Automatic detection and quantification of abdominal aortic calcification in dual energy X-ray absorptiometry

Cardiovascular disease (CVD) is a major cause of mortality and the main cause of morbidity worldwide. CVD may lead to heart attacks and strokes and most of these are caused by atherosclerosis; this is a medical condition in which the arteries become narrowed and hardened due to an excessive build-up of plaque on the inner artery wall. Arterial calcification and, in particular, abdominal aortic calcification (AAC) is a manifestation of atherosclerosis and a prognostic indicator of CVD. In this paper, a two-stage automatic method to detect and quantify the severity of AAC is described; it is based on the analysis of lateral vertebral fracture assessment (VFA) images. These images were obtained on a dual energy x-ray absorptiometry (DXA) scanner used in single energy mode. First, an active appearance model was used to segment the lumbar vertebrae L1-L4 and the aorta on VFA images; the segmentation of the aorta was based on its position with respect to the vertebrae. In the second stage, feature vectors representing calcified regions in the aorta were extracted to quantify the severity of AAC. The presence and severity of AAC was also determined using an established visual scoring system (AC24). The abdominal aorta was divided into four parts immediately anterior to each vertebra, and the severity of calcification in the anterior and posterior walls was graded separately for each part on a 0-3 scale. The results were summed to give a composite severity score ranging from 0 to 24. This severity score was classified as follows: mild AAC (score 0-4), moderate AAC (score 5-12) and severe AAC (score 12-24). Two classification algorithms (k-nearest neighbour and support vector machine) were trained and tested to assign the automatically extracted feature vectors into the three classes. There was good agreement between the automatic and visual AC24 methods and the accuracy of the automated technique relative to visual classification indicated that it is capable of identifying and quantifying AAC over a range of severit

A robust technique for the detection and quantification of abdominal aortic calcification using dual energy X-Ray absorptiometry

Arterial calcification is a manifestation of atherosclerosis, which over the last two decades has become a recognised predictor of cardiovascular disease. Abdominal Aortic Calcification (AAC) and osteoporosis have been shown to coincide in older individuals. The accepted method of diagnosing osteoporosis is through the measurement of bone mineral density by dual energy x-ray absorptiometry (DXA). Vertebral fracture assessment (VFA) images obtained alongside BMD using DXA technology provide an inexpensive resource for AAC diagnosis. Although several simple methods have been proposed for manual semi-quantitative scoring of AAC in x-ray images in the past, these methods have limitations in terms of capturing small changes in atherosclerosis progression and are time-consuming. Several automatic approaches have been proposed to measure AAC on radiographs. However, these methods have not been related to any accepted medical AAC scoring systems and thus are not likely to be adopted easily by the medical community. In addition, there has been no attempt to apply the proposed methods to VFA images. The main focus of the research presented in this thesis is the automatic quantification of AAC in VFA images acquired in single energy mode. The thesis is divided into two main parts. In the first part, an automatic method for AAC detection and quantification in VFA images is proposed and evaluated on a large number of images. In the second part, the performance of both single and dual energy VFA imaging for the detection of uniformly distributed calcification is investigated. The automatic method for AAC detection consists of two stages. In the first stage an active appearance model was employed for the purpose of segmentaion. In the second stage, adaptive thresholding techniques were used to detect AAC, whilst automatic iii classification techniques were used to quantify the detected calcification. The performance of several classifiers were investigated, and the proposed method was evaluated against the manual AC-24 scoring method using several hundred images and two human readers. A thorough statistical analysis of the results showed that, overall, the SVM classifier gave the best results. Weighted accuracy, sensitivity, specificity assessed for 4 AAC categories were 89.2%, 78.5% and 92.3% respectively while the corresponding values for 3 AAC categories were 88.6%, 86%, 90.4%. In the second part, a study using a tissue-mimicking physical phantom is described. The phantom consists of an aluminium strip within Perspex to simulate calcification and abdominal soft tissue respectively. VFA images of different phantom configurations were acquired in single energy (SE) and dual energy (DE) modes. The minimum detectable aluminium thickness was assessed visually and related to contrast and contrast-to-noise ratio. Percentage coefficient of variation was used to quantify uniformity, repeatability and reproducibility with a Perspex width of 25 cm, the smallest thickness of aluminium that could be detected was 0.20- 0.25 mm. The initial results are promising, and the system proposed in this research can be used as an alternative method to the manual scoring system (AC-24) for a wide range of AAC. The principal conclusion from the phantom work is that under idealised imaging conditions, VFA images have the potential to be used for detecting small thicknesses of calcification with good linearity, repeatability and reproducibility in SE and DE modes for patients with a body width < 30 cm

SCOL: Supervised Contrastive Ordinal Loss for Abdominal Aortic Calcification Scoring on Vertebral Fracture Assessment Scans

Abdominal Aortic Calcification (AAC) is a known marker of asymptomatic Atherosclerotic Cardiovascular Diseases (ASCVDs). AAC can be observed on Vertebral Fracture Assessment (VFA) scans acquired using Dual-Energy X-ray Absorptiometry (DXA) machines. Thus, the automatic quantification of AAC on VFA DXA scans may be used to screen for CVD risks, allowing early interventions. In this research, we formulate the quantification of AAC as an ordinal regression problem. We propose a novel Supervised Contrastive Ordinal Loss (SCOL) by incorporating a label-dependent distance metric with existing supervised contrastive loss to leverage the ordinal information inherent in discrete AAC regression labels. We develop a Dual-encoder Contrastive Ordinal Learning (DCOL) framework that learns the contrastive ordinal representation at global and local levels to improve the feature separability and class diversity in latent space among the AAC-24 genera. We evaluate the performance of the proposed framework using two clinical VFA DXA scan datasets and compare our work with state-of-the-art methods. Furthermore, for predicted AAC scores, we provide a clinical analysis to predict the future risk of a Major Acute Cardiovascular Event (MACE). Our results demonstrate that this learning enhances inter-class separability and strengthens intra-class consistency, which results in predicting the high-risk AAC classes with high sensitivity and high accuracy.Comment: Accepted in conference MICCAI 202

New targets in cardiovascular imaging

Nieuwe aanknopingspunten voor evaluatie aderverkalking Atherosclerose, in de volksmond beter bekend als aderverkalking, is een veelvoorkomende hart- en vaatziekte. De ziekte heeft een gecompliceerd en langzaam verloop. In de loop van jaren wordt een vetachtige stof (‘plaque’) afgezet in de wand van slagaders. De bloedvaten worden daardoor nauwer. Wanneer de plaque loslaat, kan deze complicaties veroorzaken, zoals een hart- of herseninfarct. Reza Golestani onderzocht hoe het moleculaire proces van aderverkalking met visuele technieken zoals PET-scans in beeld kan worden gebracht. Golestani ontwikkelde een beeldvormende techniek voor het visualiseren en meten van de moleculaire en cellulaire doelwitten die betrokken zijn bij atherosclerotische ziekten. Hij deed dat onder andere door diermodellen van de ziekte te onderzoeken en stukjes verwijderde plaque uit de halsslagaders. De promovendus concludeert dat zijn onderzoek veelbelovende doelen heeft geïdentificeerd om de ziekte van buitenaf in beeld te brengen. Op basis van deze nieuwe inzichten, die nog preklinisch van aard zijn, moet het in de toekomst makkelijker worden om patiënten in verschillende risicogroepen in te delen of om nieuwe medicijnen te ontwikkelen die de progressie van de ziekte moeten stoppen

Machine-learning assessed abdominal aortic calcification is associated with long-term fall and fracture risk in community-dwelling older Australian women

Abdominal aortic calcification (AAC), a recognized measure of advanced vascular disease, is associated with higher cardiovascular risk and poorer long-term prognosis. AAC can be assessed on dual-energy X-ray absorptiometry (DXA)-derived lateral spine images used for vertebral fracture assessment at the time of bone density screening using a validated 24-point scoring method (AAC-24). Previous studies have identified robust associations between AAC-24 score, incident falls, and fractures. However, a major limitation of manual AAC assessment is that it requires a trained expert. Hence, we have developed an automated machine-learning algorithm for assessing AAC-24 scores (ML-AAC24). In this prospective study, we evaluated the association between ML-AAC24 and long-term incident falls and fractures in 1023 community-dwelling older women (mean age, 75 ± 3 years) from the Perth Longitudinal Study of Ageing Women. Over 10 years of follow-up, 253 (24.7%) women experienced a clinical fracture identified via self-report every 4–6 months and verified by X-ray, and 169 (16.5%) women had a fracture hospitalization identified from linked hospital discharge data. Over 14.5 years, 393 (38.4%) women experienced an injurious fall requiring hospitalization identified from linked hospital discharge data. After adjusting for baseline fracture risk, women with moderate to extensive AAC (ML-AAC24 ≥ 2) had a greater risk of clinical fractures (hazard ratio [HR] 1.42; 95% confidence interval [CI], 1.10–1.85) and fall-related hospitalization (HR 1.35; 95% CI, 1.09–1.66), compared to those with low AAC (ML-AAC24 ≤ 1). Similar to manually assessed AAC-24, ML-AAC24 was not associated with fracture hospitalizations. The relative hazard estimates obtained using machine learning were similar to those using manually assessed AAC-24 scores. In conclusion, this novel automated method for assessing AAC, that can be easily and seamlessly captured at the time of bone density testing, has robust associations with long-term incident clinical fractures and injurious falls. However, the performance of the ML-AAC24 algorithm needs to be verified in independent cohorts. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)

Bone Mineral Density and Vascular Calcification in Children and Young Adults with Chronic Kidney Disease

Introduction: Older adults with chronic kidney disease (CKD) can have low bone mineral density (BMD) with concurrent vascular calcification. It is not known if mineral accrual by the growing skeleton protects young people with CKD from extraosseous calcification. My hypothesis was that children and young adults with increasing BMD do not develop vascular calcification. Methods: Multicentre longitudinal study in children and young people (5-30 years) with CKD stages 4-5 or on dialysis. Cortical (Cort) and trabecular (Trab) BMD were assessed by peripheral quantitative Computed Tomography and lumbar spine BMD by DXA (Dual Energy X-ray Absorptiometry). Vascular calcification was assessed by cardiac CT for coronary artery calcification (CAC) and ultrasound for carotid intima-media thickness (cIMT). Arterial stiffness was measured by pulse wave velocity (PWV) and carotid distensibility. Results: One hundred participants (median age 13.82 years) were assessed at baseline and 57 followed-up after a median of 1.45 years. The cohort had a significant bone and cardiovascular disease burden. 10% suffered at least one previous atraumatic fracture, and 58% reported bone pain affecting activities of daily living. The majority had evidence of vascular calcification and arterial stiffness with increased cIMT and PWV z-scores. 10% had CAC at baseline. Baseline TrabBMD was independently associated with cIMT (R2=0.10, β=0.34, p=0.001). An annualised increase in TrabBMD was an independent predictor of cIMT increase (R2=0.48, β=0.40, p=0.03), with 6-fold greater odds of an increase in ΔcIMT in those with an increase in ΔTrabBMD [(95%CI 1.88 to 18.35), p=0.003]. Young people that demonstrated statural growth (n=33) had attenuated vascular changes compared to those with static growth. Conclusion: These hypothesis generating studies suggest that children and young adults with CKD or on dialysis may develop vascular calcification even as BMD increases. A presumed buffering capacity of the growing skeleton may offer some protection against extraskeletal calcification

Advances in Clinical Application of Bone Mineral Density and Bone Turnover Markers

Bone mineral density is the main basis for the diagnosis of osteoporosis. The measurement methods of bone mineral density include dual X-ray absorptiometry (DXA), quantitative computer tomography (QCT), quantitative ultrasound (QUS), magnetic resonance imaging (MRI) and so on. Currently, bone mineral density measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for the diagnosis of osteoporosis. Bone turnover markers (BTMs) are biochemical products that reflect the activity of bone cells and the metabolic level of bone matrix, and they reflect the dynamic changes of bone tissue in the whole body earlier than bone mineral-density, procollagen type 1 N-terminal propeptide (PINP) and carboxy-terminal cross-linked telopeptide of type 1 collagen (CTX) is sensitive BTMs, widely used in clinical practice, and can predict the occurrence of fractures. Some new markers such as Periostin, AGEs/RAGE, Gelsolin, and Annexin A2 provide new clues for exploring the mechanism of osteoporosis. The combination of the two can better carry out the diagnosis and differential diagnosis of multiple metabolic bone diseases, evaluate the therapeutic response of anti-osteoporotic medicines, and predict fracture risk

Molecular Imaging of Vascular Calcification with 18 F-Sodium-Fluoride in Patients Infected with Human Immunodeficiency Virus

18F-Sodium Fluoride (NaF) accumulates in areas of active hydroxyapatite deposition and potentially unstable atherosclerotic plaques. We assessed the presence of atherosclerotic plaques in 50 adult patients with HIV (HIV+) who had undergone two cardiac computed tomography scans to measure coronary artery calcium (CAC) progression. CAC and its progression are predictive of an unfavorable prognosis. Tracer uptake was quantified in six arterial territories: aortic arch, innominate carotid artery, right and left internal carotid arteries, left coronary (anterior descending and circumflex) and right coronary artery. Thirty-one patients showed CAC progression and 19 did not. At least one territory with high NaF uptake was observed in 150 (50%) of 300 arterial territories. High NaF uptake was detected more often in non-calcified than calcified areas (68% vs. 32%), and in patients without than in those with prior CAC progression (68% vs. 32%). There was no correlation between clinical and demographic variables and NaF uptake. In clinically stable HIV+ patients, half of the arterial territories showed a high NaF uptake, often in the absence of macroscopic calcification. NaF uptake at one time point did not correlate with prior progression of CAC. Prospective studies will demonstrate the prognostic significance of high NaF uptake in HIV+ patients