Li Zhoubo, Leng Shuai, Halaweish Ahmed F, Yu Zhicong, Yu Lifeng, Ritman Erik L, McCollough Cynthia H
Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States.
Mayo Graduate School, Biomedical Engineering and Physiology Graduate Program, Rochester, Minnesota, United States.
J Med Imaging (Bellingham). 2020 Sep;7(5):053501. doi: 10.1117/1.JMI.7.5.053501. Epub 2020 Oct 1.
Conventional stenosis quantification from single-energy computed tomography (SECT) images relies on segmentation of lumen boundaries, which suffers from partial volume averaging and calcium blooming effects. We present and evaluate a method for quantifying percent area stenosis using multienergy CT (MECT) images. We utilize material decomposition of MECT images to measure stenosis based on the ratio of iodine mass between vessel locations with and without a stenosis, thereby eliminating the requirement for segmentation of iodinated lumen. The method was first assessed using simulated MECT images created with different spatial resolutions. To experimentally assess this method, four phantoms with different stenosis severity (30% to 51%), vessel diameters (5.5 to 14 mm), and calcification densities (700 to ) were fabricated. Conventional SECT images were acquired using a commercial CT system and were analyzed with commercial software. MECT images were acquired using a commercial dual-energy CT (DECT) system and also from a research photon-counting detector CT (PCD-CT) system. Three-material-decomposition was performed on MECT data, and iodine density maps were used to quantify stenosis. Clinical radiation doses were used for all data acquisitions. Computer simulation verified that this method reduced partial volume and blooming effects, resulting in consistent stenosis measurements. Phantom experiments showed accurate and reproducible stenosis measurements from MECT images. For DECT and two-threshold PCD-CT images, the estimation errors were 4.0% to 7.0%, 2.0% to 9.0%, 10.0% to 18.0%, and to (ground truth: 51%, 51%, 51%, and 30%). For four-threshold PCD-CT images, the errors were 1.0% to 3.0%, 4.0% to 6.0%, to 9.0%, and 0.0% to 6.0%. Errors using SECT were much larger, ranging from 4.4% to 46%, and were especially worse in the presence of dense calcifications. The proposed approach was shown to be insensitive to acquisition parameters, demonstrating the potential to improve the accuracy and precision of stenosis measurements in clinical practice.
基于单能计算机断层扫描(SECT)图像的传统狭窄量化依赖于管腔边界的分割,而这会受到部分容积平均效应和钙化伪影的影响。我们提出并评估了一种使用多能CT(MECT)图像量化面积狭窄百分比的方法。我们利用MECT图像的物质分解,根据有狭窄和无狭窄血管位置之间碘质量的比率来测量狭窄,从而无需对含碘管腔进行分割。该方法首先使用具有不同空间分辨率创建的模拟MECT图像进行评估。为了通过实验评估该方法,制作了四个具有不同狭窄严重程度(30%至51%)、血管直径(5.5至14毫米)和钙化密度(700至 )的体模。使用商用CT系统采集传统SECT图像,并使用商用软件进行分析。使用商用双能CT(DECT)系统以及研究用光子计数探测器CT(PCD - CT)系统采集MECT图像。对MECT数据进行三物质分解,并使用碘密度图来量化狭窄。所有数据采集均使用临床辐射剂量。计算机模拟证实该方法减少了部分容积和伪影效应,从而实现了一致的狭窄测量。体模实验表明,从MECT图像中可以准确且可重复地测量狭窄。对于DECT和双阈值PCD - CT图像,估计误差分别为4.0%至7.0%、2.0%至9.0%、10.0%至18.0%以及 至 (真实值:51%、51%、51%和30%)。对于四阈值PCD - CT图像,误差分别为1.0%至3.0%、4.0%至6.0%、 至9.0%以及0.0%至6.0%。使用SECT时的误差要大得多,范围从4.4%至46%,在存在致密钙化的情况下尤其严重。所提出的方法对采集参数不敏感,显示出在临床实践中提高狭窄测量准确性和精度的潜力。