CN107730540A - 基于高精度匹配模型的冠脉参数的计算方法 - Google Patents
基于高精度匹配模型的冠脉参数的计算方法 Download PDFInfo
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Abstract
本发明公开了一种基于高精度匹配模型的冠脉参数的计算方法,获取冠脉血管部位的血管造影图像,和血管内成像,将血管造影图像与血管内成像匹配成为高精度匹配模型,基于高精度匹配模型计算冠脉血管的血流量、血流储备分数(FFR)、微循环阻力指数(IMR)。本发明的冠脉参数血流量、血流储备分数、微循环阻力指数的计算方法,基于血管造影、血管内成像两种方式获得的图像的高精度匹配模型,其计算结果较于血管造影、血管内成像两者任一图像单独使用计算的结果更加精确,实用性较高。
Description
技术领域
本发明涉及医疗领域,尤其涉及一种基于高精度匹配模型的冠脉参数的计算方法。
背景技术
血管狭窄导致的冠心病是一种严重影响人们健康的疾病,能够准确诊断和治疗血管狭窄是非常重要的。一般而言,比较严重的冠心病需要通过做血管造影(CoronaryAngiography,CAG)来诊断,虽然几乎所有冠脉介入手术都需要做CAG,但是血管造影是一种投影成像技术,精度较差,而且基于有限角度投影,因此根据血管造影获得的三维血管管腔重建不是很准确。血管内超声(Intravascular Ultrasound,IVUS)和血管内光学相干断层成像(Optical Coherence Tomography,OCT)是血管内成像方法,具有血管造影所不能比及的精度和准确度。IVUS和OCT能够提供血管管腔和管壁的大量结构信息,帮助医生选用治疗方法。比如IVUS可以探测到血管管壁的钙化斑块,严重时可能需要使用旋磨术或者切割球囊先给予处理。无论IVUS还是OCT一般需要通过介入成像导管实现。
血管狭窄的临床生理表现是心肌血流灌注不足,然而仅仅依靠对血管影像进行形态参数测量并不能提供直接的临床生理学诊断依据,形态和功能的关系并不一定是简单明朗的。早期研究指出CAG或者IVUS测量出的最高血管狭窄百分比并不能准确预测血管的下游心肌生理缺血程度,但是存在某种相关性,因此血管的功能是否正常可能不是单一形态参数决定的,而可能是和多种参数有关一种比较复杂的关系。
血流储备分数(Fractional Flow Reserve,FFR)是一种直接测量血管供血功能的方法,定义为病变血管能提供的最大血流和该血管假如完全正常时能提供的最大血流的比值。FFR能够提供直接有用的诊断信息,一般来说FFR≤0.8需要血管重建,而FFR>0.8则可以暂时不干预。一系列的临床试验证实了基于FFR的诊断和治疗能够提高病人的预后并降低医疗成本。FFR的测量一般需要使用介入式压力导丝。
功能测量的FFR和血管内成像方法获得的形态成像是反映血管病变的两个方面,理想情况下,医生应该同时掌握这两方面的信息。然而FFR使用的压力导丝和IVUS或者OCT使用的成像导管并非一种器械,因此同时使用有费用和操作复杂度的问题。
一般来说,人体中器官组织的形态结构决定了功能,而功能反映了形态结构,因此如果能够用形态成像来推导出功能学参数,或者由功能学测量来推测形态结构,都是很有意义的,因此这方面的研究很早就引起了人们的兴趣。发明专利CN105326486A《血管压力差与血流储备分数的计算方法及***》,具体公布了一种基于血管造影图像,计算FFR的计算机模型,发明专利CN103932694A《精确诊断心肌血流储备分数(FFR)的方法和设备》,公布了一种基于计算机断层成像(Computed Tomography,CT)和超声心动图,并借助计算流体动力学(CFD)理论,计算FFR的计算机模型。上述两份对比文件都是由形态成像推导FFR,一般思路是通过计算血管远端和近端的压力差来实现。一般来说,这个压力差由两个因素决定,通过血管的血流流量和血管的面积函数。然而CAG和CT都是属于成像分辨力较差的方法,大约在0.5mm左右,不足以对直径大约为2-4毫米的冠状动脉进行准确面积测量,因此无论是血流流量还是血管的面积分布都测不准确,所以计算FFR的准确性无法得到保证。
发明专利US20130072805A1公布了一种腔形态学与血管阻力的测量采集装置及方法,具体涉及一种基于OCT图像间接计算FFR的方法,OCT成像的分辨力较高,大约在0.02mm左右,因此可以准确测量血管面积,但是一般的OCT成像无法有效测量血流流量,因此该方法使用了基于人群平均的血流参数,并使用了大量的基于人群平均的经验公式,因此计算结果的准确度较低。
心肌供血不足既有可能来自于冠状动脉堵塞,又有可能来自于微循环阻力太高。FFR仅能反映冠状动脉的狭窄程度,却无法测量微循环阻力。冠状动脉血流储备(CFR)尽管能够测量达到最大血流时来自于冠状动脉和微循环的合阻力,却无法区分堵塞到底来自于心外膜血管狭窄还是微循环病变。微循环阻力指数(IMR)是由Fearon等人于2003年(FearonW F,Balsam L B,Farouque H M O,et al.Novel index for invasively assessing thecoronary microcirculation.Circulation,107(25)2003)提出的一个新指标,定义为冠状动脉远端测得的血压和血流量之比,能够准确评估来自于微循环的阻力而排除冠状动脉近端狭窄的干扰。因而在临床上,理想情况是能同时获得FFR和IMR的信息,全面评估分别来自与冠状动脉和微循环的阻力并采取相应的治疗措施。目前,评价IMR的常用方法是通过压力导丝测量冠脉远端在最大充血状态下的压力,同时通过热稀释技术测量常温盐水注入冠状动脉的通行时间近似计算出相对血流,压力与血流的比值即为IMR。
FFR/IMR提供的功能反馈与影像学提供的结构信息形成互补,互相不可替代。理想情况是能够同时获取功能和结构信息。然而,当前并没有一种技术或设备能够同时测量功能和结构。传统测量方法需要多种测量工具,不但延长了心导管检查时间,增加了病人的花费,还累积了多种有创测量的风险。
发明内容
本发明的目的是解决现有冠脉血管参数测量方法复杂成本高,但计算方法往往结果精度较差的问题,提出一种更加精确的利用计算方法得到血流量、血流储备分数、微循环阻力指数的方法。
为实现上述发明目的,本发明的技术方案是:
一种基于高精度匹配模型的冠脉参数的计算方法,获取冠脉血管部位的血管造影图像和血管内成像,将血管造影图像与血管内成像匹配成为高精度匹配模型,基于高精度匹配模型计算冠脉血管的血流量、血流储备分数或微循环阻力指数。
所述将血管造影图像与血管内部图像匹配成为高精度匹配模型的方法,具体是在血管内成像导管内放置能与探头同步移动的对造影射线不透明的显影物,通过追踪显影物的位置及回拉轨迹,找到血管内成像的图像所对应的血管造影中血管的位置,通过信号同步和处理,将血管内成像和血管造影图像匹配成高精度匹配模型。
基于高精度匹配模型计算冠脉血管血流量的方法,在高精度匹配模型中选取一段血管,测量造影剂在血管内的渡越时间,计算高精度匹配模型中血管段的管腔体积,利用下式(1)计算血流量:
式中,Q——血流量,ΔT——渡越时间,V——管腔体积。
所述管腔体积V通过高精度匹配模型中血管内成像测得的形状参数计算得到。
测量造影剂在血管段内的渡越时间ΔT,在血管供血近端注射造影剂,并记录造影机输出第一幅图像对应时间为T1,第二幅图像对应时间为T2,并依次类推,造影剂到达冠脉血管段近端图像对应时间为Tp,造影剂到达冠脉血管段远端图像对应时间为Td,渡越时间ΔT=Td-Tp。
所述血流储备分数通过血流量,结合高精度匹配模型测得的血管形状参数计算得到。
所述微循环阻力指数通过血流储备分数计算得到。
所述微循环阻力指数通过下式(2)计算:
上式中,FFR——血流储备分数,Pa——动脉平均压,Q——血流量。
所述血管内图像包括血管内超声成像、光学相干断层成像、同时使用血管内超声成像和光学相干断层成像。
本发明的有益效果是:
本发明的冠脉参数血流量、血流储备分数、微循环阻力指数的计算方法,基于血管造影、血管内成像两种方式获得的图像的高精度匹配模型,其计算结果较于血管造影、血管内成像两者任一图像单独使用计算的结果更加精确,实用性较高。
附图说明
图1为血管造影和血管内成像高精度匹配获得血管模型示意图。
图2为造影剂在血管段内的渡越时间ΔT测量示意图;
图3为压降计算误差和直径测量误差之间的关系图。
具体实施方式
下面将结合附图对本发明实施例中的技术方案进行清楚、完整地描述。
如图1所示,一种基于高精度匹配模型的冠脉参数的计算方法,获取冠脉血管部位的血管造影图像,和内成像血管内部图像,将血管造影图像与血管内部图像匹配成为高精度匹配模型,基于高精度匹配模型计算冠脉血管的血流量、血流储备分数、微循环阻力指数。
血管造影和血管内成像是常用来评估冠状动脉病变的两种不同手段。血管造影成像是通过向血管注射造影液,使用X光对人体进行某一方向的投影,输出的是沿某个方向的最大直径管腔的二维的投影图;血管内成像是使用光学或声学探头,在血管内获得的,呈沿轴向透视的管道状圆形图。
血管造影和血管内成像对病变狭窄程度以及斑块的诊断具有互补性。血管造影分辨率较低,在量化血管直径、面积和狭窄程度方面精度不够,且无法区分动脉粥样化斑块的类别,但可以提供整个冠脉的形态信息;血管内成像分辨率很高,可以精确计算血管面积和狭窄程度,有效区分和诊断血管内斑块,但无法看到全局冠脉结构。
综上可知,血管造影与血管内成像都在某些方面存在不足,两者单独使用都没有办法真正实现精确测量。因此本发明提出了同时进行血管造影和血管内成像,并实现两种图像高精度匹配的方法。
将血管造影图像与血管内部图像匹配成为高精度匹配模型的方法,具体如下:
首先要在血管内成像导管的导丝上放置造影射线不透明的物品比如显影环,在初始阶段,通过识别血管造影图中显影环、导丝的位置,以及导丝的进入方向,大致确定显影环或导丝在血管内成像过程中可能的回拉轨迹所在的大致范围;
第二步,开启血管造影设备,通过导管向血管内释放造影剂,释放造影剂后,获得血管造影图像的时序录像,通过检测血管造影图像中的血管位置,为精确检测显影环在血管造影图中的位置信息提供精确参考量。其中血管位置的检测可以根据海森矩阵特征值或其他滤波方法检测确定。
第三步,前面确定的血管位置以及导丝位置提供了显影环位置的大致范围,下一步是在此基础上精确确定显影环的回拉轨迹;一种实现方法是通过特征匹配卷积滤波器检测每一帧造影图中的显影环位置。特征的选取可以通过预先采集的造影图中显影环的独特特征予以设计。另一种实现方法是设计一个目标特征函数用于捕捉显影环的位置,运用图割算法或马尔科夫链或贝叶斯方法,通过全局优化累积特征函数,寻找造影图像序列中显影环的最优轨迹。另外一种实现思路是在注射造影剂得到的血管造影图序列中选取一幅或多幅图手动标记显影环的位置,然后通过livewire或intelligent scissor算法获取显影环的最优回拉轨迹。
第四步,结合上步获得的显影环的最优回拉轨迹,完成血管内图像与血管造影图像的匹配,从而实现血管内成像的每一帧图像都能找到其对应的血管造影图像的位置。
基于高精度匹配模型计算冠脉血管血流量的方法,在高精度匹配模型中选取一段血管,测量造影剂在血管段内的渡越时间,计算高精度匹配模型中血管段的管腔体积,利用下式(1)计算血流量:
式中,Q——血流量,ΔT——渡越时间,V——管腔体积。
如图1所示,所述血管段管腔体积V通过高精度匹配模型中血管内成像测得的形状参数计算得到。
如图2所示,测量造影剂在血管段内的渡越时间ΔT,在血管供血近端注射造影剂,并记录造影机输出第一幅图像对应时间为T1,第二幅图像对应时间为T2,并依次类推,造影剂到达血管段近端图像对应时间为Tp,造影剂到达冠脉血管段远端图像对应时间为Td,渡越时间ΔT=Td-Tp。根据上述血管造影和血管内成像图像匹配方法,可以得到Tp和Td时造影液前沿位置对映的血管内成像得到的血管3D模型中的位置Lp和Ld。通过对血管3D模型的计算,即可获得Lp和Ld之间的管腔体积V。通过(1)计算得到的Q将用于后续压降及FFR的计算。
所述血流储备分数通过公式FFR=Pd/Pa算出。其中被考察血管远端的压力Pd可通过近端压力Pa减去压降ΔP得出。流体流过一段管道的压降,包括沿程摩擦压降,重力压降,加速压降,局部形阻压降。在正常血管中,层流摩擦压降起主导。假设血管段长度L,直径d,血流粘稠度μ,血流量Q(前面计算已得到),根据泊肃叶定律,其沿程摩擦压降公式如下:
因此要想准确计算压降,必须获得准确的血流量Q,血管段长度L,直径d,尤其是对直径的测量精度非常重要。图3显示了对于3mm粗细的血管,其压降计算误差和直径测量误差之间的关系。如图3所示,直径测量误差越大,压降计算误差越大。血管造影的分辨力一般在0.5mm,因此压降计算误差非常大,结果无法信赖,而腔内成像比如OCT的分辨力一般在0.02mm左右,压降计算误差控制的很好。由于OCT的穿透力有限,成像时需要清血,因此有时无法完全获得所有位置的高质量图像,而IVUS成像时不需要清血,因此结合OCT和IVUS图像可以获得更好的血管内成像结果。
具体来说,压降有两种计算方法,一种是解析法,即先将被考察血管段按照某种标准细分成小段,对每一小段使用明确的公式和血管模型几何参数计算其压降,再把所有血管小段的压降求和得到血管段的压降。另一种是数值计算法,即通过计算流体动力学方法,使用标准的有限元法计算血管内的每一个单位体元的流速和压力,从而获得血管段的压降。
如图2所示,以上Lp和Ld之间的管腔体积V以及压降计算方法需要通过血管内图像准确计算血管各截面的面积。首先确定对应Lp和Ld的血管内成像的帧位置,然后对血管内图像进行图像分割,获得每一张图像的血管管腔分割结果,再由这分割结果计算截面面积以及重建出血管管腔的模型,并进一步利用血流Q和血管管腔模型计算出压降和FFR。
所述微循环阻力指数通过血流储备分数计算所得。
所述微循环阻力指数通过下式(2)计算:
上式中,FFR——血流储备分数,Pa——动脉平均压,Q——血流量。在冠脉侧支循环血流存在且不可忽略时,IMR需修正为下式(3):
其中,Pw为冠状动脉锲压(coronary wedge pressure),需要在球囊冠状动脉成形术中测得,或者将冠状动脉完全堵塞后运用压力导丝在冠脉远端测得;FFRcor是只考虑冠状动脉狭窄时远端压力与动脉平均压Pa的比值;Pd为静脉平均圧。
所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。
Claims (9)
1.一种基于高精度匹配模型的冠脉参数的计算方法,其特征在于,获取冠脉血管部位的血管造影图像和血管内成像,将血管造影图像与血管内成像匹配成为高精度匹配模型,基于高精度匹配模型计算冠脉血管的血流量、血流储备分数或微循环阻力指数。
2.根据权利要求1所述的基于高精度匹配模型的冠脉参数的计算方法,其特征在于,所述将血管造影图像与血管内部图像匹配成为高精度匹配模型的方法,具体是在血管内成像导管内放置能与探头同步移动的对造影射线不透明的显影物,通过追踪显影物的位置及回拉轨迹,找到血管内成像的图像所对应的血管造影中血管的位置,通过信号同步和处理,将血管内成像和血管造影图像匹配成高精度匹配模型。
3.根据权利要求1所述的基于高精度匹配模型的冠脉参数的计算方法,其特征在于,基于高精度匹配模型计算冠脉血管血流量的方法,在高精度匹配模型中选取一段血管,测量造影剂在该血管段内的渡越时间,计算高精度匹配模型中该血管段的管腔体积,利用下式(1)计算血流量:
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式中,Q——血流量,ΔT——渡越时间,V——管腔体积。
4.根据权利要求3所述的基于高精度匹配模型的冠脉参数的计算方法,特征在于,所述血管段的管腔体积V通过高精度匹配模型中血管内成像测得的形状参数计算得到。
5.根据权利要求3所述的基于高精度匹配模型的冠脉参数的计算方法,其特征在于,测量造影剂在血管段内的渡越时间ΔT,在血管供血近端注射造影剂,并记录造影机输出第一幅图像对应时间为T1,第二幅图像对应时间为T2,并依次类推,造影剂到达冠脉血管段近端图像对应时间为Tp,造影剂到达冠脉血管段远端图像对应时间为Td,渡越时间ΔT=Td-Tp。
6.根据权利要求1所述的基于高精度匹配模型的冠脉参数的计算方法,其特征在于,所述血流储备分数通过血流量,结合高精度匹配模型测得的血管形状参数计算得到。
7.根据权利要求1所述的基于高精度匹配模型的冠脉参数的计算方法,其特征在于,所述微循环阻力指数通过血流储备分数计算得到。
8.根据权利要求6所述的基于高精度匹配模型的冠脉参数的计算方法,其特征在于,所述微循环阻力指数通过下式(2)计算:
<mrow>
<mi>I</mi>
<mi>M</mi>
<mi>R</mi>
<mo>=</mo>
<mi>F</mi>
<mi>F</mi>
<mi>R</mi>
<mo>&times;</mo>
<mfrac>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mi>Q</mi>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
上式中,FFR——血流储备分数,Pa——动脉平均压,Q——血流量。
9.根据权利要求1所述的基于高精度匹配模型的冠脉参数的计算方法,其特征在于,所述血管内图像包括血管内超声成像、光学相干断层成像、同时使用血管内超声成像和光学相干断层成像。
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