ISSN 1004-4140
CN 11-3017/P

非门控大螺距肺CT联合人工智能评估冠状动脉钙化积分的初探

刘丹丹, 李伟, 张永县, 赵波, 崔莹, 康天良, 马梓轩, 刘宇航, 牛延涛

刘丹丹,李伟,张永县,等. 非门控大螺距肺CT联合人工智能评估冠状动脉钙化积分的初探[J]. CT理论与应用研究(中英文),xxxx,x(x): 1-6. DOI: 10.15953/j.ctta.2024.085.
引用本文: 刘丹丹,李伟,张永县,等. 非门控大螺距肺CT联合人工智能评估冠状动脉钙化积分的初探[J]. CT理论与应用研究(中英文),xxxx,x(x): 1-6. DOI: 10.15953/j.ctta.2024.085.
LIU D D, LI W, ZHANG Y X, et al. Preliminary exploration of non-gated high-pitch chest CT combined with artificial intelligence in assessing coronary artery calcium score[J]. CT Theory and Applications, xxxx, x(x): 1-6. DOI: 10.15953/j.ctta.2024.085. (in Chinese).
Citation: LIU D D, LI W, ZHANG Y X, et al. Preliminary exploration of non-gated high-pitch chest CT combined with artificial intelligence in assessing coronary artery calcium score[J]. CT Theory and Applications, xxxx, x(x): 1-6. DOI: 10.15953/j.ctta.2024.085. (in Chinese).

非门控大螺距肺CT联合人工智能评估冠状动脉钙化积分的初探

详细信息
    作者简介:

    刘丹丹,女,副主任技师,医学硕士学位,首都医科大学附属北京同仁医院放射科技师长,主要从事辐射防护与剂量优化的研究。E-mail:trdandan4170@163.com

    通讯作者:

    牛延涛✉,男,主任技师,教授,博士研究生导师,首都医科大学附属北京同仁医院医学技术部主任。擅长医学影像成像技术理论和质量控制、辐射剂量管理与成像参数优化。E-mail:ytniu163@163.com

Preliminary exploration of non-gated high-pitch chest CT combined with artificial intelligence in assessing coronary artery calcium score

  • 摘要:

    目的:探讨非门控大螺距肺CT联合人工智能技术对冠脉钙化积分测量的可行性。方法:回顾性分析24例同时接受非门控大螺距肺CT和冠状动脉钙化积分CT扫描的患者图像。两个扫描组参数设置:①非门控大螺距肺CT平扫:CarekV和CareDose 4D,ref.kV 110,ref.mAs 80,螺距3,滤过核BR40。②心电门控冠状动脉钙化积分CT:CarekV,CareDose 4D,ref.kV 120,ref.mAs 50,螺距0.15,采集R-R间期35%以及75%,滤过核QR36。两组图像窗宽/窗位345/50,层厚/间隔3 mm/1.5 mm。两个扫描组图像根据3种测量方法(Syngo.via工作站测量、AI测量、AI+手动校正测量)各分为三个亚组,进行钙化积分(Agatston Score,AS)测量和风险分级,并记录工作站测量以及AI+手动校正测量所用时间。记录两种检查容积剂量指数(CTDIvol)、剂量长度乘积(DLP),并计算有效剂量(ED)。使用SPSS Statistics 27.0软件进行统计分析。结果:①大螺距肺CT管电压分别为100、110、120 kV,冠状动脉钙化积分CT均为120 kV。②大螺距肺CT和冠状动脉钙化积分CT的ED分别为(2.1±0.4)、(2.1±0.7) mSv。③大螺距肺CT的3种测量方法所得AS有统计学差异(X2=27.163,P<0.001),但AS一致性较高(ICC:0.988)。④大螺距肺CT联合AI(X2=4.795,P=0.091、ICC:0.990)、大螺距肺CT工作站(Z=0.912,P=0.362、ICC:0.988)、门控钙化积分联合AI(X2=10.900,P=0.004、ICC:0.980)分别与冠脉钙化积分工作站测得AS,均无统计学差异且一致性较高。⑤AI所得风险分级一致性很强,加权Kappa系数均在0.818-1.000之间。⑥两个扫描组的工作站与AI+手动校正测量所用时间均有统计学差异(Z =4.200、4.049,均P<0.001)。结论:非门控大螺距肺CT联合AI可获得可靠的冠脉钙化积分和风险分级结果且耗时短,具有较好的临床应用价值。

    Abstract:

    Objective: To investigate the feasibility of using non-gated high-pitch chest computed tomography (CT) combined with artificial intelligence (AI) technology to measure the coronary artery calcium score. Methods: In this retrospective analysis, we reviewed the images of 24 patients who underwent both non-gated high-pitch chest CT and coronary artery calcium scoring CT. The scanning parameters were as follows: ① non-gated high-pitch chest CT scan: CarekV and CareDose 4D, ref.kV 110, ref.mAs 80, pitch 3, filter core BR40; ② ECG-gated coronary artery calcification score CT: CarekV, CareDose 4D, ref.kV 120, ref.mAs 50, pitch 0.15, acquisition R-R interval 35% and 75%, filter core QR36. The image window width/window level of the two groups was 345/50, and the slice thickness/interval was 3 mm/1.5 mm. The images of the two scan groups were divided into three subgroups according to the three measurement methods (Syngo.via workstation measurement, AI measurement, and AI + manual correction measurement). The image calcification score (Agatston Score, AS) was measured, risk classification was performed, and the time taken for the workstation measurement and the AI + manual correction measurement was recorded. The volume dose index (CTDIvol) and dose-length product (DLP) of the two examinations were recorded, and the effective dose (ED) was calculated. SPSS Statistics software (version 27.0) was used for the statistical analysis. Results: The tube voltages of the high-pitch chest CT were 100, 110, and 120 kV, and the coronary artery calcium score was 120 kV. EDs of high-pitch chest CT and coronary artery calcium score CT were 2.1±0.4 and 2.1±0.7 mSv, respectively. The three measurement methods used for high-pitch chest CT showed significant differences in AS (X2=27.163, P < 0.001), but the consistency of AS was high (ICC: 0.988). Regarding AS measurement, high-pitch chest CT combined with AI (X2 = 4.795, P = 0.091, ICC: 0.990), high-pitch chest CT using a workstation (Z = 0.912, P = 0.362, ICC: 0.988), and gated calcium scoring combined with AI (X2 = 10.900, P = 0.004, ICC: 0.980) showed no significant differences or high consistency compared to the workstation for coronary artery calcium scoring. The risk classification obtained by the AI was highly consistent, and the weighted kappa coefficients were between 0.818 and 1.000. The time taken for measurements using the workstation and AI + manual correction in both scanning groups showed significant differences (Z = 4.200, 4.049, P < 0.001). Conclusion: Non-gated high-pitch chest CT combined with AI can provide reliable results for artery calcium scores and risk classification with reduced time consumption, demonstrating substantial clinical utility.

  • 急性小脑梗死约占急性脑梗死的1.5%~20%[1],其临床症状不特异,包括头痛、眩晕、共济失调、构音障碍、恶心、呕吐等,容易漏诊或误诊。磁共振成像(magnetic resonance imaging,MRI),尤其是扩散加权成像(diffusion weighted imaging,DWI)的广泛应用,大大提高了急性小脑梗死的诊断率[2],在此基础上,临床专家开始关注急性双侧小脑梗死的特征及其病因。既往的文献报道中[3-4],研究者更多关注的是双侧小脑上动脉(superior cerebellar artery,SCA)供血区的急性梗死,并在此基础上分析急性双侧小脑梗死的发病原因。而在临床工作中,我们发现双侧小脑后下动脉供血区(posterior inferior cerebellar artery,PICA)急性脑梗死并不少见。

    本研究对此类病变的MRI特征进行了回顾性分析,并与同期双侧SCA供血区急性梗死的MRI特征进行对比,探讨其发病原因,以期提高急性双侧小脑梗死的临床救治水平。

    收集首都医科大学附属北京友谊医院2019年1月至2022年1月间,经临床和影像学确诊的双侧小脑PICA供血区急性梗死患者共38例,其中男性32例,女性6例,年龄21~89岁(中位年龄62岁);同期经临床和影像学确诊的双侧小脑SCA供血区急性梗死患者40例,其中男性32例,女性8例,年龄38~100岁(中位年龄69岁)。

    本研究未纳入同时累及双侧小脑PICA+SCA供血区急性梗死患者。

    (1)78例患者均于发病72 h内接受MRI常规扫描,扫描设备包括GE Signa EXCITE HD 1.5 MR扫描仪、SIMENS Magnetom Prisma 3.0 T MR扫描仪、PHILIPS Ingenia 3.0 T MR扫描仪,序列包括横轴面T1 WI、T2 WI、FLAIR、DWI及矢状面T1 WI。25例患者同时进行了磁敏感加权成像(susceptibility weighted imaging,SWI)。

    (2)15例患者接受头颅磁共振血管成像(magnetic resonance angiography,MRA)检查,37例患者接受头颈CT血管成像(CT angiography,CTA)检查,其中2例患者接受了MRA及CTA检查。

    MRA成像序列:三维磁共振血管成像(3D time-of-flight magnetic resonance angiography,3D TOF-MRA),扫描范围:枕骨大孔至胼胝体顶。

    CTA采用GE revolution CT或佳能Aquilion TM Vision CT扫描仪,双筒高压注射器(STELLANT,MEDRAD Inc.,USA),非离子对比剂选用碘普罗胺(370 mgI/mL,GE Inc.,USA)。扫描范围自主动脉弓至颅底。扫描方法:经肘正中静脉以5 mL/s的流率注入A筒60 mL对比剂,之后B筒追加30 mL生理盐水。选取主动脉根部为监测层面进行预扫描,当升主动脉CT值达150 HU时自动触发,3 s后启动正式扫描。扫描结束后将采集到的头颈CTA图像传至后处理工作站进行多平面重建及三维重建。

    由两名神经影像医师双盲阅片,意见不一致时共同协商确定。进行以下分析:

    (1)根据Amarenco's解剖图谱[5],对梗死累及部位进行确认。对双侧PICA供血区急性梗死病变按PICA外侧支(lateral branch of PICA,lPICA)供血区受累、PICA内侧支(medial branch of PICA,mPICA)供血区受累、分水岭区受累、全PICA供血区受累进行分类。

    (2)按梗死大小对单支动脉供血区病灶进行分类[2],单个病灶大于1.5 cm定义为区域性梗死,单个病灶均小于1.5 cm定义为小梗死,将所有病例分为双侧区域性梗死、双侧小梗死、一侧区域性梗死+对侧小梗死。

    (3)分别记录每个患者是否伴发其他后循环供血区、前循环供血区的急性梗死。

    (4)基于头颈CTA或MRA,观察双侧PICA供血区梗死患者椎-基底动脉血管改变。

    采用SPSS 23.0进行统计描述与分析。比较双侧PICA供血区及双侧SCA供血区影像特征,采用绝对值及百分比表示计数资料,两组间比较采用χ${}^2 $检验。P<0.05为差异有统计学意义。

    按梗死大小、后循环其他部位及前循环受累情况分类,总结双侧PICA供血区、双侧SCA供血区急性梗死MRI特征(表1)。

    表  1  双侧PICA供血区、双侧SCA供血区急性梗死MRI特征
    Table  1.  MRI characteristics of bilateral PICA and SCA territory acute infarction
    MRI特征组别(例数(%))χ2P
    双侧PICA梗死
    (38例)
    双侧SCA梗死
    (40例)
    双侧区域性梗死     6(15.8)5(12.5)0.1740.677
    一侧区域性梗死+对侧梗死15(39.5)6(15.0)5.9330.015
    双侧小梗死       17(44.7)29(72.5)6.2080.013
    累及后循环其它部位   8(21.0)32(80.0)27.103<0.001
    累及前循环       2(5.2)2(5.0)0.0030.958
    下载: 导出CSV 
    | 显示表格

    38例双侧PICA供血区急性梗死中,17例为一侧全供血区受累伴对侧PICA供血区受累,15例为双侧mPICA供血区受累,4例为双侧分水岭区受累,2例为一侧分水岭区伴对侧PICA供血区受累。

    38例双侧PICA供血区急性梗死患者,20例患者接受头颈CTA检查,6例患者接受头颅MRA检查,其中2例患者接受CTA及MRA检查。40例双侧SCA供血区急性梗死患者,17例患者接受头颈CTA检查,10例患者接受头颅MRA检查。

    24例行血管检查诊断为双侧PICA供血区急性梗死患者,其中13例(54.2%)表现为单侧椎动脉V4段或PICA局限性重度狭窄/闭塞,8例(33.3%)表现为椎-基底动脉多节段或弥漫管腔狭窄、闭塞,3例(12.5%)椎-基底动脉未见管腔异常。27例行血管检查诊断为双侧SCA供血区急性梗死患者,其中8例(29.6%)表现为椎-基底动脉单一节段局限性重度狭窄,14例(51.9%)表现为椎-基底动脉多节段或弥漫管腔狭窄、闭塞,5例(18.5%)椎-基底动脉未见管腔异常。双侧PICA梗死中椎基底动脉单一节段局限性重度狭窄/闭塞的比例高于双侧SCA梗死,而椎基底动脉多处或弥漫管腔狭窄、闭塞的比例低于双侧SCA梗死,但二者均无统计学差异(表2)。

    表  2  双侧PICA供血区、双侧SCA供血区急性梗死椎-基底动脉CTA/MRA表现特征
    Table  2.  CTA/MRA features of acute infarcted vertebrobasilar artery in bilateral PICA territory and bilateral SCA territory
    椎-基底动脉CTA/MRA表现特征组别(例数(%))χ2P
    双侧PICA梗死
    (24例)
    双侧SCA梗死
    (27例)
    单一节段局限性重度狭窄/闭塞13(54.2)8(29.6)3.1580.076
    多处或弥漫管腔狭窄、闭塞  8(33.3)14(51.9)1.7760.183
    未见管腔异常        3(12.5)5(18.5)0.3480.555
    下载: 导出CSV 
    | 显示表格

    13例椎动脉V4段或PICA局限性病变患者,MRI表现为一侧区域性梗死伴对侧小梗死(图1)或双侧区域性梗死(图2);8例椎-基底动脉多处或弥漫病变患者,MRI表现为双侧小梗死(图3);3例椎-基底动脉未见管腔异常患者,MRI表现为双侧小梗死。

    图  1  双侧PICA供血区急性梗死:一侧全供血区受累伴对侧PICA供血区受累
    男性,34岁,突发眩晕、行走不稳就诊。头颅MRI检查(a)T1 WI、(b)T2 WI、(c)DWI、(d)ADC,可见左侧全PICA供血区急性区域性梗死病灶及右侧mPICA供血区急性小梗死病灶;(e)CTA显示右侧PICA起源于基底动脉(白色弧形箭头),左侧PICA起始部闭塞(白色箭头);(f)SWI可见左侧PICA起始部低信号血栓形成(黑色箭头)。本例双侧小脑梗死由左侧PICA原位血栓形成引起,由于存在解剖变异,右侧mPICA供血区可能部分由左侧PICA供血,因此引起双侧小脑PICA供血区梗死。
    Figure  1.  Acute infarction in bilateral PICA territory: Unilateral total infarct of PICA combined with the contralateral infarct of mPICA
    图  2  双侧PICA供血区急性梗死:双侧mPICA供血区受累
    男性,66岁,突发头晕就诊。头颅MRI检查(a)T1 WI、(b)T2 WI、(c)DWI,可见双侧mPICA供血区急性区域性梗死病灶,(d)头颅CTA右侧椎动脉V4段及双侧PICA均未见显示;该患者四年前头颅CTA(e)和(f)可见左侧PICA未发育,右侧PICA供应双侧小脑半球并可见局限性重度狭窄(白色箭头),本次右侧椎动脉及PICA闭塞引起双侧小脑半球急性梗死。
    Figure  2.  Acute infarction in bilateral PICA territory: Bilateral mPICA involved
    图  3  双侧PICA供血区急性小梗死灶伴后循环其它供血区梗死
    男性,62岁,意识障碍。头颅MRI检查DWI示双侧PICA供血区(a)和(b)及左侧SCA供血区、桥脑(c)多发小梗死灶,TOF-MRA双侧椎动脉颅内段、基底动脉均未显示,提示可能为椎-基底动脉弥漫病变所致动脉-动脉栓塞。
    Figure  3.  Acute small infarcts in bilateral PICA combined with other areas of the posterior circulation

    在急性小脑梗死中,双侧小脑梗死约占20%~30%,与单侧小脑梗死比较,其临床症状更重、预后更差[6]。在发病机制上,既往研究认为,单侧小脑梗死多见于PICA供血区,多为原位动脉粥样硬化引起;而双侧小脑梗死多见于SCA供血区,且常合并小脑以外的急性梗死病灶,病因多为上一级动脉粥样硬化引起动脉-动脉栓塞或心源性栓塞。

    需要注意的是,既往研究中,虽然双侧SCA供血区受累更多见,但双侧PICA供血区的受累仅略低于双侧SCA供血区[3-4],但在发病原因的探讨中并未对不同供血区受累进行独立分析,有一定的局限性。

    在本组病例中,同时间段急性双侧小脑梗死中,双侧PICA供血区受累与双侧SCA供血区受累病例数量接近,而不同供血区受累的双侧小脑梗死影像特征并不相同。急性双侧PICA供血区梗死中,区域性梗死的发生率明显高于双侧SCA供血区梗死,而合并小脑以外病灶的发生率明显低于双侧SCA供血区梗死。不同的影像特征提示:与急性双侧SCA供血区梗死相比,急性双侧PICA供血区梗死有着不同的发病机制。

    如前所述,双侧SCA供血区梗死病因多为动脉-动脉栓塞或心源性栓塞,因此小梗死灶的发生率更高,且更容易合并小脑以外的梗死灶;而双侧PICA供血区梗死中区域性梗死更为常见,头颈CTA/MRA分析结果表明双侧PICA供血区梗死患者多数由单侧椎动脉V4段或PICA局限性病变引起,提示其病因多为原位动脉粥样硬化引起、更容易发生区域性梗死[7]

    原位动脉粥样硬化引起双侧PICA供血区梗死的原因主要与PICA变异相关。小脑供血动脉中,PICA变异最为常见,包括几种类型[8-12]:①单侧优势型 PICA,优势侧动脉供应双侧小脑内侧区域;②双侧 PICA共干或分别起源于一侧椎动脉;③双侧 PICA起源于基底动脉;④双侧 PICA缺如,相应区域由小脑前下动脉供血。其中,单侧优势型PICA更为常见。本组中双侧PICA供血区梗死主要表现为一侧全供血区受累伴对侧mPICA供血区受累或双侧mPICA供血区受累,且区域性梗死常见,我们认为主要原因是优势侧PICA或起源椎动脉发生局限性动脉粥样硬化造成管腔狭窄或原位血栓形成,从而引起相应供血区梗死。

    本组中双侧PICA供血区梗死还可表现为双侧小梗死、合并后循环其它部位梗死或前循环梗死,我们分析除了解剖变异因素,双侧PICA供血区梗死还可能有:①一侧大面积 PICA供血区梗死引起占位效应,压迫对侧mPICA分支血管引起相应供血区梗死;②血流动力学变化引起 PICA远端供血区低灌注引起梗死,此时多表现为分水岭梗死;③动脉-动脉栓塞或心源性栓塞,此时,往往合并后循环其它供血区梗死或前循环梗死。

    本研究为回顾性分析,存在局限性:①样本量较小,且仅有65% 的患者接受了头颈CTA或头颅MRA检查,造成不同类型双侧小脑梗死椎-基底动脉变化特征未发现统计学差异,对责任动脉的改变评价不够充分;②临床随访资料不完善,未能对不同类型双侧小脑梗死临床预后进行对比分析。后期研究将采取前瞻性实验设计加大样本量与长期随访,对双侧小脑梗死的发病机理及预后进行更加深入研究。

    综上所述,双侧小脑梗死病因复杂,累及不同供血动脉,其发病机制、影像特征有所不同,应区别分析。累及双侧PICA供血区不伴有小脑以外梗死灶时,应首先考虑原位动脉粥样硬化所致,血管成像(CTA、MRA或DSA)有助于显示椎动脉V4段或PICA的局限病变。由于PICA变异较大,血管成像可能无法准确判断有无异常,此时,磁共振磁敏感加权成像(SWI)有助于原位血栓的显示[13]。当梗死累及双侧PICA供血区同时合并后循环其它供血区梗死或前循环梗死时,应考虑动脉-动脉栓塞或心源性栓塞,此时应重点关注上一级动脉有无异常以及有无心源性栓子。针对不同病因进行个体化诊疗,将有助于改善病人预后、减少复发。

  • 图  1   (a) 大螺距肺CT示例图;(b) 冠状动脉钙化积分CT示例图

    注:此患者性别女,BMI:23.8,大螺距肺CT与门控冠状动脉钙化积分检查结果均为“低风险”。

    Figure  1.   (a) large-pitch lung computed tomography (CT) sample; (b) Sample CT scan of coronary artery calcification

    表  1   两种扫描方法扫描参数和辐射剂量

    Table  1   Scanning parameters and radiation doses in the two scanning methods

    扫描方法 实际管电压(kV) 实际管电流(mAs) 容积剂量指数(mGy) 剂量长度乘积(mGy·cm) 有效剂量(mSv)
    非门控大螺距肺CT 120(4)* 84-102(4)* 4.0±0.7 149.2±31.2 2.1±0.4
    110(13)* 72-104(13)*
    100(7)* 102-158(7)*
    心电门控冠状动脉
    钙化积分CT
    120(24)* 22-64(24)* 9.0±2.8 152.2±49.2 2.1±0.7
    注:*括号内数值代表病例数。
    下载: 导出CSV

    表  2   两种扫描方式图像使用3种测量方法所得钙化积分统计分析

    Table  2   Calcification scores of the two scanning images using three measurement methods

    扫描方式 测量方法
    Syngo.via
    工作站测量
    AI测量 AI+手动
    校正测量
    X2 P ICC 工作站与AI
    测量Kappa
    工作站与AI+
    手动校正
    测量Kappa
    非门控大螺
    距肺CT
    99.70(32.20,374.80) 97.00(31.00,337.00)1 97.50(31.00,351.25)1 27.163 <0.001 0.988
    (0.977,0.995)*
    0.818 0.818
    心电门控冠
    脉钙化积分CT
    96.75(30.90,363.40) 90.97(38.59,329.48)2 95.29(30.52,356.43)2 10.900 0.004 0.980
    (0.960,0.990)*
    1.000 1.000
    注:1使用AI非门控钙化积分模块测量;2使用AI门控钙化积分模块测量;ICC,组内相关系数;*括号内数值为95%置信空间;Kappa,加权Kappa系数。
    下载: 导出CSV
  • [1] 周俊林, 萧毅, 张雪君, 等. 中国医学影像人工智能应用现状调研报告[J]. 中华放射学杂志, 2022, 56(11): 1248−1253. DOI: 10.3760/cma.j.cn112149-20220802-00650.

    ZHOU J L, XIAO Y, ZHANG X J, et al. A survey report on the application status of artificial intelligence in medical imaging in China. Chinese Journal of Radiology, 2022, 56(11): 1248−1253. DOI: 10.3760/cma.j.cn112149-20220802-00650. (in Chinese).

    [2]

    YU J, QIAN L, SUN W, et al. Automated total and vessel-specific coronary artery calcium (CAC) quantification on chest CT: direct comparison with CAC scoring on non-contrast cardiac CT. BMC Med Imaging. 2022 Oct 14;22(1): 177. DOI: 10.1186/s12880-022-00907-1.

    [3]

    CHANG S, REN L, TANG S, et al. Technical note: Exploring the detectability of coronary calcification using ultra-high-resolution photon-counting-detector CT. Med Phys. 2023 Nov;50(11): 6836−6843. DOI: 10.1002/mp.16712.

    [4]

    LLEWELLYN O, WILLIAMS M C. What should we do about Coronary Calcification on Thoracic CT? Rofo. 2022 Aug;194(8): 833−840. English. DOI: 10.1055/a-1752-0577.

    [5]

    JACOBS P C, ISGUM I, GONDRIE M J, et al. Coronary artery calcification scoring in low-dose ungated CT screening for lung cancer: interscan agreement. AJR Am J Roentgenol. 2010 May;194(5): 1244−9. DOI: 10.2214/AJR.09.3047.

    [6]

    PARK S H, HAN K. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. Radiology. 2018 Mar;286(3): 800−809. DOI: 10.1148/radiol.2017171920.

    [7]

    The 2007 Recommendations of the International Commission on Radiological Protection. ICRP publication 103. Ann ICRP. 2007;37(2−4): 1−332. DOI: 10.1016/j.icrp.2007.10.003.

    [8] 孙会利, 陈杰, 张焕, 等. 基于人工智能技术的非门控胸部CT平扫对冠状动脉钙化积分的准确性评价[J]. CT理论与应用研究, 2021, 30(1): 106−113. DOI: 10.15953/j.1004-4140.2021.30.01.11.

    SUN H L, CHEN J, ZHANG H, et al. Accuracy evaluation of coronary artery calcification score by non gated chest CT scan based on artificial intelligence technology[J]. CT Theory and Applications, 2021, 30(1): 106−113. DOI: 10.15953/j.1004-4140.2021.30.01.11.

    [9]

    LESSMANN N, VAN GINNEKEN B, ZREIK M, et al. Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions. IEEE Trans Med Imaging. 2018 Feb;37(2): 615−625. DOI: 10.1109/TMI.2017.2769839.

    [10]

    LIU Y, CHEN X, LIU X, et al. Accuracy of non-gated low-dose non-contrast chest CT with tin filtration for coronary artery calcium scoring. Eur J Radiol Open. 2022 Jan 17;9: 100396. DOI: 10.1016/j.ejro.2022.100396.

    [11]

    BUDOFF M J, NASIR K, KINNEY G L, et al. Coronary artery and thoracic calcium on noncontrast thoracic CT scans: comparison of ungated and gated examinations in patients from the COPD Gene cohort. J Cardiovasc Comput Tomogr. 2011 Mar-Apr;5(2): 113−8. DOI: 10.1016/j.jcct.2010.11.002.

    [12] 顾耕, 陶欣慰, 邓建宏. 无门控大螺距胸部CT扫描心脏冠状动脉钙化积分的可行性研究[J]. 实用放射学杂志, 2022, 38(6): 910−914, 918. DOI: 10.3969/j.issn.1002-1671.2022.06.010.

    GU G, TAO X W, DENG J H. Coronary artery calcification score detection using thorax CT with TurboFlash mode: a feasibility study. Journal of Practical Radiology, 2022, 38(6): 910−914, 918. DOI: 10.3969/j.issn.1002-1671.2022.06.010. (in Chinese).

    [13] 樊荣荣, 刘凯, 夏晨, 等. AI对非门控胸部LDCT平扫冠状动脉钙化积分危险分层的预测价值[J]. 国际医学放射学杂志, 2022, 45(1): 21−26. DOI: 10.19300/j.2022.L18690.

    FAN R R, LIU K, XIA C, et al. Predictive value of AI in risk stratification of coronary artery calcification score using non-gated chest LDCT. International Journal of Medical Radiology, 2022, 45(1): 21−26. DOI: 10.19300/j.2022.L18690. (in Chinese).

    [14] 闫玉辰, 胡磊, 王焰, 等. 基于深度学习技术的非门控胸部CT冠脉钙化积分系统的准确性评价[J]. 放射学实践, 2023, 38(3): 273−278. DOI: 10.13609/j.cnki.1000-0313.2023.03.006.

    YAN Y C, HU L, WANG Y, et al. Accuracy evaluation of non-gated chest CT coronary calcification scoring system based on deep learning technology. Radiology Practice, 2023, 38(3): 273−278. DOI: 10.13609/j.cnki.1000-0313.2023.03.006. (in Chinese).

    [15]

    ANDRE F, SEITZ S, FORTNER P, et al. Simultaneous assessment of heart and lungs with gated high-pitch ultra-low dose chest CT using artificial intelligence-based calcium scoring. Eur J Radiol Open. 2023 Feb 15;10: 100481. DOI: 10.1016/j.ejro.2023.100481.

    [16] 陈丽虹, 郭银霞, 李雅楠, 等. 人工智能冠状动脉钙化积分自动测量方法的临床有效性评估[J]. 实用放射学杂志, 2023, 39(1): 45−48, 69. DOI: 10.3969/j.issn.1002-1671.2023.01.012.

    CHEN L H, GUO Y X, LI Y N, et al. Clinical effectiveness evaluation of artificial intelligence automatic measurement method for coronary artery calcium score. Journal of Practical Radiology, 2023, 39(1): 45−48, 69. DOI: 10.3969/j.issn.1002-1671.2023.01.012. (in Chinese).

    [17]

    COVAS P, DE GUZMAN E, BARROWS I, et al. Artificial Intelligence Advancements in the Cardiovascular Imaging of Coronary Atherosclerosis. Front Cardiovasc Med. 2022 Mar 21;9: 839400. DOI: 10.3389/fcvm.2022.839400.

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  • 收稿日期:  2024-06-06
  • 修回日期:  2024-09-04
  • 录用日期:  2024-09-09
  • 网络出版日期:  2024-10-22

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