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PURPOSE: The aim of this study was to find the optimal strength level of QIR for ultra-high-resolution (UHR) PCCT of the lung. MATERIALS AND METHODS: This retrospective study included 24 patients who had unenhanced chest CT with the novel UHR scan protocol on the PCCT scanner between March 24, 2023 and May 18, 2023. Two sets of reconstructions were made using different slice thicknesses: standard resolution (SR, 1 mm) and ultra-high-resolution (UHR, 0.2 mm), reconstructed with all strength levels of QIR (0 to 4). Attenuation of the lung parenchyma, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were assessed as objective criteria of image quality. Two fellowship-trained radiologists compared image quality and noise level, sharpness of the images, and the airway details using a 5-point Likert scale. Wilcoxon signed-rank test was used for statistical analysis of reader scores, and one-way repeated measures analysis of variance for comparing the objective image quality scores. RESULTS: Objective image quality linearly improved with higher strength levels of QIR, reducing image noise by 66% from QIR-0 to QIR-4 (P<0.001). Subjective image noise was best for QIR-4 (P<0.001). Readers rated QIR-1 and QIR-2 best for SR, and QIR-2 and QIR-3 best for UHR in terms of subjective image sharpness and airway detail, without significant differences between them (P=0.48 and 0.56, respectively). CONCLUSIONS: Higher levels of QIR provided excellent objective image quality, but readers' preference was for intermediate levels. Considering all metrics, we recommend QIR-3 for ultra-high-resolution PCCT of the lung.

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PURPOSE: To explore the potential differences in epicardial adipose tissue (EAT) volume and attenuation measurements between photon-counting detector (PCD) and energy-integrating detector (EID)-CT systems. METHODS: Fifty patients (mean age 69 ± 8 years, 41 male [82 %]) were prospectively enrolled for a research coronary CT angiography (CCTA) on a PCD-CT within 30 days after clinical EID-based CCTA. EID-CT acquisitions were reconstructed using a Bv40 kernel at 0.6 mm slice thickness. The PCD-CT acquisition was reconstructed at a down-sampled resolution (0.6 mm, Bv40; [PCD-DS]) and at ultra-high resolutions (PCD-UHR) with a 0.2 mm slice thickness and Bv40, Bv48, and Bv64 kernels. EAT segmentation was performed semi-automatically at about 1 cm intervals and interpolated to cover the whole epicardium within a threshold of -190 to -30 HU. A subgroup analysis was performed based on quartile groups created from EID-CT data and PCD-UHRBv48 data. Differences were measured using repeated-measures ANOVA and the Friedman test. Correlations were tested using Pearson's and Spearman's rho, and agreement using Bland-Altman plots. RESULTS: EAT volumes significantly differed between some reconstructions (e.g. EID-CT: 138 ml [IQR 100, 188]; PCD-DS: 147 ml [110, 206]; P<0.001). Overall, correlations between PCD-UHR and EID-CT EAT volumes were excellent, e.g. PCD-UHRBv48: r: 0.976 (95 % CI: 0.958, 0.987); P<0.001; with good agreement (mean bias: -9.5 ml; limits of agreement [LoA]: -40.6, 21.6). On the other hand, correlations regarding EAT attenuation was moderate, e.g. PCD-UHRBV48: r: 0.655 (95 % CI: 0.461, 0.790); P<0.001; mean bias: 6.5 HU; LoA: -2.0, 15.0. CONCLUSION: EAT attenuation and volume measurements demonstrated different absolute values between PCD-UHR, PCD-DS as well as EID-CT reconstructions, but showed similar tendencies on an intra-individual level. New protocols and threshold ranges need to be developed to allow comparison between PCD-CT and EID-CT data.

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BACKGROUND: Radiomics is not yet used in clinical practice due to concerns regarding its susceptibility to technical factors. We aimed to assess the stability and interscan and interreader reproducibility of myocardial radiomic features between energy-integrating detector computed tomography (EID-CT) and photon-counting detector CT (PCD-CT) in patients undergoing coronary CT angiography (CCTA) on both systems. METHODS: Consecutive patients undergoing clinically indicated CCTA on an EID-CT were prospectively enrolled for a PCD-CT CCTA within 30 days. Virtual monoenergetic images (VMI) at various keV levels and polychromatic images (T3D) were generated for PCD-CT, with image reconstruction parameters standardized between scans. Two readers performed myocardial segmentation and 110 radiomic features were compared intraindividually between EID-CT and PDC-CT series. The agreement of parameters was assessed using the intraclass correlation coefficient and paired t-test for the stability of the parameters. RESULTS: Eighteen patients (15 males) aged 67.6 ± 9.7 years (mean ± standard deviation) were included. Besides polychromatic PCD-CT reconstructions, 60- and 70-keV VMIs showed the highest feature stability compared to EID-CT (96%, 90%, and 92%, respectively). The interscan reproducibility of features was moderate even in the most favorable comparisons (median ICC 0.50 [interquartile range 0.20-0.60] for T3D; 0.56 [0.33-0.74] for 60 keV; 0.50 [0.36-0.62] for 70 keV). Interreader reproducibility was excellent for the PCD-CT series and good for EID-CT segmentations. CONCLUSION: Most myocardial radiomic features remain stable between EID-CT and PCD-CT. While features demonstrated moderate reproducibility between scanners, technological advances associated with PCD-CT may lead to greater reproducibility, potentially expediting future standardization efforts. RELEVANCE STATEMENT: While the use of PCD-CT may facilitate reduced interreader variability in radiomics analysis, the observed interscanner variations in comparison to EID-CT should be taken into account in future research, with efforts being made to minimize their impact in future radiomics studies. KEY POINTS: Most myocardial radiomic features resulted in being stable between EID-CT and PCD-CT on certain VMIs. The reproducibility of parameters between detector technologies was limited. PCD-CT improved interreader reproducibility of myocardial radiomic features.

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BACKGROUND: We compared ultra-high resolution (UHR), standard resolution (SR), and virtual non-calcium (VNCa) reconstruction for coronary artery stenosis evaluation using photon-counting computed tomography (PC-CT). METHODS: One vessel phantom (4-mm diameter) containing solid calcified lesions with 25% and 50% stenoses inside a thorax phantom with motion simulation underwent PC-CT using UHR (0.2-mm slice thickness) and SR (0.6-mm slice thickness) at heart rates of 60 beats per minute (bpm), 80 bpm, and 100 bpm. A paired t-test or Wilcoxon test with Bonferroni correction was used. RESULTS: For 50% stenosis, differences in percent mean diameter stenosis between UHR and SR at 60 bpm (51.0 vs 60.3), 80 bpm (51.7 vs 59.6), and 100 bpm (53.7 vs 59.0) (p ≤ 0.011), as well as between VNCa and SR at 60 bpm (50.6 vs 60.3), 80 bpm (51.5 vs 59.6), and 100 bpm (53.7 vs 59.0) were significant (p ≤ 0.011), while differences between UHR and VNCa at all heart rates (p ≥ 0.327) were not significant. For 25% stenosis, differences between UHR and SR at 60 bpm (28.0 vs 33.7), 80 bpm (28.4 vs 34.3), and VNCa vs SR at 60 bpm (29.1 vs 33.7) were significant (p ≤ 0.015), while differences for UHR vs SR at 100 bpm (29.9 vs 34.0), as well as for VNCa vs SR at 80 bpm (30.7 vs 34.3) and 100 bpm (33.1 vs 34.0) were not significant (p ≥ 0.028). CONCLUSION: Stenosis quantification accuracy with PC-CT improved using either UHR acquisition or VNCa reconstruction. RELEVANCE STATEMENT: PC-CT offers to scan with UHR mode and the reconstruction of VNCa images both of them could provide improved coronary stenosis quantification at increased heart rates, allowing a more accurate stenosis grading at low and high heart rates compared to SR. KEY POINTS: Evaluation of coronary stenosis with conventional CT is challenging at high heart rates. PC-CT allows for scanning with ECG-gated UHR and SR modes. UHR and VNCa images were compared in a dynamic phantom. UHR improves stenosis quantification up to 100 bpm. VNCa reconstruction improves stenosis evaluation up to 80 bpm.

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OBJECTIVE: We previously demonstrated the potential of radiomics for prediction of severe histological Placenta Accreta Spectrum (PAS) subtypes using T2-weighted MRI. We aim is to validate our model using an additional dataset. Secondly, we explore whether performance is improved using a new approach to develop a new multivariate radiomics model. METHODS: Multi-centre retrospective analysis conducted between 2018-2023. Inclusion criteria: MRI performed for suspicion of PAS from ultrasound, clinical findings of PAS at laparotomy and/or histopathological confirmation. Radiomic features were extracted from T2-weighted MRI. The previous multivariate model was validated. Secondly, a 5-radiomic feature random forest classifier was selected from a randomised feature selection scheme to predict invasive placenta increta PAS cases. Prediction performance was assessed based on several metrics including Area Under the Curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity and specificity. RESULTS: We present 100 women (mean age 34.6 (±3.9) with PAS, 64 of whom had placenta increta. Firstly, we validated the previous multivariate model and found a Support Vector Machine classifier had a sensitivity of 0.620 (95% CI: 0.068; 1.0), specificity of 0.619 (95% CI: 0.059; 1.0), an AUC of 0.671 (95% CI: 0.440; 0.922) and accuracy of 0.602 (95% CI: 0.353; 0.817) for predicting placenta increta. From the new multivariate model, the best 5-feature subset selected via the random subset feature selection scheme comprised of 4 radiomic features and 1 clinical variable (number of previous caesareans). This clinical-radiomic model achieved an AUC of 0.713 (95% CI: 0.551; 0.854), accuracy of 0.695 (95% CI 0.563; 0.793), sensitivity of 0.843 (95% CI 0.682; 0.990) and specificity of 0.447 (95% CI 0.167; 0.667). CONCLUSION: We validated our previous model and present a new multivariate radiomic model for prediction of severe placenta increta from a well-defined, cohort of PAS cases. ADVANCES IN KNOWLEDGE: Radiomic features demonstrate good predictive potential for identifying placenta increta. This suggests radiomics may be a useful adjunct to clinicians caring for women with this high-risk pregnancy condition.

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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.

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OBJECTIVE: This study aims to assess the efficacy of polyenergetic reconstruction methods in reducing streak artifacts caused by dual source imaging in Photon Counting Detector Computed Tomography (PCD-CT) imaging, thereby improving image quality and diagnostic accuracy. METHODS: A retrospective cohort study was conducted, involving 50 patients who underwent chest Computed Tomography Angiography with PCD-CT, focusing on those with streak artifacts. Quantitative and qualitative analyses were performed on images reconstructed using monoenergetic and polyenergetic techniques. Quantitative evaluations measured the attenuation of tracheal air density in regions affected by streak artifacts, while qualitative assessments employed a modified Likert scale to rate image quality. Statistical analyses included Wilcoxon's signed-rank tests and Spearman's correlation, alongside assessments of inter-rater reliability. RESULTS: There was significantly lower attenuation of tracheal air density on the polyenergetic reconstructions (Median - 1010 ± 62 HU vs -930 ± 110 HU; P < 0.001), and significantly decreased variation on the polyenergetic reconstructions (Median 65.2 ± 79.5 HU vs 38.8 ± 33.9 HU; P < 0.001). The median modified-Likert scale were significantly better for the polyenergetic reconstructions (median modified-Likert 4 ± 0.5 vs 2.5 ± 1; P < 0.001). The inter-rater agreement was substantial and not significantly different between reconstructions (Gwet's ACPolyenergetic = 0.78 vs Gwet's ACVMI = 0.775). CONCLUSION: Polyenergetic reconstruction significantly mitigates streak artifacts in PCD-CT imaging, enhancing quantitative and qualitative image quality. This advancement addresses a known limitation of current PCD-CT reconstruction techniques, offering a promising approach to improving diagnostic reliability and accuracy in clinical practice. We demonstrate that future software implementations can resolve this artifact.

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BACKGROUND: Linear attenuation coefficients (LACs) in positron emission tomography combined with computed tomography (PET/CT) are derived from CT scans that utilize energy-integrating detectors (EID-CT). These LACs are inaccurate when iodine contrast has been injected. Photon counting detector CT (PCD-CT) may be able to improve the accuracy. PURPOSE: To investigate whether PCD-CT can improve PET/CT quantitative accuracy. METHODS: Two experiments were performed: one with CT only and one that combined PET and CT. The first experiment used an electron density phantom whose inserts were imaged with EID-CT and PCD-CT. The inserts simulated normal human tissues, including bone and iodinated blood. In the case of PCD-CT, virtual-monoenergetic images at 190 keV were created. LACs were derived in each case and compared against known values. For inserts with iodine, more accurate LACs were expected with PCD-CT. The second experiment involved a custom PET phantom with various materials simulating human tissues (blood, iodinated blood, and bone) and 18F radioactivity. Data were first acquired with an EID-CT-based PET/CT system and then separately in a PCD-CT system without PET. PET images were reconstructed using LAC from EID-CT and PCD-CT. PET image values were compared against known activity values using recovery coefficients (RC). RESULTS: In the first experiment, LAC based on EID-CT were in error by as much as 18%, whereas the corresponding PCD-CT based measurements were within 3%. In the second experiment, minimum, maximum, and mean RC were (96.1%, 115.4%, and 103.8%) for the EID-CT method, and (95.8%, 105.5%, and 101.0%) for the PCD-CT method. The consistency of PET images in body and head orientations was improved. CONCLUSIONS: PCD-CT can acquire the information needed for accurate LAC for PET reconstruction in a single spiral acquisition.

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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.

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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.

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BACKGROUND AND PURPOSE: The first-generation photon-counting detector CT was recently introduced into clinical practice and represents a promising innovation in high-resolution CT imaging. The purpose of this study was to assess the image quality of ultra-high-resolution photon-counting detector CT compared with energy-integrating detector CT and to explore different reconstruction kernel sharpness levels for the evaluation of intracranial aneurysms. MATERIALS AND METHODS: Ten patients with intracranial saccular aneurysms who had previously undergone conventional energy-integrating detector CT were prospectively enrolled. CT angiograms were acquired on a clinical dual-source photon-counting detector CT in ultra-high-resolution mode and reconstructed with 4 vascular kernels (Bv36, Bv40, Bv44, Bv48). Quantitative and qualitative image-quality parameters of the intracranial arteries were evaluated. For the quantitative analysis (image noise, SNR, contrast-to-noise ratio), ROIs were manually placed at standard anatomic intracranial and extracranial locations by 1 author. In addition, vessel border sharpness was evaluated quantitatively. For the qualitative analysis, 3 blinded neuroradiologists rated photon-counting detector CT and energy-integrating detector CT image quality for the evaluation of the intracranial vessels (ie, the aneurysms and 9 standard vascular branching locations) on a 5-point Likert-type scale. Additionally, readers independently selected their preferred kernel among the 4 kernels evaluated on photon-counting detector CT. RESULTS: In terms of quantitative image quality, Bv48, the sharpest kernel, yielded increased image noise and decreased SNR and contrast-to-noise ratio parameters compared with Bv36, the smoothest kernel. Compared with energy-integrating detector CT, the Bv48 kernel offered better quantitative image quality for the evaluation of small intracranial vessels (P < .001). Image-quality ratings of the Bv48 were superior to those of the energy-integrating detector CT and not significantly different from ratings of the B44 reconstruction kernel. When comparing side by side all 4 photon-counting detector reconstruction kernels, readers selected the B48 kernel as the best to visualize the aneurysms in 80% of cases. CONCLUSIONS: Ultra-high-resolution photon-counting detector CT provides improved image quality for neurovascular imaging. Although the less sharp kernels provided superior SNR and contrast-to-noise ratio, the sharpest kernels delivered the best subjective image quality on photon-counting detector CT for the evaluation of intracranial aneurysms.

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Purpose: Computed tomography (CT) pulmonary angiography is considered the gold standard for pulmonary embolism (PE) diagnosis, relying on the discrimination between contrast and embolus. Photon-counting detector CT (PCD-CT) generates monoenergetic reconstructions through energy-resolved detection. Virtual monoenergetic images (VMI) at low keV can be used to improve pulmonary artery opacification. While studies have assessed VMI for PE diagnosis on dual-energy CT (DECT), there is a lack of literature on optimal settings for PCD-CT-PE reconstructions, warranting further investigation. Material and methods: Twenty-five sequential patients who underwent PCD-CT pulmonary angiography for suspicion of acute PE were retrospectively included in this study. Quantitative metrics including signal-to-noise ratio (SNR) and contrast-to-noise (CNR) ratio were calculated for 4 VMI values (40, 60, 80, and 100 keV). Qualitative measures of diagnostic quality were obtained for proximal to distal pulmonary artery branches by 2 cardiothoracic radiologists using a 5-point modified Likert scale. Results: SNR and CNR were highest for the 40 keV VMI (49.3 ± 22.2 and 48.2 ± 22.1, respectively) and were inversely related to monoenergetic keV. Qualitatively, 40 and 60 keV both exhibited excellent diagnostic quality (mean main pulmonary artery: 5.0 ± 0 and 5.0 ± 0; subsegmental pulmonary arteries 4.9 ± 0.1 and 4.9 ± 0.1, respectively) while distal segments at high (80-100) keVs had worse quality. Conclusions: 40 keV was the best individual VMI for the detection of pulmonary embolism by quantitative metrics. Qualitatively, 40-60 keV reconstructions may be used without a significant decrease in subjective quality. VMIs at higher keV lead to reduced opacification of the distal pulmonary arteries, resulting in decreased image quality.

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We compared image quality of head and neck CT angiography (CTA) obtained with a photon-counting detector CT (PCD-CT), including virtual monoenergetic images and polyenergetic reconstructions, and conventional energy-integrating detectors CT (EID-CT) in three patients. PCD-CT monoenergetic reconstructions at 70 keV and lower provided excellent image quality, with improved signal-to-noise and contrast-to-noise compared to EID-CT and PCD-CT polyenergetic reconstructions. PCD-CT may enable radiation dose and iodinated contrast dose reduction for cerebrovascular imaging.

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PURPOSE: Acute chest syndrome (ACS) is secondary to occlusion of the pulmonary vasculature and a potentially life-threatening complication of sickle cell disease (SCD). Dual-energy CT (DECT) iodine perfusion map reconstructions can provide a method to visualize and quantify the extent of pulmonary microthrombi. METHODS: A total of 102 patients with sickle cell disease who underwent DECT CTPA with perfusion were retrospectively identified. The presence or absence of airspace opacities, segmental perfusion defects, and acute or chronic pulmonary emboli was noted. The number of segmental perfusion defects between patients with and without acute chest syndrome was compared. Sub-analyses were performed to investigate robustness. RESULTS: Of the 102 patients, 68 were clinically determined to not have ACS and 34 were determined to have ACS by clinical criteria. Of the patients with ACS, 82.4% were found to have perfusion defects with a median of 2 perfusion defects per patient. The presence of any or new perfusion defects was significantly associated with the diagnosis of ACS (P = 0.005 and < 0.001, respectively). Excluding patients with pulmonary embolism, 79% of patients with ACS had old or new perfusion defects, and the specificity for new perfusion defects was 87%, higher than consolidation/ground glass opacities (80%). CONCLUSION: DECT iodine map has the capability to depict microthrombi as perfusion defects. The presence of segmental perfusion defects on dual-energy CT maps was found to be associated with ACS with potential for improved specificity and reclassification.

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Positron emission tomography-computed tomography (PET-CT) has the potential to revolutionize research in infectious diseases, as it has done with cancer. There is growing interest in it as a biomarker in the setting of early-phase tuberculosis clinical trials, particularly given the limitations of current biomarkers as adequate predictors of sterilizing cure for tuberculosis. PET-CT is a real-time tool that provides a 3-dimensional view of the spatial distribution of tuberculosis within the lung parenchyma and the nature of lesions with uptake (ie, whether nodular, consolidative, or cavitary). Its ability to provide functional data on changes in metabolism, drug penetration, and immune control of tuberculous lesions has the potential to facilitate drug development and regimen selection for advancement to phase 3 trials in tuberculosis. In this narrative review, we discuss the role that PET-CT may have in evaluating responses to drug therapy in active tuberculosis treatment and the challenges in taking PET-CT forward as predictive biomarker of relapse-free cure in the setting of phase 2 clinical trials.

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PURPOSE: To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA). PATIENTS AND METHODS: A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers. RESULTS: A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%. CONCLUSIONS: The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.

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Absorbed radiation doses are essential in assessing the effects, e.g. safety and efficacy, of radiopharmaceutical therapy (RPT). Patient-specific absorbed dose calculations in the target or the organ at risk require multiple inputs. These include the number of disintegrations in the organ, i.e. the time-integrated activities (TIAs) of the organs, as well as other parameters describing the process of radiation energy deposition in the target tissue (i.e. mean energy per disintegration, radiation dose constants, etc). TIAs are then estimated by incorporating the area under the radiopharmaceutical's time-activity curve (TAC), which can be obtained by quantitative measurements of the biokinetics in the patient (typically based on imaging data such as planar scintigraphy, SPECT/CT, PET/CT, or blood and urine samples). The process of TAC determination/calculation for RPT generally depends on the user, e.g., the chosen number and schedule of measured time points, the selection of the fit function, the error model for the data and the fit algorithm. These decisions can strongly affect the final TIA values and thus the accuracy of calculated absorbed doses. Despite the high clinical importance of the TIA values, there is currently no consensus on processing time-activity data or even a clear understanding of the influence of uncertainties and variations in personalised RPT dosimetry related to user-dependent TAC calculation. As a first step towards minimising site-dependent variability in RPT dosimetry, this work provides an overview of quality assurance and uncertainty management considerations of the TIA estimation.

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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.

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Imaging biomarkers (IBs) have been proposed in medical literature that exploit images in a quantitative way, going beyond the visual assessment by an imaging physician. These IBs can be used in the diagnosis, prognosis, and response assessment of several pathologies and are very often used for patient management pathways. In this respect, IBs to be used in clinical practice and clinical trials have a requirement to be precise, accurate, and reproducible. Due to limitations in imaging technology, an error can be associated with their value when considering the entire imaging chain, from data acquisition to data reconstruction and subsequent analysis. From this point of view, the use of IBs in clinical trials requires a broadening of the concept of quality assurance and this can be a challenge for the responsible medical physics experts (MPEs). Within this manuscript, we describe the concept of an IB, examine some examples of IBs currently employed in clinical practice/clinical trials and analyze the procedure that should be carried out to achieve better accuracy and reproducibility in their use. We anticipate that this narrative review, written by the components of the EFOMP working group on "the role of the MPEs in clinical trials"-imaging sub-group, can represent a valid reference material for MPEs approaching the subject.

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