Song Yingnan, Hoori Ammar, Wu Hao, Vembar Mani, Al-Kindi Sadeer, Ciancibello Leslie, Terry James G, Jacobs David R, Carr John Jeffrey, Wilson David L
Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.
Philips Healthcare, Cleveland, Ohio, United States.
J Med Imaging (Bellingham). 2023 Jan;10(1):014002. doi: 10.1117/1.JMI.10.1.014002. Epub 2023 Jan 13.
Our long-range goal is to improve whole-heart CT calcium scores by extracting quantitative features from individual calcifications. Here, we perform deconvolution to improve bias/reproducibility of small calcification assessments, which can be degraded at the normal CT calcium score image resolution.
We analyzed features of individual calcifications on repeated standard (2.5 mm) and thin (1.25 mm) slice scans from QRM-Cardio phantom, cadaver hearts, and CARDIA study participants. Preprocessing to improve the resolution involved of Lucy-Richardson deconvolution with a measured point spread function (PSF) or three-dimensional blind deconvolution in which the PSF was iteratively optimized on high detail structures such as calcifications in images.
Using QRM with inserts having known mg-calcium, we determined that both blind and conventional deconvolution improved mass measurements nearly equally well on standard images. Further, deconvolved thin images gave an excellent recovery of actual mass scores, suggesting that such processing could be our gold standard. For CARDIA images, blind deconvolution greatly improved results on standard slices. Bias across 33 calcifications (without, with deconvolution) was (23%, 9%), (18%, 1%), and ( , ) for Agatston, volume, and mass scores, respectively. Reproducibility was (0.13, 0.10), (0.12, 0.08), and (0.11, 0.06), respectively. Mass scores were more reproducible than Agatston scores or volume scores. For many other calcification features, blind deconvolution improved reproducibility in 21 out of 24 features. Cadaver images showed similar improvements in bias/reproducibility and slightly better results with a measured PSF.
Deconvolution improves bias and reproducibility of multiple features extracted from individual calcifications in CT calcium score exams. Blind deconvolution is useful for improving feature assessments of coronary calcification in archived datasets.
我们的长期目标是通过从单个钙化中提取定量特征来提高全心CT钙评分。在此，我们进行反卷积以改善小钙化评估的偏差/可重复性，在正常CT钙评分图像分辨率下，小钙化评估可能会受到影响。
我们分析了来自QRM-心脏模型、尸体心脏和CARDIA研究参与者的重复标准（2.5毫米）和薄层（1.25毫米）切片扫描上单个钙化的特征。为提高分辨率进行的预处理包括使用测量的点扩散函数（PSF）进行Lucy-Richardson反卷积，或在图像中诸如钙化等高细节结构上迭代优化PSF的三维盲反卷积。
使用带有已知毫克钙插入物的QRM，我们确定在标准图像上，盲反卷积和传统反卷积在改善质量测量方面几乎同样有效。此外，反卷积后的薄层图像能很好地恢复实际质量评分，这表明这种处理可能是我们的金标准。对于CARDIA图像，盲反卷积极大地改善了标准切片的结果。33个钙化（无反卷积、有反卷积）的Agatston、体积和质量评分的偏差分别为（23%，9%）、（18%，1%）和（ ， ）。可重复性分别为（0.13，0.10）、（0.12，0.08）和（0.11，0.06）。质量评分比Agatston评分或体积评分更具可重复性。对于许多其他钙化特征，盲反卷积在24个特征中的21个中提高了可重复性。尸体图像在偏差/可重复性方面显示出类似的改善，使用测量的PSF时结果略好。
反卷积改善了CT钙评分检查中从单个钙化提取的多个特征的偏差和可重复性。盲反卷积有助于改善存档数据集中冠状动脉钙化的特征评估。
