Aortic wall segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation

PET imaging of atherosclerosis: artificial intelligence applications and recent advancements

PET imaging has become a valuable tool for assessing atherosclerosis by targeting key processes such as inflammation and microcalcification. Among available tracers, 18F-sodium fluoride has demonstrated superior performance compared to 18F-fluorodeoxyglucose, particularly in detecting coronary artery disease. However, the role of other tracers remains underexplored, requiring further validation. Emerging technologies such as artificial intelligence show potential in enhancing diagnostic speed and accuracy. Furthermore, the integration of the Alavi-Carlsen Calcification Score offers a novel approach to evaluating global disease burden, presenting a more clinically applicable method for predicting outcomes. Techniques such as total-body PET provide faster and more comprehensive imaging of the entire vascular system with reduced radiation exposure, representing a significant advancement in early detection and intervention. The combination of molecular imaging and advanced computational tools may revolutionize the management of atherosclerosis, facilitating earlier identification of at-risk individuals and improving long-term cardiovascular outcomes.

Auto-segmentation, radiomic reproducibility, and comparison of radiomics between manual and AI-derived segmentations for coronary arteries in cardiac [18F]NaF PET/CT images

BACKGROUND

[18F]NaF is a potential biomarker for assessing cardiac risk. Automated analysis of [18F]NaF positron emission tomography (PET) images, specifically through quantitative image analysis ("radiomics"), can potentially enhance diagnostic accuracy and personalised patient management. However, it is essential to evaluate the reproducibility and reliability of radiomic features to ensure their clinical applicability. This study aimed to (i) develop and evaluate an automated model for coronary artery segmentation using [18F]NaF PET and calcium scoring computed tomography (CSCT) images, (ii) assess inter- and intra-observer radiomic reproducibility from manual segmentations, and (iii) evaluate the radiomics reliability from AI-derived segmentations by comparison with manual segmentations.

RESULTS

141 patients from the "effects of Vitamin K and Colchicine on vascular calcification activity" (VikCoVac, ACTRN12616000024448) trial were included. 113 were used to train an auto-segmentation model using nnUNet on [18F]NaF PET and CSCT images. Reproducibility of inter- and intra-observer radiomics and reliability of radiomics from AI-derived segmentations was assessed using lower bound of intraclass correlation coefficient (ICC). The auto-segmentation model achieved an average Dice Similarity Coefficient of 0.61 ± 0.05, having no statistically significant difference compared to the intra-observer variability (p = 0.922). For the unfiltered images, 47(12.6%) CT and 25(7.5%) PET radiomics were inter-observer reproducible, while 133(35.8%) CT and 57(15.3%) PET radiomics were intra-observer reproducible. 7(9.7%) CT and 18(25.0%) PET first-order features, as well as 17(17.7%) CT GLCM features, were reproducible for both inter- and intra-observer analyses. 9.8% and 16.8% of radiomics from AI-derived segmentations showed excellent and good reliability. First-order features were most reliable (ICC > 0.75; 78/144[54.2%]) and shape features least (2/112[1.8%]). CT features demonstrated greater reliability (147/428[34.3%]) than PET (81/428 [18.9%]). Features from the left anterior descending (76/214[35.5%]) and right coronary artery (75/214[35.0%]) were more reliability than the circumflex (49/214[22.9%]) and left main (28/214[13.1%]) arteries.

CONCLUSIONS

An effective segmentation model for coronary arteries was developed and reproducible [18F]NaF PET/CSCT radiomics were identified through inter- and intra-observer assessments, supporting their clinical applicability. The reliability of radiomics from AI-derived segmentations compared to manual segmentations was highlighted. The novelty of [18F]NaF as a biomarker underscores its potential in providing unique insights into vascular calcification activity and cardiac risk assessment.

CLINICAL TRIAL REGISTRATION

VIKCOVAC trial ("effects of Vitamin K and Colchicine on vascular calcification activity"). Unique identifier: ACTRN12616000024448. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368825 .

Common carotid segmentation in 18 F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based and manual method

BACKGROUND

Carotid atherosclerosis is a major cause of stroke, traditionally diagnosed late. Positron emission tomography/computed tomography (PET/CT) with ¹⁸ F-sodium fluoride (NaF) detects arterial wall micro-calcification long before macro-calcification becomes detectable by ultrasound, CT or magnetic resonance imaging. However, manual PET/CT processing is time-consuming and requires experience. We compared a convolutional neural network (CNN) approach with manual segmentation of the common carotids.

METHODS

Segmentation in NaF-PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients were compared for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, and SUVtotal). SUVmean was the average of SUVmeans within the VOI, SUVmax the highest SUV in all voxels in the VOI, and SUVtotal the SUVmean multiplied by the Vol of the VOI. Intra and Interobserver variability with manual segmentation was examined in 25 randomly selected scans.

RESULTS

Bias for Vol, SUVmean, SUVmax, and SUVtotal were 1.33 ± 2.06, -0.01 ± 0.05, 0.09 ± 0.48, and 1.18 ± 1.99 in the left and 1.89 ± 1.5, -0.07 ± 0.12, 0.05 ± 0.47, and 1.61 ± 1.47, respectively, in the right common carotid artery. Manual segmentation lasted typically 20 min versus 1 min with the CNN-based approach. Mean Vol deviation at repeat manual segmentation was 14% and 27% in left and right common carotids.

CONCLUSIONS

CNN-based segmentation was much faster and provided SUVmean values virtually identical to manually obtained ones, suggesting CNN-based analysis as a promising substitute of slow and cumbersome manual processing.

An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images

The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.

NaF-PET Imaging of Atherosclerosis Burden

The method of 18F-sodium fluoride (NaF) positron emission tomography/computed tomography (PET/CT) of atherosclerosis was introduced 12 years ago. This approach is particularly interesting because it demonstrates microcalcification as an incipient sign of atherosclerosis before the development of arterial wall macrocalcification detectable by CT. However, this method has not yet found its place in the clinical routine. The more exact association between NaF uptake and future arterial calcification is not fully understood, and it remains unclear to what extent NaF-PET may replace or significantly improve clinical cardiovascular risk scoring. The first 10 years of publications in the field were characterized by heterogeneity at multiple levels, and it is not clear how the method may contribute to triage and management of patients with atherosclerosis, including monitoring effects of anti-atherosclerosis intervention. The present review summarizes findings from the recent 2¾ years including the ability of NaF-PET imaging to assess disease progress and evaluate response to treatment. Despite valuable new information, pertinent questions remain unanswered, not least due to a pronounced lack of standardization within the field and of well-designed long-term studies illuminating the natural history of atherosclerosis and effects of intervention.

"Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison

BACKGROUND

Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden.

METHODS

A trained convolutional neural network (CNN) was used for cardiac segmentation in 18F-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analyzed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%. Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of manual segmentation was examined by re-segmentation in 25 randomly selected scans.

RESULTS

Mean (± SD) values with manual vs. CNN-based segmentation were Vol 617.65 ± 154.99 mL vs 625.26 ± 153.55 mL (P = .21), SUVmean 0.69 ± 0.15 vs 0.69 ± 0.15 (P = .26), SUVmax 2.68 ± 0.86 vs 2.77 ± 1.05 (P = .34), and SUVtotal 425.51 ± 138.93 vs 427.91 ± 132.68 (P = .62). Limits of agreement were - 89.42 to 74.2, - 0.02 to 0.02, - 1.52 to 1.32, and - 68.02 to 63.21, respectively. Manual segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at manual re-segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators.

CONCLUSION

The CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the manually obtained ones. This AI-based segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome manual segmentation of the heart.

Advances in positron emission tomography tracers related to vascular calcification

Microcalcification, a type of vascular calcification, increases the instability of plaque and easily leads to acute clinical events. Positron emission tomography (PET) is a new examination technology with significant advantages in identifying vascular calcification, especially microcalcification. The use of the ¹⁸F-NaF is undoubtedly the benchmark, and other PET tracers related to vascular calcification are also currently in development. Despite all this, a large number of studies are still needed to further clarify the specific mechanisms and characteristics. This review aimed at providing a summary of the application and progress of different PET tracers and also the future development direction.

PET-Based Imaging with 18F-FDG and 18F-NaF to Assess Inflammation and Microcalcification in Atherosclerosis and Other Vascular and Thrombotic Disorders

Positron emission tomography (PET) imaging with ¹⁸F-fluorodeoxyglucose (FDG) represents a method of detecting and characterizing arterial wall inflammation, with potential applications in the early assessment of vascular disorders such as atherosclerosis. By portraying early-stage molecular changes, FDG-PET findings have previously been shown to correlate with atherosclerosis progression. In addition, recent studies have suggested that microcalcification revealed by ¹⁸F-sodium fluoride (NaF) may be more sensitive at detecting atherogenic changes compared to FDG-PET. In this review, we summarize the roles of FDG and NaF in the assessment of atherosclerosis and discuss the role of global assessment in quantification of the vascular disease burden. Furthermore, we will review the emerging applications of FDG-PET in various vascular disorders, including pulmonary embolism, as well as inflammatory and infectious vascular diseases.

Alavi-Carlsen Calcification Score (ACCS): A Simple Measure of Global Cardiac Atherosclerosis Burden

Multislice cardiac CT characterizes late stage macrocalcification in epicardial arteries as opposed to PET/CT, which mirrors early phase arterial wall changes in epicardial and transmural coronary arteries. With regard to tracer, there has been a shift from using mainly 18F-fluorodeoxyglucose (FDG), indicating inflammation, to applying predominantly 18F-sodium fluoride (NaF) due to its high affinity for arterial wall microcalcification and more consistent association with cardiovascular risk factors. To make NaF-PET/CT an indispensable adjunct to clinical assessment of cardiac atherosclerosis, the Alavi-Carlsen Calcification Score (ACCS) has been proposed. It constitutes a global assessment of cardiac atherosclerosis burden in the individual patient, supported by an artificial intelligence (AI)-based approach for fast observer-independent segmentation. Common measures for characterizing epicardial coronary atherosclerosis by NaF-PET/CT as the maximum standardized uptake value (SUV) or target-to-background ratio are more versatile, error prone, and less reproducible than the ACCS, which equals the average cardiac SUV. The AI-based approach ensures a quick and easy delineation of the entire heart in 3D to obtain the ACCS expressing ongoing global cardiac atherosclerosis, even before it gives rise to CT-detectable coronary calcification. The quantification of global cardiac atherosclerotic burden by the ACCS is suited for management triage and monitoring of disease progression with and without intervention.