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评估一种新型 CBCT 平台图像重建的金属伪影减少算法。

Evaluation of a Metal Artifact Reduction Algorithm for Image Reconstruction on a Novel CBCT Platform.

机构信息

Department of Physics and Atmospheric Sciences, Dalhousie University, Halifax, Canada.

Department of Radiation Oncology, Dalhousie University, Halifax, Canada.

出版信息

J Appl Clin Med Phys. 2024 Nov;25(11):e14516. doi: 10.1002/acm2.14516. Epub 2024 Sep 17.

Abstract

PURPOSE

The presence of metal implants can produce artifacts and distort Hounsfield units (HU) in patient computed tomography (CT) images. The purpose of this work was to characterize a novel metal artifact reduction (MAR) algorithm for reconstruction of CBCT images obtained by the HyperSight imaging system.

METHODS

Three tissue-equivalent phantoms were fitted with materials commonly used in medical applications. The first consisted of a variety of metal samples centered within a solid water block, the second was an Advanced Electron Density phantom with metal rods, and the third consisted of hip prostheses positioned within a water tank. CBCT images of all phantoms were acquired and reconstructed using the MAR and iCBCT Acuros algorithms on the HyperSight system. The signal-to-noise ratio (SNR), artifact index (AI), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE) were computed to assess the image quality in comparison to artifact-free reference images. The mean HU at various VOI positions around the cavity was calculated to evaluate the artifact dependence on distance and angle from the center of the cavity. The artifact volume of the phantom (excluding the cavity) was estimated by summing the volume of all voxels with HU values outside the 5th and 95th percentiles of the phantom CBCT with no artifact.

RESULTS

The SNR, AI, SSIM, PSNR, and MSE metrics demonstrated significantly higher similarity to baseline when using MAR compared to iCBCT Acuros for all high-density materials, except for aluminum. Mean HU returned to expected solid water background at a shorter distance from metal sample in the MAR images, and the standard deviation remained lower for the MAR images at all distances from the insert. The artifact volume decreased using the novel MAR algorithm for all metal samples excluding aluminum (p < 0.001) and all hip prostheses (p < 0.05).

CONCLUSION

Varian's HyperSight MAR reconstruction algorithm shows a reduction in metal artifact metrics, motivating the use of MAR reconstruction for patients with metal implants.

摘要

目的

金属植入物的存在会在患者 CT 图像中产生伪影并扭曲亨氏单位(HU)。本研究的目的是对 HyperSight 成像系统获得的锥形束 CT(CBCT)图像进行新型金属伪影减少(MAR)算法的特征描述。

方法

将三种组织等效体模分别与医疗应用中常用的材料匹配。第一个体模包含各种位于实心水块中心的金属样本,第二个是带有金属棒的高级电子密度体模,第三个是放置在水箱中的髋关节假体。使用 HyperSight 系统上的 MAR 和 iCBCT Acuros 算法获取和重建所有体模的 CBCT 图像。计算信噪比(SNR)、伪影指数(AI)、结构相似性指数度量(SSIM)、峰值信噪比(PSNR)和均方误差(MSE),以评估与无伪影参考图像相比的图像质量。计算腔周围各种感兴趣区(VOI)位置的平均 HU 值,以评估伪影对腔中心距离和角度的依赖性。通过将具有 HU 值超出 5th 和 95th 百分位数的所有体素的体积相加,估算无伪影的体模(不包括腔)的伪影体积。

结果

除了铝之外,与 iCBCT Acuros 相比,MAR 算法在所有高密度材料的 SNR、AI、SSIM、PSNR 和 MSE 指标上都表现出与基线更高的相似性。MAR 图像中,金属样本距离更近时,HU 值恢复到预期的实心水背景,所有距离插入物的 MAR 图像的标准差均较低。MAR 算法降低了除铝之外的所有金属样本(p < 0.001)和所有髋关节假体(p < 0.05)的伪影体积。

结论

Varian 的 HyperSight MAR 重建算法显示出金属伪影指标的降低,这为有金属植入物的患者使用 MAR 重建提供了依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b6e8/11539962/b06db2ad03f8/ACM2-25-e14516-g004.jpg

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