Photon-counting computed tomography (CT) is a technology that has attracted increasing interest in recent years since, thanks to new-generation detectors, it holds the promise to radically change the clinical use of CT imaging. Photon-counting detectors overcome the major limitations of conventional CT detectors by providing very high spatial resolution without electronic noise, providing a higher contrast-to-noise ratio, and optimizing spectral images. Additionally, photon-counting CT can lead to reduced radiation exposure, reconstruction of higher spatial resolution images, reduction of image artifacts, optimization of the use of contrast agents, and create new opportunities for quantitative imaging. The aim of this review is to briefly explain the technical principles of photon-counting CT and, more extensively, the potential clinical applications of this technology.

(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.

Background: Glioblastoma (GBM) is the most common primary brain tumor in adults, with a poor prognosis and survival. Although typically presenting with focal neurological deficits, seizures, or cognitive decline, GBM can occasionally mimic autoimmune encephalitis (AE), leading to significant diagnostic delay. The overlap in clinical, radiological, and serological findings between GBM and AE underscores the need for thorough evaluation. Methods: We retrospectively reviewed cases of patients diagnosed between 2016 and 2023 with pathology-confirmed GBM, critically rethinking those cases initially diagnosed with AE at symptom onset. The diagnostic workup included magnetic resonance imaging (MRI), cerebrospinal fluid (CSF) analysis, autoantibody testing, and whole-body nuclear scanning to exclude extracranial malignancies. Results: We found five female patients diagnosed with GBM who initially presented with signs and symptoms suggestive for AE. Initial MRI showed non-specific brain tissue alterations, without definitive tumor features. CSF analysis was largely unremarkable, though some cases exhibited positive autoantibodies. Despite therapy, clinical deterioration and follow-up MRI revealed infiltrative intra-axial lesions with contrast enhancement, leading to pathology-confirmed GBM diagnoses. All patients had poor prognoses, with a mean survival of 10 ± 4 months. Conclusions: GBM can mimic AE, delaying appropriate treatment. In patients with atypical MRI findings and suboptimal response to therapy, early follow-up imaging and biopsy should be considered to exclude malignancy. A multidisciplinary approach is critical for timely diagnosis and improved management.

Background: The diagnosis of acute spondylodiscitis can be very difficult because clinical onset symptoms are highly variable. The reference examination is MRI, but very often the first diagnostic investigation performed is CT, given its high availability in the acute setting. CT allows rapid evaluation of other alternative diagnoses (e.g., fractures), but scarce literature is available to evaluate the accuracy of CT, and in particular of multi-detector computed tomography (MDCT), in the diagnosis of suspected spondylodiscitis. The aim of our study was to establish MDCT accuracy and how this diagnostic method could help doctors in the depiction of acute spondylodiscitis in an emergency situation by comparing the diagnostic performance of MDCT with MRI, which is the gold standard. Methods: We searched our radiological archive for all MRI examinations of patients who had been studied for a suspicion of acute spondylodiscitis in the period between January 2017 and January 2021 (n = 162). We included only patients who had undergone MDCT examination prior to MRI examination (n = 25). The overall diagnostic value of MDCT was estimated, using MRI as the gold standard. In particular, the aim of our study was to clarify the effectiveness of CT in radiological cases that require immediate intervention (stage of complications). Therefore, the radiologist, faced with a negative CT finding, can suggest an elective (not urgent) MRI with relative serenity and without therapeutic delays. Results: MDCT allowed identification of the presence of acute spondylodiscitis in 13 of 25 patients. Specificity and positive predictive value were 100% for MDCT, while sensitivity and negative predictive value were 68% and 50%, respectively, achieving an overall accuracy of 76%. In addition, MDCT allowed the identification of paravertebral abscesses (92%), fairly pathognomonic lesions of spondylodiscitis pathology. Conclusions: The MDCT allows identification of the presence of acute spondylodiscitis in the Emergency Department (ED) with a satisfactory accuracy. In the case of a positive CT examination, this allows therapy to be started immediately and reduces complications. However, we suggest performing an elective MRI examination in negative cases in which pathological findings are hard to diagnose with CT alone.

Background and Objectives: Brainstem infarctions remain challenging to identify due to their small size, complex anatomy, and known limitations of conventional axial diffusion-weighted imaging (DWI), particularly in the posterior fossa. Thin-section coronal DWI may improve lesion conspicuity by providing higher spatial resolution and an orthogonal imaging perspective. To evaluate whether 3 mm thin-section coronal DWI improves lesion visualization and delineation compared with standard 4 mm axial DWI in patients with MRI-confirmed acute brainstem infarction. Materials and Methods: In this retrospective single-center study, 125 consecutive patients with isolated brainstem infarction confirmed by MRI (January 2021–January 2024) were included. All patients underwent both axial and coronal DWI acquisitions. Lesions were classified by anatomical location and by the imaging plane providing better visualization (“coronal better” vs. “equal”). Lesion volumes were calculated using manual segmentation. Image interpretation was performed independently by two neuroradiologists. Interobserver agreement was assessed using Cohen’s kappa and intraclass correlation coefficient (ICC). Statistical analysis included both parametric and nonparametric tests, with confidence intervals reported. Results: Coronal DWI provided improved or equivalent lesion visualization in all cases. Improved visualization was most frequent in midbrain infarctions (100%) and in a subset of medullary lesions (26.7%). Lesions better visualized on coronal DWI were significantly smaller than those equally visualized (mean volume ~0.23 mL vs. ~0.55 mL, p < 0.0001). Twelve midbrain and eight medullary lesions were identified only on coronal DWI within the imaging protocol, all showing confirmation on ADC and/or FLAIR correlation. Interobserver agreement was substantial to excellent. Conclusions: Thin-section coronal DWI improves visualization and delineation of small brainstem infarctions, particularly in anatomically compact regions. These findings support its role as a complementary sequence rather than a replacement for standard axial imaging.