Patent Application: US-55832495-A

Abstract:
registration of organ images , such as myocardial images obtained by myocardial perfusion scintigraphy , is performed by an elastic transformation which includes a rigid transformation , a global affine transformation , and local transformations . the elastic transformation eliminates normal morphological variances such as variances in orientation , size and shape , so that the remaining differences represent important functional differences . the method may be used to register a patient &# 39 ; s organ against a template obtained by averaging organ images from many patients . for scintigraphic images the boundary of the organ is determined by a &# 34 ; segmentation &# 34 ; procedure involving the analysis of spatial derivatives of the count density . after the elastic transformations of the surface of the organ , the scintigraphic count densities are redistributed . the method decreases the effects of operator variability and increases the reliability of diagnoses of organ irregularities .

Description:
the present invention is directed to a method which decreases the effect of operator variability and morphologically blind sampling in the quantification of scintigraphic myocardial perfusion studies by automatically registering patients &# 39 ; myocardial images using a non - rigid transformation . more particularly , the non - rigid transformation is a combination of a rigid transformation , followed by an affine transformation , and then a number of local splines . alternatively , the non - rigid transformation is a combination of a rigid transformation , followed by an affine transformation , and then a number of locally affine transformations as described in the feldmar and ayache reference . the method of the present invention first requires that the perfusion scintigraphy image is &# 34 ; segmented &# 34 ; to isolate the myocardium from the surrounding tissue . the registration of the stress image onto the rest image of the stress , and rest image onto a template image , can be performed using a rigid transformation , a global affine transformation and then a local spline transformation . it should be noted that the order of the alignment of stress image , rest image and template may affect the degree of registration between the images . for instance , it may be preferable to align the rest image with the stress image prior to align both of these images with the template . alternatively , it may be preferable to align the rest image with the template before aligning both of these images with the stress image . the effectiveness of the method has been tested by determining if the variability in normal cases is reduced , and if abnormal cases remain distinct from normal cases . to begin the elastic registration method of the present invention , the surface points on the myocardium must be identified by a process termed &# 34 ; segmentation &# 34 ; ( besl 92 ). according to the segmentation procedure of the present invention , surface points are minima of the first derivative along radii originating approximately at the center of the ventricular cavity . since pixels in the image are characterized by : 5 ) the order of the sign ( from center to periphery the order must be positive , negative ); the definition of a point as a surface point may also be calculated based on a combination of these attributes . in addition , during the registration , points at the extremes of the cluster of the distribution of distances between template and target points are disregarded . a first step of the transformation of the present invention is a rigid transformation &# 34 ; f &# 34 ; to achieve a minimal distance between the target and template surface points . the sign of the derivative is taken into account , as in all the subsequent steps . mathematically , the transformation of each point m on the target surface s1 to the transformed point m &# 39 ; on the transformed target surface s1 &# 39 ; is given by where &# 34 ; r &# 34 ; is a rotation and &# 34 ; t &# 34 ; is a translation . after an initial transformation f0 , subsequent iterations are performed by : finding the closest point n on the template surface for each point m on the target surface using a method such as the distance map method [ danielsson 1980 ], and determining the subsequent transformations fi using a least squares evaluation to superimpose m and n . the initial rigid transformation f0 is a rough estimate of the rigid transformation required to optimally superimpose the target and template images . for each point n on the template surface s2 the principal curvatures ( k1 , k2 ) and the principal frame e1 and e2 are determined and stored in a hash table or a kd - tree . then a point m is randomly chosen on the target surface s1 and the set of points n &# 39 ; on s2 which have principal curvatures which are approximately the same as that of point m are determined . for each point n &# 39 ; the transformation which superposes m to n &# 39 ; and the principal frames at m and n &# 39 ; is determined , and that transformation which provides the best superposition of s1 and s2 is chosen . the best superposition of s1 &# 39 ; and s2 is determined by picking a subset of points p on s1 , and for each point p determining the point q on s2 closest to p . for each point p which is within a distance is counted , where d is a diameter of s2 . if the number of points p within that distance , divided by the number of points investigated , is greater than a ratio ρ ( 0 & lt ; ρ & lt ; 1 ) then the transformation is determined to be an adequate initial rigid transformation f0 . next , a global affine transformation g is performed . the global affine transformation g maps s1 &# 39 ; to s1 &# 34 ;, i . e ., where a is a global affine transformation , i . e . a transformation which scales three orthogonal axes independently to change the size and shape of the myocardium while maintaining the overall morphological features . the matrix b represents an additional rotation and translation , and also incorporates a possible axis shift . however , since the transformation g which minimizes the distance between s1 &# 34 ; and s2 does not have a stable solution ( since an optimal transformation g has a = 0 and b equal to a point on s2 ), locally similar points are matched by defining a new distance measure which incorporates curvature information . in particular , the distance measure d between points m &# 34 ; and n is ## equ1 ## where ( nx , ny , nz ) is the normal vector of s2 at n , and ( nx &# 34 ;, ny &# 34 ;, nz &# 34 ;) is the normal vector of s1 &# 34 ; at m &# 34 ;. because this distance measure incorporates orientation and curvature information it often does a much superior job of matching points that the eye would choose to match than the standard three - dimensional euclidean distance measure . the euclidean distance measure of nearest points is illustrated in fig9 a , where two similarly shaped curves 900 and 910 are shown , and the point on curve 910 closest to the point 902 at the top of curve 900 is shown to be a point 912 on the side of the curve 910 . in comparison , according to the eight - dimensional distance measure given above , the closest point on curve 910 to the point 902 at the top of curve 900 is a point 913 very near the top of curve 910 . finally , local optimization of the distances between points on s1 &# 34 ; and s2 is then obtained by local spline deformations , which are restricted by the distance over which the deformation works and by the &# 34 ; energy &# 34 ; required for the spline ( press 1986 ). when the functions of the global affine and the local spline have been defined , pixel values in the pre - transformation stress and rest target images are mapped to pixel values in the transformed space by a process termed &# 34 ; resampling .&# 34 ; although in continuous space the relation between an original coordinate ( x , y , z ) and a transformed coordinate ( x &# 34 ;, y &# 34 ;, z &# 34 ;) is one - to - one , in a discreet space the mapping may be one - to - many or many - to - one . therefore , the resampling can be performed a number of different ways . for instance , if voxel ( k , l , m ) is mapped to voxels ( k1 &# 39 ;, l1 &# 39 ;, m1 &# 39 ;) and ( k2 &# 39 ;, l2 &# 39 ;, m2 &# 39 ;) by the transformation , the activity ( i . e ., the signal or density of counts ) in voxel ( k , l , m ) can either be placed in both voxels ( k1 &# 39 ;, l1 &# 39 ;, m1 &# 39 ;) and ( k2 &# 39 ;, l2 &# 39 ;, m2 &# 39 ;), or distributed between ( k1 &# 39 ;, l1 &# 39 ;, m1 &# 39 ;) and ( k2 &# 39 ;, l2 &# 39 ;, m1 &# 39 ;). the second approach conserves densities . the registration method of the present invention is illustrated for a template myocardium and three target myocardia in fig8 . the template myocardium 800 is shown in the upper left - hand corner of fig8 . cross - sections of the original target myocardia 820 , 840 and 860 are shown as the images in the right - hand column , and the cross - sections of the transformed myocardia 810 , 830 and 850 are shown in the middle column of fig8 . as illustrated by the two vertical lines 871 and 872 in the middle column , the transformed myocardia each have approximately the same width . the validation of the effectiveness of the method of the present invention is based on statistically significant improvement in the differentiation of patient groups with four different categories of myocardial perfusion . the four groups are : patients with normal coronary flow ; patients with coronary artery disease and no history of infarction ; and patients with an history of infarction and no clinical or electrocardiographic signs of ischemia . patients with normal coronary flow form a group with small deviations from the average normal regional flow distribution . patients with coronary artery disease , with or without a history of myocardial infarction , differ significantly from the normal average . patients with coronary artery disease with signs or symptoms of stress ischemia but without infarction show a difference between the resting study and the stress study which is larger than the average difference between stress and rest in the two other groups . the subjects in the validation study of the present invention were patients referred to the division of nuclear medicine at stanford for a myocardial perfusion rest and stress spect . the patients were stratified into a reference group if they have normal coronaries demonstrated by arteriography or their risk of coronary artery disease is less than 15 % according to the classification of diamond and forrester ( diamond 80 ). the group with coronary artery disease were patients having a greater than 80 % risk of coronary artery disease according to classification of diamond and forrester , having a history of q - wave myocardial infarction , or having a positive coronary arteriogram . the method was validated by demonstrating by f - testing that after polar sampling the standard deviation around the mean of the normal case stress polar maps is smaller when this registration method is used than if the alignment is concurrently performed by an experienced operator or if the alignment was performed in routine clinical practice prior to the conception of this study . also , the method has been validated by the reduction of variation between normal myocardial images without a reduction of the difference between normal and abnormal . finally , it has been shown that a segmental segmentation with an average normal segment value map , increases the separation between normal and abnormal cases . with expected values for the sensitivity ( the percentage of positive test results for patients having a disease ) and the specificity ( the percentage of negative test results for patients which do not have a disease ) of the polar analysis after manual centering and reorientation being about 85 %, 100 cases in each category are needed to demonstrate a 3 . 5 % change with a confidence of 0 . 05 , and 200 cases are need to demonstrate a 2 . 5 % with this confidence . however , this assumes that the patients belong with certainty in a given class , and the majority of cases cannot be stratified in that way . an alternative is to use only intermediate classes of patients , and to exploit the necessary relation between the symptom prevalence p ( s ) and the disease prevalence p ( s ) given by where b is the non - specificity and a + b the sensitivity ( diamond 86 , goris 89 ). although the above description contains many specificities , these should not be construed as limiting the scope of the invention , but as merely providing illustrations of some of the preferred embodiments of this invention . many variations are possible and are to be considered within the scope of the present invention . for instance : the technique can be applied to other organs or body parts ; the technique can be applied to parts of the heart other than the left ventricle ; the technique can be applied to images other than perfusion scintigraphic images ; the optimal rigid transform can be determined using another method ; the optimal global affine transform can be determined using another method ; the optimal splines or local affine transforms can be determined using other methods ; the segmentation can be performed using data in addition to the first derivative ; the segmentation can be performed using other combinations of data ; the resampling need not conserve the sum of the count densities ; etc . many other variations are also to be considered within the scope of the present invention . thus the scope of the invention should be determined not by the examples given herein , but rather by the appended claims and their equivalents . table 1__________________________________________________________________________reference and pt . non - interpre - valid - characteristics sensitivity specificity tation ation data type__________________________________________________________________________tamaki 1984 98 % 9 % 1 201tl ( s & amp ; d )* iskandrian 1989 well tested 88 %( 164 ) 38 %( 58 ) b 1 201tl ( s & amp ; d ) iskandrian 1989 single vessel 74 %( 39 ) -- b 1 201tl ( s & amp ; d ) iskandrian 1989 two vessel 88 %( 69 ) -- b 1 201tl ( s & amp ; d ) iskandrian 1989 three vessel 98 %( 56 ) -- b 1 201tl ( s & amp ; d ) iskandrian 1989 understressed 73 %( 108 ) -- b 1 201tl ( s & amp ; d ) iskandrian 1989 single vessel 52 %( 31 ) -- b 1 201tl ( s & amp ; d ) iskandrian 1989 two vessel 84 %( 38 ) -- b 1 201tl ( s & amp ; d ) iskandrian 1989 three vessel 79 %( 39 ) -- b 1 201tl ( s & amp ; d ) iskandrian 1989 -- 7 % b 2 201tl ( s & amp ; d ) dipasquale 88 95 % 29 % s 1 201tl ( s & amp ; d )* goris 1989 well tested 92 %( 135 ) 42 %( 135 ) s 2 201tl ( s ) goris 1989 understressed 74 %( 135 ) 7 %( 135 ) s 2 201tl ( s ) maddahi 1989 96 % 45 % 1 201tl ( s & amp ; d )* maddahi 1989 -- 14 % 2tamaki 1989 86 % 35 % 201tl ( s & amp ; d )* mahmarian 90 87 %( 221 ) 19 %( 41 ) b 1 201tl ( s & amp ; d ) mahmarian 90 95 %( 171 ) 19 %( 41 ) b 1 201tl ( s & amp ; d ) mahmarian 90 99 %( 74 ) 19 %( 41 ) s 1 201tl ( s & amp ; d ) mahmarian 90 78 %( 86 ) 19 %( 41 ) s 1 201tl ( s & amp ; d ) vantrain 1990 94 %( 111 ) 34 %( 18 ) s 1 201tl ( s only ) vantrain 1990 -- 18 %( 39 ) s 2 201tl ( s only ) vantrain 1990 single vessel 83 %( 24 ) 34 % s 1 201tl ( s only ) vantrain 1990 two vessel 98 %( 41 ) 34 % s 1 201tl ( s only ) vantrain 1990 three vessel 98 %( 46 ) 34 % s 1 201tl ( s only ) go 1990 76 %( 152 ) 20 %( 50 ) tr 1 201tl ( s & amp ; d ) stewart 1990 84 % 47 % 201tl ( s & amp ; d )* tamaki 1994 73 %( 40 ) 23 %( 35 ) 1 201tl ( s & amp ; d ) tamaki 1994 75 %( 40 ) 20 %( 35 ) 1 tetrofosmin ( s & amp ; r ) van train 1994 89 %( 102 ) 64 %( 22 ) 1 sestamibi ( s only ) van train 1994 -- 19 %( 37 ) 2 sestamibi ( s only ) __________________________________________________________________________ 1 : standard is coronary arteriogram . 2 : clinical stratification . s & amp ; d is stress injection with early and delayed imaging . s & amp ; r separate stress and rest injection . r & amp ; 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