Development of A Low-Dose Strategy for Propagation-based Imaging Helical Computed Tomography (PBI-HCT): High Image Quality and Reduced Radiation Dose.

Biomed Phys Eng Express

Department of Biomedical Engineering; Department of Chemical and Biological Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Saskatoon, Saskatchewan, S7N 5A2, CANADA.

Published: December 2024

AI Article Synopsis

  • Propagation-based imaging computed tomography (PBI-CT) is gaining popularity for visualizing low-density materials thanks to its high resolution and image contrast, but it poses radiation risks in live animal imaging due to high doses.
  • This study integrates a deep learning method called Sparse2Noise with PBI-CT to significantly reduce radiation exposure (up to 90%) while preserving image quality, showing improved results over traditional low-dose imaging methods.
  • The findings indicate that Sparse2Noise provides a better signal-to-noise ratio compared to two other advanced low-dose algorithms, enhancing the effectiveness of PBI-CT for biomedical applications.

Article Abstract

Propagation-based imaging computed tomography (PBI-CT) has been recently emerging for visualizing low-density materials due to its excellent image contrast and high resolution. Based on this, PBI-CT with a helical acquisition mode (PBI-HCT) offers superior imaging quality (e.g., fewer ring artifacts) and dose uniformity, making it ideal for biomedical imaging applications. However, the excessive radiation dose associated with high-resolution PBI-HCT may potentially harm objects or hosts being imaged, especially in live animal imaging, raising a great need to reduce radiation dose. Methods: In this study, we strategically integrated Sparse2Noise (a deep learning approach) with PBI-HCT imaging to reduce radiation dose without compromising image quality. Sparse2Noise uses paired low-dose noisy images with different photon fluxes and projection numbers for high-quality reconstruction via a convolutional neural network (CNN). Then, we examined the imaging quality and radiation dose of PBI-HCT imaging using Sparse2Noise, as compared to when Sparse2Noise was used in low-dose PBI-CT imaging (circular scanning mode). Furthermore, we conducted a comparison study on using Sparse2Noise versus two other state-of-the-art low-dose imaging algorithms (i.e., Noise2Noise and Noise2Inverse) for imaging low-density materials using PBI-HCT at equivalent dose levels. Results: Sparse2Noise allowed for a 90% dose reduction in PBI-HCT imaging while maintaining high image quality. As compared to PBI-CT imaging, the use of Sparse2Noise in PBI-HCT imaging shows more effective by reducing additional radiation dose (30%-36%). Furthermore, helical scanning mode also enhances the performance of existing low-dose algorithms (Noise2Noise and Noise2Inverse); nevertheless, Sparse2Noise shows significantly higher signal-to-noise ratio (SNR) value compared to Noise2Noise and Noise2Inverse at the same radiation dose level. Conclusions and significance: Our proposed low-dose imaging strategy Sparse2Noise can be effectively applied to the PBI-HCT imaging technique and requires lower dose for acceptable quality imaging. This would represent a significant advance imaging for low-density materials imaging and for future live animal imaging applications. .

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Source
http://dx.doi.org/10.1088/2057-1976/ad9f66DOI Listing

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