一种用于光谱光子计数 CT 图像的多通道分块匹配去噪算法。

A multichannel block-matching denoising algorithm for spectral photon-counting CT images.

机构信息

Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bethesda, MD, 20814, USA.

Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bethesda, MD, 20814, USA.

出版信息

Med Phys. 2017 Jun;44(6):2447-2452. doi: 10.1002/mp.12225. Epub 2017 May 22.

DOI:10.1002/mp.12225

PMID:28332211

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9560250/

Abstract

PURPOSE

We present a denoising algorithm designed for a whole-body prototype photon-counting computed tomography (PCCT) scanner with up to 4 energy thresholds and associated energy-binned images.

METHODS

Spectral PCCT images can exhibit low signal to noise ratios (SNRs) due to the limited photon counts in each simultaneously-acquired energy bin. To help address this, our denoising method exploits the correlation and exact alignment between energy bins, adapting the highly-effective block-matching 3D (BM3D) denoising algorithm for PCCT. The original single-channel BM3D algorithm operates patch-by-patch. For each small patch in the image, a patch grouping action collects similar patches from the rest of the image, which are then collaboratively filtered together. The resulting performance hinges on accurate patch grouping. Our improved multi-channel version, called BM3D_PCCT, incorporates two improvements. First, BM3D_PCCT uses a more accurate shared patch grouping based on the image reconstructed from photons detected in all 4 energy bins. Second, BM3D_PCCT performs a cross-channel decorrelation, adding a further dimension to the collaborative filtering process. These two improvements produce a more effective algorithm for PCCT denoising.

RESULTS

Preliminary results compare BM3D_PCCT against BM3D_Naive, which denoises each energy bin independently. Experiments use a three-contrast PCCT image of a canine abdomen. Within five regions of interest, selected from paraspinal muscle, liver, and visceral fat, BM3D_PCCT reduces the noise standard deviation by 65.0%, compared to 40.4% for BM3D_Naive. Attenuation values of the contrast agents in calibration vials also cluster much tighter to their respective lines of best fit. Mean angular differences (in degrees) for the original, BM3D_Naive, and BM3D_PCCT images, respectively, were 15.61, 7.34, and 4.45 (iodine); 12.17, 7.17, and 4.39 (galodinium); and 12.86, 6.33, and 3.96 (bismuth).

CONCLUSION

We outline a multi-channel denoising algorithm tailored for spectral PCCT images, demonstrating improved performance over an independent, yet state-of-the-art, single-channel approach.

摘要

目的

我们提出了一种针对具有多达 4 个能量阈值和相关能量-bin 图像的全身原型光子计数计算机断层扫描（PCCT）扫描仪的去噪算法。

方法

由于每个同时采集的能量-bin 中的光子计数有限，因此光谱 PCCT 图像可能会出现低信噪比（SNR）。为了解决这个问题，我们的去噪方法利用了能量-bin 之间的相关性和精确对齐，为 PCCT 调整了非常有效的块匹配 3D（BM3D）去噪算法。原始的单通道 BM3D 算法逐块操作。对于图像中的每个小补丁，补丁分组操作会从图像的其余部分收集类似的补丁，然后将它们一起协同过滤。最终的性能取决于准确的补丁分组。我们改进的多通道版本称为 BM3D_PCCT，它包含两个改进。首先，BM3D_PCCT 使用基于在所有 4 个能量-bin 中检测到的光子重建的图像的更准确的共享补丁分组。其次，BM3D_PCCT 执行跨通道去相关，为协同过滤过程增加了另一个维度。这两个改进为 PCCT 去噪生成了更有效的算法。

结果

初步结果将 BM3D_PCCT 与独立地对每个能量-bin 进行去噪的 BM3D_Naive 进行了比较。实验使用了犬腹部的三对比 PCCT 图像。在从脊柱旁肌肉、肝脏和内脏脂肪中选择的五个感兴趣区域内，与 BM3D_Naive 相比，BM3D_PCCT 将噪声标准差降低了 65.0%。校准小瓶中对比剂的衰减值也更紧密地聚集到它们各自的最佳拟合线。原始、BM3D_Naive 和 BM3D_PCCT 图像的平均角度差异（以度为单位）分别为 15.61、7.34 和 4.45（碘）；12.17、7.17 和 4.39（钆）；和 12.86、6.33 和 3.96（铋）。