Patent Abstract:
a method for simulating a blood flow in a vascular segment of a patient is proposed . a 3d image dataset of an examination region is recorded by a radiographic diagnostic device for generating a 3d vascular model . contrast agent propagation in the examination region is captured by a dynamic 2d angiography method for generating a real 2d angiography recording . a cfd simulation of the blood flow is performed in the 3d vascular model based on a blood flow parameter for generating a virtual 2d angiography recording . a degree of correspondence between the real and the virtual 2d angiography recordings is determined from identical angulation and adjusted recording geometry of the patient and compared with predefinable tolerance values . the cfd simulation is iteratively optimized while changing the blood flow parameter as a function of the comparison . the degree of correspondence is outputted when the optimum cfd simulation is achieved .

Detailed Description:
in the inventive method , apparatus and workflow a degree of correspondence between a virtual angiography from a cfd simulation and a real angiography scene is determined and this degree of correspondence is used to purposefully and iteratively optimize the cfd simulation . this degree of correspondence is based on the comparison between a virtual angiography and a real angiography from identical angulation and adjusted recording geometry of the individual patient . the inventive determination of the degree of correspondence in 2 - d is an alternative approach to sun et al . [ 4 ]. output data for this degree of correspondence is dynamic angiography scenes , which show the diffusion or passage of the contrast agent through the corresponding vascular system . the virtual dynamic angiography s * ( the values indicated by * always relate in the following to the data derived from the virtual angiography ) is obtained by means of cfd simulation . the time intensity curves tic i , j and tic * i , j are now obtained from these two scenes s and s * for each pixel ( or combination of several pixels ). in the next step one or also more characteristic variables can be extracted from these time intensity curves , as described in “ parametric color coding of digital subtraction angiography ” by strother et al . [ 5 ]. these can be time values and / or intensity values or intensity values at defined times . fig2 shows by way of example a time intensity curve ( tic ) with drawn - in characteristic variables , in which the blood flow is plotted as intensity i over time t . after a noise - like behavior of the bolus curve 10 the intensity i climbs to the intensity maximum 11 ( i max ), in order then to drop back to a noise level . the bolus curve 10 is furthermore characterized by its half - width 12 ( fwhm — full width at half maximum ), which lies between the mean rise and the mean drop of the bolus curve . the arrival time 13 ( t rise ) is the time that elapses until the occurrence of the contrast agent bolus at the point under examination and thus until the rise in the bolus curve 10 . the mean rise time 14 ( t rise , fwhm ) is the time that elapses until the occurrence of the half - width 12 of the bolus curve 10 , i . e . until the bolus curve 10 has reached half of the intensity maximum 11 ( i max ). the time until the intensity maximum 11 ( i max ) is called the maximum time 15 ( t max ). the rise time 16 or wash - in time ( t wash in ) characterizes the steep rise in the bolus curve 10 . the drop in the bolus curve 10 is characterized by the drop time 17 or wash - out time ( t wash out ). the duration of the occurrence of the contrast agent bolus is characterized by the bolus or maximum time 18 ( t peak ). in the following let t i , j or t * i , j be the extracted variable for each pixel i , j from both angiographies . this produces a degree of correspondence b i , j of both angiographies from a mathematical link between both values t i , j or t * i , j , such as a simple subtraction for example . this degree of correspondence is thus a two - dimensional field , which can be represented for example as an image ( e . g . color - coded ) and permits an evaluation of the correspondence between the virtual and the real angiography . theoretically this degree of correspondence can be determined as in sun et al . [ 4 ] as a mean quadratic error for the entire curves tic i , j and tic * i , j . however , this means it is then subsequently not possible to say anything about the nature of the deviation and thus it cannot be used for purposeful control of the cfd optimization . until now , however , the two angiography scenes have not been synchronized . in this application there are various excellent vascular regions in the angiography images , among which are the vascular regions into which the blood or the contrast agent flows . in an improved embodiment regions of interest ( roi ) in these vascular regions can be defined in both images . in this case one or more roi , or corresponding roi * in the virtual image can be selected such that they cover the vascular inflow regions . in a next step the mean value of the characteristic variable t i , j , or t * i , j under consideration can be determined : mwt i , j , or mwt * i , j . from a comparison of these mean values a normalization is calculated in the following . this can be a difference ( in the case of temporal values ) or a factor e . g . in the case of intensity values , as well as other algorithms . thus the normalized degree of correspondence b i , j of both angiography recordings can be balanced : it is especially advantageous if the contrast inflow curve from the real angiography scene is used for the ( initial ) cfd simulation . if the simulation of the virtual angiography is performed such that the virtual inflow of the contrast agent matches reality , a normalization can be dispensed with however , it can also happen that both curves are delayed in respect of one another at the time of inflow or have different grayscale values . in this case , depending on the question , normalization will bring an improvement . essential for the invention are the definition of a degree of correspondence and the use thereof to assess and optimize the cfd simulation individual to the patient which until now has not been possible in - vivo . in particular the missing information on the local flow , into and out of the vascular segment under consideration , can herewith be adjusted iteratively to the real recorded 2d angiography recordings . this results in an improvement in the cfd results . this is based on the idea of comparing a virtual angiography obtained from the cfd simulation with the real angiography , determining a degree of correspondence , or if there is a difference optimizing the cfd so that the correspondence becomes better . in the case of a patient a 3d subtraction angiography with a c - arm system of the cerebral vessels and one ( or more ) 2d subtraction angiography scenes are recorded . in a first step a 3d surface model is generated in the computer following a segmentation of the relevant vascular section around an aneurysm , which is then used as geometry for the cfd simulation . moreover inflow and outflow regions are established . fig3 shows a vascular segment 20 with an afferent vessel 21 as an example for the definition of the region of interest roi and the associated basic conditions flow q and pressure p , which vessel branches into a first efferent vessel 22 and a second efferent vessel 23 . the vascular segment 20 furthermore has an aneurysm 24 . the inlet to the afferent vessel 21 is formed by an inflow region 25 . the outlet of the first efferent vessel 22 is formed by a first outflow region 26 and the outlet of the second efferent vessel 23 by a second outflow region 27 . a flow q in ( t ) and a pressure p in ( t ) prevail in the region of interest roi in of the inflow region 25 . in the region of interest roi out1 of the first outflow region 26 a flow q out1 ( t ) and a pressure p out1 ( t ) are measured and in the region of interest roi out2 of the second outflow region 27 a flow q out2 ( t ) and a pressure p out 2 ( t ) are measured . the selected volume is adjusted here to the 2d angiography , i . e . an angulation and projection geometry corresponding to the 2d angiography are determined in the 3d angiography . if both recordings originate from an examination which involves no movement of the patient this is simple to calculate , but otherwise a registration must be performed . thus the inflow region 25 and the outflow regions 26 and 27 are now also established in the 2d angiography . in the following cfd simulation the propagation of an injected contrast agent is simulated , among other things . the temporal dynamics of the contrast agent inflow can be adjusted to the averaged time intensity curve from the 2d angiography . after the simulation a virtual 2d angiography is calculated by means of known angulation and projection geometry using forward projection ( drr ), as is described for example in de 10 2007 039 034 a1 . in a next step the normalized degree of correspondence b ′ i , j e . g . for the bolus arrival times of both angiographies ( real and virtual ) is calculated . if for example the typical vascular segment 20 with the afferent vessel 21 , the aneurysm 24 and the two efferent vessels 22 and 23 is considered , a further evaluation for control of an iterative cfd simulation can now take place . to this end the normalized degree of correspondence averaged in the roi of the outflow regions 26 and 27 of the two efferent vessels 22 and 23 is considered . if it lies within a predefined tolerance , the result of the simulation is satisfactory in respect of these parameters , but otherwise this can be interpreted as a too fast or too slow flow in the entire vascular segment 20 . physically this means that the basic condition of pressure difference between inflow region 25 and outflow regions 26 and 27 was selected suboptimally . this can happen , since the vascular resistance distally to the vascular arborization section under consideration is generally not known . the tolerances can for example be predefined by a user . it is thereby determined how closely both angiographies , the virtual and the real angiography , must correspond before the user is satisfied . if the normalized degree of correspondence is positive , the calculated flow is too low and in the subsequent cfd simulation the pressure difference or the pressure conditions must be increased at the outflow regions 26 and 27 ( or variables corresponding thereto such as flow rate at the inflow region 25 ). in the case of a negative value the pressure difference can be reduced correspondingly . if the correspondence in both outflowing vascular segments roi out1 and roi out 2 is different , this can be corrected individually for each segment by the individual selection of the parameters . another example of this is concerned with the vascular walls . in cfd simulations the vascular walls are increasingly treated elastically . a corresponding analysis can orient the regions of interest along the vascular walls . these are segmented to this end . the degree of correspondence is now determined locally for all pixels along the vascular wall and if values are too large the elasticity for the subsequent cfd simulation is adjusted . it is especially advantageous here if real angiographies from several angulations are present . the inventive method is explained in greater detail on the basis of a flow chart shown in fig4 . first comes an acquisition 30 of a 3d angiography image dataset for model generation 31 . in the further method step a recording 32 of a contrast agent propagation is generated by means of dynamic real 2d angiography . then a cfd simulation 33 is performed , wherein it is possible to input 34 blood flow parameters as basic conditions . a virtual 2d angiography 35 from angulation identical to the real angiography 32 and adjusted recording geometry of the individual patient is calculated from this data . then follows a determination 36 of a degree of correspondence based on a comparison between the virtual angiography 35 and the real angiography 32 and then a check 37 to see whether the degree of correspondence is sufficient , i . e . whether the degree of correspondence is within a predefined tolerance . if the degree of correspondence is insufficient , a change 38 in the basic conditions in terms of an optimization is performed . then follows a new optimized cfd simulation 38 , by means of which again a determination 36 is performed , followed by a check 37 on the degree of correspondence . if in contrast the degree of correspondence is sufficient , the degree of correspondence is output 40 , for example as a color - coded image and the end of the examination is initiated . a ( percentage ) figure , if a global correspondence is considered , as well as a local figure can be described , which then itself can be output as a color map ( degree of correspondence ). however , it is also possible to describe other values ( for example the time difference for the maximum grayscale values ). fig5 shows the method sequence or workflow of the inventive method with the following steps in greater detail : s 1 ) 3d imaging for model generation , e . g . by means of 3d rotational angiography . s 2 ) recording a contrast agent propagation by means of dynamic 2d angiography . s 3 ) initial cfd simulation and generation of a virtual 2d angiography . s 4 ) determining a degree of correspondence between real and virtual 2d angiography . s 5 ) if degree of correspondence is sufficient , continue with s 9 ). s 6 ) changing one or more basic conditions of the cfd simulation according to the result of the degree of correspondence . s 7 ) renewed , optimized cfd simulation with basic conditions changed in terms of an optimization . s 8 ) back to s 4 ). s 9 ) done — optimum cfd simulation was achieved . the result is an iterative optimization of cfd simulation results based on the comparison between real and virtual 2 - dsa recordings on the basis of a determination of a degree of correspondence between both recordings . image - based computational simulation of flow 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bender , y . deuerling - zheng , k . royalty , k . a . pulfer , j . baumgart , m . zellerhoff , b . aagaard - kienitz , d . b . niemann , m . l . lindstrom ; ajnr am j neuroradiol ; 2010 ; www . ajnr . org ; pages 1 - 7