UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images.

Purpose: The aim of this work is the development and characterization of a model observer (MO) based on convolutional neural networks (CNNs), trained to mimic human observers in image evaluation in terms of detection and localization of low-contrast objects in CT scans acquired on a reference phantom. The final goal is automatic image quality evaluation and CT protocol optimization to fulfill the ALARA principle. Approach: Preliminary work was carried out to collect localization confidence ratings of human observers for signal presence/absence from a dataset of 30,000 CT images acquired on a PolyMethyl MethAcrylate phantom containing inserts filled with iodinated contrast media at different concentrations. The collected data were used to generate the labels for the training of the artificial neural networks. We developed and compared two CNN architectures based respectively on Unet and MobileNetV2, specifically adapted to achieve the double tasks of classification and localization. The CNN evaluation was performed by computing the area under localization-ROC curve (LAUC) and accuracy metrics on the test dataset. Results: The mean of absolute percentage error between the LAUC of the human observer and MO was found to be below 5% for the most significative test data subsets. An elevated inter-rater agreement was achieved in terms of S-statistics and other common statistical indices. Conclusions: Very good agreement was measured between the human observer and MO, as well as between the performance of the two algorithms. Therefore, this work is highly supportive of the feasibility of employing CNN-MO combined with a specifically designed phantom for CT protocol optimization programs.

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients.

NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of making it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e., standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.

The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission.

Triage is crucial for patient's management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient's admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73-0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56-0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp . The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.

Ficoll as testing material for diffusion weighted imaging-quality assurance phantoms.

PURPOSE: The aim of this work is to test the use of aqueous solutions of Ficoll®**, a highly branched polymer displaying crowding properties, to build a phantom suitable for Diffusion Weighted Imaging (DWI) in Magnetic Resonance Imaging (MRI). METHODS: We developed a test object made of a cylindrical plastic container with a precise geometrical arrangement suitable for measuring several samples at the same time. The container was designed to host single vials with variable geometry and number, and to fit inside common commercial head coils for MRI scanners. In our experiments, vials were filled with 8 aqueous solutions of Ficoll 70 and Ficoll 400 spanning a range of polymer concentration from 5 to 30% by weight. Vials containing ultra-pure water were also used as reference. Experiments were performed on both 1.5 and 3 T clinical scanners (GE, Philips and Siemens), under the conditions of a standard clinical examination. RESULTS: The geometry of the phantom provided reduced imaging artifacts, especially image distortions at magnetic interfaces. We found that the Apparent Diffusion Coefficient (ADC) varied in the range of 0.00125-0.00223 mm2/s and decreased with Ficoll concentration. ADC vs Ficoll concentration exhibited a linear trend. Results were consistent over time and among different MRI clinical scanners, showing an average variability of 3% at 1.5 T and of 7.5% at 3 T. Moreover, no substantial difference was found between Ficoll 70 and 400. By varying Ficoll concentration, ADC can be modulated to approach tissue-mimicking values. Preliminary results for relaxation measurements proved that both T1 and T2 decreased with Ficoll concentration in the ranges 1.3-2.4 s and 150-800 ms respectively. CONCLUSIONS: In this work, we propose a 3D phantom design based on the widespread crowding agent Ficoll, which is suitable for DWI quality assurance purposes in MRI acquisitions. Aqueous Ficoll solutions provide good performance in terms of stability, ease of preparation, and safety.

Pneumatosis intestinalis in COVID-19.

INTRODUCTION: COVID-19 is a respiratory illness due to novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), described in December 2019 in Wuhan (China) and rapidly evolved into a pandemic. Gastrointestinal (GI) tract can also be involved. CASE PRESENTATION: A 44-year-old man was hospitalised for COVID-19-associated pneumonia. A rapid recovery of respiratory and general symptoms was observed after 1 week of treatment with lopinavir/ritonavir plus hydroxychloroquine and broad-spectrum antibiotics (piperacillin-tazobactam plus teicoplanin). No GI symptoms were reported during hospitalisation, but a lung contrast-enhancement CT (CE-CT) excluding thromboembolism showed, as collateral finding, intraperitoneal free bubbles not present on a previous CT examination; the subsequent abdominal CE-CT described pneumatosis intestinalis (PI) involving the caecum and the right colon. Ciprofloxacin plus metronidazole was started, and the 2-week follow-up CT showed the complete resolution of PI. DISCUSSION: The pathogenesis of PI is poorly understood. PI involving the caecum and right colon has been described for HIV and Cytomegalovirus infections, but, to our best knowledge, never before in COVID-19. We hypothesise a multifactorial aetiopathogenesis for PI, with a possible role of the bowel wall damage and microbiota impairment due to SARS-CoV-2 infection, and we suggest a conservative management in the absence of symptoms.

An operational approach to the execution of MR examinations in patients with CIED.

In the context of the increasing spread of cardiac active implantable heart devices (CIEDs) in the population and of the wide diagnostic/therapeutic utility of magnetic resonance (MRI) examinations, the goal of this paper is to provide the experience of the Santa Maria Nuova Hospital of the USL Tuscany Center in Florence and to report an organizational proposal to perform, in the hospital settings, MRI examinations on patients carrying CIED. This report is intended to show the operational choices of a Radiology Department which organizes this activity in accordance with the new Italian regulatory framework in the field of safety of MR sites (Ministero della Salute in Decreto Ministeriale 10 agosto 2018 Determinazione degli standard di sicurezza e impiego per le apparecchiature a risonanza magnetica, 2018).