目的:评估不同量子迭代重建(QIR)水平对超高分辨率(UHR)冠状动脉CT血管造影(CCTA)图像客观和主观图像质量的影响,并确定强度水平对使用光子计数探测器(PCD)-CT进行狭窄量化的影响。
方法:使用PCD-CT系统以每分钟60、80和100次的心率扫描包含两个钙化病变(25%和50%狭窄)的动态血管体模。对102例患者进行了体内CCTA检查。所有扫描均以UHR模式(切片厚度0.2mm)获取,并使用锋利的血管内核(Bv64)以四个不同的QIR水平(1-4)进行重建。图像噪声,信噪比(SNR),清晰度,并在体模中量化直径狭窄百分比(PDS),而噪音,SNR,对比噪声比(CNR),清晰度,和主观质量指标(噪声,清晰度,总体图像质量)在患者扫描中进行评估。
结果:增加QIR水平导致客观图像噪声显着降低(体外和体内:均p<0.001),更高的信噪比(p<0.001)和CNR(p<0.001)。锐度和PDS值在QIR之间没有显著差异(所有成对p>0.008)。随着QIR水平的增加,体内图像的主观噪声显着降低,在增加的QIR水平下产生显著更高的图像质量评分(所有成对p<0.001)。定性清晰度,另一方面,不同水平的QIR没有差异(p=0.15)。
结论:QIR算法可以增强CCTA数据集的图像质量,而不会影响图像清晰度或精确的狭窄测量,在最高强度水平上有最突出的好处。
OBJECTIVE: To assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT.
METHODS: A dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1-4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans.
RESULTS: Increasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15).
CONCLUSIONS: The QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level.