CT attenuation values of blood and myocardium: rationale for accurate coronary artery calcifications detection with multi-detector CT.

OBJECTIVES: To determine inter-session and intra/inter-individual variations of the attenuations of aortic blood/myocardium with MDCT in the context of calcium scoring. To evaluate whether these variations are dependent on patients' characteristics. METHODS: Fifty-four volunteers were evaluated with calcium scoring non-enhanced CT. We measured attenuations (inter-individual variation) and standard deviations (SD, intra-individual variation) of the blood in the ascending aorta and of the myocardium of left ventricle. Every volunteer was examined twice to study the inter-session variation. The fat pad thickness at the sternum and noise (SD of air) were measured too. These values were correlated with the measured aortic/ventricular attenuations and their SDs (Pearson). Historically fixed thresholds (90 and 130 HU) were tested against different models based on attenuations of blood/ventricle. RESULTS: The mean attenuation was 46 HU (range, 17-84 HU) with mean SD 23 HU for the blood, and 39 HU (10-82 HU) with mean SD 18 HU for the myocardium. The attenuation/SD of the blood were significantly higher than those of the myocardium (p < 0.01). The inter-session variation was not significant. There was a poor correlation between SD of aortic blood/ventricle with fat thickness/noise. Based on existing models, 90 HU threshold offers a confidence interval of approximately 95% and 130 HU more than 99%. CONCLUSIONS: Historical thresholds offer high confidence intervals for exclusion of aortic blood/myocardium and by the way for detecting calcifications. Nevertheless, considering the large variations of blood/myocardium CT values and the influence of patient's characteristics, a better approach might be an adaptive threshold

Computer Aided Detection and Measurement of Peripheral Artery Disease

Computer-Aided Tomography Angiography (CTA) images are the standard for assessing Peripheral artery disease (PAD). This paper presents a Computer Aided Detection (CAD) and Computer Aided Measurement (CAM) system for PAD. The CAD stage detects the arterial network using a 3D region growing method and a fast 3D morphology operation. The CAM stage aims to accurately measure the artery diameters from the detected vessel centerline, compensating for the partial volume effect using Expectation Maximization (EM) and a Markov Random field (MRF). The system has been evaluated on phantom data and also applied to fifteen (15) CTA datasets, where the detection accuracy of stenosis was 88% and the measurement accuracy was with an 8% error

Kernel Parameter Optimization for Support Vector Machine Based on Sliding Mode Control

Support Vector Machine (SVM) is a supervised machine learning algorithm, which is used for robust and accurate classification. Despite its advantages, its classification speed deteriorates due to its large number of support vectors when dealing with large scale problems and dependency of its performance on its kernel parameter. This paper presents a kernel parameter optimization algorithm for Support Vector Machine (SVM) based on Sliding Mode Control algorithm in a closed-loop manner. The proposed method defines an error equation and a sliding surface, iteratively updates the Radial Basis Function (RBF) kernel parameter or the 2-degree polynomial kernel parameters, forcing SVM training error to converge below a threshold value. Due to the closed-loop nature of the proposed algorithm, key features such as robustness to uncertainty and fast convergence can be obtained. To assess the performance of the proposed technique, ten standard benchmark databases covering a range of applications were used. The proposed method and the state-of-the-art techniques were then used to classify the data. Experimental results show the proposed method is significantly faster and more accurate than the anchor SVM technique and some of the most recent methods. These achievements are due to the closed-loop nature of the proposed algorithm, which significantly has reduced the data dependency of the proposed method

Sliding Mode Control based Support Vector Machine RBF Kernel Parameter Optimization

Support Vector Machine (SVM) is a learning-based algorithm, which is widely used for classification in many applications. Despite its advantages, its application to large scale datasets is limited due to its use of large number of support vectors and dependency of its performance on its kernel parameter. This paper presents a Sliding Mode Control based Support Vector Machine Radial Basis Function’s kernel parameter optimization (SMC-SVM-RBF) method, inspired by sliding mode closed loop control theory, which has demonstrated significantly higher performance to that of the standard closed loop control technique. The proposed method first defines an error equation and a sliding surface and then iteratively updates the RBF’s kernel parameter based on the sliding mode control theory, forcing SVM training error to converge below a predefined threshold value. The closed loop nature of the proposed algorithm increases the robustness of the technique to uncertainty and improves its convergence speed. Experimental results were generated using nine standard benchmark datasets covering wide range of applications. Results show the proposed SMC-SVM-RBF method is significantly faster than those of classical SVM based techniques. Moreover, it generates more accurate results than most of the state of the art SVM based methods

Multimodal imaging of the distal interphalangeal-joint synovio-entheseal complex in psoriatic arthritis (MIDAS): a cross-sectional study on the diagnostic accuracy of different imaging modalities comparing psoriatic arthritis to psoriasis and osteoarthritis

Objective Can ultrasound (US), MRI and X-ray applied to the distal interphalangeal (DIP)-joint and synovio-entheseal complex (SEC) discriminate between patients with psoriatic arthritis (PsA), skin psoriasis (PsO) and hand osteoarthritis (OA)? Methods In this prospective, cross-sectional study, patients with DIP-joint PsA and nail involvement (n=50), PsO with nail involvement (n=12); and OA (n=13); were consecutively recruited. Risk ratios (RR) were calculated for US, MRI and X-ray findings of the DIP-joint and SEC between diagnoses. Results New bone formation (NBF) in US and MRI was a hallmark of OA, reducing the risk of having PsA (RR 0.52 (95% CI 0.43 to 0.63) and 0.64 (95% CI 0.56 to 0.74). The OA group was different from PsA and PsO on all MRI and X-ray outcomes reflected in a lower RR of having PsA; RR ranging from 0.20 (95% CI 0.13 to 0.31) for MRI bone marrow oedema (BMO) to 0.85 (95% CI 0.80 to 0.90) in X-ray enthesitis. No outcome in US, MRI or X-ray was significantly associated with a higher risk of PsA versus PsO, although there was a trend to a higher degree of US erosions and NBF in PsA. 82% of PsA and 67% of PsO was treated with disease modifying antirheumatic drugs which commonly reflects the clinical setting. Conclusion High grade of US, MRI and X-ray NBF reduce the RR of having PsA compared with OA. In PsA versus PsO patients, there was a trend for US to demonstrate more structural changes in PsA although this did not reach significance

MRI Digital Artery Volume Index (DAVIX) as a surrogate outcome measure of digital ulcer disease in patients with systemic sclerosis: a prospective cohort study

Background Vascular fibrosis is a key manifestation of systemic sclerosis that leads to the narrowing of small and medium arteries, causing vascular clinical manifestations including digital ulcers and pulmonary arterial hypertension. We investigated the potential of the MRI-based Digital Artery Volume Index (DAVIX) as a surrogate outcome measure of vascular fibrosis by using it to quantify and predict the burden of digital ulcer disease in patients with systemic sclerosis. Methods Two independent cohorts of patients participating in the prospective observational study STRIKE were consecutively enrolled from the Scleroderma Clinic of the Leeds Teaching Hospitals Trust, Leeds, UK. Eligible patients were aged 18 years or older and fulfilled the very early diagnosis of systemic sclerosis (VEDOSS) or the 2013 American College of Rheumatology (ACR)–European Alliance of Associations for Rheumatology (EULAR) systemic sclerosis classification criteria. DAVIX was calculated as the percentage mean of the ratio of digital artery volume to finger volume in the four fingers of the dominant hand. Data were collected at baseline and 12-month follow-up, and the primary outcome was the presence of digital ulcers at 12-month follow-up. Findings Between Feb 7, 2018, and April 11, 2022, we included 85 patients in the exploratory cohort and 150 in the validation cohort. In the exploratory cohort, the mean age was 54·5 years (SD 11·6), 75 (88%) of 85 patients were women, ten (12%) were men, and 69 (82%) were White. In the validation cohort, the mean age was 53·5 years (SD 13·8), 136 (91%) of 150 patients were women, 14 (9%) were men, and 127 (85%) were White. In the exploratory cohort, DAVIX was significantly lower in patients with previous or active digital ulcers (0·34% [IQR 0·16–0·69]) than in those without digital ulcer disease (0·65% [0·42–0·88]; p=0·015); this finding was substantiated in the validation cohort (0·43% [0·20–0·73] vs 0·73% [0·53–0·97]; p<0·0001). Patients who developed new digital ulcers during 12-month follow-up had a lower DAVIX (0·23% [0·10–0·66]) than those who did not (0·65% [0·45–0·91]; p=0·0039). DAVIX was negatively correlated with disease duration (r=−0·415; p<0·0001), the ratio of forced vital capacity to the diffusing capacity of the lungs for carbon monoxide (r=−0·334; p=0·0091), nailfold capillaroscopy pattern (r=−0·447; p<0·0001), and baseline modified Rodnan skin score (r=−0·305; p=0·014) and was positively correlated with the diffusing capacity of carbon monoxide (r=0·368; p=0·0041). DAVIX was negatively correlated with change in score on the Scleroderma Health Assessment Questionnaire-Disability Index (r=−0·308; p=0·024), Visual Analogue Scale (VAS) Raynaud's (r=−0·271; p=0·044), and VAS digital ulcers (r=−0·291; p=0·044). Interpretation DAVIX is a promising surrogate outcome measure of digital ulcer disease in patients with systemic sclerosis. The ability of DAVIX to non-invasively predict future digital ulcers and worsening of patient-reported outcomes could aid patient enrichment and stratification in clinical trials. Clinically, DAVIX could offer insights into the assessment of vascular activity. The sensitivity of DAVIX to change over time and with treatment will establish its value as an imaging outcome measure of vascular disease. Funding National Institute for Health Research Biomedical Research Centre and University of Leeds Industry Engagement Accelerator Fund

Prevalence of Grey Matter Pathology in Early Multiple Sclerosis Assessed by Magnetization Transfer Ratio Imaging

The aim of the study was to assess the prevalence, the distribution and the impact on disability of grey matter (GM) pathology in early multiple sclerosis. Eighty-eight patients with a clinically isolated syndrome with a high risk developing multiple sclerosis were included in the study. Forty-four healthy controls constituted the normative population. An optimized statistical mapping analysis was performed to compare each subject's GM Magnetization Transfer Ratio (MTR) imaging maps with those of the whole group of controls. The statistical threshold of significant GM MTR decrease was determined as the maximum p value (p<0.05 FDR) for which no significant cluster survived when comparing each control to the whole control population. Using this threshold, 51% of patients showed GM abnormalities compared to controls. Locally, 37% of patients presented abnormalities inside the limbic cortex, 34% in the temporal cortex, 32% in the deep grey matter, 30% in the cerebellum, 30% in the frontal cortex, 26% in the occipital cortex and 19% in the parietal cortex. Stepwise regression analysis evidenced significant association (p = 0.002) between EDSS and both GM pathology (p = 0.028) and T2 white matter lesions load (p = 0.019). In the present study, we evidenced that individual analysis of GM MTR map allowed demonstrating that GM pathology is highly heterogeneous across patients at the early stage of MS and partly underlies irreversible disability

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis

Classification of disease subgroup and correlation with disease severity using magnetic resonance imaging whole-brain histograms: application to magnetization transfer ratios and multiple sclerosis

This paper presents a new approach to characterize subtle diffuse changes in multiple sclerosis (MS) using histograms derived from magnetization transfer ratio (MTR) images. Two major parts dominate our histogram analysis; (1) Classification of MTR histograms into control and MS subgroups; (2) Correlation with current disability, as measured by the EDSS scale (a measure of disease severity). Two data reduction schemes are used to reduce the complexity of the analysis: linear discriminant analysis (LDA) and principal component analysis (PCA). LDA is better for the classification of MTR histograms as it takes into account the between-class variation. By using LDA, the space of MTR histograms is transformed to the optimal discriminant space for a nearest mean classifier. In contrast, PCA is useful for correlation with current disability as it takes into account the variation within each subgroup in its process. A multiple regression analysis is used to evaluate the multiple correlation of those principal components with the degree of disability in MS. This is the first application of such classification and correlation techniques to magnetic resonance imaging histogram data. Our MTR histogram analysis approach give improved classification success and improved correlation compared with methods that use traditional histogram features such as peak height and peak locatio