OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging

A new framework named ReBrain has been introduced for reconstructing brain MRI from sparse CT slices using a retrieval-augmented diffusion approach. This method utilizes a Brownian Bridge Diffusion Model to synthesize MRI slices and retrieves similar CT slices from a prior database to enhance reconstruction accuracy.

A new foundation model named SPECTRE has been introduced, utilizing a fully transformer-based architecture for volumetric computed tomography (CT). This model employs self-supervised and cross-modal pretraining strategies to effectively learn CT representations, addressing challenges such as extreme token scaling and weak clinical supervision.

A novel artificial intelligence framework has been developed to measure human spine aging using MRI, leveraging a deep learning method that analyzes over 18,000 MRI series focused on age-related spine degeneration. The model employs advanced clustering techniques to identify degenerative conditions and evaluates its clinical utility by comparing actual spine age with model predictions.

A recent study evaluated the association between pretreatment MRI descriptors and the pathologic complete response (pCR) of breast cancer patients undergoing neoadjuvant chemotherapy. The retrospective analysis included 129 patients treated between 2016 and 2020, revealing that 46% achieved pCR, with significant differences noted across breast cancer subtypes.

A new statistical framework for crack pre-localization in 3D concrete images has been introduced, addressing the challenges of effectively segmenting cracks in large-scale computed tomography (CT) images. This method utilizes a Hessian-based filter and geometric descriptors to identify regions likely to contain cracks, relying on minimal calibration data rather than extensive annotated datasets.

A new publicly accessible model for automated segmentation of muscle and fat in computed tomography (CT) images has been introduced, enhancing body composition analysis. This model effectively segments skeletal muscle, subcutaneous adipose tissue, and visceral adipose tissue in axial CT images, addressing a significant gap in available tools for clinical applications.