Computed tomography-osteoaboorptiometry

A method of making a visual display of subchondral mineralization in the major synovial joints is described. Unlike existing procedures, it can be used on the living subject. A modified application of computed tomography-densitometry, computed tomography-osteoabsorptiometry makes it possible to explore the mechanical adaptability to the prevailing mechanical force. This claim is based upon the comparison of information obtained from 20 anatomical specimens with CT-osteoabsorptiometry and x-ray densitometry of sections; both methods yielding virtually identical results. The distribution of the subchondral density was then expressed as a map of the articular surface with the aid of an image analyser. This method can make a useful contribution to basic clinical research, as well as providing a diagnostic technique which can also be used for observing progress after a corrective osteotomy or any other procedure causing a change in mechanical function. Examples of its use on living patients are given

Modelling and simulation of magnetic induction in magnetic particle imaging system

In the last century, tomographic imaging has become an essential tool for disease diagnosis. There are several dominant tomographic imaging methods used for medical application such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and single photon emission computed tomography (SPECT)

Radiogenomics in clear cell renal cell carcinoma: correlations between advanced CT imaging (texture analysis) and microRNAs expression

Purpose: A relevant challenge for the improvement of clear cell renal cell carcinoma management could derive from the identification of novel molecular biomarkers that could greatly improve the diagnosis, prognosis, and treatment choice of these neoplasms. In this study, we investigate whether quantitative parameters obtained from computed tomography texture analysis may correlate with the expression of selected oncogenic microRNAs. Methods: In a retrospective single-center study, multiphasic computed tomography examination (with arterial, portal, and urographic phases) was performed on 20 patients with clear cell renal cell carcinoma and computed tomography texture analysis parameters such as entropy, kurtosis, skewness, mean, and standard deviation of pixel distribution were measured using multiple filter settings. These quantitative data were correlated with the expression of selected microRNAs (miR-21-5p, miR-210-3p, miR-185-5p, miR-221-3p, miR-145-5p). Both the evaluations (microRNAs and computed tomography texture analysis) were performed on matched tumor and normal corticomedullar tissues of the same patients cohort. Results: In this pilot study, we evidenced that computed tomography texture analysis has robust parameters (eg, entropy, mean, standard deviation) to distinguish normal from pathological tissues. Moreover, a higher coefficient of determination between entropy and miR-21-5p expression was evidenced in tumor versus normal tissue. Interestingly, entropy and miR-21-5p show promising correlation in clear cell renal cell carcinoma opening to a radiogenomic strategy to improve clear cell renal cell carcinoma management. Conclusion: In this pilot study, a promising correlation between microRNAs and computed tomography texture analysis has been found in clear cell renal cell carcinoma. A clear cell renal cell carcinoma can benefit from noninvasive evaluation of texture parameters in adjunction to biopsy results. In particular, a promising correlation between entropy and miR-21-5p was found

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

Comparison of multidetector-row computed tomography and duplex Doppler ultrasonography in detecting atherosclerotic carotid plaques complicated with intraplaque hemorrhage [Usporedba višeslojne kompjuterizirane tomografije i duplex Doppler ultrazvuka u otkrivanju aterosklerotskih karotidnih plakova kompliciranih krvarenjem u plak ]

This study compared sensitivity and specificity of multidetector-row computed tomography and duplex Doppler ultrasonography in detecting atherosclerotic carotid plaques complicated with intraplaque hemorrhage. Carotid plaques from 50 patients operated for carotid artery stenosis were analyzed. Carotid endarterectomy was performed within one week of diagnostic evaluation. Results of multidetector-row computed tomography and duplex Doppler ultrasonography diagnostic evaluation were compared with results of histological analysis of the same plaque areas. American Heart Association classification of atherosclerotic plaques was applied for histological classification. Median tissue density of carotid plaques complicated with intraplaque hemorrhage was 14.7 Hounsfield units. Median tissue density of noncalcified segments of uncomplicated plaques was 54.3 Hounsfield units (p = 0.00003). The highest tissue density observed for complicated plaques was 31.8 Hounsfield units. Multidetector-row computed tomography detected plaques complicated with hemorrhage with sensitivity of 100% and specificity of 70.4%, with tissue density of 33.8 Hounsfield units as a threshold value. Duplex Doppler ultrasonography plaque analysis based on visual in-line classification showed sensitivity of 21.7% and specificity of 89.6% in detecting plaques complicated with intraplaque hemorrhage. Multidetector-row computed tomography showed a very high level of sensitivity and a moderate level of specificity in detecting atherosclerotic carotid plaques complicated with hemorrhage. Duplex Doppler ultrasonography plaque analysis based on visual in-line classification showed a low level of sensitivity and a moderate-high level of specificity in detecting atherosclerotic carotid plaques complicated with hemorrhage