Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization.

Untrained networks inspired by deep image priors have shown promising capabilities in recovering high-quality images from noisy or partial measurements . Their success is widely attributed to implicit regularization due to the spectral bias of suitable network architectures. However, the application of such network-based priors often entails superfluous architectural decisions, risks of overfitting, and lengthy optimization processes, all of which hinder their practicality.

UltimateSynth: MRI Physics for Pan-Contrast AI.

Magnetic resonance imaging (MRI) is commonly used in healthcare for its ability to generate diverse tissue contrasts without ionizing radiation. However, this flexibility complicates downstream analysis, as computational tools are often tailored to specific MRI types and lack generalizability across the full spectrum of scans used in healthcare. Here, we introduce a versatile framework for the development and validation of pan-contrast AI models that can exhaustively cater to the full spectrum of scans achievable with MRI, enabling model deployment across scanner models, scan types, and age groups.

Disentangled latent energy-based style translation: An image-level structural MRI harmonization framework.

Brain magnetic resonance imaging (MRI) has been extensively employed across clinical and research fields, but often exhibits sensitivity to site effects arising from non-biological variations such as differences in field strength and scanner vendors. Numerous retrospective MRI harmonization techniques have demonstrated encouraging outcomes in reducing the site effects at image level. However, existing methods generally suffer from high computational requirements and limited generalizability, restricting their applicability to unseen MRIs.

Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning methods often rely on labeled data to construct learning models, while the data annotation process typically requires significant time and resource investment. Graph contrastive learning offers a promising solution to address the small labeled data issue, by augmenting fMRI time series for self-supervised learning.

Assessment of hypolipidemic potential of cholesteryl esterase inhibitory peptides in different probiotic fermented milk through in vitro, , and molecular docking studies.

Fermented milk (FM) is well-known to confer health-promoting benefits, particularly for managing chronic metabolic diseases. However, the specific cholesterol esterase (CE) inhibitory activities of FM produced from different animal milk sources have not been extensively explored. This study for the first time investigates the CE inhibition potential of FM derived from bovine (F_BM), camel (F_CM), sheep (F_SM), and goat milk (F_GM), each fermented with five different probiotic strains and stored for 14 days under refrigeration.