能谱CT单能量重建结合迭代算法对冠状动脉优化显示的初步探讨

Monochromatic Imaging Combined with ASIR of Dual Energy Spectral CT for Optimized Coronary Artery Imaging

目的：评价能谱CT单能量重建技术结合ASIR重建算法对冠脉优化显示的作用;方法：前瞻性收集能谱冠状动脉CT检查20例,患者均采用能谱扫描模式、单源瞬时（0.5 ms）管电压（140、80 kVp）切换技术行心脏能谱CT检查。将扫描获得的原始图像采用40%自适应统计迭代重组技术重组为轴位单能量图像（60、70、80、90、100、110、120、130、140 keV）,并常规重组轴位混合能量图像。在不同重建模式下,分别测量主动脉根部、左主干、前降支中段、右冠状动脉根部、右冠状动脉中段血管腔内的噪声,信噪比、对比噪声比。将9个单能量水平的上述测量指标分别与混合能量的对应指标进行统计学分析比较,统计学比较方法均采用随机区组设计的方差分析。结果：1噪声：混合能量水平主动脉根部、左主干、右冠状动脉根部、前降支中段、右冠状动脉中段血管腔内的噪声分别为（24.32±5.84）HU、（25.65±10.83）HU、（33.27±11.95）HU、（42.16±15.52）HU、（35.58±13.21）HU,随着keV上升噪声逐渐下降,与混合能量相比,所测冠脉噪声均在90-140keV水平得到了改善（P均〈0.05）,其中除主动脉根部血管腔内噪声在130keV达到最低水平（10.85±2.49）HU外,余各测量位置血管腔内的噪声均在140keV水平达到最低水平,分别为左主干（10.65±6.55）HU、右冠状动脉根部（13.07±5.06）HU、前降支中段（21.94±8.31）HU、右冠中段（16.83±6.05）HU;2信噪比：混合能量水平主动脉根部、左主干、右冠状动脉根部、前降支中段、右冠状动脉中段血管腔内的信噪比分别11.47±1.97、15.23±7.51、10.19±3.98、6.94±2.85、7.60±3.28,与混合能量相比,上述测量点信噪比分别在60-90keV、60-80keV、60-70keV、60keV、60-70keV水平得到明显改善（P均〈0.05）,且均在60keV水平得到最佳改善,分别为22.20±5.74、23.82±11.19、16.61±8.15、8.78±3.67、8.91±4.12;3、对比噪声比：混合能量水平主动脉根部、左主干、右冠状动脉根部、前降支中段、右冠状动脉中段血管腔内的对比噪声比分别18.68±6.90、18.74±7.12、17.58±6.56、12.29±2.40、17.88±7.16,与混合能量相比,上述测量点对比噪声比分别在60-80keV、60-80keV、60-80keV、60-70keV、60-70keV水平得到明显改善（P均〈0.05）,其中前降支中段的对比噪声比在60keV得到了最佳改善,为17.82±5.40,余各测量点的对比噪声比均在70keV水平得到了最佳改善,分别为主动脉根部29.73±8.46、左主干28.69±7.65、右冠状动脉根部25.70±7.59、右冠状动脉中段21.62±10.23;结论：冠状动脉能谱CT成像的单能量图像结合迭代重建算法,能够有效降低冠脉的噪声,提高冠脉信噪比及对比噪声比,从而达到优化冠脉显示的目的。

Purpose： To evaluate the image quality of monochromatic reconstruction combined with ASIR of coronary artery CT angiography（CCTA） by the use of Gemstone Spectral Imaging（GSI）. Methods： Twenty patients who received CCTA on single-source dual-energy CT were enrolled in this study prospectively. The scanning mode was GSI, single-source instantaneous（0.5ms） k Vp（140k Vp and 80 k Vp） switch. The original images acquired were reconstructed into monochromatic energy（60, 70, 80, 90, 100, 110, 120, 130, 140 keV） axial images via 40% ASIR and the polychromatic axial images. The standard deviation（SD）, signal-to-noise ratios（SNR） and contrast-to-noise ratios（CNR） were measured in aortic root, LMA, RCA root, mid-LAD and mid-RCA at different monochromatic energy levels and polychromatic images. Randomized block designed ANOVA were used to compare the difference of SD, SNR, CNR between monochromatic images and polychromatic images. Results： 1. The SD of aortic root, LMA, RCA root, mid-LAD, and mid-RCA at polychromatic images were（24.32±5.84） HU,（25.65±10.83） HU,（33.27±11.95） HU,（42.16±15.52） HU, and（35.58±13.21） HU. They were decreased when keV was increased, and all got the lowest level at 140 keV images [SDLMA（10.65±6.55） HU, SDmid-LAD（21.94±8.31） HU, SDRCA root（13.07±5.06） HU and SDmid-RCA（16.83±6.05） HU] except SDaortic root got the lowest level（10.85±2.49）HU at 130 keV images; 2. The SNR of aortic root, LMA, RCA root, mid-LAD, and mid-RCA at polychromatic images were 11.47±1.97, 15.23±7.51, 10.19±3.98, 6.94±2.85, and 7.60±3.28, they were improved at（60-90）keV,（60-80）keV,（60-70）keV, 60 keV and（60-70）keV images（P〈0.05）, and got the highest level at 60 keV images（SNRaortic root 22.20±5.74, SNRLMA 23.82±11.19, SNRmid-LAD8.78±3.67, SNRRCA root 16.61±8.15 and SNRmidRCA 8.91±4.12）; 3. The CNR of aortic root, LMA, RCA root, mid-LAD and mid-RCA at polychromatic images were 18.68±6.90, 18.74±7.12, 17.58±6.56, 12.29±2.40, and 17.88±7.16, they were improved at（60-80）keV,（60-80）keV,（60-80）keV,（60-70）keV,（60-70）keV images（P〈0.05）, and got the highest level at 60 keV images in LAD（17.82±5.40）, at 70 keV images in all the other locations（CNRaortic root 29.73±8.46, CNRLMA 28.69±7.65, CNRRCA root 25.70±7.59 and CNRmid-RCA 21.62±10.23）. Conclusion： Compared with the polychromatic images, monochromatic energy images combined with ASIR resulted in significant SD reduction and SNR, CNR increment, thus coronary artery imaging could be optimized.