Development and characterization of OA-UDNet (IMAGE)

BMEF (BME Frontiers)

Development and characterization of OA-UDNet

Caption

 (A) Paired sparse-sampling inputs (32-, 64-, and 128-detector) and 256-detector full-view ground truth preparation across diverse murine organs and human tissues. (B) End-to-end training pipeline structured into 3 sequential stages: stage I for initial reconstruction via the encoder–decoder, stage II for the iterative image-space diffusion loop, and stage III for final high-fidelity generation. (C) Core architectural components illustrating the EAS block and EUNet configuration, complemented by a comprehensive symbol glossary defining all image tensors, latent representations, and operational data flows.

Credit

IMU Lab @FDU

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