Multidetector (64+) Computed Tomography Angiography of the Lower Limb in Symptomatic Peripheral Arterial Disease: Assessment of Image Quality and Accuracy in a Tertiary Care Setting

Improving image quality consistency and diagnostic accuracy in lower extremity CT angiography using a split-bolus contrast injection protocol

OBJECTIVES

To evaluate the clinical value of using a split-bolus contrast injection protocol in improving image quality consistency and diagnostic accuracy in lower extremity CT angiography (CTA).

METHODS

Fifty (mean age, 66 ± 12 years) and 39 (mean age, 66 ± 11 years) patients underwent CTA in the lower extremity arteries using split-bolus and fixed-bolus injection schemes, respectively. The objective and subjective image quality of the 2 groups were compared and the diagnostic efficacy for the degree of vessel stenosis was compared using digital subtraction angiography as the gold standard. A P < .05 was considered statistically significant.

RESULTS

In comparison with the fixed-bolus scheme, the split-bolus scheme greatly improved the consistency of image quality of the low extremities by significantly increasing the arterial enhancement (337.87 ± 64.67HU vs. 254.74 ± 71.58HU, P < .001), signal-to-noise ratio (22.58 ± 11.64 vs. 7.14 ± 1.98, P < .001), and contrast-to-noise ratio (37.21 ± 10.46 vs. 31.10 ± 15.40, P = .041) in the infrapopliteal segment. The subjective image quality was better (P < .001) and the diagnostic accuracy was higher in the split-bolus group than in the fixed-bolus group (96.00% vs. 91.67%, P < .05, for diagnosing >50% stenosis, and 97.00% vs. 89.10%, P < .05, for diagnosing occlusion) for the infrapopliteal segment arteries.

CONCLUSIONS

Compared with the fixed-bolus injection scheme, the split-bolus injection scheme improves the image quality consistency and diagnostic accuracy especially for the infrapopliteal segment arteries in lower extremity CTA.

ADVANCES IN KNOWLEDGE

(1) The split-bolus injection scheme of CTA of the lower extremity arteries improves the overall image quality, uniformity of contrast enhancement. (2) Compared with the fixed-bolus injection scheme, the split-bolus injection scheme especially improves the infrapopliteal segment arteries image quality and diagnostic efficacy.

Multi-Modality Imaging of Atheromatous Plaques in Peripheral Arterial Disease: Integrating Molecular and Imaging Markers

Peripheral artery disease (PAD) is a common and debilitating condition characterized by the narrowing of the limb arteries, primarily due to atherosclerosis. Non-invasive multi-modality imaging approaches using computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging have emerged as valuable tools for assessing PAD atheromatous plaques and vessel walls. This review provides an overview of these different imaging techniques, their advantages, limitations, and recent advancements. In addition, this review highlights the importance of molecular markers, including those related to inflammation, endothelial dysfunction, and oxidative stress, in PAD pathophysiology. The potential of integrating molecular and imaging markers for an improved understanding of PAD is also discussed. Despite the promise of this integrative approach, there remain several challenges, including technical limitations in imaging modalities and the need for novel molecular marker discovery and validation. Addressing these challenges and embracing future directions in the field will be essential for maximizing the potential of molecular and imaging markers for improving PAD patient outcomes.

Applications of artificial intelligence for patients with peripheral artery disease

OBJECTIVE

Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field.

METHODS

We performed a narrative review based on studies reporting applications of AI in patients with PAD. The MEDLINE database was independently searched by two authors using a combination of keywords to identify studies published between January 1995 and December 2021. Three main fields of AI were investigated including natural language processing (NLP), computer vision and machine learning (ML).

RESULTS

NLP and ML brought new tools to improve the screening, the diagnosis and classification of the severity of PAD. ML was also used to develop predictive models to better assess the prognosis of patients and develop real-time prediction models to support clinical decision-making. Studies related to computer vision mainly aimed at creating automatic detection and characterization of arterial lesions based on Doppler ultrasound examination or computed tomography angiography. Such tools could help to improve screening programs, enhance diagnosis, facilitate presurgical planning, and improve clinical workflow.

CONCLUSIONS

AI offers various applications to support and likely improve the management of patients with PAD. Further research efforts are needed to validate such applications and investigate their accuracy and safety in large multinational cohorts before their implementation in daily clinical practice.

Automatic Plaque Removal Using Dual-Energy Computed Tomography Angiography: Diagnostic Accuracy and Utility in Patients with Peripheral Artery Disease

Background and Objectives: This study aimed to evaluate the utility and accuracy of dual-energy automatic plaque removal (DE-APR) in patients with symptomatic peripheral arterial disease (PAD) using digital subtraction angiography (DSA) as the reference standard. Materials and Methods: We retrospectively analyzed 100 patients with PAD who underwent DE computed tomography angiography (DE-CTA) and DSA of the lower extremities. DE-CTA was used to generate APR subtracted images. In the three main arterial segments (aorto-iliac segment, femoro-popliteal segment, and below-the-knee segment), the presence or absence of hemodynamically significant stenosis (>50%) and calcification was assessed using the images. CTA data were analyzed using different imaging approaches (DE-standard reconstruction image (DE-SR), DE-APR maximum intensity projection image (APR), and DE-SR with APR). Results: For all segments evaluated, the sensitivity, specificity, and accuracy for detecting significant stenosis were 98.16%, 81.01%, and 89.58%, respectively, with DE-SR; 97.79%, 83.33%, and 90.56%, respectively, with APR; and 98.16%, 92.25%, and 95.20%, respectively, with DE-SR with APR. DE-SR with APR had greater accuracy than DE-SR or APR alone (p < 0.001 and p < 0.001, respectively). When analyzed based on vascular wall calcification, the accuracy of DE-SR with APR remained greater than 90% regardless of calcification severity, whereas DE-SR showed a considerable reduction in accuracy in moderate to severe calcification. In the case of APR, the degree of vascular wall calcification did not significantly influence the accuracy in the aorto-iliac and femoro-popliteal segments. DE-SR with APR achieved significantly higher diagnostic accuracy for all lower extremity segments in evaluating hemodynamically significant stenosis in patients with symptomatic PAD and transcended the impact of vascular wall calcification compared with DE-SR. Conclusions: APR demonstrated favorable diagnostic performance in the aorto-iliac and femoro-popliteal segments, exhibiting good agreement with DSA even in cases of moderate to severe vascular wall calcification.

Deep learning-based classification of lower extremity arterial stenosis in computed tomography angiography

PURPOSE

The purpose of this study is to develop and evaluate a deep learning model to assist radiologists in classifying lower extremity arteries based on the degree of arterial stenosis caused by plaque in lower extremity computed tomography angiography (CTA) of patients with peripheral artery disease.

METHODS

In this retrospective study, 265 patients who underwent lower-extremity CTA between January 1, 2016 and October 31, 2019 were selected. A total of 17050 axial images of iliac, femoropopliteal and infrapopliteal artery from these patients were used for the training and validation of the parallel efficient network (p-EffNet), a kind of supervised convolutional neural network, to classify the lower-extremity artery segments according to the degree of stenosis with digital subtraction angiography as reference standard. The classification results of the p-EffNet were then compared with those obtained from radiologists. Receiver operating characteristic curve (ROC) was used to evaluate the performance of the p-EffNet and accuracy, specificity, sensitivity and area under the curve (AUC) were used as measure metrics to compare the performance of the p-EffNet and that of radiologists.

RESULTS

The p-EffNet exhibited a good performance of 91.5 % accuracy, 0.987 AUC and 90.2 % sensitivity and 97.7 % specificity in classifying above-knee artery and 90.9 % accuracy, 0.981 AUC, 91.3 % sensitivity and 95.2 % specificity in classifying below-knee artery. When compared with human readers, for both above-knee and below-knee artery, the p-EffNet had comparable accuracy (p = 0.266 and p = 0.808, respectively) and specificity (p = 0.118 and p = 0.971, respectively) but lower sensitivity (p < 0.001 and p = 0.022, respectively).

CONCLUSIONS

The p-EffNet demonstrates promising diagnostic performance and has the potential to reduce the workload of radiologists and help to find the plaques that might otherwise have been missed or misjudged.