Mild traumatic brain injury (mTBI) caused by sports-related incidents in children and youth often leads to prolonged cognitive impairments but remains difficult to diagnose. In order to identify clinically relevant imaging and behavioral biomarkers associated concussion, a closed-head mTBI was induced in adolescent pigs. Twelve (n = 4 male and n = 8 female), 16-week old Yucatan pigs were tested; n = 6 received mTBI and n = 6 received a sham procedure. T1-weighted imaging was used to assess volumetric alterations in different regions of the brain and diffusion tensor imaging (DTI) to examine microstructural damage in white matter. The pigs were imaged at 1- and 3-month post-injury. Neuropsychological screening for executive function and anxiety were performed before and in the months after the injury. The volumetric analysis showed significant longitudinal changes in pigs with mTBI compared with sham, which may be attributed to swelling and neuroinflammation. Fractional anisotropy (FA) values derived from DTI images demonstrated a 21% increase in corpus callosum from 1 to 3 months in mTBI pigs, which is significantly higher than in sham pigs (4.8%). Additionally, comparisons of the left and right internal capsules revealed a decrease in FA in the right internal capsule for mTBI pigs, which may indicate demyelination. The neuroimaging results suggest that the injury had disrupted the maturation of white and gray matter in the developing brain. Behavioral testing showed that compare to sham pigs, mTBI pigs exhibited 23% increased activity in open field tests, 35% incraesed escape attempts, along with a 65% decrease in interaction with the novel object, suggesting possible memory impairments and cognitive deficits. The correlation analysis showed an associations between volumetric features and behavioral metrics. Furthermore, a machine learning model, which integrated FA, volumetric features and behavioral test metrics, achieved 67% accuracy, indicating its potential to differentiate the two groups. Thus, the imaging biomarkers were indicative of long-term behavioral impairments and could be crucial to the clinical management of concussion in youth.

To compare the quality of standard 512-matrix, standard 1024-matrix, and Swin2SR-based 2048-matrix phantom images under different scanning protocols. The Catphan 600 phantom was scanned using a multidetector CT scanner under two protocols: 120 kV/100 mA (CT dose index volume = 3.4 mGy) to simulate low-dose CT, and 70 kV/40 mA (0.27 mGy) to simulate ultralow-dose CT. Raw data were reconstructed into standard 512-matrix images using three methods: filtered back projection (FBP), adaptive statistical iterative reconstruction at 40% intensity (ASIR-V), and deep learning image reconstruction at high intensity (DLIR-H). The Swin2SR super-resolution model was used to generate 2048-matrix images (Swin2SR-2048), while the super-resolution convolutional neural network (SRCNN) model generated 2048-matrix images (SRCNN-2048). The quality of 2048-matrix images generated by the two models (Swin2SR and SRCNN) was compared. Image quality was evaluated by ImQuest software (v7.2.0.0, Duke University) based on line pair clarity, task-based transfer function (TTF), image noise, and noise power spectrum (NPS). At equivalent radiation doses and reconstruction method, Swin2SR-2048 images identified more line pairs than both standard-512 and standard-1024 images. Except for the 0.27 mGy/DLIR-H/standard kernel sequence, TTF-50% of Teflon increased after super-resolution processing. Statistically significant differences in TTF-50% were observed between the standard 512, 1024, and Swin2SR-2048 images (all p < 0.05). Swin2SR-2048 images exhibited lower image noise and NPS_peak compared to both standard 512- and 1024-matrix images, with significant differences observed in all three matrix types (all p < 0.05). Swin2SR-2048 images also demonstrated superior quality compared to SRCNN-2048, with significant differences in image noise (p < 0.001), NPS_peak (p < 0.05), and TTF-50% for Teflon (p < 0.05). Transformer-enhanced 2048-matrix CT images improve spatial resolution and reduce image noise compared to standard-512 and -1024 matrix images.

In this work, we introduce a benchtop, turn-table photon-counting (PC) micro-CT scanner and highlight its application for dynamic small animal perfusion imaging.&#xD;Approach: Built on recently published hardware, the system now features a CdTe-based photon-counting detector (PCD). We validated its static spectral PC micro-CT imaging using conventional phantoms and assessed dynamic performance with a custom flow-configurable dual-compartment perfusion phantom. The phantom was scanned under varied flow conditions during injections of a low molecular weight iodinated contrast agent. In vivo mouse studies with identical injection settings demonstrated potential applications. A pretrained denoising CNN processed large multi-energy, temporal datasets (20 timepoints × 4 energies × 3 spatial dimensions), reconstructed via weighted filtered back projection. A separate CNN, trained on simulated data, performed gamma variate-based 2D perfusion mapping, evaluated qualitatively in phantom and in vivo tests.&#xD;Main Results: Full five-dimensional reconstructions were denoised using a CNN in ~3% of the time of iterative reconstruction, reducing noise in water at the highest energy threshold from 1206 HU to 86 HU. Decomposed iodine maps, which improved contrast to noise ratio from 16.4 (in the lowest energy CT images) to 29.4 (in the iodine maps), were used for perfusion analysis. The perfusion CNN outperformed pixelwise gamma variate fitting by ~33%, with a test set error of 0.04 vs. 0.06 in blood flow index (BFI) maps, and quantified linear BFI changes in the phantom with a coefficient of determination of 0.98.&#xD;Significance: This work underscores the PC micro-CT scanner's utility for high-throughput small animal perfusion imaging, leveraging spectral PC micro-CT and iodine decomposition. It provides a versatile platform for preclinical vascular research and advanced, time-resolved studies of disease models and therapeutic interventions.

B-line artifacts in lung ultrasound, pivotal for diagnosing pulmonary conditions, warrant automated recognition to enhance diagnostic accuracy. In this paper, a lung ultrasound B-line vertical artifact identification method based on radio frequency (RF) signal was proposed. B-line regions were distinguished from non-B-line regions by inputting multiple characteristic parameters into nonlinear support vector machine (SVM). Six characteristic parameters were evaluated, including permutation entropy, information entropy, kurtosis, skewness, Nakagami shape factor, and approximate entropy. Following the evaluation that demonstrated the performance differences in parameter recognition, Principal Component Analysis (PCA) was utilized to reduce the dimensionality to a four-dimensional feature set for input into a nonlinear Support Vector Machine (SVM) for classification purposes. Four types of experiments were conducted: a sponge with dripping water model, gelatin phantoms containing either glass beads or gelatin droplets, and in vivo experiments. By employing precise feature selection and analyzing scan lines rather than full images, this approach significantly reduced the dependency on large image datasets without compromising discriminative accuracy. The method exhibited performance comparable to contemporary image-based deep learning approaches, which, while highly effective, typically necessitate extensive data for training and require expert annotation of large datasets to establish ground truth. Owing to the optimized architecture of our model, efficient sample recognition was achieved, with the capability to process between 27,000 and 33,000 scan lines per second (resulting in a frame rate exceeding 100 FPS, with 256 scan lines per frame), thus supporting real-time analysis. The results demonstrate that the accuracy of the method to classify a scan line as belonging to a B-line region was up to 88%, with sensitivity reaching up to 90%, specificity up to 87%, and an F1-score up to 89%. This approach effectively reflects the performance of scan line classification pertinent to B-line identification. Our approach reduces the reliance on large annotated datasets, thereby streamlining the preprocessing phase.

Magnetic resonance spectroscopic imaging has potential for non-invasive metabolic imaging of the human brain. Here we report a method that overcomes several long-standing technical barriers associated with clinical magnetic resonance spectroscopic imaging, including long data acquisition times, limited spatial coverage and poor spatial resolution. Our method achieves ultrafast data acquisition using an efficient approach to encode spatial, spectral and J-coupling information of multiple molecules. Physics-informed machine learning is synergistically integrated in data processing to enable reconstruction of high-quality molecular maps. We validated the proposed method through phantom experiments. We obtained high-resolution molecular maps from healthy participants, revealing metabolic heterogeneities in different brain regions. We also obtained high-resolution whole-brain molecular maps in regular clinical settings, revealing metabolic alterations in tumours and multiple sclerosis. This method has the potential to transform clinical metabolic imaging and provide a long-desired capability for non-invasive label-free metabolic imaging of brain function and diseases for both research and clinical applications.

Atherosclerosis involves not only the narrowing of blood vessels and plaque accumulation but also changes in plaque composition and stability, all of which are critical for disease progression. Conventional imaging techniques such as magnetic resonance angiography (MRA) and digital subtraction angiography (DSA) primarily assess luminal narrowing and plaque size, but have limited capability in identifying plaque instability and inflammation within the vascular muscle wall. This study aimed to develop and evaluate a novel imaging approach using ligand-modified nanomagnetic contrast (lmNMC) nanoprobes in combination with molecular magnetic resonance imaging (mMRI) to visualize and quantify vascular inflammation and plaque characteristics in a rabbit model of atherosclerosis. A rabbit model of atherosclerosis was established and underwent mMRI before and after administration of lmNMC nanoprobes. Radiomic features were extracted from segmented images using discrete wavelet transform (DWT) to assess spatial frequency changes and gray-level co-occurrence matrix (GLCM) analysis to evaluate textural properties. Further radiomic analysis was performed using neural network-based regression and clustering, including the application of self-organizing maps (SOMs) to validate the consistency of radiomic pattern between training and testing data. Radiomic analysis revealed significant changes in spatial frequency between pre- and post-contrast images in both the horizontal and vertical directions. GLCM analysis showed an increase in contrast from 0.08463 to 0.1021 and a slight decrease in homogeneity from 0.9593 to 0.9540. Energy values declined from 0.2256 to 0.2019, while correlation increased marginally from 0.9659 to 0.9708. Neural network regression demonstrated strong convergence between target and output coordinates. Additionally, SOM clustering revealed consistent weight locations and neighbor distances across datasets, supporting the reliability of the radiomic validation. The integration of lmNMC nanoprobes with mMRI enables detailed visualization of atherosclerotic plaques and surrounding vascular inflammation in a preclinical model. This method shows promise for enhancing the characterization of unstable plaques and may facilitate early detection of high-risk atherosclerotic lesions, potentially improving diagnostic and therapeutic strategies.

Nano-immunotherapy shows great promise in improving patient outcomes, as seen in advanced triple-negative breast cancer, but it does not cure the disease, with median survival under two years. Therefore, understanding resistance mechanisms and developing strategies to enhance its effectiveness in breast cancer is crucial. A key resistance mechanism is the pronounced desmoplasia in the tumor microenvironment, which leads to dysfunction of tumor blood vessels and thus, to hypoperfusion, limited drug delivery and hypoxia. Ultrasound sonopermeation and agents that normalize the tumor stroma have been employed separately to restore vascular abnormalities in tumors with some success. Here, we performed in vivo studies in two murine, orthotopic breast tumor models to explore if combination of ultrasound sonopermeation with a stroma normalization drug can synergistically improve tumor perfusion and enhance the efficacy of nano-immunotherapy. We found that the proposed combinatorial treatment can drastically reduce primary tumor growth and in many cases tumors were no longer measurable. Overall survival studies showed that all mice that received the combination treatment survived and rechallenge experiments revealed that the survivors obtained immunological memory. Employing ultrasound elastography and contrast enhanced ultrasound along with proteomics analysis, flow cytometry and immunofluorescene staining, we found the combinatorial treatment reduced tumor stiffness to normal levels, restoring tumor perfusion and oxygenation. Furthermore, it increased infiltration and activity of immune cells and altered the levels of immunosupportive chemokines. Finally, using machine learning analysis, we identified that tumor stiffness, CD8⁺ T cells and M2-type macrophages were strong predictors of treatment response.

Ultrasound (US) imaging is a promising modality for real-time monitoring of robotic capsule endoscopes navigating through the gastrointestinal (GI) tract. It offers high temporal resolution and safety but is limited by a narrow field of view, low visibility in gas-filled regions and challenges in detecting out-of-plane motions. This work addresses these issues by proposing a novel robotic ultrasound tracking system capable of long-distance 3D tracking and active re-localization when the capsule is lost due to motion or artifacts. We develop a hybrid deep learning-based tracking framework combining convolutional neural networks (CNNs) and a transformer backbone. The CNN component efficiently encodes spatial features, while the transformer captures long-range contextual dependencies in B-mode US images. This model is integrated with a robotic arm that adaptively scans and tracks the capsule. The system's performance is evaluated using ex vivo colon phantoms under varying imaging conditions, with physical perturbations introduced to simulate realistic clinical scenarios. The proposed system achieved continuous 3D tracking over distances exceeding 90 cm, with a mean centroid localization error of 1.5 mm and over 90% detection accuracy. We demonstrated 3D tracking in a more complex workspace featuring two curved sections to simulate anatomical challenges. This suggests the strong resilience of the tracking system to motion-induced artifacts and geometric variability. The system maintained real-time tracking at 9-12 FPS and successfully re-localized the capsule within seconds after tracking loss, even under gas artifacts and acoustic shadowing. This study presents a hybrid CNN-transformer system for automatic, real-time 3D ultrasound tracking of capsule robots over long distances. The method reliably handles occlusions, view loss and image artifacts, offering millimeter-level tracking accuracy. It significantly reduces clinical workload through autonomous detection and re-localization. Future work includes improving probe-tissue interaction handling and validating performance in live animal and human trials to assess physiological impacts.

This proof-of-concept study introduces a novel, deep learning-based, parameter-free, automatic slice-tracking technique for continuous catheter tracking and visualization during MR-guided cardiac catheterization. The proposed sequence includes Calibration and Runtime modes. Initially, Calibration mode identifies the catheter tip's three-dimensional coordinates using a fixed stack of contiguous slices. A U-Net architecture with a ResNet-34 encoder is used to identify the catheter tip location. Once identified, the sequence then switches to Runtime mode, dynamically acquiring three contiguous slices automatically centered on the catheter tip. The catheter location is estimated from each Runtime stack using the same network and fed back to the sequence, enabling prospective slice tracking to keep the catheter in the central slice. If the catheter remains unidentified over several dynamics, the sequence reverts to Calibration mode. This artificial intelligence (AI)-based approach was evaluated prospectively in a three-dimensional-printed heart phantom and 3 patients undergoing MR-guided cardiac catheterization. This technique was also compared retrospectively in 2 patients with a previous non-AI automatic tracking method relying on operator-defined parameters. In the phantom study, the tracking framework achieved 100% accuracy/sensitivity/specificity in both modes. Across all patients, the average accuracy/sensitivity/specificity were 100 ± 0/100 ± 0/100 ± 0% (Calibration) and 98.4 ± 0.8/94.1 ± 2.9/100.0 ± 0.0% (Runtime). The parametric, non-AI technique and the proposed parameter-free AI-based framework yielded identical accuracy (100%) in Calibration mode and similar accuracy range in Runtime mode (Patients 1 and 2: 100%-97%, and 100%-98%, respectively). An AI-based prospective slice-tracking framework was developed for real-time, parameter-free, operator-independent, automatic tracking of gadolinium-filled balloon catheters. Its feasibility was successfully demonstrated in patients undergoing MRI-guided cardiac catheterization.

Scanner-related changes in data quality are common in medical imaging, yet monitoring their impact on diagnostic AI performance remains challenging. In this study, we performed standardized consistency testing of an FDA-cleared and CE-marked AI for triage and notification of intracranial aneurysms across changes in image data quality caused by dose and image reconstruction. Our assessment was based on repeated examinations of a head CT phantom designed for AI evaluation, replicating a patient with three intracranial aneurysms in the anterior, middle and posterior circulation. We show that the AI maintains stable performance within the medium dose range but produces inconsistent results at reduced dose and, unexpectedly, at higher dose when filtered back projection is used. Data quality standards required for AI are stricter than those for neuroradiologists, who report higher aneurysm visibility rates and experience performance degradation only at substantially lower doses, with no decline at higher doses.