Medical image segmentation is becoming a growing crucial step in assisting with disease detection and diagnosis. However, medical images often exhibit complex structures and textures, resulting in the need for highly complex methods. Particularly, when Deep Learning methods are utilized, they often require large-scale pretraining, leading to significant memory demands and increased computational costs. The well-known Convolutional Neural Networks (CNNs) have become the backbone of medical image segmentation tasks thanks to their effective feature extraction abilities. However, they often struggle to capture global context due to the limited sizes of their kernels. To address this, various Transformer-based models have been introduced to learn long-range dependencies through self-attention mechanisms. However, these architectures typically incur relatively high computational complexity.
Methods: To address the aforementioned challenges, we propose a lightweight and computationally efficient model named ADC-MambaNet, which combines the conventional Depthwise Convolutional layers with the Mamba algorithm that can address the computational complexity of Transformers. In the proposed model, a new feature extractor named Harmonious Mamba-Convolution (HMC) block, and the Multi-Dimensional Priority Attention (MDPA) block have been designed. These blocks enhance the feature extraction process, thereby improving the overall performance of the model. In particular, the mechanisms enable the model to effectively capture local and global patterns from the feature maps while keeping the computational costs low. A novel loss function called the Balanced Normalized Cross Entropy is also introduced, bringing promising performance compared to other losses. Evaluations on five public medical image datasets: ISIC 2018 Lesion Segmentation, PH2, Data Science Bowl 2018, GlaS, and Lung X-ray demonstrate that ADC-MambaNet achieves higher evaluation scores while maintaining compact parameters and low computational complexity.
Conclusion: ADC-MambaNet offers a promising solution for accurate and efficient medical image segmentation, especially in resource-limited or edge-computing environments. Implementation code will be publicly accessible at: https://github.com/nqnguyen812/mambaseg-model.

Image enhancement of the Hypoxic-Ischemic Encephalopathy (HIE) lesion region in neonatal brain MR images is a challenging task due to the diffuse (i.e., multi-focal) nature, small size, and low contrast of the lesions. Classifying the stages of HIE is also difficult because of the unclear boundaries and edges of the lesions, which are dispersedthroughout the brain. Moreover, unclear boundaries and edges are due to chemical shifts, partial volume artifacts, and motion artifacts. Further, voxels may reflect signals from adjacent tissues. Existing algorithms perform poorly in HIE lesion enhancement due to artifacts, voxels, and the diffuse nature of the lesion. In this paper, we propose a Rough Neural Network and Anti-Homomorphic Filter (RNN-AHF) framework for the enhancement of the HIE lesion region. The RNN-AHF framework reduces the pixel dimensionality of the feature space, eliminates unnecessary pixels, and preserves essential pixels for lesion enhancement. The RNN efficiently learns and identifies pixel patterns and facilitates adaptive enhancement based on different weights in the neural network. The proposed RNN-AHF framework operates using optimized neural weights and an optimized training function. The hybridization of optimized weights and the training function enhances the lesion region with high contrast while preserving the boundaries and edges. The proposed RNN-AHF framework achieves a lesion image enhancement and classification accuracy of approximately 93.5%, which is better than traditional algorithms.

Despite the established association between chronic obstructive pulmonary disease (COPD) severity and risk of osteoporosis, even after accounting for the known shared confounding variables (e.g., age, smoking, history of exacerbations, steroid use), there is paucity of data on bone loss among mild to moderate COPD, which is more prevalent in the general population. We conducted a longitudinal analysis using data from the Multi-Ethnic Study of Atherosclerosis. Participants with chest CT at Exam 5 (2010-2012) and Exam 6 (2016-2018) were included. Mild to moderate COPD was defined as forced expiratory volume in 1 s (FEV₁) to forced vital capacity ratio of <0.70 and FEV₁ of 50 % or higher. Vitamin D deficiency was defined as serum vitamin D < 20 ng/mL. We utilized a validated deep learning algorithm to perform automated multilevel segmentation of vertebral bodies (T1-T10) from chest CT and derive 3D volumetric thoracic vertebral BMD measurements at Exam 5 and 6. Of the 1226 participants, 173 had known mild to moderate COPD at baseline, while 1053 had no known COPD. After adjusting for age, race/ethnicity, sex, body mass, index, bisphosphonate use, alcohol consumption, smoking, diabetes, physical activity, C-reactive protein and vitamin D deficiency, mild to moderate COPD was associated with faster decline in BMD (estimated difference, β = -0.38 g/cm³/year; 95 % CI: -0.74, -0.02). A significant interaction between COPD and vitamin D deficiency (p = 0.001) prompted stratified analyses. Among participants with vitamin D deficiency (47 % of participants), COPD was associated with faster decline in BMD (-0.64 g/cm³/year; 95 % CI: -1.17 to -0.12), whereas no significant association was observed among those with normal vitamin D in both crude and adjusted models. Mild to moderate COPD is associated with longitudinal declines in vertebral BMD exclusively in participants with vitamin D deficiency over 6-year follow-up. Vitamin D deficiency may play a crucial role in bone loss among patients with mild to moderate COPD.

The integration of artificial intelligence (AI) with radiology signifies a transformative era in medicine. Vision foundation models have been adopted to enhance radiologic imaging analysis. However, the inherent complexities of 2D and 3D radiologic data present unique challenges that existing models, which are typically pretrained on general nonmedical images, do not adequately address. To bridge this gap and harness the diagnostic precision required in radiologic imaging, we introduce radiologic contrastive language-image pretraining (RadCLIP): a cross-modal vision-language foundational model that utilizes a vision-language pretraining (VLP) framework to improve radiologic image analysis. Building on the contrastive language-image pretraining (CLIP) approach, RadCLIP incorporates a slice pooling mechanism designed for volumetric image analysis and is pretrained using a large, diverse dataset of radiologic image-text pairs. This pretraining effectively aligns radiologic images with their corresponding text annotations, resulting in a robust vision backbone for radiologic imaging. Extensive experiments demonstrate RadCLIP's superior performance in both unimodal radiologic image classification and cross-modal image-text matching, underscoring its significant promise for enhancing diagnostic accuracy and efficiency in clinical settings. Our key contributions include curating a large dataset featuring diverse radiologic 2D/3D image-text pairs, pretraining RadCLIP as a vision-language foundation model on this dataset, developing a slice pooling adapter with an attention mechanism for integrating 2D images, and conducting comprehensive evaluations of RadCLIP on various radiologic downstream tasks.

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal lowrankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses stateof-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. DeepSSL is efficient under highly limited training data and adaptive to patients and prospective undersampling. This approach holds promise in addressing the escalating demand for high-dimensional data reconstruction in MRI applications.

Chemical Exchange Saturation Transfer (CEST) MRI has demonstrated its remarkable ability to enhance the detection of macromolecules and metabolites with low concentrations. While CEST mapping is essential for quantifying molecular information, conventional methods face critical limitations: model-based approaches are constrained by limited sensitivity and robustness depending heavily on parameter setups, while data-driven deep learning methods lack generalizability across heterogeneous datasets and acquisition protocols. To overcome these challenges, we propose a Lorentzian-model Informed Neural Representation (LINR) framework for high-quality CEST mapping. LINR employs a self-supervised neural architecture embedding the Lorentzian equation - the fundamental biophysical model of CEST signal evolution - to directly reconstruct high-sensitivity parameter maps from raw z-spectra, eliminating dependency on labeled training data. Convergence of the self-supervised training strategy is guaranteed theoretically, ensuring LINR's mathematical validity. The superior performance of LINR in capturing CEST contrasts is revealed through comprehensive evaluations based on synthetic phantoms and in-vivo experiments (including tumor and Alzheimer's disease models). The intuitive parameter-free design enables adaptive integration into diverse CEST imaging workflows, positioning LINR as a versatile tool for non-invasive molecular diagnostics and pathophysiological discovery.

Pancreatic cancer is a highly aggressive malignancy with rising incidence and mortality rates, often diagnosed at advanced stages. Conventional imaging methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), struggle to assess tumor characteristics and vascular involvement, which are crucial for treatment planning. This paper explores the potential of diffusion magnetic resonance imaging (dMRI) in enhancing pancreatic cancer diagnosis and treatment. Diffusion-based techniques, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), combined with emerging AI‑powered analysis, provide insights into tissue microstructure, allowing for earlier detection and improved evaluation of tumor cellularity. These methods may help assess prognosis and monitor therapy response by tracking diffusion and perfusion metrics. However, challenges remain, such as standardized protocols and robust data analysis pipelines. Ongoing research, including deep learning applications, aims to improve reliability, and dMRI shows promise in providing functional insights and improving patient outcomes. Further clinical validation is necessary to maximize its benefits.

Advancements in magnetic resonance imaging (MRI) analysis over the past decades have significantly reshaped the field of neuroradiology. The ability to extract multiple quantitative measures from each MRI scan, alongside the development of extensive data repositories, has been fundamental to the emergence of advanced methodologies such as radiomics and artificial intelligence (AI). This educational review aims to delineate the importance of image analysis, highlight key paradigm shifts, examine their implications, and identify existing constraints that must be addressed to facilitate integration into clinical practice. Particular attention is given to aiding junior neuroradiologists in navigating this complex and evolving landscape. A comprehensive review of the available analysis toolboxes was conducted, focusing on major technological advancements in MRI analysis, the evolution of data repositories, and the rise of AI and radiomics in neuroradiology. Stakeholders within the field were identified and their roles examined. Additionally, current challenges and barriers to clinical implementation were analyzed. The analysis revealed several pivotal shifts, including the transition from qualitative to quantitative imaging, the central role of large datasets in developing AI tools, and the growing importance of interdisciplinary collaboration. Key stakeholders-including academic institutions, industry partners, regulatory bodies, and clinical practitioners-were identified, each playing a distinct role in advancing the field. However, significant barriers remain, particularly regarding standardization, data sharing, regulatory approval, and integration into clinical workflows. While advancements in MRI analysis offer tremendous potential to enhance neuroradiology practice, realizing this potential requires overcoming technical, regulatory, and practical barriers. Education and structured support for junior neuroradiologists are essential to ensure they are well-equipped to participate in and drive future developments. A coordinated effort among stakeholders is crucial to facilitate the seamless translation of these technological innovations into everyday clinical practice.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To estimate the total lung volume (TLV) from real and synthetic frontal chest radiographs (CXR) on a pixel level using lung thickness maps generated by a U-Net deep learning model. Materials and Methods This retrospective study included 5,959 chest CT scans from two public datasets: the lung nodule analysis 2016 (<i>n</i> = 656) and the Radiological Society of North America (RSNA) pulmonary embolism detection challenge 2020 (<i>n</i> = 5,303). Additionally, 72 participants were selected from the Klinikum Rechts der Isar dataset (October 2018 to December 2019), each with a corresponding chest radiograph taken within seven days. Synthetic radiographs and lung thickness maps were generated using forward projection of CT scans and their lung segmentations. A U-Net model was trained on synthetic radiographs to predict lung thickness maps and estimate TLV. Model performance was assessed using mean squared error (MSE), Pearson correlation coefficient <b>(r)</b>, and two-sided Student's t-distribution. Results The study included 72 participants (45 male, 27 female, 33 healthy: mean age 62 years [range 34-80]; 39 with chronic obstructive pulmonary disease: mean age 69 years [range 47-91]). TLV predictions showed low error rates (MSEPublic-Synthetic = 0.16 L², MSEKRI-Synthetic = 0.20 L², MSEKRI-Real = 0.35 L²) and strong correlations with CT-derived reference standard TLV (nPublic-Synthetic = 1,191, r = 0.99, <i>P</i> < .001; nKRI-Synthetic = 72, r = 0.97, <i>P</i> < .001; nKRI-Real = 72, r = 0.91, <i>P</i> < .001). When evaluated on different datasets, the U-Net model achieved the highest performance for TLV estimation on the Luna16 test dataset, with the lowest mean squared error (MSE = 0.09 L²) and strongest correlation (<i>r</i> = 0.99, <i>P</i> <.001) compared with CT-derived TLV. Conclusion The U-Net-generated pixel-level lung thickness maps successfully estimated TLV for both synthetic and real radiographs. ©RSNA, 2025.