Caterina Ghetti

The aim of this study was to assess the impact of a state-of-the-art 32 cm axial field-of-view GE Omni Legend PET/CT system on administered activity and image quality, and to update Diagnostic Reference Levels (DRLs). System performance was evaluated using NEMA NU 2-2018 measurements and EARL2 accreditation tests. A comparison with the previous 5-ring GE Discovery IQ scanner was performed to analyze the administered activity, scan duration and image quality with and without Artificial Intelligence driven reconstruction. A retrospective analysis of standard-sized adult examinations was conducted to derive updated DRLs values. Omni Legend demonstrated improved sensitivity and Noise Equivalent Count Rate, enabling up to 60% reduction in injected activity while maintaining or improving image quality. Deep learning reconstruction enhanced image quality, particularly at lower administered activities. The DRLs were lower than the national and international reference levels, supporting dose optimization in clinical practice.

To quantitatively evaluate the possible advantages of quantifying and differentiating various soft tissues using virtual monochromatic images (VMI) derived from different dual-energy computed tomography (DECT) technologies. This study involved four DECT scanners with different technologies. CIRS phantom images were acquired in single-energy (SECT) and DECT modes with each scanner. The analysis focused on five equivalent soft-tissue inserts: adipose, breast, liver, muscle, and bone (200 mg). The signal-to-noise ratio (SNR) was calculated for each equivalent soft-tissue insert. Finally, the contrasts of tissue pairs between DECT and SECT images were compared using Wilcoxon signed-rank tests adjusted for multiple comparisons. Average CT numbers and noise showed a significant difference pattern between DECT with respect to SECT for each CT scanner. Generally, energy levels of 70 keV or higher led to improved SNR in VMI for most of the equivalent soft-tissue inserts. However, energy levels of 40–50 keV showed significantly higher contrasts in most of the equivalent soft-tissue insert pairs. DECT images at low energies, especially at 40–50 keV, outperform SECT images in discriminating soft tissues across all four DECT technologies. The combined use of DECT images reconstructed at different energy levels provides a more comprehensive set of information for diagnostic and/or radiotherapy evaluation compared to SECT. Some differences between scanners are evident, depending on the DECT acquisition technique and reconstruction method.

(1) Background and (2) Methods: In this retrospective, observational, monocentric study, we selected a cohort of eighty-five patients (age range 38–87 years old, 51 men), enrolled between January 2014 and December 2020, with a newly diagnosed renal mass smaller than 4 cm (SRM) that later underwent nephrectomy surgery (partial or total) or tumorectomy with an associated histopatological study of the lesion. The radiomic features (RFs) of eighty-five SRMs were extracted from abdominal CTs bought in the portal venous phase using three different CT scanners. Lesions were manually segmented by an abdominal radiologist. Image analysis was performed with the Pyradiomic library of 3D-Slicer. A total of 108 RFs were included for each volume. A machine learning model based on radiomic features was developed to distinguish between benign and malignant small renal masses. The pipeline included redundant RFs elimination, RFs standardization, dataset balancing, exclusion of non-reproducible RFs, feature selection (FS), model training, model tuning and validation of unseen data. (3) Results: The study population was composed of fifty-one RCCs and thirty-four benign lesions (twenty-five oncocytomas, seven lipid-poor angiomyolipomas and two renal leiomyomas). The final radiomic signature included 10 RFs. The average performance of the model on unseen data was 0.79 ± 0.12 for ROC-AUC, 0.73 ± 0.12 for accuracy, 0.78 ± 0.19 for sensitivity and 0.63 ± 0.15 for specificity. (4) Conclusions: Using a robust pipeline, we found that the developed RFs signature is capable of distinguishing RCCs from benign renal tumors.

Purpose: To test radiomics for prognostication of intrahepatic mass-forming cholangiocarcinoma (IMCC) and to develop a comprehensive risk model. Methods: Histologically proven IMCC (representing the full range of stages) were retrospectively analyzed by volume segmentation on baseline hepatic venous phase computed tomography (CT), by two readers with different experience (R1 and R2). Morphological CT features included: tumor size, hepatic satellite lesions, lymph node and distant metastases. Radiomic features (RF) were compared across CT protocols and readers. Univariate analysis against overall survival (OS) warranted ranking and selection of RF into radiomic signature (RSign), which was dichotomized into high and low-risk strata (RSign*). Models without and with RSign* (Model 1 and 2, respectively) were compared. Results: Among 78 patients (median follow-up 262 days, IQR 73–957), 62/78 (79%) died during the study period, 46/78 (59%) died within 1 year. Up to 10% RF showed variability across CT protocols; 37/108 (34%) RF showed variability due to manual segmentation. RSign stratified OS (univariate: HR 1.37 for R1, HR 1.28 for R2), RSign* was different between readers (R1 0.39; R2 0.57). Model 1 showed AUC 0.71, which increased in Model 2: AUC 0.81 (p < 0.001) and AIC 89 for R1, AUC 0.81 (p = 0.001) and AIC 90.2 for R2. Conclusion: The use of RF into a unified RSign score stratified OS in patients with IMCC. Dichotomized RSign* classified survival strata, its inclusion in risk models showed adjunct yield. The cut-off value of RSign* was different between readers, suggesting that the use of reference values is hampered by interobserver variability.

The aim of this article was to characterise the performance of four different digital breast tomosynthesis (DBT) systems in terms of dose and image quality parameters. One of them, GE Pristina, has never been tested before. Average glandular doses were measured both in DBT and 2D full field digital mammography mode. Several phantoms were employed to perform signal difference to noise ratio, slice sensitivity profile, slice to slice incrementation, chest wall offset, z-axis geometry, artefact spread function, low contrast detectability, contrast detail evaluations, image uniformity and in-plane MTF in chest wall-nipple and in tube-travel directions. There are many differences in DBT systems explored: the angular range, detector type, reconstruction algorithms, and the presence or not of the grid. Even if it is not simple to calculate a global figure of merit, the analysis of all the collected data can be useful in a contest of a quality assurance program to define a set of values that could be used as benchmarks.

Purpose. Prospective, controlled cohort study to investigate possible alterations in brain glucose metabolism (CMRglc) in patients with Cogan’s syndrome (CS).Patients and Methods. Functional mapping of the CMRglc was obtained by quantitative molecular imaging positron emission tomography, combined with computed tomography (FDG-PET/CT). The patients were divided into three clinical groups: typical CS; atypical CS (ACS); autoimmune inner ear disease (AIED). The unmatched control group (CG) consisted of subjects requiring FDG-PET/CT for an extracranial pathology. Statistical mapping searched areas of significant glucose hypometabolism in all the affected patients (DG) and in each clinical subgroup. The results were compared with those of the CG.Results. 44 patients were enrolled (DG) and assigned to the three study groups: 8 patients to the CS group; 21 patients to the ACS group; and 15 to the AIED group. Sixteen subjects formed the CG group. Areas of significant brain glucose hypometabolism were identified in all the study groups, with the largest number and extension in the DG and CS.Conclusions. This study revealed areas of significantly altered CMRglc in patients with CS (any subform) without neurologic complains and normal conventional neuroimaging. Our results suggest that FDG-PET/CT may represent a very useful tool for the global assessment of patients with Cogan’s syndrome.