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三重源鞍形曲线锥形束光子计数 CT 图像重建:一项模拟研究。

Triple-source saddle-curve cone-beam photon counting CT image reconstruction: A simulation study.

机构信息

Institute of Radiation Medicine, Fudan University, Shanghai, China.

School of Biomedical Engineering, Hainan University, Haikou, China.

出版信息

Z Med Phys. 2024 Aug;34(3):408-418. doi: 10.1016/j.zemedi.2022.10.003. Epub 2022 Nov 3.

DOI:10.1016/j.zemedi.2022.10.003
PMID:36336554
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11384087/
Abstract

PURPOSE

The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution.

METHODS

Triple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich's theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins.

RESULTS

Root mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20-33, 33-47, 47-58, 58-69, 69-80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image.

CONCLUSIONS

A new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution.

摘要

目的

在 PC CT 系统中,最常见的探测器材料在相对较高的光子通量率下无法达到最佳性能。在重建视图中,最常用的滤波反投影(filtered back projection)在光谱 CT 中无法提供足够的重建图像质量。开发三源鞍形曲线锥束光子计数 CT 图像重建方法可以提高时间分辨率。

方法

将三源鞍形曲线锥束轨迹重新排列为四个轨迹集进行模拟和重建。通过正向投影和光子计数 CT 响应模型模拟,模拟不同能量bins 的投影图像。模拟后,使用 Katsevich 理论对经过光子计数校正后的物体进行重建,使用光子计数 CT 响应矩阵的伪逆校正光子计数。可以基于不同能量bins 的图像进行材料分解。

结果

均方根误差(root mean square error,RMSE)和结构相似性指数(structural similarity index,SSIM)用于量化重建图像的图像质量。与 FDK 图像相比,三重源图像的 RMSE 在 20-33keV、33-47keV、47-58keV、58-69keV 和 69-80keV 能量bins 的重建图像分别提高了 27%、21%、14%、8%和 6%。SSIM 分别提高了 1.031%、0.665%、0.396%、0.235%和 0.174%。基于校正图像的分解图像显示 RMSE 和 SSIM 均有提高,分别提高了 33.861%和 0.345%。碘校正分解图像的 SSIM 达到原始图像的 99.415%。

结论

本工作开发了一种新的三源鞍形曲线锥束 PC CT 图像重建方法。三源鞍形曲线的精确重建提高了图像质量和时间分辨率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/7ed5d8bb1518/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/13c4d84e927a/gr1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/53cc3469259e/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/cdc0d5ef3a8d/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/c01eb513b60e/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/6e1914211715/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/74e5f52d49c2/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/8d9341e1ef1f/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/7ed5d8bb1518/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/13c4d84e927a/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/52ec0a187efe/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/53cc3469259e/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/cdc0d5ef3a8d/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/c01eb513b60e/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/6e1914211715/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/74e5f52d49c2/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/8d9341e1ef1f/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be04/11384087/7ed5d8bb1518/gr9.jpg

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