To develop PerAIDE, an AI-driven system for automated analysis of pulmonary perfusion blood volume (PBV) using dual-energy computed tomography pulmonary angiography (DE-CTPA) in patients with chronic pulmonary thromboembolism (CPE). In this prospective observational study, 32 patients with chronic thromboembolic pulmonary disease (CTEPD) and 151 patients with chronic thromboembolic pulmonary hypertension (CTEPH) were enrolled between January 2022 and July 2024. PerAIDE was developed to automatically quantify three distinct perfusion patterns-normal, reduced, and defective-on DE-CTPA images. Two radiologists independently assessed PBV scores. Follow-up imaging was conducted 3 months after balloon pulmonary angioplasty (BPA). PerAIDE demonstrated high agreement with the radiologists (intraclass correlation coefficient = 0.778) and reduced analysis time significantly (31 ± 3 s vs. 15 ± 4 min, p < 0.001). CTEPH patients had greater perfusion defects than CTEPD (0.35 vs. 0.29, p < 0.001), while reduced perfusion was more prevalent in CTEPD (0.36 vs. 0.30, p < 0.001). Perfusion defects correlated positively with pulmonary vascular resistance (ρ = 0.534) and mean pulmonary artery pressure (ρ = 0.482), and negatively with oxygenation index (ρ = -0.441). PerAIDE effectively differentiated CTEPH from CTEPD (AUC = 0.809, 95% CI: 0.745-0.863). At the 3-month post-BPA, a significant reduction in perfusion defects was observed (0.36 vs. 0.33, p < 0.01). CTEPD and CTEPH exhibit distinct perfusion phenotypes on DE-CTPA. PerAIDE reliably quantifies perfusion abnormalities and correlates strongly with clinical and hemodynamic markers of CPE severity. ClinicalTrials.gov, NCT06526468. Registered 28 August 2024- Retrospectively registered, https://clinicaltrials.gov/study/NCT06526468?cond=NCT06526468&rank=1 . PerAIDE is a dual-energy computed tomography pulmonary angiography (DE-CTPA) AI-driven system that rapidly and accurately assesses perfusion blood volume in patients with chronic pulmonary thromboembolism, effectively distinguishing between CTEPD and CTEPH phenotypes and correlating with disease severity and therapeutic response. Right heart catheterization for definitive diagnosis of chronic pulmonary thromboembolism (CPE) is invasive. PerAIDE-based perfusion defects correlated with disease severity to aid CPE-treatment assessment. CTEPH demonstrates severe perfusion defects, while CTEPD displays predominantly reduced perfusion. PerAIDE employs a U-Net-based adaptive threshold method, which achieves alignment with and faster processing relative to manual evaluation.

One of today's major health threats is brain tumours, yet current systems focus mainly on diagnostic methods and medical imaging to understand them. Here, the Shepard Quantum Dilated Forward Harmonic Net (ShQDFHNet) is developed for brain tumour detection using MRI scans. It starts by enhancing images with high boost filtering to highlight key features, then uses Log-Cosh Point-Wise Pyramid Attention Network (Log-Cosh PPANet) for accurate tumour segmentation, guided by a refined Log-Cosh Dice Loss. To capture texture details, features like Spatial Grey-Level Dependence Matrix (SGLDM) and Gray-Level Co-occurrence Matrix (GLCM) are extracted. The final detection uses ShQDFHNet, combining Shepard Convolutional Neural Network (ShCNN) and Quantum Dilated Convolutional Neural Network (QDCNN), with layers enhanced by a Forward Harmonic Analysis Network. ShQDFHNet achieved strong performance on the Brain Tumour MRI dataset, with 90.69% accuracy, 91.14% True Positive Rate (TPR), and 90.61% True Negative Rate (TNR) using K-fold of 9. The use of high boost filtering, Log-Cosh PPANet, and texture-based features improves the input data quality and enables accurate tumor segmentation in MRI scans. The proposed ShQDFHNet model improves feature learning and achieves strong performance on brain tumor MRI data.

Skull-stripping is an essential preprocessing step in the analysis of brain Magnetic Resonance Imaging (MRI). While deep learning-based methods have shown success with this task, strong domain shifts between adult and newborn brain MR images complicate model transferability. We previously developed unsupervised domain adaptation techniques to address the domain shift between these data, without requiring newborn MRI data to be labeled. In this work, we build upon our previous domain adaptation framework by extensively expanding the training and validation datasets using weakly labeled newborn MRI scans from the Developing Human Connectome Project (dHCP), our private newborn dataset, and synthetic data generated by a Gaussian Mixture Model (GMM). While the core model architecture remains similar, we focus on validating the model's generalization across four diverse domains, adult, synthetic, public newborn, and private newborn MRI, demonstrating improved performance and robustness over our prior methods. These results highlight the impact of incorporating broader training data under weak supervision for newborn brain imaging analysis. The experimental results reveal that our proposed approach outperforms our previous work achieving a Dice coefficient of 0.9509±0.0055 and a Hausdorff distance of 3.0883±0.1833 for newborn MRI data, surpassing state-of-the-art models such as SynthStrip (Dice =0.9412±0.0063, Hausdorff =3.1570±0.1389). These results reveal that including weakly labeled newborn data results in improvements in model performance and generalization and is useful for newborn brain imaging analysis. Our code is available at: https://github.com/abbasomidi77/Weakly-Supervised-DAUnet.

Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disorder of the central nervous system (CNS) and represents the leading cause of non-traumatic disability among young adults. Magnetic resonance imaging (MRI) has revolutionized both the clinical management and scientific understanding of MS, serving as an indispensable paraclinical tool. Its high sensitivity and diagnostic accuracy enable early detection and timely therapeutic intervention, significantly impacting patient outcomes. Recent technological advancements have facilitated the integration of artificial intelligence (AI) algorithms for automated lesion identification, segmentation, and longitudinal monitoring. The ongoing refinement of deep learning (DL) and machine learning (ML) techniques, alongside their incorporation into clinical workflows, holds great promise for improving healthcare accessibility and quality in MS management. Despite the encouraging performance of DL models in MS lesion segmentation and disease progression tracking, their effectiveness is frequently constrained by the scarcity of large, diverse, and publicly available datasets. Open-source initiatives such as MSLesSeg, MS-Baghdad, MS-Shift, and MSSEG-2 have provided valuable contributions to the research community. Building upon these foundations, we introduce the SibBMS dataset to further advance data-driven research in MS. In this study, we present the SibBMS dataset, a carefully curated, open-source resource designed to support MS research utilizing structural brain MRI. The dataset comprises imaging data from 93 patients diagnosed with MS or radiologically isolated syndrome (RIS), alongside 100 healthy controls. All lesion annotations were manually delineated and rigorously reviewed by a three-tier panel of experienced neuroradiologists to ensure clinical relevance and segmentation accuracy. Additionally, the dataset includes comprehensive demographic metadata--such as age, sex, and disease duration--enabling robust stratified analyses and facilitating the development of more generalizable predictive models. Our dataset is available via a request-access form at https://forms.gle/VqTenJ4n8S8qvtxQA.

[¹⁸F]-meta-fluorobenzylguanidine ([¹⁸F]MFBG) PET/CT is a promising imaging modality for neural crest-derived tumors, particularly neuroblastoma. Accurate interpretation necessitates an understanding of normal biodistribution and variations in physiological uptake. This study aimed to systematically characterize the physiological distribution and variability of [¹⁸F]MFBG uptake in pediatric patients to enhance clinical interpretation and differentiate normal from pathological uptake. We retrospectively analyzed [¹⁸F]MFBG PET/CT scans from 169 pediatric neuroblastoma patients, including 20 in confirmed remission, for detailed biodistribution analysis. Organ uptake was quantified using both manual segmentation and deep learning(DL)-based automatic segmentation methods. Patterns of physiological uptake variants were categorized and illustrated using representative cases. [¹⁸F]MFBG demonstrated consistent physiological uptake in the salivary glands (SUVmax 9.8 ± 3.3), myocardium (7.1 ± 1.7), and adrenal glands (4.6 ± 0.9), with low activity in bone (0.6 ± 0.2) and muscle (0.8 ± 0.2). DL-based analysis confirmed uniform, mild uptake across vertebral and peripheral skeletal structures (SUVmean 0.47 ± 0.08). Three physiological liver uptake patterns were identified: uniform (43%), left-lobe predominant (31%), and marginal (26%). Asymmetric uptake in the pancreatic head, transient brown adipose tissue activity, gallbladder excretion, and symmetric epiphyseal uptake were also recorded. These variants were not associated with structural abnormalities or clinical recurrence and showed distinct patterns from pathological lesions. This study establishes a reference for normal [¹⁸F]MFBG biodistribution and physiological variants in children. Understanding these patterns is essential for accurate image interpretation and the avoidance of diagnostic pitfalls in pediatric neuroblastoma patients.

The claustrum is a band-like gray matter structure located between putamen and insula whose exact functions are still actively researched. Its sheet-like structure makes it barely visible in in vivo magnetic resonance imaging (MRI) scans at typical resolutions, and neuroimaging tools for its study, including methods for automatic segmentation, are currently very limited. In this paper, we propose a contrast- and resolution-agnostic method for claustrum segmentation at ultra-high resolution (0.35 mm isotropic); the method is based on the SynthSeg segmentation framework, which leverages the use of synthetic training intensity images to achieve excellent generalization. In particular, SynthSeg requires only label maps to be trained, since corresponding intensity images are synthesized on the fly with random contrast and resolution. We trained a deep learning network for automatic claustrum segmentation, using claustrum manual labels obtained from 18 ultra-high resolution MRI scans (mostly ex vivo). We demonstrated the method to work on these 18 high resolution cases (Dice score = 0.632, mean surface distance = 0.458 mm, and volumetric similarity = 0.867 using 6-fold cross validation (CV)), and also on in vivo T1-weighted MRI scans at typical resolutions (≈1 mm isotropic). We also demonstrated that the method is robust in a test-retest setting and when applied to multimodal imaging (T2-weighted, proton density, and quantitative T1 scans). To the best of our knowledge this is the first accurate method for automatic ultra-high resolution claustrum segmentation, which is robust against changes in contrast and resolution. The method is released at https://github.com/chiara-mauri/claustrum_segmentation and as part of the neuroimaging package FreeSurfer.

Non-contrast CT (NCCT) is widely used in clinical practice and holds potential for large-scale atherosclerosis screening, yet its application in detecting and grading aortic atherosclerosis remains limited. To address this, we propose Aortic-AAE, an automated segmentation system based on a cascaded attention mechanism within the nnU-Net framework. The cascaded attention module enhances feature learning across complex anatomical structures, outperforming existing attention modules. Integrated preprocessing and post-processing ensure anatomical consistency and robustness across multi-center data. Trained on 435 labeled NCCT scans from three centers and validated on 388 independent cases, Aortic-AAE achieved 81.12% accuracy in aortic stenosis classification and 92.37% in Agatston scoring of calcified plaques, surpassing five state-of-the-art models. This study demonstrates the feasibility of using deep learning for accurate detection and grading of aortic atherosclerosis from NCCT, supporting improved diagnostic decisions and enhanced clinical workflows.

Dark-field radiography is a novel X-ray imaging modality that enables complementary diagnostic information by visualizing the microstructural properties of lung tissue. Implemented via a Talbot-Lau interferometer integrated into a conventional X-ray system, it allows simultaneous acquisition of perfectly temporally and spatially registered attenuation-based conventional and dark-field radiographs. Recent clinical studies have demonstrated that dark-field radiography outperforms conventional radiography in diagnosing and staging pulmonary diseases. However, the polychromatic nature of medical X-ray sources leads to beam-hardening, which introduces structured artifacts in the dark-field radiographs, particularly from osseous structures. This so-called beam-hardening-induced dark-field signal is an artificial dark-field signal and causes undesired cross-talk between attenuation and dark-field channels. This work presents a segmentation-based beam-hardening correction method using deep learning to segment ribs and clavicles. Attenuation contribution masks derived from dual-layer detector computed tomography data, decomposed into aluminum and water, were used to refine the material distribution estimation. The method was evaluated both qualitatively and quantitatively on clinical data from healthy subjects and patients with chronic obstructive pulmonary disease and COVID-19. The proposed approach reduces bone-induced artifacts and improves the homogeneity of the lung dark-field signal, supporting more reliable visual and quantitative assessment in clinical dark-field chest radiography.

Soft X-ray tomography (SXT) provides detailed structural insight into whole cells but is hindered by experimental artifacts such as the missing wedge and by limited availability of annotated datasets. We present \method, a simulation pipeline that generates realistic cellular phantoms and applies synthetic artifacts to produce paired noisy volumes, sinograms, and reconstructions. We validate our approach by training a neural network primarily on synthetic data and demonstrate effective few-shot and zero-shot transfer learning on real SXT tomograms. Our model delivers accurate segmentations, enabling quantitative analysis of noisy tomograms without relying on large labeled datasets or complex reconstruction methods.

Deep learning segmentation relies heavily on labeled data, but manual labeling is laborious and time-consuming, especially for volumetric images such as brain magnetic resonance imaging (MRI). While recent domain-randomization techniques alleviate the dependency on labeled data by synthesizing diverse training images from label maps, they offer limited anatomical variability when very few label maps are available. Semi-supervised self-training addresses label scarcity by iteratively incorporating model predictions into the training set, enabling networks to learn from unlabeled data. In this work, we combine domain randomization with self-training to train three-dimensional skull-stripping networks using as little as a single labeled example. First, we automatically bin voxel intensities, yielding labels we use to synthesize images for training an initial skull-stripping model. Second, we train a convolutional autoencoder (AE) on the labeled example and use its reconstruction error to assess the quality of brain masks predicted for unlabeled data. Third, we select the top-ranking pseudo-labels to fine-tune the network, achieving skull-stripping performance on out-of-distribution data that approaches models trained with more labeled images. We compare AE-based ranking to consistency-based ranking under test-time augmentation, finding that the AE approach yields a stronger correlation with segmentation accuracy. Our results highlight the potential of combining domain randomization and AE-based quality control to enable effective semi-supervised segmentation from extremely limited labeled data. This strategy may ease the labeling burden that slows progress in studies involving new anatomical structures or emerging imaging techniques.