Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Ste 236, Boston, MA 02114.
Harvard Medical School, Boston, MA.
AJR Am J Roentgenol. 2020 Mar;214(3):566-573. doi: 10.2214/AJR.19.21809. Epub 2020 Jan 22.
The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. Our prospective multiinstitutional study included 59 adult patients (33 women, 26 men; mean age ± SD, 65 ± 12 years old; mean body mass index [weight in kilograms divided by the square of height in meters] = 27 ± 5) who underwent routine chest ( = 22; 16 women, six men) and abdominopelvic ( = 37; 17 women, 20 men) CT on a 640-MDCT scanner (Aquilion ONE, Canon Medical Systems). All patients gave written informed consent for the acquisition of low-dose (LD) CT (LDCT) after a clinically indicated standard-dose (SD) CT (SDCT). The SDCT series (120 kVp, 164-644 mA) were reconstructed with interactive reconstruction (IR) (adaptive iterative dose reduction [AIDR] 3D, Canon Medical Systems), and the LDCT (100 kVp, 120 kVp; 30-50 mA) were reconstructed with filtered back-projection (FBP), IR (AIDR 3D and forward-projected model-based iterative reconstruction solution [FIRST], Canon Medical Systems), and deep learning reconstruction (DLR) (Advanced Intelligent Clear-IQ Engine [AiCE], Canon Medical Systems). Four subspecialty-trained radiologists first read all LD image sets and then compared them side-by-side with SD AIDR 3D images in an independent, randomized, and blinded fashion. Subspecialty radiologists assessed image quality of LDCT images on a 3-point scale (1 = unacceptable, 2 = suboptimal, 3 = optimal). Descriptive statistics were obtained, and the Wilcoxon sign rank test was performed. Mean volume CT dose index and dose-length product for LDCT (2.1 ± 0.8 mGy, 49 ± 13mGy·cm) were lower than those for SDCT (13 ± 4.4 mGy, 567 ± 249 mGy·cm) ( < 0.0001). All 31 clinically significant abdominal lesions were seen on SD AIDR 3D and LD DLR images. Twenty-five, 18, and seven lesions were detected on LD AIDR 3D, LD FIRST, and LD FBP images, respectively. All 39 pulmonary nodules detected on SD AIDR 3D images were also noted on LD DLR images. LD DLR images were deemed acceptable for interpretation in 97% (35/37) of abdominal and 95-100% (21-22/22) of chest LDCT studies ( = 0.2-0.99). The LD FIRST, LD AIDR 3D, and LD FBP images had inferior image quality compared with SD AIDR 3D images ( < 0.0001). At submillisievert chest and abdominopelvic CT doses, DLR enables image quality and lesion detection superior to commercial IR and FBP images.
本研究的目的是比较深度学习重建(DLR)和迭代重建(IR)在亚毫西弗胸部和腹部 CT 中的图像质量和临床显著病变检测。我们的前瞻性多中心研究纳入了 59 名成年患者(33 名女性,26 名男性;平均年龄±标准差,65±12 岁;平均体重指数[体重千克除以身高米的平方]=27±5),他们在 640 层 MDCT 扫描仪(Aquilion ONE,佳能医疗系统)上进行了常规胸部(=22;16 名女性,6 名男性)和腹部(=37;17 名女性,20 名男性)CT 检查。所有患者均在临床指示的标准剂量(SD)CT(SDCT)后签署书面知情同意书,以进行低剂量(LD)CT(LDCT)采集。SDCT 系列(120 kVp,164-644 mA)采用交互式重建(IR)(自适应迭代剂量降低[AIDR]3D,佳能医疗系统)进行重建,LDCT(100 kVp,120 kVp;30-50 mA)采用滤波反投影(FBP)、IR(AIDR 3D 和正向投影基于模型的迭代重建解决方案[FIRST],佳能医疗系统)和 DLR(高级智能清晰 IQ 引擎[AiCE],佳能医疗系统)进行重建。四名专业放射科医生首先阅读所有 LD 图像集,然后以独立、随机和盲法的方式将其与 SD AIDR 3D 图像并排比较。专业放射科医生对 LDCT 图像的图像质量进行了 3 分制评估(1=不可接受,2=不理想,3=理想)。获得描述性统计数据,并进行了 Wilcoxon 符号秩检验。LDCT 的平均容积 CT 剂量指数和剂量长度乘积(2.1±0.8 mGy,49±13mGy·cm)低于 SDCT(13±4.4 mGy,567±249 mGy·cm)(<0.0001)。所有 31 个临床显著腹部病变均在 SD AIDR 3D 和 LD DLR 图像上可见。LD AIDR 3D、LD FIRST 和 LD FBP 图像上分别检测到 25、18 和 7 个病变。SD AIDR 3D 图像上检测到的所有 39 个肺结节也在 LD DLR 图像上可见。LD DLR 图像在 97%(35/37)的腹部 LDCT 研究和 95-100%(21-22/22)的胸部 LDCT 研究中被认为可用于解释(=0.2-0.99)。与 SD AIDR 3D 图像相比,LD FIRST、LD AIDR 3D 和 LD FBP 图像的图像质量较差(<0.0001)。在亚毫西弗胸部和腹部 CT 剂量下,DLR 可实现优于商业 IR 和 FBP 图像的图像质量和病变检测。