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PURPOSE: We aimed to assess the association between features of epicardial adipose tissue and demographic, morphometric and clinical data, in a large population of symptomatic patients with clinical indication to cardiac computed tomography (CT) angiography. METHODS: Epicardial fat volume (EFV) and adipose CT density of 1379 patients undergoing cardiac CT angiography (918 men, 66.6%; age range, 18–93 years; median age, 64 years) were semi-automatically quantified. Clinical variables were compared between diabetic and nondiabetic patients to assess potential differences in EFV and adipose CT density. Multiple regression models were calculated to find the clinical variables with a significant association with EFV and adipose CT density. RESULTS: The median EFV in diabetic patients (112.87 mL) was higher compared with nondiabetic patients (82.62 mL; P < 0.001). The explanatory model of the multivariable analysis showed the strongest associations between EFV and BMI (β=0.442) and age (β=0.365). Significant yet minor association was found with sex (β=0.203), arterial hypertension (β=0.072), active smoking (β=0.068), diabetes (β=0.068), hypercholesterolemia (β=0.046) and cardiac height (β=0.118). The mean density of epicardial adipose tissue was associated with BMI (β=0.384), age (β=0.105), smoking (β=0.088), and diabetes (β=0.085). CONCLUSION: In a large population of symptomatic patients, EFV is higher in diabetic patients compared with nondiabetic patients. Clinical variables are associated with quantitative features of epicardial fat.

Photon-counting detector (PCD) is a novel computed tomography detector technology (photon-counting computed tomography—PCCT) that presents many advantages in the neurovascular field, such as increased spatial resolution, reduced radiation exposure, and optimization of the use of contrast agents and material decomposition. In this overview of the existing literature on PCCT, we describe the physical principles, the advantages and the disadvantages of conventional energy integrating detectors and PCDs, and finally, we discuss the applications of the PCD, focusing specifically on its implementation in the neurovascular field.

Transcranial MR-guided Focused ultrasound (tcMRgFUS) is a surgical procedure that adopts focused ultrasounds beam towards a specific therapeutic target through the intact skull. The convergence of focused ultrasound beams onto the target produces tissue effects through released energy. Regarding neurosurgical applications, tcMRgFUS has been successfully adopted as a non-invasive procedure for ablative purposes such as thalamotomy, pallidotomy, and subthalamotomy for movement disorders. Several studies confirmed the effectiveness of tcMRgFUS in the treatment of several neurological conditions, ranging from motor disorders to psychiatric disorders. Moreover, using low-frequencies tcMRgFUS systems temporarily disrupts the blood–brain barrier, making this procedure suitable in neuro-oncology and neurodegenerative disease for controlled drug delivery. Nowadays, tcMRgFUS represents one of the most promising and fascinating technologies in neuroscience. Since it is an emerging technology, tcMRgFUS is still the subject of countless disparate studies, even if its effectiveness has been already proven in many experimental and therapeutic fields. Therefore, although many studies have been carried out, many others are still needed to increase the degree of knowledge of the innumerable potentials of tcMRgFUS and thus expand the future fields of application of this technology.

The Corovirus Disease 2019 (COVID-19) represents the first medical catastrophe of the new millennium. Although imaging is not a screening test for COVID-19, it plays a crucial role in evaluation and follow-up of COVID-19 patients. In this paper, we will review typical and atypical imaging findings of COVID-19.

This study was aimed to investigate the predictive value of the radiomics features extracted from pericoronaric adipose tissue — around the anterior interventricular artery (IVA) — to assess the condition of coronary arteries compared with the use of clinical characteristics alone (i.e., risk factors). Clinical and radiomic data of 118 patients were retrospectively analyzed. In total, 93 radiomics features were extracted for each ROI around the IVA, and 13 clinical features were used to build different machine learning models finalized to predict the impairment (or otherwise) of coronary arteries. Pericoronaric radiomic features improved prediction above the use of risk factors alone. In fact, with the best model (Random Forest + Mutual Information) the AUROC reached 0.820 ± 0.076 . As a matter of fact, the combined use of both types of features (i.e., radiomic and clinical) allows for improved performance regardless of the feature selection method used. Experimental findings demonstrated that the use of radiomic features alone achieves better performance than the use of clinical features alone, while the combined use of both clinical and radiomic biomarkers further improves the predictive ability of the models. The main contribution of this work concerns: (i) the implementation of multimodal predictive models, based on both clinical and radiomic features, and (ii) a trusted system to support clinical decision-making processes by means of explainable classifiers and interpretable features.

The incidence of non-uniform expansion in the context of the self-expandable transcatheter heart valve (THV) is little investigated, along with stent-frame decoupling, which is a form of stent adaptation, in which the lower part of the THV stent conforms to both the ellipticity of the left ventricle outflow tract and the native annulus while maintaining the higher part of the valve more circular. We analyzed post-implant multi-detector computed tomography scans in 50 patients. Prosthesis non-uniform expansion was assessed by computing the prosthesis eccentricity on 6 prespecified levels: (1) frame inflow, (2) native annulus, (3) leaflet inflow, (4) prosthesis waist, (5) leaflet outflow, and (6) frame outflow. Stent-frame decoupling was assessed by comparing the mean eccentricity on 6 different prosthesis levels. Implantation depth, leaflet expansion and alignment, and residual anatomic sinus area ratios were also calculated. Subclinical leaflet thrombosis was defined as hypoattenuated lesion of a meniscal shape. At a 12-month follow-up, non-uniform expansion was consistently detected at each valvular level. Highest eccentricity was measured at the native annulus level (eccentricity: 0.54 ±  0.12), while the lowest index at the frame outflow level (0.23 ± 0.11). Similar results were observed in the subgroup analyses of sizes 23, 26, 29, and 34. Eccentricity significantly decreased from the annulus level to the prosthesis frame outflow (p <0.001). Notably, the incidence of mild-to-severe subclinical leaflet thrombosis was relevant (16%). In conclusion, prosthesis non-uniform expansion and stent frame decoupling frequently occur after self-expandable THV replacement. The clinical and hemodynamic implications remain uncertain. [Display omitted]

Objectives The purpose of our retrospective review of prospectively acquired Whole Body Magnetic Resonance (WB-MRI) scans was to assess the incidence of osteonecrosis in patients who received different chemotherapies. Methods We evaluated the WB-MRI scans performed on 42 patients with Hodgkin Lymphoma treated by three chemotherapy regimens (6ABVD, 2ABVD + 4BEACOPP, 2ABVD + 8BEACOPP), excluding patients with the main risk factors for osteonecrosis. Results Six out of seven patients (86 %) who received eight BEACOPP and one out of five patients (20 %) treated by four BEACOPP presented osteonecrosis, with a statistically significant difference of frequency between the two groups of patients (p < 0.05); no injury has been reported in patients treated by only ABVD. Among a total of 48 osteonecrotic lesions observed, 48 % were detected in the knee; multifocal osteonecrosis were detected in six out of seven patients (86 %). Conclusions The development of osteonecrosis is strictly related to the chemotherapy protocol adopted and the number of cycles received, with a strong correlation between the dose of corticosteroids included in the BEACOPP scheme and this complication. WB-MRI can be considered as a helpful tool that allows detecting earlier osteonecrotic lesions in patients treated with corticosteroids. Key Points • Osteonecrosis is a possible complication of patients with Lymphoma treated by chemotherapy. • Osteonecrosis is related to the corticosteroids included within the BEACOPP protocol. • WB-MRI allows detecting osteonecrotic lesions in patients treated with corticosteroids.

The widespread use of imaging examinations has increased the detection of incidental adrenal lesions, which are mostly benign and non-functioning adenomas. The differentiation of a benign from a malignant adrenal mass can be crucial especially in oncology patients since it would greatly affect treatment and prognosis. In this setting, imaging plays a key role in the detection and characterization of adrenal lesions, with several imaging tools which can be employed by radiologists. A thorough knowledge of the imaging features of adrenal masses is essential to better characterize these lesions, avoiding a misinterpretation of imaging findings, which frequently overlap between benign and malignant conditions, thus helping clinicians and surgeons in the management of patients. The purpose of this paper is to provide an overview of the main imaging features of adrenal masses and tumor-like conditions recalling the strengths and weaknesses of imaging modalities commonly used in adrenal imaging.

Attenuation variability (measured in Hounsfield Units, HU) of human coronary plaques using multislice computed tomography (MSCT) was evaluated in an ex vivo model with increasing convolution kernels. MSCT was performed in seven ex vivo left coronary arteries sunk into oil followingthe instillation of saline (1/infinity) and a 1/50 solution of contrast material (400 mgI/ml iomeprol). Scan parameters were: slices/collimation, 16/0.75 mm; rotation time, 375 ms. Four convolution kernels were used: b30f-smooth, b36f-medium smooth, b46f-medium and b60f-sharp. An experienced radiologist scored for the presence of plaques and measured the attenuation in lumen, calcified and noncalcified plaques and the surrounding oil. The results were compared by the ANOVA test and correlated with Pearson's test. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The mean attenuation values were significantly different between the four filters (p < 0.0001) in each structure with both solutions. After clustering for the filter, all of the noncalcified plaque values (20.8 +/- 39.1, 14.2 +/- 35.8, 14.0 +/- 32.0, 3.2 +/- 32.4 HU with saline; 74.7 +/- 66.6, 68.2 +/- 63.3, 66.3 +/- 66.5, 48.5 +/- 60.0 HU in contrast solution) were significantly different, with the exception of the pair b36f-b46f, for which a moderate-high correlation was generally found. Improved SNRs and CNRs were achieved by b30f and b46f. The use of different convolution filters significantly modifief the attenuation values, while sharper filtering increased the calcified plaque attenuation and reduced the noncalcified plaque attenuation.

Multimodal datasets offer valuable support for developing Clinical Decision Support Systems (CDSS), which leverage predictive models to enhance clinicians’ decision-making. In this observational study, we present a dataset of suspected Coronary Artery Disease (CAD) patients - called MultiD4CAD - comprising imaging and clinical data. The imaging data obtained from Coronary Computed Tomography Angiography (CCTA) includes epicardial (EAT) and pericoronary (PAT) adipose tissue segmentations. These metabolically active fat tissues play a key role in cardiovascular diseases. In addition, clinical data include a set of biomarkers recognized as CAD risk factors. The validated EAT and PAT segmentations make the dataset suitable for training predictive models based on radiomics and deep learning architectures. The inclusion of CAD disease labels allows for its application in supervised learning algorithms to predict CAD outcomes. MultiD4CAD has revealed important correlations between imaging features, clinical biomarkers, and CAD status. The article concludes by discussing some challenges, such as classification, segmentation, radiomics, and deep training tasks, that can be investigated and validated using the proposed dataset.