Accurate brain tumor segmentation is essential for clinical decision-making, yet remains difficult to automate. Key obstacles include the small volume of lesions, their morphological diversity, poorly defined MRI boundaries, and nonuniform intensity profiles. Furthermore, while traditional segmentation approaches often focus on intralayer relevance, they frequently underutilize the rich semantic correlations between features extracted from adjacent network layers. Concurrently, classical attention mechanisms, while effective for highlighting salient regions, often lack explicit mechanisms for directing feature refinement along specific dimensions. To solve these problems, this paper presents CAGs-Net, a novel network that progressively constructs semantic dependencies between neighboring layers in the UNet hierarchy, enabling effective integration of local and global contextual information. Meanwhile, the channel attention gate was embedded within this adjacent-context network. These gates strategically fuse shallow appearance features and deep semantic information, leveraging channel-wise relationships to refine features by recalibrating voxel spatial responses. In addition, the hybrid loss combining generalized dice loss and binary cross-entropy loss was employed to avoid severe class imbalance inherent in lesion segmentation. Therefore, CAGs-Net uniquely combines adjacent-context modeling with channel attention gates to enhance feature refinement, outperforming traditional UNet-based methods, and the experimental results demonstrated that CAGs-Net shows better segmentation performance in comparison with some state-of-the-art methods for brain tumor image segmentation.

Security issues of telemedicine‐based secure transmission of medical images find a very thin line drawn between diagnostic acceptability and cybersecurity. Partial but imperfect solutions emerge. JPEG2000 and HEVC concentrate only on compression, failing to provide any security consideration. Although secure, blockchain‐based systems introduce latency that impedes fine‐grained medical telepresence. The methods of homomorphic encryption are one of the very secure options, but they are almost impossible to carry out computationally. Watermarking schemes are usually incapable of providing real‐time detection of tampering. Given these drawbacks, such as missing real‐time tamper detection and poor integration between compression and security, as well as prohibitively high computational overhead, are our points to tackle; we recommend a hybrid framework based on adaptive compressed sensing (ACS), Secure Hash Algorithm 3 (SHA‐3), and lightweight encryption. The proposed framework obtains an improved CR of up to 30% better than JPEG2000 (13.5 bpp against 10.2 on x‐ray images), an 8.8% improvement in PSNR (43.2 vs. 39.7 dB), and a 6.6% increase in SSIM (0.97 compared to 0.91). Real‐time tampering detection (5.6 ms) safeguards the system against replay attacks (6.2 ms). When optimized for low‐latency transmission over constrained telemedicine scenarios, the algorithm shows greater efficiency and robustness than previously proposed methods.

Background Hyperpolarized gas (HPG) magnetic resonance imaging, recently FDA‐approved, offers an innovative approach to evaluating gas distribution and lung function in both adults and children. Purpose In this study, we present an algorithm for calculating maps of changes in regional ventilation in asthma, cystic fibrosis, and COPD patients before and after receiving treatment. We validate the results with a radiologist′s evaluation for accuracy. Our hypothesis is that the change map would be in congruence with a radiologist′s visual examination. Assessment Nine asthmatics, six cystic fibrosis patients, and five COPD patients underwent hyperpolarized 3He MRI. N4ITK bias correction, voxel smoothing, and normalization to the signal distribution′s 95th percentile voxel signal value were performed on images. For calculating regional ventilation change maps, posttreatment images were registered to baseline images, and difference maps were created. Difference‐map voxel values of > 60% of the baseline mean signal value were identified as improved, and those of < −60% were identified as worsened. In addition, short‐term improvement (STI) was identified where voxels improved at Timepoint 2 but returned to baseline at Timepoint 3. A grading rubric was developed for radiologist scoring that had the following assessment categories: “level of volume discrepancy” and “discrepancy causes” for each ventilation change map. Results In 15 out of the 20 cases (75% of the data), there was a small to no volume disparity between the change map and the radiologists′ visual evaluation. The rest of the two cases had moderate volume differences, and three cases had large ones. Conclusion Our regional change maps demonstrated congruence with visual examination and may be a useful tool for clinicians evaluating ventilation changes longitudinally.

Background: The quality of CT images obtained from hepatocellular carcinoma (HCC) patients is complex, affecting diagnostic accuracy, precision, and radiation dose assessment due to increased exposure risks. Objectives: The study evaluated image quality qualitatively and quantitatively by comparing quality levels with an effective radiation dose to ensure acceptable quality accuracy. Materials and Methods: This study retrospectively reviewed 100 known HCC patients (Li-RADS-5) who underwent multidetector computed tomography (MDCT) multiphasic scans for follow-up of their health condition between January and October 2023. The evaluation involved quantitative and qualitative analyses of parameters such as SD, SNR, and CNR, as well as a qualitative assessment by two radiology consultants. The outcomes were compared, and the effective dose was calculated and compared with both quantitative and qualitative assessments of image quality. Results: ROC curve analysis revealed significant differences in CT image quality, with high to moderate specificity and sensitivity across all the quantitative parameters. However, multivariate examination revealed decreasing importance levels, except for CNR (B, 0.203; p = 0.001) and SD BG (B, 0.330; p = 0.002), which increased in B. The CNR and SD BG remained independent variables for CT image quality prediction, but no statistically significant relationship was found between the effective dose and image quality, either quantitatively or qualitatively. Conclusion: This study underscores the vital role of both quantitative and qualitative assessments of CT images in evaluating their quality for patients with HCC and highlights the predictive importance of CNR, SNR, and SD. These findings emphasize the value of these devices in assessing and predicting outcomes to minimize the effective dose.

Wideband antennas are extensively used in many medical applications, which require the placement of the antenna on or near a human body. The performance of the antenna should remain compliant with the requirements of the target application when placed in front of the subject under investigation. Since the performance of an antenna varies when the distance from the subject is changed, the effect of varying the distance of a miniaturized wideband antipodal Vivaldi antenna from a numerical head model is analyzed in this work. The analyses can demonstrate whether the antenna performance and its effect on the head aptly comply with the requirements for the intended application of microwave brain imaging. It is observed that, when the antenna-head distance is increased, the background noise in the received signal is enhanced, whereas when the distance is reduced, the radiation-safety consideration on the head is affected. Hence, the optimum distance should provide a good compromise in terms of both signal receptibility by the antenna and radiation safety on the head. As the optimum antenna-to-head distance may vary with the change in antenna, measurement system, and the surrounding medium, this work presents a basic analysis procedure to find the appropriate antenna distance for the intended application.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a re-emerging disease that necessitates early and accurate detection. While Ziehl–Neelsen (ZN) staining is effective in highlighting bacterial morphology, automation significantly accelerates the diagnostic workflow. However, detecting TB bacilli—which are typically much smaller than white blood cells (WBCs)—in stained images remains a considerable challenge. This study leverages the ZNSM-iDB dataset, which comprises approximately 2000 publicly available images captured using different staining methods. Notably, 800 images are fully stained with the ZN technique. We propose a novel two-stage pipeline where a RetinaNet-based object detection model functions as a preprocessing step to localize and isolate TB bacilli and WBCs from ZN-stained images. To address the challenges posed by low spatial resolution and background interference, the RetinaNet model is enhanced with dilated convolutional layers to improve fine-grained feature extraction. This approach not only facilitates accurate detection of small objects but also achieves an average precision (AP) of 0.94 for WBCs and 0.97 for TB bacilli. Following detection, a patch-based convolutional neural network (CNN) classifier is employed to classify the extracted regions. The proposed CNN model achieves a remarkable classification accuracy of 93%, outperforming other traditional CNN architectures. This framework demonstrates a robust and scalable solution for automated TB screening using ZN-stained microscopy images.

Introduction: Doctors can play a significant role in attributing to patient safety concerning exposure to ionizing radiation. Therefore, healthcare professionals should have adequate knowledge about radiation risk and protection of different medical imaging examinations. This study aims to evaluate the knowledge about radiation protection (RP) and applications of different imaging modalities (IMs) among medical students in their clinical years and intern, in Jeddah, Saudi Arabia. Materials and Methods: A cross-sectional study based on an online questionnaire was performed in Jeddah, Saudi Arabia, on 170 medical students during January 2024; the study participants included clinical years medical students (from Years 4 to 6) and interns of both gender and basic year medical students, and specialists and consultants were excluded. For each participant, the percentage of correct answers was calculated for the knowledge RP and knowledge in IMs separately, and each participant will have two scores, RP knowledge score (RPKS) and IM knowledge score (IMKS). Results: A total of 170 medical students responded and completed the questionnaire. The overall levels of awareness and knowledge of the students was determined through calculations of their scores in answering the questionnaire; students in this study group have low average knowledge score in RP, which is 43, while they have moderate-high knowledge score in IMs, which is 68. Regarding the knowledge score, for the RPKS, the best participant scored 82, while the worst scored 0, whereas for IMKS, the best participant score 100, while the worst scored 0. However, according to the SD, participants generally differ between each other by 19 in RPKS and 31 in IMKS. Conclusions: The assessments of medical students' knowledge regarding radiation exposure in diagnostic modalities reveal a low level of confidence in their knowledge of ionizing radiation dose parameters. Furthermore, the mean scores on overall knowledge assessments indicate a need for improvement in RP knowledge for medical students. To address this gap, a comprehensive modification of the undergraduate medical curriculum's radiology component is required by enhancing active learning approaches and integrating radiation safety courses early in the medical curriculum. Medical education institutions could implement ongoing workshops, online modules, and certification programs to reinforce radiation safety principles.

Objectives: This study is aimed at assessing glymphatic function by diffusion tensor image analysis along the perivascular space (DTI-ALPS) and its associations with cortical morphological changes and severity of accommodative asthenopia (AA). Methods: We prospectively enrolled 50 patients with AA and 47 healthy controls (HCs). All participants underwent diffusion tensor imaging (DTI) and T1-weighted imaging and completed the asthenopia survey scale (ASS). Differences in brain morphometry and the analysis along the perivascular space (ALPS) index between the two groups were compared. The correlation and mediation analyses were conducted to explore the relationships between them. Results: Compared to HCs, patients with AA exhibited significantly increased sulcal depth in the left superior occipital gyrus (SOG.L) and increased cortical thickness in the left superior temporal gyrus (STG.L), left middle occipital gyrus (MOG.L), left postcentral gyrus (PoCG.L), and left precuneus (PCUN.L). Additionally, patients with AA had a significantly lower ALPS index than HCs. The sulcal depth of the SOG.L was significantly positively correlated with the ASS score in patients with AA, and a positive correlation was found between the cortical thickness of the MOG.L and ASS score. The ALPS index was negatively associated with the sulcal depth of the SOG.L and cortical thickness of the MOG.L. Mediation analysis revealed that the sulcal depth of SOG.L and cortical thickness of MOG.L partially mediated the impact of the DTI-ALPS index on the ASS score. Conclusion: Our findings suggested that patients with AA exhibit impaired glymphatic function, which may contribute to the severity of asthenopia through its influence on cortical morphological changes. The ALPS index is anticipated to become a potential imaging biomarker for patients with AA. Trial Registration: Chinese Registry of Clinical Trials: ChiCTR1900028306.

The objective of AI research and development is to create intelligent systems capable of performing tasks and reasoning like humans. Artificial intelligence extends beyond pattern recognition, planning, and problem-solving, particularly in the realm of machine learning, where deep learning frameworks play a pivotal role. This study focuses on enhancing brain tumour detection in MRI scans using deep learning techniques. Malignant brain tumours result from abnormal cell growth, leading to severe neurological complications and high mortality rates. Early diagnosis is essential for effective treatment, and our research aims to improve detection accuracy through advanced AI methodologies. We propose a modified DarkNet-53 architecture, optimized with invasive weed optimization (IWO), to extract critical features from preprocessed MRI images. The model's presentation is assessed using accuracy, recall, loss, and AUC, achieving a 95% success rate on a dataset of 3264 MRI scans. The results demonstrate that our approach surpasses existing methods in accurately identifying a wide range of brain tumours at an early stage, contributing to improved diagnostic precision and patient outcomes.

Background: This study is aimed at solving the misalignment and semantic gap caused by multiple convolutional and pooling operations in U-Net while segmenting subabdominal MR images during rectal cancer treatment.Methods: We propose a new approach for MR Image Segmentation based on a multiscale feature pyramid network and a bidirectional cross-attention mechanism. Our approach comprises two innovative modules: (1) We use dilated convolution and a multiscale feature pyramid network in the encoding phase to mitigate the semantic gap, and (2) we implement a bidirectional cross-attention mechanism to preserve spatial information in U-Net and reduce misalignment.Results: Experimental results on a subabdominal MR image dataset demonstrate that our proposed method outperforms existing methods.Conclusion: A multiscale feature pyramid network effectively reduces the semantic gap, and the bidirectional cross-attention mechanism facilitates feature alignment between the encoding and decoding stages.