calcified and mixed plaques in patients. RESULTS. In the phantom, PDS decreased for 25% stenosis from 59.9% (40 keV) to 13.4% (140 keV) and for 50% stenosis from 81.6% (40 keV) to 42.3% (140 keV). PDS showed lowest bias for 25% stenosis at 90 keV (bias, 1.4%) and for 50% stenosis at 100 keV (bias, -0.4%). Blooming artifacts decreased for 25% stenosis from 61.5% (40 keV) to 35.4% (140 keV) and for 50% stenosis from 82.7% (40 keV) to 52.1% (140 keV). In patients, PDS for calcified plaque decreased from 70.8% (40 keV) to 57.3% (140 keV), for mixed plaque decreased from 69.8% (40 keV) to 56.3% (140 keV), and for noncalcified plaque was 46.6% at 40 keV and 54.6% at 140 keV. PDS showed lowest bias for calcified plaque at 100 keV (bias, 17.2%), for mixed plaque at 140 keV (bias, 5.0%), and for noncalcified plaque at 40 keV (bias, -0.5%). Blooming artifacts decreased for calcified plaque from 78.4% (40 keV) to 48.6% (140 keV) and for mixed plaque from 73.1% (40 keV) to 44.7% (140 keV). CONCLUSION. For calcified and mixed plaque, stenosis severity measurements and blooming artifacts decreased at increasing VMI reconstruction levels. CLINICAL IMPACT. PCD CT with VMI reconstruction helps overcome current limitations in stenosis quantification on coronary CTA.

Implementing Artificial Intelligence for Emergency Radiology Impacts Physicians' Knowledge and Perception: A Prospective Pre- and Post-Analysis.

PURPOSE: The aim of this study was to evaluate the impact of implementing an artificial intelligence (AI) solution for emergency radiology into clinical routine on physicians' perception and knowledge. MATERIALS AND METHODS: A prospective interventional survey was performed pre-implementation and 3 months post-implementation of an AI algorithm for fracture detection on radiographs in late 2022. Radiologists and traumatologists were asked about their knowledge and perception of AI on a 7-point Likert scale (-3, "strongly disagree"; +3, "strongly agree"). Self-generated identification codes allowed matching the same individuals pre-intervention and post-intervention, and using Wilcoxon signed rank test for paired data. RESULTS: A total of 47/71 matched participants completed both surveys (66% follow-up rate) and were eligible for analysis (34 radiologists [72%], 13 traumatologists [28%], 15 women [32%]; mean age, 34.8 ± 7.8 years). Postintervention, there was an increase that AI "reduced missed findings" (1.28 [pre] vs 1.94 [post], P = 0.003) and made readers "safer" (1.21 vs 1.64, P = 0.048), but not "faster" (0.98 vs 1.21, P = 0.261). There was a rising disagreement that AI could "replace the radiological report" (-2.04 vs -2.34, P = 0.038), as well as an increase in self-reported knowledge about "clinical AI," its "chances," and its "risks" (0.40 vs 1.00, 1.21 vs 1.70, and 0.96 vs 1.34; all P 's ≤ 0.028). Radiologists used AI results more frequently than traumatologists ( P < 0.001) and rated benefits higher (all P 's ≤ 0.038), whereas senior physicians were less likely to use AI or endorse its benefits (negative correlation with age, -0.35 to 0.30; all P 's ≤ 0.046). CONCLUSIONS: Implementing AI for emergency radiology into clinical routine has an educative aspect and underlines the concept of AI as a "second reader," to support and not replace physicians.

Intra-individual comparison of coronary CT angiography-based FFR between energy-integrating and photon-counting detector CT systems.

BACKGROUND: Coronary computed tomography angiography (CCTA)-based fractional flow reserve (CT-FFR) allows for noninvasive determination of the functional severity of anatomic lesions in patients with coronary artery disease. The aim of this study was to intra-individually compare CT-FFR between photon-counting detector (PCD) and conventional energy-integrating detector (EID) CT systems. METHODS: In this single-center prospective study, subjects who underwent clinically indicated CCTA on an EID-CT system were recruited for a research CCTA on PCD-CT within 30 days. Image reconstruction settings were matched as closely as possible between EID-CT (Bv36 kernel, iterative reconstruction strength level 3, slice thickness 0.5 mm) and PCD-CT (Bv36 kernel, quantum iterative reconstruction level 3, virtual monoenergetic level 55 keV, slice thickness 0.6 mm). CT-FFR was measured semi-automatically using a prototype on-site machine learning algorithm by two readers. CT-FFR analysis was performed per-patient and per-vessel, and a CT-FFR ≤ 0.75 was considered hemodynamically significant. RESULTS: A total of 22 patients (63.3 ± 9.2 years; 7 women) were included. Median time between EID-CT and PCD-CT was 5.5 days. Comparison of CT-FFR values showed no significant difference and strong agreement between EID-CT and PCD-CT in the per-vessel analysis (0.88 [0.74-0.94] vs. 0.87 [0.76-0.93], P = 0.096, mean bias 0.02, limits of agreement [LoA] -0.14/0.19, r = 0.83, ICC = 0.92), and in the per-patient analysis (0.81 [0.60-0.86] vs. 0.76 [0.64-0.86], P = 0.768, mean bias 0.02, LoA -0.15/0.19, r = 0.90, ICC = 0.93). All included patients were classified into the same category (CT-FFR > 0.75 vs ≤0.75) with both CT systems. CONCLUSIONS: CT-FFR evaluation is feasible with PCD-CT and it shows a strong agreement with EID-CT-based evaluation when images are similarly reconstructed.

Photon-counting detector CT-based virtual monoenergetic reconstructions: repeatability and reproducibility of radiomics features of an organic phantom and human myocardium.

BACKGROUND: Photon-counting detector computed tomography (PCD-CT) may influence imaging characteristics for various clinical conditions due to higher signal and contrast-to-noise ratio in virtual monoenergetic images (VMI). Radiomics analysis relies on quantification of image characteristics. We evaluated the impact of different VMI reconstructions on radiomic features in in vitro and in vivo PCD-CT datasets. METHODS: An organic phantom consisting of twelve samples (four oranges, four onions, and four apples) was scanned five times. Twenty-three patients who had undergone coronary computed tomography angiography on a first generation PCD-CT system with the same image acquisitions were analyzed. VMIs were reconstructed at 6 keV levels (40, 55, 70, 90, 120, and 190 keV). The phantoms and the patients' left ventricular myocardium (LVM) were segmented for all reconstructions. Ninety-three original radiomic features were extracted. Repeatability and reproducibility were evaluated through intraclass correlations coefficient (ICC) and post hoc paired samples ANOVA t test. RESULTS: There was excellent repeatability for radiomic features in phantom scans (all ICC = 1.00). Among all VMIs, 36/93 radiomic features (38.7%) in apples, 28/93 (30.1%) in oranges, and 33/93 (35.5%) in onions were not significantly different. For LVM, the percentage of stable features was high between VMIs ≥ 90 keV (90 versus 120 keV, 77.4%; 90 versus 190 keV, 83.9%; 120 versus 190 keV, 89.3%), while comparison to lower VMI levels led to fewer reproducible features (40 versus 55 keV, 8.6%). CONCLUSIONS: VMI levels influence the stability of radiomic features in an organic phantom and patients' LVM; stability decreases considerably below 90 keV. RELEVANCE STATEMENT: Spectral reconstructions significantly influence radiomic features in vitro and in vivo, necessitating standardization and careful attention to these reconstruction parameters before clinical implementation. KEY POINTS: • Radiomic features have an excellent repeatability within the same PCD-CT acquisition and reconstruction. • Differences in VMI lead to decreased reproducibility for radiomic features. • VMI ≥ 90 keV increased the reproducibility of the radiomic features.

Intra-individual comparison of image quality of the coronary arteries between photon-counting detector and energy-integrating detector CT systems.

PURPOSE: To intra-individually compare the objective and subjective image quality of coronary computed tomography angiography (CCTA) between photon-counting detector CT (PCD-CT) and energy-integrating detector CT (EID-CT). METHOD: Consecutive patients undergoing clinically indicated CCTA on an EID-CT system were prospectively enrolled for a research CCTA performed on a PCD-CT system within 30 days. Polychromatic images were reconstructed for both EID- and PCD-CT, while virtual monoenergetic images (VMI) were generated at 40, 45, 50, 55, 60 and 70 keV for PCD-CT. Two blinded readers calculated contrast-to-noise ratio (CNR) for each major coronary artery and rated image noise, vessel attenuation, vessel sharpness, and overall quality on a 1-5 Likert scale. Patients were then stratified by body mass index (BMI) [high (>30 kg/m2) vs low (<30 kg/m2)] for subgroup analysis. RESULTS: A total of 20 patients (67.5 ± 9.0 years, 75% male) were included in the study. Compared with EID-CT, coronary artery CNR values from PCD-CT monoenergetic and polychromatic reconstructions were all significantly higher than CNR values from EID-CT, with incrementally greater differences in obese subjects (all p < 0.008). Subjective image noise and sharpness were also significantly higher for all VMI reconstructions compared to EID-CT (all p < 0.008). All subjective scores were significantly higher for 55, 60, and 70 keV PCD-CT than EID-CT values (all p < 0.05). CONCLUSIONS: The improved objective and subjective image quality of PCD-CT compared to EID-CT may provide better visualization of the coronary arteries for a wide array of patients, especially those with a high BMI.

Impact of Cardiac Motion on coronary artery calcium scoring using a virtual non-iodine algorithm on photon-counting detector CT: a dynamic phantom study.

This study assessed the impact of cardiac motion and in-vessel attenuation on coronary artery calcium (CAC) scoring using virtual non-iodine (VNI) against virtual non-contrast (VNC) reconstructions on photon-counting detector CT. Two artificial vessels containing calcifications and different in-vessel attenuations (500, 800HU) were scanned without (static) and with cardiac motion (60, 80, 100 beats per minute [bpm]). Images were post-processed using a VNC and VNI algorithm at 70 keV and quantum iterative reconstruction (QIR) strength 2. Calcium mass, Agatston scores, cardiac motion susceptibility (CMS)-indices were compared to physical mass, static scores as well as between reconstructions, heart rates and in-vessel attenuations. VNI scores decreased with rising heart rate (p < 0.01) and showed less underestimation than VNC scores (p < 0.001). Only VNI scores were similar to the physical mass at static measurements, and to static scores at 60 bpm. Agatston scores using VNI were similar to static scores at 60 and 80 bpm. Standard deviation of CMS-indices was lower for VNI-based than for VNC-based CAC scoring. VNI scores were higher at 500 than 800HU (p < 0.001) and higher than VNC scores (p < 0.001) with VNI scores at 500 HU showing the lowest deviation from the physical reference. VNI-based CAC quantification is influenced by cardiac motion and in-vessel attenuation, but least when measuring Agatston scores, where it outperforms VNC-based CAC scoring.

Optimization of Kernel Type and Sharpness Level Improves Objective and Subjective Image Quality for High-Pitch Photon Counting Coronary CT Angiography.

(1) Background: Photon-counting detector (PCD) CT offers a wide variety of kernels and sharpness levels for image reconstruction. The aim of this retrospective study was to determine optimal settings for coronary CT angiography (CCTA). (2) Methods: Thirty patients (eight female, mean age 63 ± 13 years) underwent PCD-CCTA in a high-pitch mode. Images were reconstructed using three different kernels and four sharpness levels (Br36/40/44/48, Bv36/40/44/48, and Qr36/40/44/48). To analyze objective image quality, the attenuation, image noise, contrast-to-noise ratio (CNR), and vessel sharpness were quantified in proximal and distal coronaries. For subjective image quality, two blinded readers assessed image noise, visually sharp reproduction of coronaries, and the overall image quality using a five-point Likert scale. (3) Results: Attenuation, image noise, CNR, and vessel sharpness significantly differed across kernels (all p < 0.001), with the Br-kernel reaching the highest attenuation. With increasing kernel sharpness, image noise and vessel sharpness increased, whereas CNR continuously decreased. Reconstruction with Br-kernel generally had the highest CNR (Br > Bv > Qr), except Bv-kernel had a superior CNR at sharpness level 40. Bv-kernel had significantly higher vessel sharpness than Br- and Qr-kernel (p < 0.001). Subjective image quality was rated best for kernels Bv40 and Bv36, followed by Br36 and Qr36. (4) Conclusion: Reconstructions with kernel Bv40 are beneficial to achieve optimal image quality in spectral high-pitch CCTA using PCD-CT.

How to exclude pulmonary embolism in patients hospitalized with COVID-19: a comparison of predictive scores.

BACKGROUND: Pulmonary embolism (PE) is an important complication of Coronavirus disease 2019 (COVID-19). COVID-19 is associated with respiratory impairment and a pro-coagulative state, rendering PE more likely and difficult to recognize. Several decision algorithms relying on clinical features and D-dimer have been established. High prevalence of PE and elevated Ddimer in patients with COVID-19 might impair the performance of common decision algorithms. Here, we aimed to validate and compare five common decision algorithms implementing age adjusted Ddimer, the GENEVA, and Wells scores as well as the PEGeD- and YEARS-algorithms in patients hospitalized with COVID-19. METHODS: In this single center study, we included patients who were admitted to our tertiary care hospital in the COVID-19 Registry of the LMU Munich. We retrospectively selected patients who received a computed tomography pulmonary angiogram (CTPA) or pulmonary ventilation/perfusion scintigraphy (V/Q) for suspected PE. The performances of five commonly used diagnostic algorithms (age-adjusted D-dimer, GENEVA score, PEGeD-algorithm, Wells score, and YEARS-algorithm) were compared. RESULTS: We identified 413 patients with suspected PE who received a CTPA or V/Q confirming 62 PEs (15%). Among them, 358 patients with 48 PEs (13%) could be evaluated for performance of all algorithms. Patients with PE were older and their overall outcome was worse compared to patients without PE. Of the above five diagnostic algorithms, the PEGeD- and YEARS-algorithms performed best, reducing diagnostic imaging by 14% and 15% respectively with a sensitivity of 95.7% and 95.6%. The GENEVA score was able to reduce CTPA or V/Q by 32.2% but suffered from a low sensitivity (78.6%). Age-adjusted D-dimer and Wells score could not significantly reduce diagnostic imaging. CONCLUSION: The PEGeD- and YEARS-algorithms outperformed other tested decision algorithms and worked well in patients admitted with COVID-19. These findings need independent validation in a prospective study.

Radiation Dose Reduction for Coronary Artery Calcium Scoring Using a Virtual Noniodine Algorithm on Photon-Counting Detector Computed-Tomography Phantom Data.

Background: On the basis of the hypothesis that virtual noniodine (VNI)-based coronary artery calcium scoring (CACS) is feasible at reduced radiation doses, this study assesses the impact of radiation dose reduction on the accuracy of this VNI algorithm on a photon-counting detector (PCD)-CT. Methods: In a systematic in vitro setting, a phantom for CACS simulating three chest sizes was scanned on a clinical PCD-CT. The standard radiation dose was chosen at volumetric CT dose indices (CTDIVol) of 1.5, 3.3, 7.0 mGy for small, medium-sized, and large phantoms, and was gradually reduced by adjusting the tube current resulting in 100, 75, 50, and 25%, respectively. VNI images were reconstructed at 55 keV, quantum iterative reconstruction (QIR)1, and at 60 keV/QIR4, and evaluated regarding image quality (image noise (IN), contrast-to-noise ratio (CNR)), and CACS. All VNI results were compared to true noncontrast (TNC)-based CACS at 70 keV and standard radiation dose (reference). Results: INTNC was significantly higher than INVNI, and INVNI at 55 keV/QIR1 higher than at 60 keV/QIR4 (100% dose: 16.7 ± 1.9 vs. 12.8 ± 1.7 vs. 7.7 ± 0.9; p < 0.001 for every radiation dose). CNRTNC was higher than CNRVNI, but it was better to use 60 keV/QIR4 (p < 0.001). CACSVNI showed strong correlation and agreement at every radiation dose (p < 0.001, r > 0.9, intraclass correlation coefficient > 0.9). The coefficients of the variation in root-mean squared error were less than 10% and thus clinically nonrelevant for the CACSVNI of every radiation dose. Conclusion: This phantom study suggests that CACSVNI is feasible on PCD-CT, even at reduced radiation dose while maintaining image quality and CACS accuracy.

Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification.

Purpose: To investigate the chest radiograph classification performance of vision transformers (ViTs) and interpretability of attention-based saliency maps, using the example of pneumothorax classification. Materials and Methods: In this retrospective study, ViTs were fine-tuned for lung disease classification using four public datasets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. Saliency maps were generated using transformer multimodal explainability and gradient-weighted class activation mapping (GradCAM). Classification performance was evaluated on the Chest X-Ray 14, VinBigData, and Society for Imaging Informatics in Medicine-American College of Radiology (SIIM-ACR) Pneumothorax Segmentation datasets using the area under the receiver operating characteristic curve (AUC) analysis and compared with convolutional neural networks (CNNs). The explainability methods were evaluated with positive and negative perturbation, sensitivity-n, effective heat ratio, intra-architecture repeatability, and interarchitecture reproducibility. In the user study, three radiologists classified 160 chest radiographs with and without saliency maps for pneumothorax and rated their usefulness. Results: ViTs had comparable chest radiograph classification AUCs compared with state-of-the-art CNNs: 0.95 (95% CI: 0.94, 0.95) versus 0.83 (95%, CI 0.83, 0.84) on Chest X-Ray 14, 0.84 (95% CI: 0.77, 0.91) versus 0.83 (95% CI: 0.76, 0.90) on VinBigData, and 0.85 (95% CI: 0.85, 0.86) versus 0.87 (95% CI: 0.87, 0.88) on SIIM-ACR. Both saliency map methods unveiled a strong bias toward pneumothorax tubes in the models. Radiologists found 47% of the attention-based and 39% of the GradCAM saliency maps useful. The attention-based methods outperformed GradCAM on all metrics. Conclusion: ViTs performed similarly to CNNs in chest radiograph classification, and their attention-based saliency maps were more useful to radiologists and outperformed GradCAM.Keywords: Conventional Radiography, Thorax, Diagnosis, Supervised Learning, Convolutional Neural Network (CNN) Online supplemental material is available for this article. © RSNA, 2023.

Gender Differences in Epicardial Adipose Tissue and Plaque Composition by Coronary CT Angiography: Association with Cardiovascular Outcome.

Background: To investigate gender differences in epicardial adipose tissue (EAT) and plaque composition by coronary CT angiography (CCTA) and the association with cardiovascular outcome. Methods: Data of 352 patients (64.2 ± 10.3 years, 38% female) with suspected coronary artery disease (CAD) who underwent CCTA were retrospectively analyzed. EAT volume and plaque composition from CCTA were compared between men and women. Major adverse cardiovascular events (MACE) were recorded from follow-up. Results: Men were more likely to have obstructive CAD, higher Agatston scores, and a larger total and non-calcified plaque burden. In addition, men displayed more adverse plaque characteristics and EAT volume compared to women (all p < 0.05). After a median follow-up of 5.1 years, MACE occurred in 8 women (6%) and 22 men (10%). In multivariable analysis, Agatston calcium score (HR 1.0008, p = 0.014), EAT volume (HR 1.067, p = 0.049), and low-attenuation plaque (HR 3.82, p = 0.036) were independent predictors for MACE in men, whereas only low-attenuation plaque (HR 2.42, p = 0.041) showed predictive value for events in women. Conclusion: Women demonstrated less overall plaque burden, fewer adverse plaque characteristics, and a smaller EAT volume compared to men. However, low-attenuation plaque is a predictor for MACE in both genders. Thus, a differentiated plaque analysis is warranted to understand gender differences of atherosclerosis to guide medical therapy and prevention strategies.

Ultra-high resolution photon-counting coronary CT angiography improves coronary stenosis quantification over a wide range of heart rates - A dynamic phantom study.

PURPOSE: To investigate the effect of using photon-counting detector (PCD)-CT with ultra-high resolution (UHR) on stenosis quantification accuracy and blooming artifacts from low to high heart rates in a dynamic motion phantom. METHOD: Two vessel phantoms (diameter: 4 mm) containing solid calcified lesions (25%, 50% stenoses), filled with different concentrations of iodine, inside an anthropomorphic thorax phantom attached to a coronary motion simulator were used. Scanning was performed on a PCD-CT system using an ECG-gated mode at UHR and standard resolution (SR) (0.2, 0.6 mm slice thickness, respectively). Images were reconstructed at 60, 80 and 100 beats per minute (bpm) (UHR: Bv56 kernel, quantum iterative reconstruction (QIR) level 3; SR: 55 keV, Bv40 kernel, QIR3). Percent diameter stenosis (PDS) and blooming artifacts were measured by two readers. RESULTS: PDS measurements derived from UHR were more accurate than SR for both lesions at every heart rate (p ≤ 0.005 for all, e.g. 50% lesion SR vs. UHR: at 60 bpm 57.1% [55.2-59.2] vs. 50.0% [48.5-51.2], at 100 bpm 61.0% [58.6-64.3] vs. 52.4% [51.3-54.3]). Overall mean difference across heart rates and lesions compared to the nominal stenoses was 9.2% (Limit of Agreement (LoA), 2.4%/16.0%) for SR vs. 2.4% (LoA, -2.8%/7.5%) for UHR. Blooming artifacts decreased with UHR compared to SR for both lesions at every heart rate (p < 0.001 for all, e.g. 50% lesion SR vs. UHR: at 60 bpm 63.8% [60.6-69.5] vs. 52.5% [50.0-57.5], at 100 bpm 70.2% [64.8-78.1] vs. 56.1% [51.2-60.8]). CONCLUSIONS: This motion phantom study demonstrates improved stenosis quantification accuracy and reduced blooming artifacts with UHR-PCD-CT compared to SR, independent of heart rate.