Radiology and Imaging Sciences - National Institutes of Health Clinical Center, Bethesda, MD, USA; Department of Imaging and Pathology, Medical Imaging Research Centre, University Hospitals Leuven, Leuven, Belgium.
Radiology and Imaging Sciences - National Institutes of Health Clinical Center, Bethesda, MD, USA.
J Cardiovasc Comput Tomogr. 2018 Mar-Apr;12(2):108-114. doi: 10.1016/j.jcct.2018.01.006. Epub 2018 Feb 2.
To optimize spectral coronary computed tomography angiography (CTA) for quantification of coronary artery plaque components.
Fifty-one subjects were prospectively enrolled (88.2% male) (NCT02740699). Dual energy coronary CTA was performed at 90/Sn150 kVp using a 3rd generation dual-source CT scanner (SOMATOM Force, Siemens Healthcare). Dual energy images were reconstructed with a) linear mixed blending of 90 and Sn150 kVp data, b) virtual monoenergetic algorithm from 40 to 150 keV (at 10- keV intervals), and c) noise-optimized virtual monoenergetic algorithm from 40 to 150 keV. Image noise, iodine signal-to-noise-ratio (SNR), and contrast-to-noise ratio (CNR) for calcified and non-calcified plaque were measured. Qualitative readings of image quality were performed. Semi-automated software (QAngioCT, Medis) was used to quantify coronary plaque. Linear mixed-models that account for within-subject correlation of plaques were used to compare the results.
100-150 keV noise-optimized virtual monoenergetic images had lower image noise than linear mixed images (all P < 0.05). The highest iodine SNR was achieved in 40 keV noise-optimized virtual monoenergetic images (33.3 ± 0.6 vs 23.3 ± 0.7 for linear mixed images, P < 0.001). 40-70 keV noise-optimized virtual monoenergetic images and 70 keV virtual monoenergetic images had superior coronary plaque CNR versus linear mixed images (all P < 0.01) with a maximum improvement of 20.1% and 22.7% for calcified plaque and non-calcified plaque (38.8 ± 2.2 vs 32.3 ± 2.3 and 17.3 ± 1.3 vs 14.1 ± 1.4, respectively). Using 90/Sn150 kVp linear mixed images as a reference, the plaque quantity was similar for 70 keV noise-optimized virtual monoenergetic images whereas low keV images (e.g. 40 keV) yielded significantly higher coronary plaque volumes (all P < 0.001).
Spectral coronary CTA with low energy (40-70 keV) post-processing can improve the CNR of coronary plaque components. However, low energies (such as 40 keV) resulted in different absolute volumes of coronary plaque compared to "conventional" mixed 90/Sn150 kVp images.
优化冠状动脉能谱 CT 血管造影（CTA）以定量评估冠状动脉斑块成分。
前瞻性纳入 51 例受试者（88.2%为男性）（NCT02740699）。使用第三代双源 CT 扫描仪（SOMATOM Force，西门子医疗）以 90/Sn150kVp 进行双能冠状动脉 CTA。采用 a）90 和 Sn150kVp 数据的线性混合混合，b）40 至 150keV 的虚拟单能量算法（每隔 10keV），和 c）40 至 150keV 的噪声优化虚拟单能量算法来重建双能图像。测量钙化和非钙化斑块的图像噪声、碘信号噪声比（SNR）和对比噪声比（CNR）。进行图像质量的定性评估。使用半自动软件（QAngioCT，Medis）来定量评估冠状动脉斑块。使用考虑斑块内相关性的线性混合模型来比较结果。
100-150keV 噪声优化虚拟单能量图像的图像噪声低于线性混合图像（均 P<0.05）。在 40keV 噪声优化虚拟单能量图像中获得了最高的碘 SNR（33.3±0.6 比线性混合图像的 23.3±0.7，P<0.001）。40-70keV 噪声优化虚拟单能量图像和 70keV 虚拟单能量图像与线性混合图像相比，具有更好的冠状动脉斑块 CNR（均 P<0.01），钙化斑块和非钙化斑块的最大改善分别为 20.1%和 22.7%（38.8±2.2 比 32.3±2.3 和 17.3±1.3 比 14.1±1.4）。以 90/Sn150kVp 线性混合图像作为参考，70keV 噪声优化虚拟单能量图像的斑块量相似，而低 keV 图像（例如 40keV）则导致冠状动脉斑块体积显著增加（均 P<0.001）。
冠状动脉能谱 CTA 采用低能量（40-70keV）后处理可以提高冠状动脉斑块成分的 CNR。然而，与“传统”混合 90/Sn150kVp 图像相比，低能量（如 40keV）会导致冠状动脉斑块的绝对体积不同。
