自适应统计迭代重建技术对PET/CT全身扫描CT图像质量的影响
摘要
目的探讨自适应统计迭代重建(ASIR)技术对PET/CT全身扫描CT图像质量的影响。方法采用CT性能模型测量CT值准确度和噪声,对模型进行两次扫描,扫描条件分别为:140 kV、120 mA和140 kV、250 mA。随机选取30例(肺癌7例,鼻咽癌4例,宫颈癌、前列腺癌、乳腺癌、子宫内膜癌各2例,胆管癌、结肠癌、卵巢癌、膀胱癌、舌癌、脑梗死各1例,另5例为高危肿瘤筛查者)受检者作为研究对象,使用PET/CT Discovery 690(配128层CT)进行全身扫描,每个受检者分别用30%ASIR和滤波反投影(FBP)重建技术对原始数据进行3.75 mm层厚重建,测量正常组织(脑、纵膈、肺、肝脏、腰椎、膀胱、脂肪)及病灶组织(纵膈、肺、肝脏)的CT值,以测得CT值的标准差(standard deviation,SD)作为图像噪声;然后对测得的各组图像的CT值、噪声进行统计学分析。结果 (1)30%ASIR、FBP重建图像上正常组织(脑、纵膈、肝脏、腰椎、膀胱、脂肪)及病灶组织(脑、纵膈、肺、肝脏、腰椎)的噪声差异有统计学意义(P<0.05)。(2)30%ASIR、FBP图像中测得正常组织(脑、纵膈、肺、肝脏、腰椎、膀胱、脂肪)及病灶组织(脑、纵膈、肺、肝脏、腰椎)的CT值差异无统计学意义(P>0.05),两组图像测得的CT值差异无统计学意义(P>0.05)。结论在同等曝光剂量条件下,30%ASIR技术较FBP重建算法具有更低的图像噪声和更高的图像质量。
引文
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[2] Brady SL,Shulkin L. Ultralow dose computed tomography attenuation correction for pediatric PET CT using adaptive statistical iterative reconstruction[J]. Med Phys, 2015,42(2):558-566.
[3] Zinsser D, Marcus R, Othman AE, et al. Dose Reduction and Dose Management in Computed Tomography-State of the Art[J]. Rofo,2018,190(6):531-541.
[4] Silva AC, Lawder HJ, Hara A, et al. Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm[J]. AJR Am J Roentgenol, 2010,194(1):191-199.
[5] Geyer LL, Schoepf UJ, Meinel FG, et al. State of the Art: Iterative CT Reconstruction Techniques[J]. Radiology,2015,276(2):339-357.
[6] Nagayama Y, Oda S, Nakaura T, et al. Radiation Dose Reduction at Pediatric CT: Use of Low Tube Voltage and Iterative Reconstruction[J]. Radiographics, 2018,38(5):1421-1440.
[7] Miéville FA, Gudinchet F, Brunelle F, et al. Iterative reconstruction methods in two different MDCT scanners: physical metrics and 4-alternative forced-choice detectability experiments-a phantom approach[J]. Phys Med,2013,29(1):99-110.
[8] Deák Z, Grimm JM, Treitl M, et al. Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study[J].Radiology,2013,266(1):197-206.
[9] Barca P, Giannelli M, Fantacci ME,et al. Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction[J]. Australas Phys Eng Sci Med,2018,41(2):463-473.
[10] Ikeda M, Makino R, Imai K, et al. A method for estimating noise variance of CT image[J]. Comput Med Imaging Graph,2010,34(8):642-650.
[11] de Oliveira MV, Wenzel A, Campos PS, et al. Quality assurance phantoms for cone beam computed tomography: a systematic literature review[J]. Dentomaxillofac Radiol,2017,46(3):20160329.
[12] McCollough CH, Yu L, Kofler JM, et al. Degradation of CT Low-Contrast Spatial Resolution Due to the Use of Iterative Reconstruction and Reduced Dose Levels[J]. Radiology,2015,276(2):499-506.
[13] Hussain FA, Mail N, Shamy AM, et al. A qualitative and quantitative analysis of radiation dose and image quality of computed tomography images using adaptive statistical iterative reconstruction[J]. Appl Clin Med Phys, 2016, 17(3): 419-432.
[14] 辛军,赵周社,李红,等.自适应统计迭代重建技术在PET-CT全身扫描中的应用[J].国际放射医学核医学杂志,2013,37(5):279-282.