Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques.

BACKGROUND: The image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from metallic implants obstruct the clear depiction of stent and isolation lumens, and also adjacent soft tissues. However, current techniques to reduce these artifacts still need further advancements due to higher radiation doses, longer processing times and so on. Thus, the aim of this study is to assess the impact of utilizing Single-Energy Metal Artifact Reduction (SEMAR) alongside a novel deep learning image reconstruction technique, known as the Advanced Intelligent Clear-IQ Engine (AiCE), on image quality of CTA follow-ups conducted after EVAR. MATERIALS: This retrospective study included 47 patients (mean age ± standard deviation: 68.6 ± 7.8 years; 37 males) who underwent CTA examinations following EVAR. Images were reconstructed using four different methods: hybrid iterative reconstruction (HIR), AiCE, the combination of HIR and SEMAR (HIR + SEMAR), and the combination of AiCE and SEMAR (AiCE + SEMAR). Two radiologists, blinded to the reconstruction techniques, independently evaluated the images. Quantitative assessments included measurements of image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the longest length of artifacts (AL), and artifact index (AI). These parameters were subsequently compared across different reconstruction methods. RESULTS: The subjective results indicated that AiCE + SEMAR performed the best in terms of image quality. The mean image noise intensity was significantly lower in the AiCE + SEMAR group (25.35 ± 6.51 HU) than in the HIR (47.77 ± 8.76 HU), AiCE (42.93 ± 10.61 HU), and HIR + SEMAR (30.34 ± 4.87 HU) groups (p < 0.001). Additionally, AiCE + SEMAR exhibited the highest SNRs and CNRs, as well as the lowest AIs and ALs. Importantly, endoleaks and thrombi were most clearly visualized using AiCE + SEMAR. CONCLUSIONS: In comparison to other reconstruction methods, the combination of AiCE + SEMAR demonstrates superior image quality, thereby enhancing the detection capabilities and diagnostic confidence of potential complications such as early minor endleaks and thrombi following EVAR. This improvement in image quality could lead to more accurate diagnoses and better patient outcomes.

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities.

PURPOSE: To examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and hybrid iterative reconstruction (HIR [AIDR 3D, adaptive iterative dose reduction three dimensional]) algorithms on the conventional enhanced and CE-boost (contrast-enhancement-boost) images of indirect computed tomography venography (CTV) of lower extremities. MATERIALS AND METHODS: In this retrospective study, seventy patients who underwent CTV from June 2021 to October 2022 to assess deep vein thrombosis and varicose veins were included. Unenhanced and enhanced images were reconstructed for AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images were obtained using subtraction software. Objective and subjective image qualities were assessed, and radiation doses were recorded. RESULTS: The CT values of the inferior vena cava (IVC), femoral vein ( FV), and popliteal vein (PV) in the CE-boost images were approximately 1.3 (1.31-1.36) times higher than in those of the enhanced images. There were no significant differences in mean CT values of IVC, FV, and PV between AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images. Noise in AiCE, AiCE-boost images was significantly lower than in AIDR 3D and AIDR 3D-boost images ( P < 0.05). The SNR (signal-to-noise ratio), CNR (contrast-to-noise ratio), and subjective scores of AiCE-boost images were the highest among 4 groups, surpassing AiCE, AIDR 3D, and AIDR 3D-boost images (all P < 0.05). CONCLUSION: In indirect CTV of the lower extremities images, DLR with the CE-boost technique could decrease the image noise and improve the CT values, SNR, CNR, and subjective image scores. AiCE-boost images received the highest subjective image quality score and were more readily accepted by radiologists.

Dynamic computed tomography angiography for noninvasive diagnosis of bow Hunter's syndrome: a case report.

The focus of this case report is to technically describe a noninvasive diagnostic evaluation of bow Hunter's syndrome using a dynamic computed tomography angiography protocol and discuss its advantages. In addition, we aimed to exemplify the quality of the study by presenting images of a 3D-printed model generated to help plan the surgical treatment for the patient. The dynamic computed tomography angiography protocol consisted of a first image acquisition with the patient in the anatomic position of the head and neck. This was followed by a second acquisition with the head and neck rotated to the side that triggered the symptoms, with technical parameters similar to the first acquisition. The acquired images were used to print a 3D model to better depict the findings for the surgical team. The dynamic computed tomography angiography protocol developed in this study helped visualize the vertebrobasilar arterial anatomy, detect vertebral artery stenosis produced by head and neck rotation, depict the structure responsible for artery stenosis (e.g., bony structure or membranes), and study possible complications of the disease (e.g., posterior cerebral circulation infarction). Additionally, the 3D-printed model better illustrated the findings of stenosis, aiding in surgical planning. In conclusion, dynamic computed tomography angiography for the evaluation of bow Hunter's syndrome is a feasible noninvasive technique that can be used as an alternative to traditional diagnostic methods.

Assessing accuracy and consistency in intracranial aneurysm sizing: human expertise vs. artificial intelligence.

Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth of more than 1 mm is considered a surrogate of rupture risk, therefore, this study presents a comprehensive analysis of intracranial aneurysm measurements utilizing a dataset comprising 358 IA from 248 computed tomography angiography (CTA) scans measured by four junior raters and one senior rater. The study explores the variability in sizing assessments by employing both human raters and an Artificial Intelligence (AI) system. Our findings reveal substantial inter- and intra-rater variability among junior raters, contrasting with the lower intra-rater variability observed in the senior rater. Standard deviations of all raters were above the threshold for IA growth (1 mm). Additionally, the study identifies a systemic bias, indicating a tendency for human experts to measure aneurysms smaller than the AI system. Our findings emphasize the challenges in human assessment while also showcasing the capacity of AI technology to improve the precision and reliability of intracranial aneurysm assessments, especially beneficial for junior raters. The potential of AI was particularly evident in the task of monitoring IA at various intervals, where the AI-based approach surpassed junior raters and achieved performance comparable to senior raters.

Occlusion of vertebral artery and anterior spinal artery in cervical facet dislocation: a prospective study using computed tomography angiography.

STUDY DESIGN: A prospective study. OBJECTIVE: To investigate the incidence of vertebral artery (VA) occlusion and whether anterior spinal artery (ASA) is occluded in cervical facet dislocation. SETTING: University hospital, China. METHODS: During a 2-year period, 21 conventional patients with cervical facet dislocation were prospectively enrolled. All patients received computed tomography angiography (CTA) to assess the patency of the VA, anterior radiculomedullary arteries (ARAs), and ASA at the time of injury. Clinical data were documented, including demographics, symptomatic vertebrobasilar ischemia, American Spinal Injury Association Impairment Scale (ASIA) grades, and ASA and VA radiological characteristics. RESULTS: VA unilateral occlusion occurred in 5 of 21 patients (24%), including 2 with unilateral facet dislocation and 3 with bilateral facet dislocation. No ASA occlusion was found in all 21 patients, including 5 with VA unilateral occlusion. No patients had symptomatic vertebrobasilar ischemia. CONCLUSIONS: VA occlusion occurs in approximately one-fourth of cervical facet dislocations, with infrequent symptomatic vertebrobasilar ischemia. ASA is not occluded following cervical facet dislocation, even with unilateral VA occlusion.

nnU-Net-based deep-learning for pulmonary embolism: detection, clot volume quantification, and severity correlation in the RSPECT dataset.

OBJECTIVES: CT pulmonary angiography is the gold standard for diagnosing pulmonary embolism, and DL algorithms are being developed to manage the increase in demand. The nnU-Net is a new auto-adaptive DL framework that minimizes manual tuning, making it easier to develop effective algorithms for medical imaging even without specific expertise. This study assesses the performance of a locally developed nnU-Net algorithm on the RSPECT dataset for PE detection, clot volume measurement, and correlation with right ventricle overload. MATERIALS & METHODS: User input was limited to segmentation using 3DSlicer. We worked with the RSPECT dataset and trained an algorithm from 205 PE and 340 negatives. The test dataset comprised 6573 exams. Performance was tested against PE characteristics, such as central, non-central, and RV overload. Blood clot volume (BCV) was extracted from each exam. We employed ROC curves and logistic regression for statistical validation. RESULTS: Negative studies had a median BCV of 1 µL, which increased to 345 µL in PE-positive cases and 7,378 µL in central PEs. Statistical analysis confirmed a significant BCV correlation with PE presence, central PE, and increased RV/LV ratio (p < 0.0001). The model's AUC for PE detection was 0.865, with an 83 % accuracy at a 55 µL threshold. Central PE detection AUC was 0.937 with 91 % accuracy at 850 µL. The RV overload AUC stood at 0.848 with 79 % accuracy. CONCLUSION: The nnU-Net algorithm demonstrated accurate PE detection, particularly for central PE. BCV is an accurate metric for automated severity stratification and case prioritization. CLINICAL RELEVANCE STATEMENT: The nnU-Net framework can be utilized to create a dependable DL for detecting PE. It offers a user-friendly approach to those lacking expertise in AI and rapidly extracts the Blood Clot Volume, a metric that can evaluate the PE's severity.

Detectability of intracranial vessel wall atherosclerosis using black-blood spectral CT: a phantom and clinical study.

BACKGROUND: Computed tomography (CT) is the usual modality for diagnosing stroke, but conventional CT angiography reconstructions have limitations. METHODS: A phantom with tubes of known diameters and wall thickness was scanned for wall detectability, wall thickness, and contrast-to-noise ratio (CNR) on conventional and spectral black-blood (SBB) images. The clinical study included 34 stroke patients. Diagnostic certainty and conspicuity of normal/abnormal intracranial vessels using SBB were compared to conventional. Sensitivity/specificity/accuracy of SBB and conventional were compared for plaque detectability. CNR of the wall/lumen and quantitative comparison of remodeling index, plaque burden, and eccentricity were obtained for SBB imaging and high-resolution magnetic resonance imaging (hrMRI). RESULTS: The phantom study showed improved detectability of tube walls using SBB (108/108, 100% versus conventional 81/108, 75%, p < 0.001). CNRs were 75.9 ± 62.6 (mean ± standard deviation) for wall/lumen and 22.0 ± 17.1 for wall/water using SBB and 26.4 ± 15.3 and 101.6 ± 62.5 using conventional. Clinical study demonstrated (i) improved certainty and conspicuity of the vessels using SBB versus conventional (certainty, median score 3 versus 0; conspicuity, median score 3 versus 1 (p < 0.001)), (ii) improved sensitivity/specificity/accuracy of plaque (≥ 1.0 mm) detectability (0.944/0.981/0.962 versus 0.239/0.743/0.495) (p < 0.001), (iii) higher wall/lumen CNR of SBB of (78.3 ± 50.4/79.3 ± 96.7) versus hrMRI (18.9 ± 8.4/24.1 ± 14.1) (p < 0.001), and (iv) excellent reproducibility of remodeling index, plaque burden, and eccentricity using SBB versus hrMRI (intraclass correlation coefficient 0.85-0.94). CONCLUSIONS: SBB can enhance the detectability of intracranial plaques with an accuracy similar to that of hrMRI. RELEVANCE STATEMENT: This new spectral black-blood technique for the detection and characterization of intracranial vessel atherosclerotic disease could be a time-saving and cost-effective diagnostic step for clinical stroke patients. It may also facilitate prevention strategies for atherosclerosis. KEY POINTS: • Blooming artifacts can blur vessel wall morphology on conventional CT angiography. • Spectral black-blood (SBB) images are generated from material decomposition from spectral CT. • SBB images reduce blooming artifacts and noise and accurately detect small plaques.

Clinical Impact of Assessment of Myocardial Flow Reserve in Identifying the Cause of Chest Discomfort. / Impacto Clínico da Avaliação da Reserva de Fluxo Miocárdico na Identificação da Causa do Desconforto Torácico.

BACKGROUND: Gamma cameras with cadmium-zinc telluride (CZT) detectors allowed the quantification of myocardial flow reserve (MBF), which can increase the accuracy of myocardial perfusion scintigraphy (MPS) to detect the cause of chest discomfort. OBJECTIVE: To assess the clinical impact of MBF to detect the cause of chest discomfort. METHODS: 171 patients with chest discomfort who underwent coronary angiography or coronary CT angiography also underwent MPS and MBF in a time interval of <30 days. The acquisitions of dynamic imaging of rest and stress were initiated simultaneously with the 99mTc injection sestamibi (10 and 30mCi, respectively), both lasting eleven minutes, followed by immediately acquiring perfusion images for 5 minutes. The stress was performed with dipyridamole. A global or per coronary territory MBF <2.0 was classified as abnormal. RESULTS: The average age was 65.9±10 years (60% female). The anatomical evaluation showed that 115 (67.3%) patients had coronary obstruction significant, with 69 having abnormal MPs and 91 having abnormal MBF (60.0% vs 79.1%, p<0.01). Among patients without obstruction (56 - 32.7%), 7 had abnormal MPS, and 23 had reduced global MBF. Performing MBF identified the etiology of the chest discomfort in 114 patients while MPS identified it in 76 (66.7% vs 44.4%, p<0.001). CONCLUSION: MBF is a quantifiable physiological measure that increases the clinical impact of MPS in detecting the cause of chest discomfort through greater accuracy for detecting obstructive CAD, and it also makes it possible to identify the presence of the microvascular disease.

FUNDAMENTO: Gama-câmaras com detectores de telureto-cádmio-zinco (CZT) permitiram a quantificação da reserva de fluxo miocárdico (RFM), podendo aumentar a acurácia da cintilografia miocárdica de perfusão (CMP) para detectar a causa do desconforto torácico. OBJETIVO: Avaliar o impacto clínico da RFM para detectar a causa do desconforto torácico. MÉTODOS: 171 pacientes com desconforto torácico que foram submetidos a coronariografia ou angiotomografia de coronárias também realizaram CMP e RFM num intervalo de tempo <30 dias. As aquisições das imagens dinâmicas de repouso e estresse foram iniciadas simultaneamente à injeção de 99mTc sestamibi (10 e 30mCi, respectivamente), ambas com duração de onze minutos, seguidas imediatamente pela aquisição das imagens de perfusão durante 5 minutos. O estresse foi realizado com dipiridamol. Uma RFM global ou por território coronariano <2,0 foi classificada como anormal. RESULTADOS: A idade média foi de 65,9±10 anos (60% do sexo feminino). A avaliação anatômica mostrou que 115 (67,3%) pacientes apresentavam obstrução coronariana significativa, sendo que, 69 apresentavam CMP anormal e 91 apresentavam RFM anormal (60,0% vs. 79,1%, p<0,01). Dentre os pacientes sem obstrução (56 ­ 32,7%), 7 tinham CMP anormais e 23 tinham RFM global reduzida. A realização da RFM identificou a etiologia do desconforto torácico em 114 pacientes enquanto a CMP identificou em 76 (66,7% vs. 44,4%, p<0,001). CONCLUSÃO: A RFM é uma medida fisiológica quantificável que aumenta o impacto clínico da CMP na detecção da causa do desconforto torácico através de uma maior acurácia para detecção de DAC obstrutiva e ainda possibilita identificar a presença de doença microvascular.

Diagnostic performance of the quantitative flow ratio and CT-FFR for coronary lesion-specific ischemia.

Fractional flow reserve (FFR) has become the gold standard for evaluating coronary lesion-specific ischemia. However, FFR is an invasive method that may cause possible complications in the coronary artery and requires expensive equipment, which limits its use. Promising noninvasive diagnostic methods, such as computed tomography angiography-derived FFR (CT-FFR) and the quantitative flow ratio (QFR), have been proposed. In this study, we evaluated the diagnostic performance of the QFR and CT-FFR in predicting coronary lesion-specific ischemia, with the FFR serving as the reference standard. Patients with suspected or known coronary artery disease who underwent coronary CT angiography revealing 30-90% diameter stenosis in the main coronary artery (≥ 2.0 mm reference diameter) were enrolled. The FFR was measured during invasive coronary angiography (within 15 days after coronary CT angiography). An FFR ≤ 0.8 was the reference standard for coronary lesion-specific ischemia. A total of 103 vessels from 92 consecutive patients (aged 59.8 ± 9.2 years; 60.9% were men) were evaluated. The diagnostic performance of a QFR ≤ 0.80 for predicting coronary lesion-specific ischemia demonstrated good diagnostic accuracy, sensitivity, and specificity (92.2%, 87.2%, and 96.4%, respectively), with an area under the receiver operating characteristic curve (AUC) of 0.987 (P < 0.0001). The diagnostic performance of a CT-FFR ≤ 0.80 for predicting coronary lesion-specific ischemia also demonstrated good diagnostic accuracy, sensitivity, and specificity (96.1%, 95.7%, and 96.4%, respectively), with an AUC of 0.967 (P < 0.0001). However, there was no significant difference in the AUC between a QFR ≤ 0.80 and a CT-FFR ≤ 0.80 for predicting coronary lesion-specific ischemia (P = 0.319). There was an excellent correlation between the QFR and FFR (r = 0.856, P < 0.0001). The CT-FFR and FFR also showed a good direct correlation (r = 0.816, P < 0.0001). The QFR and CT-FFR are strongly correlated with the FFR and can provide excellent clinical diagnostic performance for coronary lesion-specific ischemia detection.

The role of coronary CT angiography in athletes.

The sudden death of a young or high-level athlete or adolescent during recreational sports is one of the events with the greatest impact on public opinion in modern society. Sudden cardiac death (SCD) is the principal medical cause of death in athletes and can be the first and last clinical presentation of underlying disease. To prevent such episodes, pre-participation screening has been introduced in many countries to guarantee cardiovascular safety during sports and has become a common target among medical sports/governing organizations. Different cardiac conditions may cause SCD, with incidence depending on definition, evaluation methods, and studied populations, and a prevalence and etiology changing according to the age of athletes, with CAD most frequent in master athletes, while coronary anomalies and non-ischemic causes prevalent in young. To detect silent underlying causes early would be of considerable clinical value. This review summarizes the pre-participation screening in athletes, the specialist agonistic suitability visit performed in Italy, the anatomical characteristics of malignant coronary anomalies, and finally, the role of coronary CT angiography in such arena. In particular, the anatomical conditions suggesting potential disqualification from sport, the post-treatment follow-up to reintegrate young athletes, the diagnostic workflow to rule-out CAD in master athletes, and their clinical management are analyzed.

The Plaque Analysis Classifies the Coronary Artery Disease-Reporting and Data System (CAD-RADS) Stenosis and Plaque Burden Categories: Association of the Plaque Features, Fat Attenuation Index, Coronary Computed Tomography Fractional Flow Reserve, and the Combination of Stenosis and Calcification.

BACKGROUND: The coronary artery disease-reporting and data system (CAD-RADS) 2.0 is used to standardize the reporting of coronary computed tomography angiography (CCTA) results. Artificial intelligence software can quantify the plaque composition, fat attenuation index, and fractional flow reserve. OBJECTIVE: To analyze plaque features of varying severity in patients with a combination of CAD-RADS stenosis and plaque burden categorization and establish a random forest classification model. METHODS: The data of 100 patients treated between April 2021 and February 2022 were retrospectively collected. The most severe plaque observed in each patient was the target lesion. Patients were categorized into three groups according to CAD-RADS: CAD-RADS 1-2 + P0-2, CAD-RADS 3-4B + P0-2, and CAD-RADS 3-4B + P3-4. Differences and correlations between variables were assessed between groups. AUC, accuracy, precision, recall, and F1 score were used to evaluate the diagnostic performance. RESULTS: A total of 100 patients and 178 arteries were included. The differences of computed tomography fractional flow reserve (CT-FFR) (H = 23.921, p < 0.001), the volume of lipid component (H = 12.996, p = 0.002), the volume of fibro-lipid component (H = 8.692, p = 0.013), the proportion of lipid component volume (H = 22.038, p < 0.001), the proportion of fibro-lipid component volume (H = 11.731, p = 0.003), the proportion of calcification component volume (H = 11.049, p = 0.004), and plaque type (χ2 = 18.110, p = 0.001) was statistically significant. CONCLUSION: CT-FFR, volume and proportion of lipid and fibro-lipid components of plaques, the proportion of calcified components, and plaque type were valuable for CAD-RADS stenosis + plaque burden classification, especially CT-FFR, volume, and proportion of lipid and fibro-lipid components. The model built using the random forest was better than the clinical model (AUC: 0.874 vs. 0.647).

Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease.

OBJECTIVE: To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD). MATERIALS AND METHODS: The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic. RESULTS: Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p < 0.05). The clinical radiomics model had significant higher C-index than clinical model (p = 0.030). There were no significant differences in C-index between clinical or clinical radiomics model and radiomics model (p values were 0.796 and 0.147 respectively). The AUC increased from 0.674 for clinical model to 0.721 for radiomics model, 0.759 for clinical radiomics model and 0.773 for combined model. CONCLUSION: Radiomics analysis of lesion-specific PCAT is useful in predicting MACE. Combination of lesion-specific Rad-score and CT-FFR shows incremental value over traditional risk factors.

Diagnostic value of multimodal cardiovascular imaging technology coupled with biomarker detection in elderly patients with coronary heart disease.

Aims/Background Coronary heart disease is a common disease in the elderly and has a complex pathogenesis, which complicates the clinical diagnostic process. Thus, enhancing the diagnostic efficiency for coronary heart disease is imperative to improve the life expectancy of the elderly. This study aimed to explore the diagnostic value of multimodal cardiovascular imaging technology coupled with biomarker detection in elderly patients with coronary heart disease. Methods The medical records of 421 patients with suspected coronary heart disease obtained from the geriatric department of the First Affiliated Hospital of Hebei North University from February 2020 to February 2023 were retrospectively analysed. After excluding 10 patients who did not meet the inclusion criteria, the remaining 411 patients were included in this study. The included subjects had undergone coronary computed tomography angiography and were divided into coronary heart disease group (n=208) and non-coronary heart disease group (n=203) according to the diagnostic results. Multimodal cardiovascular imaging (coronary computed tomography angiography and echocardiography) and detection of serum biomarkers such as small dense low-density lipoprotein, lipoprotein a, and gamma-glutamyl transferase were performed in both groups. The clinical indicators of the two groups were compared, and the combined diagnostic efficacy of multimodal cardiovascular imaging and biomarker detection was evaluated. Results Compared to the non-coronary heart disease group, the coronary heart disease group had significantly higher levels of maximum area stenosis, total plaque volume, total plaque burden and fibrotic plaque volume (p < ..001), and lower left ventricular ejection fraction level (p < ..001). Additionally, the coronary heart disease group exhibited higher levels of left ventricular end-diastolic volume, left ventricular end-systolic volume and stroke volume than the non-coronary heart disease group (p < ..001), and had higher levels of small dense low-density lipoprotein, lipoprotein a and gamma-glutamyl transferase (p < ..001). Our results demonstrated that combined diagnosis had better diagnostic efficacy than individual approaches, marked by higher area under the curve and sensitivity of the former (p < ..001). Conclusion Multimodal cardiovascular imaging technology combined with biomarker detection can distinctly improve the accuracy of coronary heart disease diagnosis in elderly patients.

Clinical audit of the image quality and customised contrast volume using P3T contrast injection software versus standard injection protocol in CT coronary angiography.

INTRODUCTION: The implications of shorter scan time and lower tube voltage in the dual-source CT coronary angiography (CTCA) scan protocol necessitate the adaptation of contrast media (CM) injection parameters. This audit evaluates the coronary arteries' vascular attenuation and image quality by comparing the personalised patient protocol technology (P3T) contrast injection software with standard injection protocol. The secondary aim is to determine the relationship between CM volume and the patient's weight. METHODOLOGY: A Siemens Somatom Definition Force CT Unit was used to scan 30 sets of patients between August 2020 and October 2020. Patients were selected retrospectively and separated into Standard Injection and P3T injection protocols. An experienced radiologist blinded to the groups reviewed the coronary vessels' contrast enhancement and image quality. RESULTS: Overall, the mean HU of all the main coronary artery vessels obtained from P3T injection software reached above 350 HU and was diagnostically sufficient. The mean attenuation at the proximal region of RCA in the 80-99 kg weight category was significantly higher in the P3T injection software than the standard injection protocol (p < 0.001). The CM volume proposed by P3T injection software for 40-59 kg was approximately 57 ± 5 mls, while 75 ml was used for the standard injection protocol. CONCLUSION: P3T injection software in CTCA resulted in an adequate diagnostic attenuation of coronary arteries (>350HU) in all weight groups, most effectively in the higher weight group, while maintaining diagnostic image quality. Further, the P3T software reduces CM volumes in lower-weight patients. IMPLICATIONS: P3T software enables reducing CM volume in lower-weight patients while improving vascular enhancement in CTCA scans in higher-weight patients.

Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography.

BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis. METHODS: The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration. RESULTS: This retrospective, single-center study evaluated computed tomography angiography scans of 268 patients with both symptomatic and asymptomatic carotid atherosclerosis (163 for the derivation set and 106 for the testing set) performed between March 2013 and October 2019. The area-under-receiver-operating characteristics curve by machine learning on the testing cohort (0.89) was significantly higher than the areas under the curve of traditional logit analysis based on the degree of stenosis (0.51, P<0.001), presence of intraplaque hemorrhage (0.69, P<0.001), and plaque composition (0.78, P<0.001), respectively. Comparable performance was obtained on internal validation. The identified plaque components and associated cutoff values that were significantly associated with a higher likelihood of symptomatic status after adjustment were the ratio of intraplaque hemorrhage to lipid volume (≥50%, 38.5 [10.1-205.1]; odds ratio, 95% CI) and percentage of intraplaque hemorrhage volume (≥10%, 18.5 [5.7-69.4]; odds ratio, 95% CI). CONCLUSIONS: This study presented an interpretable machine learning model that accurately identifies symptomatic carotid plaques using computed tomography angiography derived plaque composition features, aiding clinical decision-making.

Accuracy of duplex ultrasonography versus angiotomography for the diagnosis of extracranial internal carotid stenosis.

INTRODUCTION: Internal carotid artery (ICA) stenosis causes about 15% of ischemic strokes. Duplex ultrasonography (DUS) is the first line of investigation of ICA stenosis, but its accuracy varies in the literature and it is usual to complement the study with another more accurate exam when faced with significant stenosis. There is a lack of studies that compare DUS with angiotomography (CTA) in the present literature. METHODS: we performed an accuracy study, which compared DUS to CTA of patients in a tertiary hospital with a maximum interval of three months between tests. Patients were selected retrospectively, and two independent and certified vascular surgeons evaluated each image in a masked manner. When there was discordance, a third evaluator was summoned. We evaluated the diagnostic accuracy of ICA stenosis of 50-94% and 70-94%. RESULTS: we included 45 patients and 84 arteries after inclusion and exclusion criteria applied. For the 50-94% stenosis range, DUS accuracy was 69%, sensitivity 89%, and specificity 63%. For the 70-94% stenosis range, DUS accuracy was 84%, sensitivity 61%, and specificity 93%. There was discordance between CTA evaluators with a change from clinical to surgical management in at least 37.5% of the conflicting reports. CONCLUSION: DUS had an accuracy of 69% for stenoses of 50-94% and 84% for stenoses of 70-94% of the ICA. The CTA analysis depended directly on the evaluator with a change in clinical conduct in more than 37% of cases.