Stabilizing Deep Tomographic Reconstruction

TL;DR

This paper introduces ACID, a novel framework that stabilizes deep learning-based tomographic reconstruction, making it accurate, robust, and suitable for clinical use by addressing known instabilities.

Contribution

The paper proposes the ACID framework, combining deep learning, compressed sensing principles, and iterative refinement to stabilize deep tomographic reconstruction networks.

Findings

ACID achieves accurate and stable reconstructions.

ACID is resilient against adversarial attacks.

ACID eliminates three major instabilities in deep reconstruction.

Abstract

Tomographic image reconstruction with deep learning is an emerging field, but a recent landmark study reveals that several deep reconstruction networks are unstable for computed tomography (CT) and magnetic resonance imaging (MRI). Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missing in a deeply reconstructed image, and (3) decreased imaging performance with increased input data. On the other hand, compressed sensing (CS) inspired reconstruction methods do not suffer from these instabilities because of their built-in kernel awareness. For deep reconstruction to realize its full potential and become a mainstream approach for tomographic imaging, it is thus critically important to meet this challenge by stabilizing deep reconstruction networks. Here we propose an Analytic Compressed Iterative Deep (ACID) framework to address this challenge. ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement. Our study demonstrates that the deep reconstruction using ACID is accurate and stable, and sheds light on the converging mechanism of the ACID iteration under a Bounded Relative Error Norm (BREN) condition. In particular, the study shows that ACID-based reconstruction is resilient against adversarial attacks, superior to classic sparsity-regularized reconstruction alone, and eliminates the three kinds of instabilities. We anticipate that this integrative data-driven approach will help promote development and translation of deep tomographic image reconstruction networks into clinical applications.