Suppr超能文献

一种新颖可靠的用于光谱光子计数 CT 成像的像素响应校正方法(DAC 移位)。

A Novel and Reliable Pixel Response Correction Method (DAC-Shifting) for Spectral Photon-Counting CT Imaging.

机构信息

Department of Clinical Medicine, Aarhus University, 8200 Aarhus, Denmark.

Danish Centre for Particle Therapy, Aarhus University Hospital, 8200 Aarhus, Denmark.

出版信息

Tomography. 2024 Jul 22;10(7):1168-1191. doi: 10.3390/tomography10070089.

Abstract

Spectral photon-counting cone-beam computed tomography (CT) imaging is challenged by individual pixel response behaviours, which lead to noisy projection images and subsequent image artefacts like rings. Existing methods to correct for this either use calibration measurements, like signal-to-thickness calibration (STC), or perform a post-processing ring artefact correction of sinogram data or scan reconstructions without taking the pixel response explicitly into account. Here, we present a novel post-processing method (digital-to-analogue converter (DAC)-shifting) which explicitly measures the current pixel response using flat-field images and subsequently corrects the projection data. The DAC-shifting method was evaluated using a repeat series of the spectral photon-counting imaging (Medipix3) of a phantom with different density inserts and iodine K-edge imaging. The method was also compared against polymethyl methacrylate (PMMA)-based STC. The DAC-shifting method was shown to be effective in correcting individual pixel responses and was robust against detector instability; it led to a 47.4% average reduction in CT-number variation in homogeneous materials, with a range of 40.7-55.6%. On the contrary, the STC correction showed varying results; a 13.7% average reduction in CT-number variation, ranging from a 43.7% increase to a 45.5% reduction. In K-edge imaging, DAC-shifting provides a sharper attenuation peak and more uniform CT values, which are expected to benefit iodine concentration quantifications.

摘要

光谱光子计数锥形束 CT(CT)成像是受到单个像素响应行为的挑战,这会导致投影图像噪声和随后的图像伪影,如环状伪影。现有的校正方法要么使用校准测量,如信号厚度校准(STC),要么在不明确考虑像素响应的情况下对正弦图数据或扫描重建进行后处理环状伪影校正。在这里,我们提出了一种新的后处理方法(数模转换器(DAC)移位),该方法使用平场图像明确测量当前像素响应,并随后校正投影数据。使用具有不同密度插入物和碘 K 边成像的光谱光子计数成像(Medipix3)的重复系列对 DAC 移位方法进行了评估。该方法还与基于聚甲基 methacrylate(PMMA)的 STC 进行了比较。DAC 移位方法被证明可以有效地校正单个像素响应,并且对探测器不稳定具有鲁棒性;它导致均匀材料的 CT 数变化平均减少 47.4%,范围为 40.7-55.6%。相反,STC 校正显示出不同的结果;CT 数变化的平均减少 13.7%,范围从 43.7%增加到 45.5%减少。在 K 边成像中,DAC 移位提供了更尖锐的衰减峰和更均匀的 CT 值,这有望有益于碘浓度定量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcec/11281142/074d45644384/tomography-10-00089-g001.jpg

相似文献

1
A Novel and Reliable Pixel Response Correction Method (DAC-Shifting) for Spectral Photon-Counting CT Imaging.
Tomography. 2024 Jul 22;10(7):1168-1191. doi: 10.3390/tomography10070089.
2
Fast kV-switching and dual-layer flat-panel detector enabled cone-beam CT joint spectral imaging.
Phys Med Biol. 2024 May 14;69(11). doi: 10.1088/1361-6560/ad40f3.
3
Development and validation of a noise insertion algorithm for photon-counting-detector CT.
Med Phys. 2024 Sep;51(9):5943-5953. doi: 10.1002/mp.17263. Epub 2024 Jun 23.
4
Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT.
Phys Med Biol. 2024 May 8;69(10). doi: 10.1088/1361-6560/ad40fa.
5
Influence of phantom design on evaluation metrics in photon counting spectral head CT: a simulation study.
J Med Imaging (Bellingham). 2025 Jul;12(4):043501. doi: 10.1117/1.JMI.12.4.043501. Epub 2025 Jul 12.
6
Ultra-High-Resolution and K-Edge Imaging of Prosthetic Heart Valves With Spectral Photon-Counting CT: A Phantom Study.
Invest Radiol. 2024 Aug 1;59(8):589-598. doi: 10.1097/RLI.0000000000001068. Epub 2024 Jan 27.
7
In silico modeling of a clinical photon-counting CT system: Verification and validation.
Med Phys. 2025 Jun;52(6):3840-3853. doi: 10.1002/mp.17886. Epub 2025 May 13.
9
Artifact suppression for breast specimen imaging in micro CBCT using deep learning.
BMC Med Imaging. 2024 Feb 6;24(1):34. doi: 10.1186/s12880-024-01216-5.
10
Dual-focal-spot single-detector CT: A simulation study to assess cone-beam artifacts and noise.
J Appl Clin Med Phys. 2025 Sep;26(9):e70220. doi: 10.1002/acm2.70220.

本文引用的文献

1
Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction.
IEEE Trans Med Imaging. 2024 Nov;43(11):3830-3842. doi: 10.1109/TMI.2024.3402079. Epub 2024 Nov 4.
3
A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images.
Tomography. 2023 Jul 2;9(4):1286-1302. doi: 10.3390/tomography9040102.
4
Photon Counting CT: Technical Principles, Clinical Applications, and Future Prospects.
Acad Radiol. 2023 Oct;30(10):2362-2382. doi: 10.1016/j.acra.2023.05.029. Epub 2023 Jun 25.
5
Detector shifting and deep learning based ring artifact correction method for low-dose CT.
Med Phys. 2023 Jul;50(7):4308-4324. doi: 10.1002/mp.16225. Epub 2023 Jan 25.
7
One-step iterative reconstruction approach based on eigentissue decomposition for spectral photon-counting computed tomography.
J Med Imaging (Bellingham). 2022 Jul;9(4):044003. doi: 10.1117/1.JMI.9.4.044003. Epub 2022 Jul 27.
8
SciPy 1.0: fundamental algorithms for scientific computing in Python.
Nat Methods. 2020 Mar;17(3):261-272. doi: 10.1038/s41592-019-0686-2. Epub 2020 Feb 3.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验