Advanced Brain Tissue Imaging with Data-Consistent Diffusion Priors in Laminographic X-Ray Nanoimaging
为解决X射线层析成像中因数据缺失导致的脑组织成像失真问题，提出LUCID框架，结合多视角扩散先验与投影域数据一致性方法恢复未测量信息。
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LoRCA: LoRA Cycle Adaptation for Histology to HiP-CT Translation with DINOv3
This work addresses the challenge of nonlinear registration between histological sections and HiP-CT volumetric data, which arises from significant modality discrepancies. To tackle this without requiring paired training data, the authors propose a structure-preserving cross-modal image translation method. Leveraging a frozen DINOv2 backbone as a semantic structural anchor and modality-specific LoRA adapters, the approach enables efficient and generalizable cross-modal representation learning within a cycle-consistent adversarial training framework. This design effectively mitigates content drift while enhancing structural consistency. Experimental results demonstrate that the proposed method outperforms CycleGAN in terms of Fréchet Inception Distance (FID), mutual information, and edge preservation metrics, and substantially improves feature correspondence in downstream registration tasks.
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Advanced Brain Tissue Imaging with Data-Consistent Diffusion Priors in Laminographic X-Ray Nanoimaging
为解决X射线层析成像中因数据缺失导致的脑组织成像失真问题，提出LUCID框架，结合多视角扩散先验与投影域数据一致性方法恢复未测量信息。
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LoRCA: LoRA Cycle Adaptation for Histology to HiP-CT Translation with DINOv3
This work addresses the challenge of nonlinear registration between histological sections and HiP-CT volumetric data, which arises from significant modality discrepancies. To tackle this without requiring paired training data, the authors propose a structure-preserving cross-modal image translation method. Leveraging a frozen DINOv2 backbone as a semantic structural anchor and modality-specific LoRA adapters, the approach enables efficient and generalizable cross-modal representation learning within a cycle-consistent adversarial training framework. This design effectively mitigates content drift while enhancing structural consistency. Experimental results demonstrate that the proposed method outperforms CycleGAN in terms of Fréchet Inception Distance (FID), mutual information, and edge preservation metrics, and substantially improves feature correspondence in downstream registration tasks.
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