Immunoglobulin G4-related disease (IgG4-RD) is a systemic immune-mediated fibro-inflammatory disorder. Coronary IgG4-RD has been scarcely reported and may present as “tumor-like” lesions. These pseudo-masses may be underdiagnosed mainly due to a vague clinical picture that can vary from complete lack of symptoms to acute coronary syndrome or sudden cardiac death. Early recognition of coronary IgG4-RD is essential to monitor disease activity and prevent life-threatening complications. We report a comprehensive non-invasive imaging evaluation of a patient affected by coronary IgG4-RD, which was diagnosed as an incidental finding during routine pre-laparoscopic cholecystectomy checkup. Non-invasive imaging revealed the presence of a peri-coronary soft-tissue mass that was stable at 12 months follow-up.

The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists’ workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.

Pneumothorax and pneumomediastinum are life-threatening conditions especially in critically ill patients. One of the most common situations in which they occur is prolonged invasive and non-invasive mechanical ventilation with high end-expiratory pressure. Probably due to the high number of patients with SARS-CoV-2 respiratory infection being treated with mechanical ventilation, increasing number of pulmonary barotrauma cases have been reported.

Background: Type 2 diabetes mellitus (DM) is the most common metabolic disorder in the world and an important risk factor for peripheral arterial disease (PAD). CT angiography represents the method of choice for the diagnosis, pre-operative planning, and follow-up of vascular disease. Low-energy dual-energy CT (DECT) virtual mono-energetic imaging (VMI) has been shown to improve image contrast, iodine signal, and may also lead to a reduction in contrast medium dose. In recent years, VMI has been improved with the use of a new algorithm called VMI+, able to obtain the best image contrast with the least possible image noise in low-keV reconstructions. Purpose: To evaluate the impact of VMI+ DECT reconstructions on quantitative and qualitative image quality in the evaluation of the lower extremity runoff. Materials and Methods: We evaluated DECT angiography of lower extremities in patients suffering from diabetes who had undergone clinically indicated DECT examinations between January 2018 and January 2023. Images were reconstructed with standard linear blending (F_0.5) and low VMI+ series were generated from 40 to 100 keV, in an interval of 15 keV. Vascular attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for objective analysis. Subjective analysis was performed using five-point scales to evaluate image quality, image noise, and diagnostic assessability of vessel contrast. Results: Our final study cohort consisted of 77 patients (41 males). Attenuation values, CNR, and SNR were higher in 40-keV VMI+ reconstructions compared to the remaining VMI+ and standard F_0.5 series (HU: 1180.41 ± 45.09; SNR: 29.91 ± 0.99; CNR: 28.60 ± 1.03 vs. HU 251.32 ± 7.13; SNR: 13.22 ± 0.44; CNR: 10.57 ± 0.39 in standard F_0.5 series) (p < 0.0001). Subjective image rating was significantly higher in 55-keV VMI+ images compared to the other VMI+ and standard F_0.5 series in terms of image quality (mean score: 4.77), image noise (mean score: 4.39), and assessability of vessel contrast (mean value: 4.57) (p < 0.001). Conclusions: DECT 40-keV and 55-keV VMI+ showed the highest objective and subjective parameters of image quality, respectively. These specific energy levels for VMI+ reconstructions could be recommended in clinical practice, providing high-quality images with greater diagnostic suitability for the evaluation of lower extremity runoff, and potentially needing a lower amount of contrast medium, which is particularly advantageous for diabetic patients.