Kusakari Sota, Sato Kazuki, Tsushima Yuta, Matsuoka Masahiro, Sasaya Tenta, Sunaguchi Naoki, Matsubara Keisuke, Kawashima Hidekazu, Hyodo Kazuyuki, Yuasa Tetsuya, Zeniya Tsutomu
Graduate School of Science and Technology, Hirosaki University, Hirosaki, Japan.
Graduate School of Science and Engineering, Yamagata University, Yonezawa, Japan.
Int J Comput Assist Radiol Surg. 2025 Apr;20(4):665-676. doi: 10.1007/s11548-024-03307-8. Epub 2024 Dec 29.
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.
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.
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.
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.
我们正在开发一种三维X射线荧光计算机断层扫描（3D XFCT）系统，该系统使用非放射性标记化合物进行临床前研究，作为一种能够提供生物功能图像的新模态。体内成像需要提高图像质量和检测限。本研究的目的是通过将深度图像先验（DIP，一种卷积神经网络类型）应用于投影图像作为去噪方法来提高XFCT图像的质量，然后与DIP后去噪进行比较。
DIP仅使用XFCT获取的投影图像就能恢复图像。对投影图像进行DIP处理以进行去噪。使用有序子集期望最大化方法对具有多针孔准直器的XFCT系统重建三维图像。为了评估DIP预去噪的有效性，我们使用同步辐射构建了一个XFCT系统，并对物理体模和小鼠脑样本进行了成像实验。将所提出的方法与DIP后去噪和其他去噪方法进行比较。
所提出的DIP预去噪方法降低了噪声，显著提高了图像质量，并且优于DIP后去噪和其他方法。对比噪声比提高了3.7至4.6倍，空间分辨率几乎没有下降，检测限从0.069提高到0.035 mg/mL。碘浓度与像素值之间存在很强的线性关系。最后，小鼠脑的图像质量得到了改善。
通过使用体模和小鼠脑的实验，本研究表明将DIP作为预去噪方法应用于投影图像可以显著提高3D XFCT的图像质量和检测限，而不会降低空间分辨率。DIP作为预去噪应用时比后去噪更有效，并且可为未来的体内三维成像做出贡献。
