Fundamental study on improving the quality of X-ray fluorescence computed tomography images by applying deep image prior to projection images as a pre-denoising method

Sota Kusakari; Kazuki Sato; Yuta Tsushima; Masahiro Matsuoka; Tenta Sasaya; Naoki Sunaguchi; Keisuke Matsubara; Hidekazu Kawashima; Kazuyuki Hyodo; Tetsuya Yuasa; Tsutomu Zeniya

doi:10.1007/s11548-024-03307-8

Fundamental study on improving the quality of X-ray fluorescence computed tomography images by applying deep image prior to projection images as a pre-denoising method

Int J Comput Assist Radiol Surg. 2024 Dec 29. doi: 10.1007/s11548-024-03307-8. Online ahead of print.

Authors

Affiliations

¹ Graduate School of Science and Technology, Hirosaki University, Hirosaki, Japan.
² Graduate School of Science and Engineering, Yamagata University, Yonezawa, Japan.
³ Department of Radiological and Medical Laboratory Science, Nagoya University Graduate School of Medicine, Nagoya, Japan.
⁴ Department of Management Science and Engineering, Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo, Japan.
⁵ Radioisotope Research Center, Kyoto Pharmaceutical University, Kyoto, Japan.
⁶ High Energy Accelerator Research Organization, Tsukuba, Japan.
⁷ Graduate School of Science and Technology, Hirosaki University, Hirosaki, Japan. zeniya@hirosaki-u.ac.jp.

PMID: 39739291
DOI: 10.1007/s11548-024-03307-8

Abstract

Purpose: We are developing a three-dimensional X-ray fluorescence computed tomography (3D XFCT) system using non-radioactive-labeled compounds for preclinical studies as a new modality that provides images of biological functions. Improvements in image quality and detection limits are required for the in vivo imaging. The aim of this study was to improve the quality of XFCT images by applying a deep image prior (DIP), which is a type of convolutional neural network, to projection images as a pre-denoising method, and then compare with DIP post-denoising.

Methods: DIP can restore images using only the projection images acquired by XFCT. The projected images were processed with DIP for denoising. Three-dimensional images were reconstructed using the ordered subsets expectation maximization method for XFCT systems with multi-pinhole collimators. To evaluate the effectiveness of DIP pre-denoising, we constructed an XFCT system using synchrotron radiation and performed imaging experiments on a physical phantom and a mouse brain sample. The proposed method was compared with the DIP post-denoising and other denoising methods.

Results: The proposed DIP pre-denoising method reduced noise and significantly improved the image quality and was superior to the DIP post-denoising and other methods. The contrast-to-noise ratio improved by 3.7 to 4.6 times with almost no deterioration in spatial resolution, and the detection limit improved from 0.069 to 0.035 mg/mL. There was a strong linear relationship between the iodine concentration and pixel values. Finally, image quality of the mouse brain improved.

Conclusions: Through experiments using phantoms and mouse brains, this study demonstrated that the application of DIP to projection images as a pre-denoising method can significantly improve the image quality and detection limit of 3D XFCT without degrading the spatial resolution. DIP was more effective when applied as pre-denoising than as post-denoising and can contribute to in vivo 3D imaging in the future.

Keywords: Biofunctional imaging; Deep image prior; Denoising; X-ray fluorescence computed tomography.

Abstract

Grants and funding