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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic

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    This study presents a novel algorithm for mapping molecular transcriptomics to tissue atlases, integrating diverse spatial datasets into a unified coordinate system for enhanced biological insights.

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    Area of Science:

    • Computational biology
    • Bioinformatics
    • Spatial transcriptomics

    Background:

    • Bridging the gap between molecular-level gene expression and macroscopic tissue structures is crucial for understanding biological systems.
    • Existing methods struggle to integrate diverse spatial transcriptomics datasets with existing anatomical atlases.

    Approach:

    • Developed a generalized function model for spatial transcriptomics, encoding both molecular position and identity.
    • Modeled atlas compartments as random fields to infer transcriptomic feature distributions.
    • Employed alternating LDDMM optimization and quadratic programming for simultaneous coordinate transformation and feature fraction estimation.

    Key Points:

    • The algorithm successfully maps gene-based and cell-based MERFISH datasets to tissue atlases.
    • Demonstrated universality across different spatial transcriptomics modalities and atlas scales.
    • Jointly estimates coordinate transformations and latent feature distributions for robust data integration.

    Conclusions:

    • Enables the integration of diverse molecular and cellular datasets into a single, unified coordinate system.
    • Provides a framework for comparing different atlas ontologies, facilitating future research and development in spatial biology.