OBJECTIVES:The Swedish CArdioPulmonary bioImage Study reexamination (SCAPIS reexamination) is the first population-based study to employ coronary CT angiography (CCTA) using photon-counting detector CT (PCD-CT). It includes 15,000 participants from SCAPIS baseline. This work aims to describe the PCD-CT protocol in SCAPIS reexamination, compare diagnostic image quality with energy-integrating detector CT (EID-CT) at SCAPIS baseline, and assess the comparability of Agatston scores and degree of stenosis between the studies. METHODS:The PCD-CT protocol in SCAPIS reexamination is provided. CCTA data from 1,147 participants in SCAPIS reexamination (51% women, age 65 [IQR 61-69]) and 29,554 participants in SCAPIS (52% women, age 58 [54-61]) were analyzed. The image quality in eleven proximal and middle coronary segments was compared, stratified by Agatston score. Agatston scores and stenosis degree were compared in age- and sex-matched samples. RESULTS:Full diagnostic image quality was more frequent in SCAPIS reexamination (1,036/1,147, 90%) compared with SCAPIS baseline (20,468/26,188, 78%), p < 0.001, despite a higher calcium burden (Agatston score 8 [0-106] vs. 0 [0-20]). Of participants with Agatston score > 400, 89/116, 77%, and 246/945, 26%, had full diagnostic image quality, respectively, p < 0.001. Agatston score distributions and stenosis ≥ 50% were similar in matched samples (p = 0.51 and p = 0.08). Of those with ≥ 50% stenosis at SCAPIS baseline, 30% (12/40) were reclassified to < 50% in SCAPIS reexamination. CONCLUSION:The optimized PCD-CT protocol in SCAPIS reexamination provided high image quality, irrespective of Agatston scores, outperforming the EID-CT protocol used at SCAPIS baseline. Agatston scores were comparable, but potential differences in stenosis grading warrant further investigation.

Background Coronary CT angiography (CCTA) is a key non-invasive tool for evaluating coronary artery disease (CAD). While energy-integrating detector CT (EID-CT) offers high negative predictive value (NPV), its positive predictive value (PPV) is limited in heavily calcified vessels. Photon-counting detector CT (PCD-CT), with higher spatial resolution and reduced blooming, may enhance diagnostic performance. Current PCD-CT systems provide both standard-resolution (SR) and ultra-high-resolution (UHR) modes, but the clinical impact of these modes remains under investigation. Objectives To compare the diagnostic accuracy and image quality of SR-PCD-CT versus EID-CT in quantifying coronary stenosis, using quantitative coronary angiography (QCA) as reference. Materials and methods In this prospective, single-centre study, 21 patients (5 women, mean age 71.5 years) with suspected CAD underwent CCTA with both EID-CT and SR-PCD-CT prior to QCA. A total of 301 coronary segments were assessed for stenosis severity, with ≥50 ​% stenosis deemed significant. Image quality was graded using a 5-point scale. Results No significant differences in percentage diameter stenosis (%DS) were found between imaging techniques (p ​= ​0.20). Both EID-CT and SR-PCD-CT showed good agreement with QCA (AUC: PCD-CT 0.89, EID-CT 0.86). Specificity and NPV were high for both; sensitivity and PPV were moderate. SR-PCD-CT yielded higher image quality compared to EID-CT (p ​< ​0.001). Conclusions In standard resolution mode, PCD-CT offers excellent image quality for quantifying coronary stenosis at comparable diagnostic accuracy compared to EID-CT.

The aim was to compare the image quality of photon-counting detector CT (PCD-CT) and energy-integrating detector CT (EID-CT) in patients with tibial plateau fractures treated with metallic osteosynthesis material, and to identify optimal reconstruction parameters for PCD-CT. After ethical approval, twelve patients underwent PCD-CT and EID-CT scans. Images were reconstructed using bone and soft-tissue kernels with metal artifact reduction (iMAR). PCD-CT virtual monoenergetic images (VMI) at 70, 110, and 150 keV were generated. Five radiologists assessed metal artifact severity and bone and soft-tissue visualization using a 7-point Likert scale. Visual grading characteristics analysis was performed. Noise levels were quantified and compared using Wilcoxon’s signed-rank test. EID-CT was rated superior in reducing metal-artifact streaks (AUC: 0.10–0.21). No significant difference was found between EID-CT iMAR and PCD-CT VMI at 110 keV and 150 keV (AUC: 0.40–0.49) concerning the metal-bone interface. An ultra-high-resolution PCD-CT kernel outperformed its EID-CT counterpart in bone visualization, with AUC values of 0.67–0.92 across all bone criteria, including those incorporating artifact-affected regions. PCD-CT showed lower noise. Metal artifact reduction was superior in EID-CT iMAR images compared to PCD-CT iMAR and VMI 110/150 keV, except at the metal-bone interface, where EID-CT iMAR images and PCD-CT VMI 110/150 keV performed comparably. The ultra-high-resolution PCD-CT kernel provided the best bone visualization, even when artifact-affected areas were included. Noise levels were lower in PCD-CT. This is the first in vivo comparison of photon-counting and energy-integrating CT for postoperative knee imaging with metallic osteosynthesis material. These findings highlight the need for improved metal artifact reduction in PCD‑CT, while demonstrating superior bone visualization, and support a complementary interpretation strategy in postoperative knees using high‑energy VMI and MAR‑corrected reconstructions.

Importance:Risk stratification strategies in primary prevention of coronary events lack precision. Objective:To determine whether prediction of first coronary events is improved by adding information on coronary atherosclerosis from coronary computed tomography angiography (CCTA) to a model using the pooled cohort equation (PCE) risk score tool and the coronary artery calcification score (CACS). Design, Setting, and Participants:Observational cohort study including individuals aged 50 to 64 years randomly recruited from the general population and examined at 6 university hospitals in Sweden from 2013 to 2018, with a median follow-up of 7.8 years. A sample of 30 154 individuals underwent cardiopulmonary imaging, physical examinations, routine laboratory tests, questionnaires, and/or functional tests. This study included 24 791 individuals without previous cardiovascular disease for whom high-quality CCTA images were available. Events were followed up via registers until September 2024. Exposures:The information used from the CCTA images was the extent of coronary atherosclerosis (segment involvement score), presence of noncalcified atherosclerosis, and presence of coronary obstructive disease (stenosis ≥50%). Main Outcomes and Measures:The outcome was a composite of first occurrence of nonfatal myocardial infarction or death from coronary heart disease. Results:During follow-up, 304 coronary events occurred. Segment involvement scores of 3 to 4 and greater than 4 and presence of noncalcified atherosclerosis were associated with hazard ratios of 2.71 (95% CI, 1.34-5.44), 5.27 (95% CI, 2.50-11.07), and 1.66 (95% CI, 1.23-2.22), respectively. In a model based on the PCE and CACS, CCTA-derived data improved risk discrimination (C statistic improved from 0.764 to 0.779; P = .004) and risk reclassification (net reclassification improvement of 0.133 [95% CI, 0.031-0.165]), conferred a net correct upward reclassification of 14.2% in those with events and incorrectly classified 1.6% of participants not experiencing an event into a higher-risk category. Because of the low event rate in the cohort, reclassification mainly occurred in the group classified as at low risk (<5%) according to the PCE. Conclusions and Relevance:Information on coronary atherosclerosis from CCTA modestly improved risk prediction beyond traditional risk factors and CACS in identifying individuals at risk of coronary events and in need of primary prevention.

To evaluate osseointegration, bone quality, and periprosthetic osteolysis following total wrist arthroplasty using photon-counting detector CT (PCD-CT). Nineteen patients with total wrist arthroplasty were examined with PCD-CT the day after surgery, at 6 and 12 months postoperatively. CT images were reviewed and osseointegration and periprosthetic osteolysis were evaluated. Cortical thickness and cortical density were measured. The bones around the implant were divided into zones, and the evaluations were made in each zone. Osseointegration, visualized as bone ingrowth to the implant, increased in all zones except for the capitate during the 12-month follow-up period. The highest percentage of osseointegration was found in the zones most distant from the joint space, with more than 50

We compared photon-counting detector computed tomography (PCD-CT) polyenergetic images, PCD-CT virtual monoenergetic images (VMI), and energy-integrating detector computed tomography (EID-CT) polyenergetic images regarding bone visualization and metal artifacts in patients with titanium wrist prostheses. After ethical approval, 15 patients were examined with PCD-CT and EID-CT. Polyenergetic images were reconstructed, as well as 130-keV VMI for PCD-CT. Five radiologists evaluated bone visualization, interpretability at metal-bone interface and metal artifacts using a 7-point ordinal scale. Streak artifacts and artifacts at the bone-metal interface were quantitatively assessed. Differences between image setups were analyzed using Friedman test and one-way ANOVA with post hoc tests. Bone visualization was superior in PCD-CT polyenergetic images (median rating 6, range 3–7) compared with VMI (5, 3–7; p < 0.001) and EID-CT (5, 3–7; p = 0.018). Streak artifacts were more pronounced with PCD-CT polyenergetic images (4, 3–6) compared with EID-CT (5, 4–6; p = 0.003) and PCD-CT VMI (5, 3–7; p = 0.002), with quantitative results showing least streak artifacts in PCD-CT VMI, followed by EID-CT and PCD-CT polyenergetic images (50 ± 7

Aims:Assessment of cardiac function is essential for diagnosis and treatment planning in cardiovascular disease. Volume of cardiac regions and the derived measures of stroke volume (SV) and ejection fraction (EF) are most accurately calculated from imaging. This study aims to develop a fully automatic deep learning approach for calculation of cardiac function from computed tomography (CT). Methods and results:Time-resolved CT data sets from 39 patients were used to train segmentation models for the left side of the heart including the left ventricle (LV), left atrium (LA), and left atrial appendage (LAA). We compared nnU-Net, 3D TransUNet, and UNETR. Dice Similarity Scores (DSS) were similar between nnU-Net (average DSS = 0.91) and 3D TransUNet (DSS = 0.89) while UNETR performed less well (DSS = 0.69). Intra-class correlation analysis showed nnU-Net and 3D TransUNet both accurately estimated LVSV (ICCnnU-Net = 0.95; ICC3DTransUNet = 0.94), LVEF (ICCnnU-Net = 1.00; ICC3DTransUNet = 1.00), LASV (ICCnnU-Net = 0.91; ICC3DTransUNet = 0.80), LAEF (ICCnnU-Net = 0.95; ICC3DTransUNet = 0.81), and LAASV (ICCnnU-Net = 0.79; ICC3DTransUNet = 0.81). Only nnU-Net significantly predicted LAAEF (ICCnnU-Net = 0.68). UNETR was not able to accurately estimate cardiac function. Time to convergence during training and time needed for inference were both faster for 3D TransUNet than for nnU-Net. Conclusion:nnU-Net outperformed two different vision transformer architectures for the segmentation and calculation of function parameters for the LV, LA, and LAA. Fully automatic calculation of cardiac function parameters from CT using deep learning is fast and reliable.

Photon-Counting Computed Tomography (PCCT) is a novel imaging modality that simultaneously acquires volumetric data at multiple X-ray energy levels, generating separate volumes that capture energy-dependent attenuation properties. Attenuation refers to the reduction in X-ray intensity as it passes through different tissues or materials, which depends on their density and atomic composition. This spectral information enhances tissue and material differentiation, enabling more accurate diagnosis and analysis. However, the resulting multivolume datasets are often complex and redundant, making visualization and interpretation challenging. To address these challenges, we propose a method for fusing spectral PCCT data into a single representative volume that enables direct volume rendering and segmentation by leveraging both shared and complementary information across different channels. Our approach starts by computing 2D histograms between pairs of volumes to identify those that exhibit prominent structural features. These histograms reveal relationships and variations that may be difficult to discern from individual volumes alone. Next, we construct an extremum graph from the 2D histogram of two minimally correlated yet complementary volumes—selected to capture both shared and distinct features—thereby maximizing the information content. The graph captures the topological distribution of histogram extrema. By extracting prominent structure within this graph and projecting each grid point in histogram space onto it, we reduce the dimensionality to one, producing a unified volume. This representative volume retains key structural and material characteristics from the original spectral data while significantly reducing the analysis scope from multiple volumes to one. The result is a topology-aware, information-rich fusion of multi-energy CT datasets that facilitates more effective visualization and segmentation.

OBJECTIVES:Coronary computed tomography angiography is the primary modality for noninvasive assessment of coronary artery disease. Photon-counting computed tomography (PCCT) offers superior spatial resolution and spectral imaging for detailed characterization of atherosclerotic plaques. This study aimed to evaluate the impact of virtual monoenergetic imaging (VMI) energy levels and reconstruction kernels on segmentation-based measurement of plaque volume in individuals with coronary atherosclerosis using PCCT. MATERIALS AND METHODS:Fifty study participants underwent coronary computed tomography angiography with ultra-high-resolution PCCT. Both polyenergetic, 120 kVp (T3D) images and spectral images at varying VMI energy levels were reconstructed using different kernels. Plaque volumes were measured using semiautomated attenuation-based segmentation, adjusting segmentation thresholds for each VMI energy level. In addition, absolute plaque volume measurements were conducted using a coronary phantom simulating different plaque types. RESULTS:Using a sharper kernel (Bv64 vs Bv48) significantly increased noncalcified plaque volume measurements ( P < 0.005) in study participants, whereas a 0.2-mm slice thickness reduced calcified plaque volumes compared with 0.4 mm ( P < 0.005). VMI energy level had no impact on measured volumes. Phantom measurements confirmed significant variability in measured volumes of calcified and noncalcified plaques depending on reconstruction method, as well as a minor effect of VMI level. CONCLUSIONS:In PCCT, the reconstruction kernel predominantly affects noncalcified coronary plaque quantification, whereas slice thickness mainly impacts calcified plaque volumes. In study participants, measured plaque volumes were not affected by VMI energy level when energy-specific segmentation thresholds were used, although a minor effect of VMI was observed in the phantom model.

Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS). In total, 5057 CSCT examinations were performed on one CT system at Linköping University Hospital between October 8, 2015, and June 12, 2018. AI evaluations were compared to semi-automatic CSCT results from expert reader evaluations rendered within SCAPIS. Pearson correlation, intraclass correlation coefficients (ICC), and Bland–Altman analysis were applied for Agatston (AS), volume (VS), mass scores (MS), number of lesions and lesion location. Agreement of Agatston score classifications into cardiovascular (CV) risk categories was evaluated with weighted kappa analysis. The evaluation included 4567 subjects, 2229 (48.8

PurposeScaphoid fractures in patients and assessment of healing using PCD-CT have, as far as we know, not yet been studied. Therefore, the aim was to compare photon counting detector CT (PCD-CT) with energy integrating detector CT (EID-CT) in terms of fracture visibility and evaluation of fracture healing.MethodEight patients with scaphoid fracture were examined with EID-CT and PCD-CT within the first week post-trauma, and with additional scans at 4, 6 and 8 weeks. Our clinical protocol for wrist examination with EID-CT was used (CTDIvol 3.1 ± 0.1 mGy, UHR kernel Ur77). For PCD-CT matched radiation dose, reconstruction kernel Br89. Quantitative analyses of noise, CNR, trabecular and cortical sharpness, and bone volume fraction were conducted. Five radiologists evaluated the images for fracture visibility, fracture gap consolidation and image quality, and rated their confidence in the diagnosis.ResultsThe trabecular and cortical sharpness were superior in images obtained with PCD-CT compared with EID-CT. A successive reduction in trabecular bone volume fraction during the immobilized periods was found with both systems. Despite higher noise and lower CNR with PCD-CT, radiologists rated the image quality of PCD-CT as superior. The visibility of the fracture line within 1-week post-trauma was rated higher with PCD-CT as was diagnostic confidence, but the subsequent assessments of fracture gap consolidation during healing process and the confidence in diagnosis were found equivalent between both systems.ConclusionPCD-CT offers superior visibility of bone microstructure compared with EID-CT. The evaluation of fracture healing and confidence in diagnosis were rated equally with both systems, but the radiologists found primary fracture visibility and overall image quality superior with PCD-CT.

Reduced lung function is associated with cardiovascular mortality, but the relationships with atherosclerosis are unclear. The population-based Swedish CArdioPulmonary BioImage study measured lung function, emphysema, coronary CT angiography, coronary calcium, carotid plaques and ankle-brachial index in 29,593 men and women aged 50–64 years. The results were confirmed using 2-sample Mendelian randomization. Lower lung function and emphysema were associated with more atherosclerosis, but these relationships were attenuated after adjustment for cardiovascular risk factors. Lung function was not associated with coronary atherosclerosis in 14,524 never-smokers. No potentially causal effect of lung function on atherosclerosis, or vice versa, was found in the 2-sample Mendelian randomization analysis. Here we show that reduced lung function and atherosclerosis are correlated in the population, but probably not causally related. Assessing lung function in addition to conventional cardiovascular risk factors to gauge risk of subclinical atherosclerosis is probably not meaningful, but low lung function found by chance should alert for atherosclerosis.

PURPOSE:Photon counting CT (PCCT) holds promise for mitigating metal artifacts and can produce virtual mono-energetic images (VMI), while maintaining temporal resolution, making it a valuable tool for characterizing the heart. This study aimed to evaluate and optimize PCCT for cardiac imaging in patients during left ventricular assistance device (LVAD) therapy by conducting an in-depth objective assessment of metal artifacts and visual grading. METHODS:Various scan and reconstruction settings were tested on a phantom and further evaluated on a patient acquisition to identify the optimal protocol settings. The phantom comprised an empty thoracic cavity, supplemented with heart and lungs from a cadaveric lamb. The heart was implanted with an LVAD (HeartMate 3) and iodine contrast. Scans were performed on a PCCT (NAEOTOM Alpha, Siemens Healthcare). Metal artifacts were assessed by three objective methods: Hounsfield units (HU)/SD measurements (DiffHU and SDARTIFACT), Fourier analysis (AmplitudeLowFreq), and depicted LVAD volume in the images (BloomVol). Radiologists graded metal artifacts and the diagnostic interpretability in the LVAD lumen, cardiac tissue, lung tissue, and spinal cord using a 5-point rating scale. Regression and correlation analysis were conducted to determine the assessment method most closely associated with acquisition and reconstruction parameters, as well as the objective method demonstrating the highest correlation with visual grading. RESULTS:Due to blooming artifacts, the LVAD volume fluctuated between 27.0 and 92.7 cm3. This variance was primarily influenced by kVp, kernel, keV, and iMAR (R2 = 0.989). Radiologists favored pacemaker iMAR, 3 mm slice thickness, and T3D keV and kernel Bv56f for minimal metal artifacts in cardiac tissue assessment, and 110 keV and Qr40f for lung tissue interpretation. The model adequacy for DiffHU SDARTIFACT, AmplitueLowFreq, and BloomVol was 0.28, 0.76, 0.29, and 0.99 respectively for phantom data, and 0.95, 0.98, 1.00, and 0.99 for in-vivo data. For in-vivo data, the correlation between visual grading (VGSUM) and DiffHU SDARTIFACT, AmplitueLowFreq, and BloomVol was -0.16, -0.01, -0.48, and -0.40 respectively. CONCLUSION:We found that optimal scan settings for LVAD imaging involved using 120 kVp and IQ level 80. Employing T3D with pacemaker iMAR, the sharpest allowed vascular kernel (Bv56f), and VMI at 110 keV with kernel Qr40 yields images suitable for cardiac imaging during LVAD-therapy. Volumetric measurements of the LVAD for determination of the extent of blooming artifacts was shown to be the best objective method to assess metal artifacts.

Rationale: Chronic obstructive pulmonary disease (COPD) includes respiratory symptoms and chronic airflow limitation (CAL). In some cases, emphysema and impaired diffusing capacity of the lung for carbon monoxide (DlCO) are present, but characteristics and symptoms vary with smoking exposure. Objective: To study the prevalence of CAL, emphysema, and impaired DlCO in relation to smoking and respiratory symptoms in a middle-aged population. Methods: We investigated 28,746 randomly invited individuals (52% women) aged 50-64 years across six Swedish sites. We performed spirometry, DlCO testing, and high-resolution computed tomography and asked for smoking habits and respiratory symptoms. CAL was defined as post-bronchodilator forced expiratory volume in 1 second divided by forced vital capacity (FEV1/FVC) < 0.7. Results: The overall prevalence was 8.8% for CAL, 5.7% for impaired DlCO (DlCO < LLN), and 8.8% for emphysema, with a higher prevalence in current smokers than in ex-smokers and never-smokers. The proportion of never-smokers among those with CAL, emphysema, and impaired DlCO was 32%, 19%, and 31%, respectively. Regardless of smoking habits, the prevalence of respiratory symptoms was higher among people with CAL and impaired DlCO than those with normal lung function. Asthma prevalence in never-smokers with CAL was 14%. In this group, asthma was associated with lower FEV1 and more respiratory symptoms. Conclusions: In this large population-based study of middle-aged people, CAL and impaired DlCO were associated with common respiratory symptoms. Self-reported asthma was not associated with CAL in never-smokers. Our findings suggest that CAL in never-smokers signifies a separate clinical phenotype that may be monitored and, possibly, treated differently from smoking-related COPD.

Accurate segmentation of the heart is essential for personalized blood flow simulations and surgical intervention planning. Segmentations need to be accurate in every spatial dimension, which is not ensured by segmenting data slice by slice. Two cardiac computed tomography (CT) datasets consisting of 760 volumes across the whole cardiac cycle from 39 patients, and of 60 volumes from 60 patients respectively were used to train networks to simultaneously segment multiple regions representing the whole heart in 3D. The segmented regions included the left and right atrium and ventricle, left ventricular myocardium, ascending aorta, pulmonary arteries, pulmonary veins, and left atrial appendage. The widely used 3D U-Net and the UNETR architecture were compared to our proposed method optimized for large volumetric inputs. The proposed network architecture, termed Transformer Residual U-Net (TRUNet), maintains the cascade downsampling encoder, cascade upsampling decoder and skip connections from U-Net, while incorporating a Vision Transformer (ViT) block in the encoder alongside a modified ResNet50 block. TRUNet reached higher segmentation performance for all structures within approximately half the training time needed for 3D U-Net and UNETR. The proposed method achieved more precise vessel boundary segmentations and better captured the heart's overall anatomical structure compared to the other methods. The fast training time and accurate delineation of adjacent structures makes TRUNet a promising candidate for medical image segmentation tasks. The code for TRUNet is available at github.com/ljollans/TRUNet.

Introduction:Atrial fibrillation (AF) is associated with an increased risk of stroke, often caused by thrombi that form in the left atrium (LA), and especially in the left atrial appendage (LAA). The underlying mechanism is not fully understood but is thought to be related to stagnant blood flow, which might be present despite sinus rhythm. However, measuring blood flow and stasis in the LAA is challenging due to its small size and low velocities. We aimed to compare the blood flow and stasis in the left atrium of paroxysmal AF patients with controls using computational fluid dynamics (CFD) simulations.Methods:The CFD simulations were based on time-resolved computed tomography including the patient-specific cardiac motion. The pipeline allowed for analysis of 21 patients with paroxysmal AF and 8 controls. Stasis was estimated by computing the blood residence time.Results and Discussion:Residence time was elevated in the AF group (p < 0.001). Linear regression analysis revealed that stasis was strongest associated with LA ejection ratio (p < 0.001, R2 = 0.68) and the ratio of LA volume and left ventricular stroke volume (p < 0.001, R2 = 0.81). Stroke risk due to LA thrombi could already be elevated in AF patients during sinus rhythm. In the future, patient specific CFD simulations may add to the assessment of this risk and support diagnosis and treatment.

Purpose: To find the optimal imaging parameters for a photon-counting detector CT (PCD-CT) and to compare it to an energy-integrating detector CT (EID-CT) in terms of image quality and metal artefact severity using a metal-containing bovine knee specimen. Methods: A bovine knee with a stainless-steel plate and screws was imaged in a whole-body research PCD-CT at 120 kV and 140 kV and in an EID dual-source CT (DSCT) at Sn150 kV and 80/Sn150 kV. PCD-CT virtual monoenergetic 72 and 150 keV images and EID-CT images processed with and without metal artefact reduction algorithms (iMAR) were compared. Four radiologists rated the visualisation of bony structures and metal artefact severity. The Friedman test and Wilcoxon signed-rank test with Bonferroni's correction were used. P-values of <= 0.0001 were considered statistically significant. Distributions of HU values of regions of interest (ROIs) in artefact-affected areas were analysed.Results: PCD-CT 140 kV 150 keV images received the highest scores and were significantly better than EID-CT Sn150 kV images. PCD-CT 72 keV images were rated significantly lower than all the others. HU-value variation was larger in the 120 kV and the 72 keV images. The ROI analysis revealed no large difference between scanners regarding artefact severity.Conclusion: PCD-CT 140 kV 150 keV images of a metal-containing bovine knee specimen provided the best image quality. They were superior to, or as good as, the best EID-CT images; even without the presumed advantage of tin filter and metal artefact reduction algorithms. PCD-CT is a promising method for reducing metal artefacts.

Difficulties in achieving knowledge about physiology and anatomy of the beating heart highlight the challenges with more traditional pedagogical methods. Recent research regarding anatomy education has mainly focused on digital three‐dimensional models. However, these pedagogical improvements may not be entirely applicable to cardiac anatomy and physiology due to the multidimensional complexity with moving anatomy and complex blood flow. The aim of this study was therefore to evaluate whether high quality time‐resolved anatomical images combined with realistic blood flow simulations improve the understanding of cardiac structures and function. Three time‐resolved datasets were acquired using time‐resolved computed tomography and blood flow was computed using Computational Fluid Dynamics. The anatomical and blood flow information was combined and interactively visualized using volume rendering on an advanced stereo projection system. The setup was tested in interactive lectures for medical students. Ninety‐seven students participated. Summative assessment of examinations showed significantly improved mean score (18.1 ± 4.5 vs 20.3 ± 4.9, p = 0.002). This improvement was driven by knowledge regarding myocardial hypertrophy and pressure–velocity differences over a stenotic valve. Additionally, a supplementary formative assessment showed significantly more agreeing answers than disagreeing answers (p < 0.001) when the participants subjectively evaluated the contribution of the visualizations to their education and knowledge. In conclusion, the use of simultaneous visualization of time‐resolved anatomy data and simulated blood flow improved medical students' results, with a particular effect on understanding of cardiac physiology and these simulations may be useful educational tools for teaching complex anatomical and physiological concepts.

Accurate segmentation of the heart is essential for personalized blood flow simulations and surgical intervention planning. Segmentations need to be accurate in every spatial dimension, which is not ensured by segmenting data slice by slice. Two cardiac computed tomography (CT) datasets consisting of 760 volumes across the whole cardiac cycle from 39 patients, and of 60 volumes from 60 patients respectively were used to train networks to simultaneously segment multiple regions representing the whole heart in 3D. The segmented regions included the left and right atrium and ventricle, left ventricular myocardium, ascending aorta, pulmonary arteries, pulmonary veins, and left atrial appendage. The widely used 3D U-Net and the UNETR architecture were compared to our proposed method optimized for large volumetric inputs. The proposed network architecture, termed Transformer Residual U-Net (TRUNet), maintains the cascade downsampling encoder, cascade upsampling decoder and skip connections from U-Net, while incorporating a Vision Transformer (ViT) block in the encoder alongside a modified ResNet50 block. TRUNet reached higher segmentation performance for all structures within approximately half the training time needed for 3D U-Net and UNETR. The proposed method achieved more precise vessel boundary segmentations and better captured the heart's overall anatomical structure compared to the other methods. The fast training time and accurate delineation of adjacent structures makes TRUNet a promising candidate for medical image segmentation tasks. The code for TRUNet is available at github.com/ljollans/TRUNet.