Introduction: The classification of lesion types in Digital Breast Tomosynthesis (DBT) images is crucial for the early diagnosis of breast cancer. However, the task remains challenging due to the complexity of breast tissue and the subtle nature of lesions. To alleviate radiologists’ workload, computer-aided diagnosis (CAD) systems have been developed. The breast lesion regions vary in size and complexity, which leads to performance degradation. Methods: To tackle this problem, we propose a novel DBT Dual-Net architecture comprising two complementary neural network branches that extract both low-level and high-level features. By fusing different-level feature representations, the model can better capture subtle structure. Furthermore, we introduced a pseudo-color enhancement procedure to improve the visibility of lesions on DBT. Moreover, most existing DBT classification studies rely on two-dimensional (2D) slice-level analysis, neglecting the rich three-dimensional (3D) spatial context within DBT volumes. To address this limitation, we used majority voting for image-level classification from predictions across slices. Results: We evaluated our method on a public DBT dataset and compared its performance with several existing classification approaches. The results showed that our method outperforms baseline models. Discussion: The use of pseudo-color enhancement, extracting high and low-level features and inter-slice majority voting proposed method is effective for lesion classification in DBT. The code is available at https://github.com/xiaoerlaigeid/DBT-Dual-Net.

Ultrasound imaging is vital for the early detection of breast cancer, where accurate lesion segmentation supports clinical diagnosis and treatment planning. However, existing deep learning-based methods rely on pixel-level annotations, which are costly and labor-intensive to obtain. This study presents a weakly supervised framework for breast lesion segmentation in ultrasound images. The framework combines morphological enhancement with Class Activation Map (CAM)-guided lesion localization and utilizes the Segment Anything Model (SAM) for refined segmentation without pixel-level labels. By adopting a lightweight region synthesis strategy and relying solely on SAM inference, the proposed approach substantially reduces model complexity and computational cost while maintaining high segmentation accuracy. Experimental results on the BUSI dataset show that our method achieves a Dice coefficient of 0.7063 under five-fold cross-validation and outperforms several fully supervised models in Hausdorff distance metrics. These results demonstrate that the proposed framework effectively balances segmentation accuracy, computational efficiency, and annotation cost, offering a practical and low-complexity solution for breast ultrasound analysis. The code for this study is available at: https://github.com/YueXin18/MorSeg-CAM-SAM-Segmentation.

Skeletal muscle contains a highly hierarchical structure, leading to anisotropic mechanical properties, with varying morphological responses to mechanical loadings from different directions. However, this feature is rarely studied in clinical studies, mainly due to the challenges in quantifying muscle anisotropic mechanical properties in vivo. The aim of the current study was to quantify the anisotropic mechanical properties of skeletal muscle using an integrated approach combining multi-frequency magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI). Muscle fascicle orientation was determined through DTI tractography. Direct inversion of the curl-based wave equation was used to quantify three complex-valued moduli (μ⊥∗, μ‖∗, and E‖∗) assuming muscle as an incompressible transversely isotropic material. This approach was evaluated on one ex vivo muscle sample by comparing MRE-derived moduli to rheometry measurements, and further assessed in vivo in the ankle plantarflexors of nine able-bodied subjects. Consistency in the anisotropic ratio was observed between rheometry and MRE measurements in the ex vivo muscle sample, though discrepancies were noted in absolute shear moduli values. In vivo, the anisotropy of skeletal muscle was observed by the relationship of μ⊥∗≠1/3E‖∗ and μ‖∗≠1/3E‖∗ at different MRE driving frequencies with higher parallel shear modulus (μ‖∗) than the perpendicular shear modulus (μ⊥∗). This study demonstrated a promising approach for quantifying the muscle anisotropic mechanical properties in vivo, which can be useful in various clinical applications.

Alzheimer's disease (AD) subjects usually show more profound morphological changes with time compared to cognitively normal (CN) individuals. These changes are the combination of two major biological processes: normal aging and AD pathology. Investigating normal aging and residual morphological changes separately can increase our understanding of the disease. This paper proposes two scores, the aging score (AS) and the AD-specific score (ADS), whose purpose is to measure these two components of brain atrophy independently. For this, in the first step, we estimate the atrophy due to the normal aging of CN subjects by computing the expected deformation required to match imaging templates generated at different ages. We used a state-of-the-art generative deep learning model for generating such imaging templates. In the second step, we apply deep learning-based diffeomorphic registration to align the given image of a subject with a reference imaging template. Parametrization of this deformation field is then decomposed voxel-wise into their parallel and perpendicular components with respect to the parametrization of the expected atrophy of CN individuals in one year computed in the first step. AS and ADS are the normalized scores of these two components, respectively. We evaluated these two scores on the OASIS-3 dataset with 1,014 T1-weighted MRI scans. Of these, 326 scans were from CN subjects, and 688 scans were from subjects diagnosed with AD at various stages of clinical severity, as defined by clinical dementia rating (CDR) scores. Our results reveal that AD is marked by both disease-specific brain changes and an accelerated aging process. Such changes affect brain regions differently. Moreover, the proposed scores were sensitive to detect changes in the early stages of the disease, which is promising for its potential future use in clinical studies. Our code is freely available at https://github.com/Fjr9516/DBM_with_DL.

Magnetic Resonance Elastography (MRE) is a novel technique to study the brain by measuring its mechanical properties, such as stiffness and viscosity. These properties may provide insights into how the microstructure of the brain changes due to a pathology, however the connection between these microstructural mechanisms and the measured biomechanical properties are still largely unknown. For this reason, the present exploratory study utilizes multidimensional diffusion magnetic resonance imaging (MD-dMRI), apart from MRE, to extract microstructural parameters of the whole brain tissue for a small cohort of 12 Parkinson disease (PD) patients and 17 healthy controls. A combination of these methods provides valuable insights into subtle changes due to PD as it probes variables such as microscopic fractional anisotropy (mu FA) combined with measures of shear stiffness. MRE and MD-dMRI quantities across the brain are compared between the two groups and analyzed. It was found that there were significant softening effects in the temporal and occipital lobes due to PD, associated with an increase in the mean diffusivity in those regions, whereas other microstructural properties remained largely unchanged. The mesencephalon, on the other hand, displays changes in the MD-dMRI parameters consistent with neuronal atrophy, however no softening of this region was detected. In most regions, stiffness is significantly reduced due to age, which is correlated with a decrease in mu FA and increase in MD. We hypothesize that age effects can mostly explain neuronal atrophy, whereas softening due to PD effects involve additional mechanisms.

High-quality synthetic medical images can enlarge training datasets in different deep learning-based applications. Recently, diffusion-based methods for image synthesis have outperformed GAN-based methods, even for medical images. Unfortunately, using diffusion models is costly in terms of training time and computational resources. We propose a two-stage method that combines diffusion models and GANs to tackle this problem. First, we use diffusion models or GANs to generate low-resolution images. Then, we use a GAN-based super-resolution model to interpolate high-resolution images from these low-resolution images. Experimental results on synthetic breast CT slices show that the proposed framework is more efficient and performs better than state-of-the-art methods that generate the images in a single step. The proposed methods will be available at https://github.com/xiaoerlaigeid/Image-Frequency-Score.git.

Rigid registration aims to determine the translations and rotations necessary to align features in a pair of images. While recent machine learning methods have become state-of-the-art for linear and deformable registration across subjects, they have demonstrated limitations when applied to longitudinal (within-subject) registration, where achieving precise alignment is critical. Building on an existing framework for anatomy-aware, acquisition-agnostic affine registration, we propose a model optimized for longitudinal, rigid brain registration. By training the model with synthetic within-subject pairs augmented with rigid and subtle nonlinear transforms, the model estimates more accurate rigid transforms than previous cross-subject networks and performs robustly on longitudinal registration pairs within and across magnetic resonance imaging (MRI) contrasts.

The widespread success of nnU-Net as a state-of-the-art tool for medical image segmentation has driven its adoption as a baseline, but its limited portability and lack of clinical integration have limited broader deployment in real-world healthcare workflows. To address these challenges, we present the MONet Bundle, extending nnU-Net within the MONAI ecosystem, providing a modular benchmarking tool for Federated Learning (FL) that is directly compatible with downstream clinical operations such as model deployment, active learning, and DICOM-based PACS integration. MONet enables federated training across distributed clinical datasets while maintaining standardized preprocessing and harmonized workflows. Its flexibility is validated on two representative segmentation tasks: lymphoma lesion segmentation in PET-CT and brain tumor segmentation from the BraTS challenge. In both settings, MONet’s federated models consistently outperformed cross-site baselines and approached, or in some cases outperformed, the performance of centralized task-fusion models with minimal user intervention. The code is available at https://github.com/SimoneBendazzoli93/MONet-Bundle.

Simulating prospective magnetic resonance imaging (MRI) scans from a given individual brain image is challenging, as it requires accounting for canonical changes in aging and/or disease progression while also considering the individual brain's current status and unique characteristics. While current deep generative models can produce high-resolution anatomically accurate templates for population-wide studies, their ability to predict future aging trajectories for individuals remains limited, particularly in capturing subject-specific neuroanatomical variations over time. In this study, we introduce Individualized Brain Synthesis (InBrainSyn), a framework for synthesizing high-resolution subject-specific longitudinal MRI scans that simulate neurodegeneration in both Alzheimer's disease (AD) and normal aging. InBrainSyn uses a parallel transport algorithm to adapt the population-level aging trajectories learned by a generative deep template network, enabling individualized aging synthesis. As InBrainSyn uses diffeomorphic transformations to simulate aging, the synthesized images are topologically consistent with the original anatomy by design. We evaluated InBrainSyn both quantitatively and qualitatively on AD and healthy control cohorts from the Open Access Series of Imaging Studies - version 3 dataset. Experimentally, InBrainSyn can also model neuroanatomical transitions between normal aging and AD. An evaluation of an external set supports its generalizability. Overall, with only a single baseline scan, InBrainSyn synthesizes realistic 3D spatiotemporal T1w MRI scans, producing personalized longitudinal aging trajectories. The code for InBrainSyn is available at https://github.com/Fjr9516/InBrainSyn.

In this study, we propose a deep learning based two-stage breast CT reconstruction in the image domain. Unlike most methods, we use two separate models to improve the Breast CT image quality. In the first stage, a deep learning-based denoiser was used to remove the noise. In the second stage, a deep learning based image enhancement model is used to improve the image quality. We evaluated the proposed method on the AAPM 2021 sparse view CT reconstruction challenge dataset.1 The experimental results demonstrate that the proposed method performs better than all comparison methods.