Tomography

Background: This study aimed to evaluate whether low-concentration iodine contrast-enhanced multiphase low-monoenergetic computed tomography (LCLM CT; 270 mg I/mL, 40 keV) is non-inferior to standard-dose computed tomography (SDCT; 350 mg I/mL) in image quality and lesion detectability for chronic liver disease patients. Methods: Sixty-seven patients underwent both protocols. Image quality was assessed using a 5-point scale with a non-inferiority margin of −0.5. Quantitative metrics included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Lesion detectability was evaluated using jackknife free-response receiver operating characteristic (JAFROC) analysis with a −0.1 margin. Results: LCLM CT reduced iodine dose per kilogram by 21.9%. Despite higher image noise, it achieved higher CNR for the aorta and hepatic lesions, as well as superior hepatic artery clarity. Image quality was non-inferior (difference: −0.119; 95% CI: −0.192 to −0.047), and lesion detectability (FOM: 0.744 vs. 0.721; difference: 0.023; 95% CI: −0.170 to 0.218) also showed non-inferiority. Conclusions: LCLM CT maintains diagnostic performance and improves vascular contrast while reducing iodine burden, supporting its clinical utility in longitudinal HCC surveillance. Full article

Background: As a diagnostic radiology procedure, computed tomography (CT) contributes to patient radiation exposure; hence, it deserves special consideration. The use of diagnostic reference levels (DRLs) is an efficient way to optimize patient radiation dosage. The computed tomography dose index volume (CTDIv) and the dose-length product (DLP) help to measure DRLs. Methods: A retrospective analysis was conducted on 106 patients (43.9% male, 56.1% female; mean age of 48.18 years) who underwent computed tomography chest, abdomen, and pelvis (CT CAP) scans using a Toshiba Aquilion Prime 160-slice CT scanner. Data included patient demographics, CT parameters (mA, tube rotation time, pitch, slice thickness, and slice count), and dose indices: dose length product (DLP), computed tomography dose index volume (CTDIvol), and effective dose. Cancer risks were calculated based on effective dose, patient demographics, and scan parameters. Results: This study demonstrated that the mean values for DLP, CTDIvol, and effective dose were 1719.64 ± 488.45 mGy·cm, 25.97 ± 6.96 mGy, and 27.5 ± 7.82 mSv, respectively. Cancer risk estimates ranged from 0.048% to 1.58%, with higher risks observed for females, younger patients. Significant correlations were found between dose indices and technical parameters, including pitch, kVp, tube rotation time, and slice thickness (p < 0.005). Conclusions: The mean values for DLP, CTDIvol, and effective dose for abdominopelvic scans were higher than those found in previous studies, with significant correlation of weight on these values. Optimizing CT protocols and establishing DRLs tailored to clinical indications are critical for minimizing radiation exposure and enhancing patient safety. Full article

Objectives: To assess amide proton transfer weighted (APTw) MR imaging capabilities in differentiating high-grade glial tumors across alpha-thalassemia/mental retardation X-linked (ATRX) expression, tumor-suppressor protein p53 expression (p53), O6-methylguanine-DNA methyltransferase promoter (MGMTp) methylation, isocitrate dehydrogenase (IDH) status, and proliferation marker Ki-67 (Ki-67 index) as a preoperative diagnostic aid. Material & Methods: A total of 42 high-grade glioma WHO grade 4 (HGG) patients were evaluated prospectively (30 males and 12 females). All patients were examined using conventional MRI, including the following: T1w-MPRAGE pre- and post-contrast administration, conventional T2w and 3D FLAIR, and APTw imaging with a 3T MR scanner. Receiver operating characteristic (ROC) curves were calculated for the APTw% mean, median, and max signal for the different molecular biomarkers. A logistic regression model was constructed for combined mean and median APTw% signals for p53 expression. Results: The whole-tumor max APTw% signal could significantly differentiate MGMTp from non-MGMTp HGG, p = 0.035. A cutoff of 4.28% max APTw% signal yielded AUC (area under the curve) = 0.702, with 70.6% sensitivity and 66.7% specificity. The mean/median APTw% signals differed significantly in p53 normal versus p53-overexpressed HGG s: 1.81%/1.83% vs. 1.15%/1.18%, p = 0.002/0.006, respectively. Cutoffs of 1.25%/1.33% for the mean/median APTw% signals yielded AUCs of 0.786/0.757, sensitivities of 76.9%/76.9%, and specificities of 50%/66.2%, p = 0.002/0.006, respectively. A logistic regression model with a combined mean and median APTw% signal for p53 status yielded an AUC = 0.788 and 76.9% sensitivity and 66.2% specificity. ATRX-, IDH- wild type (wt) vs. mutation (mut), and the level of Ki-67 did not differ significantly, but trends were found: IDH-wt and low Ki-67 showed higher mean/median/max APTw% signals vs. IDH-mut and high Ki-67, respectively. ATRX-wt vs. mutation showed higher mean and median APTw% signals but lower max APTw% signal. Conclusions: APTw imaging can potentially be a useful marker for the stratification of p53 expression and MGMT status in high-grade glioma in the preoperative setting and potentially aid surgical decision-making. Full article

Introduction: Diffusion-weighted imaging (DWI) is a non-invasive technique for acquiring liver pathology data and characterizing liver lesions. This modality shows promise for applications in the initial diagnosis and monitoring of liver diseases, providing valuable insights for clinical assessment and treatment strategies. Intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion tensor imaging (DTI) are advanced forms of DWI. These techniques have proven effective for assessing liver lesions, including liver tumors and fibrosis. However, the results can be inconsistent. Thus, it is essential to summarize the current applications of these methods in liver fibrosis, identify existing limitations, and suggest future directions for development. Methods: This review assessed studies concerning liver DWI and its applications published in the PubMed database over the last nine years. It presents these techniques’ fundamental principles and key factors before discussing their application in liver fibrosis. Results and conclusions: It has been observed that advanced DWI sequences remain unreliable in ensuring the robustness and reproducibility of measurements when assessing liver fibrosis grades, due to inconsistent results and significant overlap among these techniques across different stages of fibrotic conditions. Full article

Aortic valve stenosis (AS) is a valvular heart disease that imposes a high afterload on the left ventricle (LV) due to restricted opening of the aortic valve, resulting in LV hypertrophy. Severe AS can lead to syncope, angina pectoris, and heart failure. The number of patients with AS has been increasing due to aging populations, the growing prevalence of lifestyle-related diseases, and advances in diagnostic technologies. Therefore, accurate diagnosis and appropriate treatment of AS are essential. In recent years, transcatheter aortic valve implantation (TAVI) has become feasible, and the number of procedures has rapidly increased, particularly among elderly patients. As treatment options for AS expand and diversify, detailed pre-procedural evaluation has become increasingly important. In particular, diagnostic imaging modalities such as computed tomography (CT) have advanced significantly, with notable improvements in image quality. With recent advancements in CT technology—such as increased detector rows, faster gantry rotation speeds, new image reconstruction methods, and the introduction of dual-energy imaging—the scope of cardiac assessment has expanded beyond the coronary arteries to include valves, myocardium, and the entire heart. This includes evaluating restricted AV opening and cardiac function using four-dimensional imaging, assessing AV annulus diameter and AS severity via calcium scoring with a novel motion correction algorithm, and detecting myocardial damage through late-phase contrast imaging using new reconstruction techniques. In cases of pre-TAVI evaluation or congenital bicuspid valves, CT is also valuable for assessing extracardiac structures, such as access routes and associated congenital heart anomalies. In addition, recent advancements in CT technology have made it possible to significantly reduce radiation exposure during cardiac imaging. CT has become an extremely useful tool for comprehensive cardiac evaluation in patients with aortic stenosis, especially those being considered for surgical treatment. Full article

Background: Recent advances in single-photon emission computed tomography (SPECT) technology, particularly cadmium–zinc–telluride (CZT) detectors, have improved spatial resolution and contrast in cerebral blood flow imaging. This study aimed to investigate whether these improvements translate to enhanced diagnostic accuracy for Alzheimer’s disease (AD). Methods: We compared conventional SPECT (eCAM) with CZT SPECT in 29 patients (mean age 60.9 ± 17.6 years, 69% female) with suspected neurodegenerative diseases. Results: Gray matter/white matter contrast was significantly higher in CZT SPECT compared to eCAM (1.615 ± 0.096 vs. 1.458 ± 0.068, p < 0.001). However, diagnostic accuracy for AD did not improve with CZT SPECT. For the participating psychiatrist, sensitivity decreased from 0.750 (eCAM) to 0.625 (CZT), while for the radiologist, specificity dropped from 0.571 (eCAM) to 0.429 (CZT). Overall accuracy slightly decreased for both readers. Conclusions: These findings suggest that while CZT SPECT offers superior image quality, it may not immediately translate to improved diagnostic accuracy for AD. The study highlights the importance of specialized training for clinicians in interpreting higher-resolution CZT SPECT images to fully leverage their potential in neurodegenerative disease diagnosis. Future research should focus on developing standardized training protocols and larger, multi-center studies to validate these findings. Full article

Background: CT image guidance and navigation, although routinely used in complex endovascular procedures, is an unexplored territory in evolving vascular robotic procedures. In robotic surgery, it promises the better localization of vasculature, the optimization of port placement, less inadvertent tissue damage, and increased patient safety during the dissection of retroperitoneal structures. However, unknown tissue displacement resulting from induced pneumoperitoneum and positional changes compared to the preoperative CT scan can pose significant limitations to the reliability of image guidance. We aimed to study the displacement of retroperitoneal organs and vasculature due to factors such as increased intra-abdominal pressure (IAP) due to CO₂ insufflation and patient positioning (PP) using intraoperative CT imaging in a cadaveric model. Methods: A thawed, fresh-frozen human cadaveric model was positioned according to simulated procedural workflows. Intra-arterial, contrast-enhanced CT scans were performed after the insertion of four laparoscopic ports in the abdomen. CT scans were performed with 0–5–15–25 mmHg IAPs in supine, left lateral decubitus, right lateral decubitus, Trendelenburg, and reverse Trendelenburg positions. Euclidean distances between fixed anatomical bony and retroperitoneal vascular landmarks were measured and compared across different CT scans. Results: Comparing the effects of various IAPs to the baseline (zero IAP) in the same PP, an average displacement for retroperitoneal vascular landmarks ranged from 0.6 to 3.0 mm (SD 1.0–2.8 mm). When changing the PPs while maintaining the same IAP, the average displacement of the retroperitoneal vasculature ranged from 2.0 to 15.0 mm (SD 1.7–7.2 mm). Conclusions: Our preliminary imaging findings from a single cadaveric model suggest minimal (~3 mm maximum) target vasculature displacement in the retroperitoneum due to elevated IAP in supine position and higher displacement due to changes in patient positioning. Similar imaging studies are needed to quantify procedural workflow-specific and anatomy-specific deformation, which would be invaluable in developing and validating advanced tissue deformation models, facilitating the routine applicability and usefulness of CT image guidance for target delineation during robotic vascular procedures. Full article

Advances in breast cancer treatment have shifted the focus from maximizing local control to balancing oncologic efficacy with treatment de-escalation and toxicity reduction. Whole-breast irradiation (WBI) following breast-conserving surgery remains the standard of care, but with up to 90% of recurrences occurring near the tumor bed, partial breast irradiation (PBI) has emerged as a viable alternative. Large randomized controlled trials (such as IMPORT LOW, Florence, and GEC-ESTRO) have demonstrated comparable ipsilateral breast tumor recurrence (IBTR) rates between PBI and WBI, reinforcing its oncologic safety in well-selected patients. However, challenges remain in optimizing fractionation schedules, refining patient selection, and minimizing late toxicity. Recent innovations, including MRI-guided radiotherapy (MRgRT) and neoadjuvant PBI, offer improved tumor targeting, real-time plan adaptation, and enhanced normal tissue sparing. These advancements hold promise for further reducing radiation-related morbidity and improving cosmetic outcomes. As PBI progresses, integrating novel imaging modalities and hypofractionated regimens will be crucial to refining protocols. This review synthesizes the latest evidence on PBI techniques, clinical outcomes, and emerging technologies to guide future research and clinical decision-making in precision breast radiotherapy. Full article

Background: Neonatal birth-related intracranial subdural hemorrhages (SDHs) represent a form of bleeding inside the skull that occurs in newborns. This condition includes the extravasation of blood both in the encephalic parenchyma and in the extra-axial spaces. Recent studies have shown that SDH and particularly post-traumatic birth-related hemorrhages represent a frequent occurrence, but they are often asymptomatic. The gold standard for the diagnosis and follow-up of patients with SDH is multiparametric Magnetic Resonance Imaging. The aim of this study is to describe our experience by reporting several cases of SDH with different distribution and Central Nervous System involvement by the MRI of this pathology in infants up to 30 days of age. Methods: We analyzed the age and sex of the patients included in this study, the localization of SDH in different CNS areas, and their frequency using distribution plots and pie charts. Results: About the analysis of the SDH locations in the 32 patients, the most common location was the cerebellum (31/32, 96.9%), followed by parietal and occipital lobes (19/32, 59.4%; 18/32, 56.2%, respectively), falx cerebri (11/32, 34.4%), tentorium cerebelli (10/32, 31.2%), temporal lobes (6/32, 18.7%), and finally cervical and dorsal spine in the same patients (4/32, 12.5%). According to SDH locations, the patients were divided into supratentorial, infratentorial, both, and Spinal Canal. Conclusions: Our study confirmed the literature data regarding the neonatal birth-related SDH high frequency, but also allowed us to focus our attention on the rarest spinal SDH localizations with the same benign evolution. Full article

Objective: This study aimed to assess the diagnostic value of contrast-enhanced ultrasound (CEUS) time–intensity curve (TIC) parameters in detecting non-ossified thyroid cartilage invasion in patients with laryngeal squamous cell carcinoma (SCC). Methods: A CEUS TIC analysis was performed on 32 cases from 27 patients with histologically confirmed laryngeal SCC. The diagnostic performance of time to peak (TTP), peak intensity (PI), wash-in slope (WIS), area under the curve (AUC), and their quantitative differences (∆TTP, ∆PI, ∆WIS, and ∆AUC) to discriminate between the invaded and the non-invaded non-ossified thyroid cartilage was determined using ROC analysis. A logistic regression analysis was employed to identify significant predictors. Results: In an ROC analysis, of all TIC parameters analyzed separately, ∆TTP showed the greatest diagnostic performance (AUC: 0.85). A ∆TTP cut-off of ≤ 8.9 s differentiated between the invaded and the non-invaded non-ossified thyroid cartilage with a sensitivity of 100%, specificity of 76.9%, and accuracy of 81.3%. A combination of ∆TTP and PI increased the AUC to 0.93, specificity to 100%, and accuracy to 96.8%, but reduced the sensitivity to 83.3%. Meanwhile, the visual assessment of enhancement on CEUS to detect cartilage invasion had 83.3% sensitivity and 84.6% specificity. In a univariate logistic regression, only ∆TTP was a significant predictor of non-ossified thyroid cartilage invasion (OR: 0.80; 95% CI: 0.64–1.00). For every second increase in ∆TTP, the probability of thyroid cartilage invasion decreased by 20%. Conclusions: CEUS TIC parameters, particularly a combination of ∆TTP and PI, showed high diagnostic performance in the detection of non-ossified thyroid cartilage invasion in laryngeal SCC. Full article

Background: According to the World Health Organization (WHO), approximately 5% of children and 2.5% of adults suffer from attention deficit hyperactivity disorder (ADHD). This disorder can have significant negative consequences on people’s lives, particularly children. In recent years, methods based on artificial intelligence and neuroimaging techniques, such as MRI, have made significant progress, paving the way for development of more reliable diagnostic tools. In this proof of concept study, our aim was to investigate the potential utility of neuroimaging data and clinical information in combination with a deep learning-based analytical approach, more precisely, a novel feature extraction technique for the diagnosis of ADHD with high accuracy. Methods: Leveraging the ADHD200 dataset, which encompasses demographic information and anatomical MRI scans collected from a diverse ADHD population, our study focused on developing modern deep learning-based diagnostic models. The data preprocessing employed a pre-trained Visual Geometry Group16 (VGG16) network to extract two-dimensional (2D) feature maps from three-dimensional (3D) anatomical MRI data to reduce computational complexity and enhance diagnostic power. The inclusion of personal attributes, such as age, gender, intelligence quotient, and handedness, strengthens the diagnostic models. Four deep-learning architectures—convolutional neural network 2D (CNN2D), CNN1D, long short-term memory (LSTM), and gated recurrent units (GRU)—were employed for analysis of the MRI data, with and without the inclusion of clinical characteristics. Results: A 10-fold cross-validation test revealed that the LSTM model, which incorporated both MRI data and personal attributes, had the best diagnostic performance among all tested models in the diagnosis of ADHD with an accuracy of 0.86 and area under the receiver operating characteristic (ROC) curve (AUC) score of 0.90. Conclusions: Our findings demonstrate that the proposed approach of extracting 2D features from 3D MRI images and integrating these features with clinical characteristics may be useful in the diagnosis of ADHD with high accuracy. Full article

Objectives: Extensive-stage small-cell lung cancer (SCLC) has a poor prognosis, but recently, the combination of immunotherapy and chemotherapy has improved treatment outcomes in some patients, and treatment plans may vary depending on the individual’s general condition and tumor response. In addition, intrathoracic tumor control remains a major challenge for this disease. In the current study, we aim to share our clinical experience and demonstrate that consolidative high-dose thoracic radiotherapy effectively reduces intrathoracic tumor recurrence while maintaining acceptable treatment-related toxicities. Materials and Methods: The medical records of 81 SCLC patients treated at Korea University Guro Hospital from January 2019 to December 2023 were reviewed retrospectively. Among them, 22 patients with extensive-stage SCLC who had a favorable tumor response after systemic therapy, including those with oligo-progressive disease limited to the thoracic region and suitable for curative local therapy, received consolidative radiotherapy. A total dose of 52.5 Gy in 25 fractions was administered over 5 weeks to all patients with extensive-stage SCLC. Results and Conclusions: The median follow-up time was 22 months (range: 8–59 months), with the median follow-up period after completing consolidative radiotherapy being 13 months (range: 4–35 months). In-field local recurrence occurred in only one patient after consolidative thoracic radiotherapy. Most importantly, 10 patients with oligo-progressive disease at the thoracic site, at the time of tumor response, remained stable without further intrathoracic in-field recurrence. Additionally, no severe cases of radiation pneumonitis or esophagitis were observed. Based on our institution’s experience, consolidative high-dose thoracic radiotherapy is well-tolerated and associated with fewer intrathoracic recurrences, leading to improved long-term survival in carefully selected patients with extensive-stage SCLC. Given these findings, we believe consolidative radiotherapy should be considered more proactively in clinical practice. Furthermore, these results may help guide the design of future clinical trials. Full article

Background. Statins appear to be useful in patients with acute ischemic stroke. Our aim was to evaluate the association between premorbid statin treatment and CT perfusion characteristics of acute ischemic stroke. Methods. A retrospective analysis of patients with acute stroke secondary to occlusion of large vessels in the anterior circulation was performed to assess collateral flow, ischemic core volume, and ischemic penumbra using CT angiography and CT perfusion maps. Fisher’s exact test was used to compare baseline characteristics of patients in the two groups. The Wilcoxon rank-sum test for independent groups was used to compare all variables obtained for the two different groups with and without statin use. Results. We identified 61 patients, including 29 treated with statins and 32 not treated with statins before stroke onset matched by age, gender, and vascular risk factors except for hypercholesterolemia. The statin group showed lower National Institutes of health Stroke Scale scores at onset (14 ± 6.1 vs. 16 ± 4.5; p = 0.04) and lower volumes of brain tissue characterized by impaired cerebral blood flow (CBF), cerebral blood volume (CBV), and ${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}^{9.5-25\mathrm{s}}$; otherwise, no statistically significant difference was found in the volume of the ${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}^{16-25\mathrm{s}}$ between the two groups. Conclusions. Premorbid statin treatment is associated with a favorable imaging condition of acute ischemic stroke in terms of ischemic core and ischemic penumbra volume. Full article

Objectives: Shoulder injury related to vaccine administration (SIRVA), previously observed with influenza vaccines, has gained clinical significance with widespread COVID-19 vaccination. However, few studies correlate vaccine types and demographic factors with the MRI findings of SIRVA. This study aimed to evaluate MRI findings of SIRVA following COVID-19 vaccination and assess associations with vaccine type and patient characteristics. Methods: A retrospective cohort study was conducted on 35 patients with new-onset shoulder complaints within six weeks of COVID-19 vaccination between May 2021 and May 2022. MRI findings suggestive of SIRVA were reviewed, including subacromial bursitis, rotator cuff tears, and adhesive capsulitis. Demographic data, vaccine type, clinical symptoms, and treatments were collected. Follow-up interviews (1–30 September 2024) assessed symptom persistence and vaccine hesitancy. Descriptive statistics and Chi-square tests were used to explore associations. Results: Of the 35 patients (mean age 53.6 ± 9.0 years; 54.3% female), subacromial bursitis was the most common MRI finding (89.5%), followed by tendonitis (47.4%) and adhesive capsulitis (36.8%). Tendonitis correlated with older age (p = 0.024) and AstraZeneca vaccination (p = 0.033). Subacromial bursitis was linked to female sex (p = 0.013) and higher BMI (p = 0.023). Adhesive capsulitis was associated with receiving the Sinopharm vaccine (p = 0.029). Persistent symptoms (22.9%) were more common in younger patients, women, and those with right-sided injections. Conclusions: SIRVA following COVID-19 vaccination showed different MRI patterns associated with female sex, higher BMI, and vaccine type. Awareness of these patterns may expedite recognition of COVID-19-associated SIRVA in routine practice. Full article

Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues. However, compared to natural images, medical images present unique challenges, such as low resolution, poor contrast, inconsistency, and scattered target regions. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements. In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. This paper categorizes, reviews, and summarizes the current representative methods and research status in the field of medical image segmentation. A comparative analysis of relevant experiments is presented, along with an introduction to commonly used public datasets, performance evaluation metrics, and loss functions in medical image segmentation. Finally, potential future research directions and development trends in this field are predicted and analyzed. Full article

Objectives: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. Methods: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was scanned at both 10 mGy and 5 mGy dose levels. Virtual monoenergetic images (VMIs) at different energy levels of 40, 50, 60, 68, 74, and 100 keV were generated. The images at 10 mGy reconstructed with 50% adaptive statistical iterative reconstruction veo (ASIR-V50%) were used to train an image segmentation model based on U-Net. The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. U-Net was employed as a tool to compare algorithm performance. Image noise and segmentation metrics, such as the DICE coefficient, intersection over union (IOU), sensitivity, and Hausdorff distance, were calculated to assess both image quality and segmentation performance. Results: DLIR-M and DLIR-H consistently achieved lower image noise and better segmentation performance, with the highest results observed at 60 keV, and DLIR-H had the lowest image noise across all energy levels. The performance metrics, including IOU, DICE, and sensitivity, were ranked in descending order with energy levels of 60 keV, 68 keV, 50 keV, 74 keV, 40 keV, and 100 keV. Specifically, at 60 keV, the average IOU values for each reconstruction method were 0.60 for FBP, 0.67 for ASIR-V50%, 0.68 for ASIR-V100%, 0.72 for DLIR-L, 0.75 for DLIR-M, and 0.75 for DLIR-H. The average DICE values were 0.75, 0.80, 0.82, 0.83, 0.85, and 0.86. The sensitivity values were 0.93, 0.91, 0.96, 0.95, 0.98, and 0.98. Conclusions: For low-density, non-enhancing objects under a low dose, the 60 keV VMIs performed better in automatic segmentation. DLIR-M and DLIR-H algorithms delivered the best results, whereas DLIR-H provided the lowest image noise and highest sensitivity. Full article

Background/Objectives: Developing a treatment strategy that effectively prolongs the lives of people with brain tumors requires an accurate diagnosis of the condition. Therefore, improving the preoperative classification of meningiomas is a priority. Machine learning (ML) has made great strides thanks to the development of convolutional neural networks (CNNs) and computer-aided tumor detection systems. The deep convolutional layers automatically extract important and dependable information from the input space, in contrast to more traditional neural network layers. One recent and promising advancement in this field is ML. Still, there is a dearth of studies being carried out in this area. Methods: Therefore, starting with the analysis of magnetic resonance images, we have suggested in this research work a tried-and-tested and methodical strategy for real-time meningioma diagnosis by image segmentation using a very deep transfer learning CNN model or DNN model (VGG-16) with CUDA. Since the VGGNet CNN model has a greater level of accuracy than other deep CNN models like AlexNet, GoogleNet, etc., we have chosen to employ it. The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. Our VGGNet model takes in an sMRI FLAIR image input. The VGG’s convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the smallest possible size that still captures up/down and left/right. Moreover, there are also 1 × 1 convolution filters acting as a linear transformation of the input. This is followed by a ReLU unit. The convolution stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution. All the hidden layers in our VGG network also use ReLU. A dataset consisting of 264 3D FLAIR sMRI image segments from three different classes (meningioma, tuberculoma, and normal) was employed. The number of epochs in the Sequential Model was set to 10. The Keras layers that we used were Dense, Dropout, Flatten, Batch Normalization, and ReLU. Results: According to the simulation findings, our suggested model successfully classified all of the data in the dataset used, with a 99.0% overall accuracy. The performance metrics of the implemented model and confusion matrix for tumor classification indicate the model’s high accuracy in brain tumor classification. Conclusions: The good outcomes demonstrate the possibility of our suggested method as a useful diagnostic tool, promoting better understanding, a prognostic tool for clinical outcomes, and an efficient brain tumor treatment planning tool. It was demonstrated that several performance metrics we computed using the confusion matrix of the previously used model were very good. Consequently, we think that the approach we have suggested is an important way to identify brain tumors. Full article

Background: This study aims to systematically evaluate the findings from computed tomography (CT) examinations conducted at least three months post-diagnosis of COVID-19 in patients diagnosed between 2020 and 2024. Objective: To determine the frequency and characteristics of CT findings in the post-COVID-19 period, analyze long-term effects on lung parenchyma, and contribute to the development of clinical follow-up and treatment strategies based on the collected data. Materials and Methods: Ethical approval was obtained for this retrospective study, and individual consent was waived. A total of 76 patients were included in the study, aged 18 and older, diagnosed with COVID-19 between March 2020 and November 2024, who underwent follow-up chest CT scans at 3–6 months, 6–12 months, and/or 12 months post-diagnosis. CT images were obtained in the supine position without contrast and evaluated by two experienced radiologists using a CT severity score (CT-SS) system, which quantifies lung involvement. Statistical analyses were performed using IBM SPSS 23.0, with significance set at p < 0.05. Results: The results indicated a mean CT-SS of 10.58 ± 0.659. Significant associations were found between age, CT scores, and the necessity for intensive care or mechanical ventilation. The most common CT findings included ground-glass opacities, reticular patterns, and traction bronchiectasis, particularly increasing with age and over time. Conclusion: This study emphasizes the persistent alterations in lung parenchyma following COVID-19, highlighting the importance of continuous monitoring and tailored treatment strategies for affected patients to improve long-term outcomes. Full article

Background: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexplored in clinical practice. Purpose: The purpose of this study was to evaluate the diagnostic accuracy of 2-fold and 4-fold DL-accelerated shoulder MRI protocols compared to standard protocols in clinical practice. Materials and Methods: In this prospective single-center study, 88 consecutive patients (49 males, 39 females; mean age, 51 years) underwent shoulder MRI examinations using standard, 2-fold (DL2), and 4-fold (DL4) accelerated protocols between June 2023 and January 2024. Four independent radiologists (experience range: 4–25 years) evaluated the presence of bone marrow edema (BME), rotator cuff tears, and labral lesions. The sensitivity, specificity, and interobserver agreement were calculated. Diagnostic confidence was assessed using a 4-point scale. The impact of reader experience was analyzed by stratifying the radiologists into ≤10 and >10 years of experience. Results: Both accelerated protocols demonstrated high diagnostic accuracy. For BME detection, DL2 and DL4 achieved 100% sensitivity and specificity. In rotator cuff evaluation, DL2 showed a sensitivity of 98–100% and specificity of 99–100%, while DL4 maintained a sensitivity of 95–98% and specificity of 99–100%. Labral tear detection showed perfect sensitivity (100%) with DL2 and slightly lower sensitivity (89–100%) with DL4. Interobserver agreement was excellent across the protocols (Kendall’s W = 0.92–0.98). Reader experience did not significantly impact diagnostic performance. The area under the ROC curve was 0.94 for DL2 and 0.90 for DL4 (p = 0.32). Clinical Implications: The implementation of DL-accelerated protocols, particularly DL2, could improve workflow efficiency by reducing acquisition times by 50% while maintaining diagnostic reliability. This could increase patient throughput and accessibility to MRI examinations without compromising diagnostic quality. Conclusions: DL-accelerated shoulder MRI protocols demonstrate high diagnostic accuracy, with DL2 showing performance nearly identical to that of the standard protocol. While DL4 maintains acceptable diagnostic accuracy, it shows a slight sensitivity reduction for subtle pathologies, particularly among less experienced readers. The DL2 protocol represents an optimal balance between acquisition time reduction and diagnostic confidence. Full article