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• ILV significantly outperforms existing feed-forward and optimization-based methods in reconstruction quality and speed for sparse-view CBCT reconstruction.result 90%
• ILV enables the recovery of fine structural details beyond the reach of prior feed-forward models in sparse-view CBCT reconstruction.technical 85%
• ILV integrates data-driven priors with classical iterative reconstruction principles to overcome key limitations of prior feed-forward models.method 90%
• ILV constructs an explicit 3D latent volume that is repeatedly updated by conditioning on multi-view X-ray features and learned anatomical prior.method 90%
• The method enables fast and accurate sparse-view CBCT reconstruction suitable for clinical use.market 80%
• ILV reduces radiation exposure, lowers system cost, and enables timely imaging in clinical workflows.market 70%
• The framework incorporates several key architectural components including X-ray feature volume, group cross-attention, efficient self-attention, and view-wise feature aggregation.technical 90%
• Recent feed-forward approaches for sparse-view CT reconstruction still suffer from artifacts and loss of fine details.limitation 85%

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