ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction

2026-03-16

AI Summary

This work proposes a novel feedforward reconstruction framework to address artifacts and loss of fine details in sparse-view cone-beam CT caused by insufficient projection data. The method introduces, for the first time in a feedforward model, an explicitly represented 3D latent volumetric space that is iteratively refined by integrating multi-view X-ray features with data-driven anatomical priors. Key components—including grouped cross-attention, efficient self-attention, and view-feature aggregation—enable precise optimization of the latent volume. Evaluated on a large-scale dataset of approximately 14,000 clinical CT scans, the proposed approach significantly outperforms existing feedforward and iterative methods in both reconstruction quality and speed, demonstrating strong potential for clinical deployment.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Feature Construction/Reformulation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

A long-term goal in CT imaging is to achieve fast and accurate 3D reconstruction from sparse-view projections, thereby reducing radiation exposure, lowering system cost, and enabling timely imaging in clinical workflows. Recent feed-forward approaches have shown strong potential toward this overarching goal, yet their results still suffer from artifacts and loss of fine details. In this work, we introduce Iterative Latent Volumes (ILV), a feed-forward framework that integrates data-driven priors with classical iterative reconstruction principles to overcome key limitations of prior feed-forward models in sparse-view CBCT reconstruction. At its core, ILV constructs an explicit 3D latent volume that is repeatedly updated by conditioning on multi-view X-ray features and the learned anatomical prior, enabling the recovery of fine structural details beyond the reach of prior feed-forward models. In addition, we develop and incorporate several key architectural components, including an X-ray feature volume, group cross-attention, efficient self-attention, and view-wise feature aggregation, that efficiently realize its core latent volume refinement concept. Extensive experiments on a large-scale dataset of approximately 14,000 CT volumes demonstrate that ILV significantly outperforms existing feed-forward and optimization-based methods in both reconstruction quality and speed. These results show that ILV enables fast and accurate sparse-view CBCT reconstruction suitable for clinical use. The project page is available at: https://sngryonglee.github.io/ILV/.

Problem

• sparse-view CT reconstruction
• 3D reconstruction
• radiation reduction
• clinical imaging
• cone-beam CT

Innovation

• Iterative Latent Volumes
• Sparse-View CT Reconstruction
• Feed-Forward Framework
• Latent Volume Refinement
• Cross-Attention