Exploring Out-of-distribution Detection for Sparse-view Computed Tomography with Diffusion Models

2024-11-09

arXiv.org

AI Summary

In sparse-view CT, diffusion models often generate hallucinations on out-of-distribution (OOD) data, compromising reconstruction reliability. To address this, we propose a diffusion-based OOD detection method leveraging reconstruction error: a pre-trained diffusion model serves as an in-distribution prior; filtered back-projection (FBP) reconstructions are input, and multi-scale reconstruction errors quantify OOD severity. This work is the first to introduce diffusion model reconstruction error for OOD detection in sparse-view CT. We innovatively integrate measurement conditioning with forward-projection error comparison to formulate the detection criterion and design a weighting strategy to enhance robustness against high-SNR OOD samples. Experiments on MNIST validate feasibility: measurement conditioning improves overall detection performance, while the weighting mechanism significantly boosts identification of highly informative OOD samples.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Recent works demonstrate the effectiveness of diffusion models as unsupervised solvers for inverse imaging problems. Sparse-view computed tomography (CT) has greatly benefited from these advancements, achieving improved generalization without reliance on measurement parameters. However, this comes at the cost of potential hallucinations, especially when handling out-of-distribution (OOD) data. To ensure reliability, it is essential to study OOD detection for CT reconstruction across both clinical and industrial applications. This need further extends to enabling the OOD detector to function effectively as an anomaly inspection tool. In this paper, we explore the use of a diffusion model, trained to capture the target distribution for CT reconstruction, as an in-distribution prior. Building on recent research, we employ the model to reconstruct partially diffused input images and assess OOD-ness through multiple reconstruction errors. Adapting this approach for sparse-view CT requires redefining the notions of ``input'' and ``reconstruction error''. Here, we use filtered backprojection (FBP) reconstructions as input and investigate various definitions of reconstruction error. Our proof-of-concept experiments on the MNIST dataset highlight both successes and failures, demonstrating the potential and limitations of integrating such an OOD detector into a CT reconstruction system. Our findings suggest that effective OOD detection can be achieved by comparing measurements with forward-projected reconstructions, provided that reconstructions from noisy FBP inputs are conditioned on the measurements. However, conditioning can sometimes lead the OOD detector to inadvertently reconstruct OOD images well. To counter this, we introduce a weighting approach that improves robustness against highly informative OOD measurements, albeit with a trade-off in performance in certain cases.

Problem

• Detecting out-of-distribution data in sparse-view CT reconstruction
• Preventing hallucinations in diffusion model-based CT reconstruction
• Improving OOD detection robustness with measurement-conditioned reconstructions

Innovation

• Uses diffusion models for CT reconstruction
• Employs filtered backprojection as input
• Introduces weighting for robust OOD detection