One-step Method for Material Quantitation using In-line Tomography with Single Scanning

TL;DR

This paper introduces a novel one-step iterative method for material quantitation in in-line tomography that improves accuracy and reduces noise by integrating phase retrieval and reconstruction with feedback, suitable for live samples.

Contribution

The paper presents a new one-step iterative approach that directly reconstructs material properties from single-scanning data, enhancing accuracy and noise robustness over traditional two-step methods.

Findings

Accuracy of material decomposition exceeds 97.2%

Effective noise and error reduction during iterative feedback

High-quality quantitative imaging for live samples and clinical studies

Abstract

Objective: Quantitative technique based on In-line phase-contrast computed tomography with single scanning attracts more attention in application due to the flexibility of the implementation. However, the quantitative results usually suffer from artifacts and noise, since the phase retrieval and reconstruction are independent ("two-steps") without feedback from the original data. Our goal is to develop a method for material quantitative imaging based on a priori information specifically for the single-scanning data. Method: An iterative method that directly reconstructs the refractive index decrement delta and imaginary beta of the object from observed data ("one-step") within single object-to-detector distance (ODD) scanning. Simultaneously, high-quality quantitative reconstruction results are obtained by using a linear approximation that achieves material decomposition in the iterative process. Results: By comparing the equivalent atomic number of the material decomposition results in experiments, the accuracy of the proposed method is greater than 97.2%. Conclusion: The quantitative reconstruction and decomposition results are effectively improved, and there are feedback and corrections during the iteration, which effectively reduce the impact of noise and errors. Significance: This algorithm has the potential for quantitative imaging research, especially for imaging live samples and human breast preclinical studies.