US20090167755A1  Method and system for generating surface models of geometric structures  Google Patents
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 US20090167755A1 US20090167755A1 US11/966,097 US96609707A US2009167755A1 US 20090167755 A1 US20090167755 A1 US 20090167755A1 US 96609707 A US96609707 A US 96609707A US 2009167755 A1 US2009167755 A1 US 2009167755A1
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 238000004458 analytical methods Methods 0.000 description 3
 281000009616 St. Jude Medical companies 0.000 description 2
 238000002405 diagnostic procedure Methods 0.000 description 2
 238000010586 diagrams Methods 0.000 description 2
 238000002560 therapeutic procedure Methods 0.000 description 2
 206010003658 Atrial fibrillation Diseases 0.000 description 1
 210000002837 Heart Atria Anatomy 0.000 description 1
 210000003540 Papillary Muscles Anatomy 0.000 description 1
 210000003492 Pulmonary Veins Anatomy 0.000 description 1
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 230000015572 biosynthetic process Effects 0.000 description 1
 238000010276 construction Methods 0.000 description 1
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 G06—COMPUTING; CALCULATING; COUNTING
 G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
 G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
 G06T17/20—Finite element generation, e.g. wireframe surface description, tesselation
Abstract
A method and system of generating a surface model of an anatomic structure includes a catheter with which a plurality of location data points are collected from the surface of the anatomic structure. The method and system further includes a computer system that is configured to receive the collected data points from the catheter and to compute the alpha shape of collected data points to thereby generate a surface model of the anatomic structure. The computer system may be further configured to process the computed alpha shape to generate a simplicial surface model of the anatomic structure.
Description
 a. Field of the Invention
 The present invention relates generally to a method and system for generating a surface model of a geometric shape. More particularly, the present invention relates to a computerimplemented method and system for generating a surface model of an anatomic structure, such as the heart, or a particular portion thereof, using surface point data.
 b. Background Art
 For many years, computerimplemented methods and systems have been used to generate surface models of geometric shapes including, for example, anatomic structures. More specifically, a variety of methods or techniques have been used to generate surface models of the heart and/or particular portions thereof (e.g., the heart as a whole or particular structures and/or portions thereof).
 In one particular method, a plurality of sample points are taken on the surface of the structure being modeled that correspond to the relative location of the structure at that particular point. A surface model of the structure is then constructed based on the convex hull of the collection of sample points. In general terms, to collect the sample points, the surface of the structure is swept with a catheter and the various points on the surface of the structure visited by the catheter are recorded using known methods. These individual points collectively form a cloud of points (See, for example,
FIG. 4 ). The convex hull of the cloud of points is then computed using known convex hull algorithms (See, for example,FIG. 5 ). The resulting convex hull shape estimates the boundary of the structure from the set of points, and therefore, provides a surface model of the structure. An advantage of this type of method/technique is that areas of the modeled structure that are not visited by the catheter, either because the catheter cannot reach the particular area or the clinician taking the samples did not collect samples from that area, are “filled in” during the model construction phase to create a complete model.  This advantage, however, may also be the principal disadvantage of these methods/techniques. For instance, because areas of the structure are “filled in”, these techniques cannot reconstruct features of the modeled structure that are concave. Accordingly, with respect to the modeling of the heart, for example, these techniques cannot reconstruct certain anatomic features within the heart, such as papillary muscles or pulmonary vein ostia, which are both concave structures that would normally “indent” the heart surface model. Thus, while these techniques provide a good generalized model of the structure, they do not provide the level of detail that would be useful for many different applications.
 Accordingly, there is a need for a method and system of generating surface models, such as, for example, cardiac surface models, that will minimize and/or eliminate one or more of the aboveidentified deficiencies.
 The present invention is directed to a method system of generating a surface model of a geometric structure, such as, for example, an anatomic structure.
 The inventive method includes a number of steps. In a first step, a plurality of location data points from the surface of the anatomic structure being modeled are collected. In a second step, the alpha shape of the collected location data points is computed to generate a surface model of the structure.
 In an exemplary embodiment, the computed alpha shape is then subjected to an additional step wherein it is processed to generate a simplicial surface model of the underlying structure. This processing step includes two principal substeps. First, at least a point of the alpha shape from which to begin the generation of the simplicial model is identified. In alternate embodiments, this point comprises, for example, a facet, edge, or vertex of the alpha shape. Second, the simplicial surface model is generated beginning with the identified point of the alpha shape.
 The inventive system that performs the above described method comprises a catheter configured to collect the surface data points from the surface of the anatomic structure being modeled, and a computer system electrically connected to the catheter. The computer system is configured to receive the collected surface data points and to perform the remaining steps in the inventive method described above.
 The foregoing and other aspects, features, details, utilities, and advantages of the present invention will be apparent from reading the following description and claims, and from reviewing the accompanying drawings.

FIG. 1 is a schematic view of a system for generating surface models in accordance with the present invention. 
FIG. 2 is a schematic view of the catheter of the system illustrated inFIG. 1 disposed in the chamber of a heart. 
FIG. 3 is a flow chart illustrating a method of generating a surface model in accordance with the present invention. 
FIG. 4 is a schematic diagram of a collection of surface data points developed in the method illustrated inFIG. 3 . 
FIG. 5 is a schematic diagram of a computed convex hull of the collection of points illustrated inFIG. 4 . 
FIG. 6 is a schematic view of a computed alpha shape of the collection of points illustrated inFIG. 4 . 
FIG. 7 is a flow chart illustrating a method of generating a simplicial surface model in accordance with the present invention. 
FIG. 8 is a schematic view of a simplicial surface model generated from the alpha shape illustrated inFIG. 6 . 
FIG. 9 is a schematic view of a portion of the surface of an alpha shape such as that illustrated inFIG. 6 . 
FIGS. 10 a10 d are diagrammatic views of various arrangements of adjacent facets of the alpha shape illustrated inFIG. 6 .  Referring now to the drawings where like reference numerals are used to identify identical components in the various views,
FIG. 1 illustrates an exemplary embodiment of a surface model generating system 10. It should be noted that while the following description focuses primarily on the use of system 10 in the generation of cardiac surface models, the present invention is not meant to be so limited. Rather, system 10, and the methods and techniques used therein, may be applied to the generation of surface models of any number of geometric structures, including anatomic structures other than the heart. Accordingly, system 10 is described below with particular reference to the modeling of the heart, and/or the structures thereof, for ease of description purposes only.  In an exemplary embodiment, system 10 comprises, in the most general sense, a plurality of surface patch electrodes 12, a catheter 14, and a computer system 16. As shown in
FIG. 1 , three sets of surface patch electrodes 12 (12 a12 c) are placed on the body of a patient 17, which is represented inFIG. 1 by the oval shape labeled with reference number 17. The first set of surface patch electrodes, electrodes 12 a _{1}, 12 a _{2}, correspond to the Xaxis; the second set, 12 b _{1}, 12 b _{2}, correspond to the Yaxis; and the third set, electrodes 12 c _{1}, 12 c _{2}, correspond to the Zaxis. These electrodes are strategically placed on the surface of a patient's body in such a way that the heart of the patient is disposed therebetween. As will be described more fully below, electrodes 12 a12 c are configured to be selectively excited to create axesspecific electric fields in the body of the patient, which allows for the location of catheter 14 to be determined in threedimensional space relative to the heart.  With reference to
FIGS. 1 and 2 , catheter 14 will be described. Catheter 14 is configured to be inserted into a patient's body, and in some instances, into the patient's heart. Catheter 14 includes one or more electrodes 18 (18 _{1}, . . . , 18 _{n}). Electrodes 18 may serve any number of purposes, such as, for example, to perform diagnostic or therapeutic procedures, electrophysiological studies, cardiac mapping, etc. For the sake of clarity, a single electrode 18 is depicted inFIG. 1 at the distal end of catheter 14 and it works in concert with patch electrodes 12 to provide, among other things, the location of catheter 14 to computer system 16 at certain points in time. Accordingly, catheter 14 can be used to collect location data points corresponding to the surface of the desired structure being modeled. These data points can then be used to construct a surface model of the desired structure. It should be noted that system 10 may further include other components known in the art that are not described in detail here, such as, for example, reference electrodes disposed within the heart or on the surface of the patient's body, switches responsively coupled to computer system 16 to excite surface electrodes 12, signal generators, etc. These additional components operate in conjunction with surface electrodes 12 and catheter electrode 18 to, among other things, determine the location information for catheter 14 and the various sampled data points corresponding to the surface of the structure that are provided to computer system 16. However, for purposes of clarity, these known components are not described in detail here.  Computer system 16 is electrically connected to electrodes 12 a12 c and electrode 18. Computer system 16 is configured, in part, to cause electrodes 12 a12 c to be selectively excited and to also record and process data collected by electrode 18. In an exemplary embodiment, and as will be described in greater detail below, computer system 16 includes a processor 20 configured to process data provided to computer system 16 by electrode 18, for example. Computer system 16 further includes a display 22 upon which models of anatomic structures generated by computer system 16 can be displayed, such as those generated by processor 20 based on the data provided by electrode 18. Additionally, various information relating to the modeled structure may also be displayed on display 22. While the illustrated embodiment depicts both processor 20 and display 22 as being integral parts of computer system 16, in alternate embodiments, either one or both of processor 20 and display 22 may be separate and distinct components of system 10. Accordingly, alternate arrangements of system 10, and computer system 16 thereof, in particular, remain within the spirit and scope of the present invention.
 Accordingly, in practice, patch electrodes 12, catheter 14, and computer system 16 function together to perform a surface model generating method 24 that ultimately generates a surface model of a desired structure, such as the heart, or at least a desired portion thereof (e.g., a particular chamber or a partial surface of a portion of the heart).
FIG. 3 illustrates an exemplary embodiment of method 24 that will now be described in detail.  In a first step 26, a plurality of location data points corresponding to the surface of the desired structure are collected. Any number of methodologies may be utilized to collect these data points, such as, for example, the method described in U.S. Pat. No. 7,263,397 entitled Method and Apparatus for Catheter Navigation and Location and Mapping in the Heart, issued to Hauck et al. and assigned to St. Jude Medical, Atrial Fibrillation Division, Inc., which is hereby incorporated by reference in its entirety. In general terms that are more fully described in the referenced patent, the collection of data points is accomplished by a clinician/physician sweeping catheter 14, and more particularly, the portion thereof containing electrode 18, around the surface of the desired structure. As catheter 14 is swept about the particular area of interest, patch electrodes 12 a12 c are selectively excited. The excitation of these electrodes creates an electric field(s) in the patient's body. As catheter 14 is swept, the location of electrode 18 is monitored by computer system 16 and using various known algorithms, the position of electrode 18 is determined and recorded by computer system 16 as a location point 28. The collection of location points 28 (28 _{1}, . . . , 28 _{n}) taken over time results in the formation of a cloud of points 30.
FIG. 4 is illustrative of a point cloud corresponding to a particular desired structure to be modeled, which in this instance is a portion of the inner surface of the left atrium of the heart.  It should be noted that while only one method of collecting location data points has been described in detail, the present invention is not meant to be limited to such a methodology. Rather, any number of methodologies may be used to collect location data points and these methodologies remain within the spirit and scope of the present invention. Therefore, the abovedescribed method is provided for exemplary purposes only.
 In a second step 32 of method 24, the plurality of location points 28 are processed by computer system 16 to generate a surface model of the desired structure—be it a complete surface model of the heart, a particular chamber, or a partial surface thereof, for example. In the embodiment illustrated in
FIG. 3 , this step is accomplished by computer system 16, and processor 20, in particular, using an alpha shape algorithm. Accordingly, in this step, the points 28 of point cloud 30 are subjected to an alpha shape algorithm to compute an alpha shape 34 corresponding to the desired structure.FIG. 6 is illustrative of the alpha shape of point cloud 30 depicted inFIG. 4 . Any known alpha shape algorithm may be used to compute alpha shape 34. Regardless of the specific algorithm used, the algorithm triangulates points 28 to form one or more facets 36 that, when taken together, create a model of the desired structure. Accordingly, as is illustrated inFIG. 6 , the alpha shape created by this process is typically a multifaceted model wherein each facet 36 (36 _{1}, . . . , 36 _{n}) comprises a triangle, and therefore, has three edges 38. Once alpha shape 34 has been computed, it may be displayed on display 22 and/or saved to a storage medium resident within computer system 16 or otherwise part of system 10.  As those of ordinary skill in the art will appreciate, the more facets in the model, the more detail of the underlying structure is represented. The number of facets, and therefore, the level of detail, depends substantially on the particular value of alpha (a measure of distance on the order of millimeters) that is used in the algorithm. For example, if alpha is zero, the alpha shape is simply the original set of points that comprise cloud 30. On the other hand, if alpha is infinity, the alpha shape is simply the convex hull of cloud 30. Thus, if the value of alpha is relatively small, the model will have a greater degree of detail (i.e., more facets 36) and may allow for concave surfaces in the structure to be modeled. The increased level of detail and the ability to potentially model concave surfaces provide a significant advantage over conventional surface model generating systems such as those described in the Background section above. This point may be realized by comparing the convex hull of point cloud 30, which is illustrated in
FIG. 5 , with the alpha shape of point cloud 30, which is illustrated inFIG. 6 . One of ordinary skill in the art will recognize and appreciate that the alpha shape provides a greater level of detail and a higher degree of accuracy as to the actual surface of the model structure as compared to the convex hull. Accordingly, a value of alpha between zero and infinity is chosen or otherwise determined and is used by the alpha shape algorithm to generate a model having a particular degree of detail. Typically, the alpha value will be on the order of five to ten millimeters, however, the present invention is not meant to be so limited. Rather, in alternate embodiments, alpha values that are more or less than five to ten millimeters may be used. In an exemplary embodiment, the value of alpha is determined by the clinician/physician choosing a desired value. Alternatively, one or more predetermined default values of alpha may be programmed into computer system 16 and/or processor 20, which may or may not provide the clinician/physician the ability to choose the value.  In one alternate exemplary embodiment, rather than computing the alpha shape as described above, alpha shape 34 may be constructed wherein different regions of the computed shape have different levels of detail. In such an embodiment, each point 28 in point cloud 30 is assigned a weight. The weight is a factor that determines how much detail the clinician/physician wants to preserve in that particular region of the desired structure. If a greater amount of detail is desired, the weight is lower, and conversely, if less detail is desired, the weight is greater. Accordingly, once all of the points are collected, each point 28 is assigned a weight that may be, for example, inversely proportional to the local point density surrounding that particular point, directly proportional to the distance to the closest neighboring point 28, or directly proportional to the average distance of a particular number of closest points 28. Thus, the overall level of detail of the alpha shape depends on the local density of points 28 in each region of the desired structure. Accordingly, depending on the weights assigned to each point, particular portions of the surface model may have different levels of detail. This concept is commonly referred to as weighted alpha shapes, and thus, in such an embodiment, alpha shape 34 is computed as a weighted alpha shape.
 Irrespective of whether a “regular” alpha shape or “weighted” alpha shape model is constructed, it is desirable that each edge 38 of each facet 36 be shared by no more than one other neighboring or adjacent facets 36 (i.e., each edge of the alpha shape is shared by no more than two adjacent facets). However, due to the level of detail the alpha shape algorithm may provide, it is possible that one or more edges 38 of a facet 36 in the completed/generated model will be shared by more than one other adjacent facet 36. This results in a surface model that is not manifold, which makes it difficult, if not impossible, to determine what the true outer surface of the model is and what surface in the model is actually inside the outer surface (in other words, it is difficult to tell in which direction the surface is actually facing). In the event that this occurs, a postprocessing procedure is required to “clean up” the generated model in order to provide a smooth simplicial surface model of the desired structure. In the context of this particular application, the term “simplicial surface” is intended to mean “a connected, orientable, and locally a twodimensional manifold surface” (i.e., each edge in the alpha shape is shared by at most two facets). In other words, for each facet 36 having at least one edge that is shared by two or more neighboring/adjacent facets 36, it must be determined which of the adjacent facets 36 will be kept as part of the final simplicial surface model, and which ambiguous adjacent facet(s) 36 will be discarded and not made part of the simplicial surface model (See, for example,
FIG. 6 wherein several “ambiguous adjacent” facets are identified by stippling and are discarded in the postprocessing procedure). This process ensures that in the simplicial surface model, each edge 38 of each facet 36 is shared by no more than one neighboring facet 36. Accordingly, in an exemplary embodiment, surface model generating method 24 includes a third step 40 that comprises a postprocessing procedure to generate a simplicial surface model 41 that is based on computed alpha shape 34.  Third step 40, which is illustrated in
FIG. 3 and in greater detail inFIG. 7 , is performed by computer system 16, and more particularly, processor 20, and includes processing alpha shape 34 with a simplicial surface or “skin” algorithm to generate a smooth, simplicial surface model 41 (best shown inFIG. 8 ). In an exemplary embodiment, third step 40 includes a series of substeps.  In a first substep 42, the algorithm identifies at least a point of alpha shape 34 from which to begin the generation process. In an exemplary embodiment, this “start point” is at least a point of the alpha shape that is shared by the convex hull of point cloud 30. In alternate embodiments, rather than or in addition to identifying points or vertices of alpha shape 34 that are shared by the convex hull, the algorithm identifies one or more edges 38 of alpha shape 34, and/or, most commonly, one or more facets 36 of alpha shape 34 that are shared by the convex hull.
 In one exemplary embodiment, this identification step is accomplished by computing the convex hull of point cloud 30, and then comparing the convex hull with alpha shape 34. In such an embodiment, the convex hull of point cloud 30 is computed using one of any number of known convex hull algorithms. The computed surface model generated by the convex hull algorithm, which is illustrated in
FIG. 5 and is identified therein as reference numeral 43, represents the connections between the most exterior points 28 in point cloud 30, and therefore, a model comprising the outermost surfaces of the desired structure is generated. In this particular embodiment, once convex hull 43 has been computed, it is compared with alpha shape 34. In an exemplary embodiment, each facet 36 of alpha shape 34 that is also part of or shared by convex hull 43 is identified and, for reasons more fully described below, placed into a queue. These “shared” facets 36 are identified to provide a starting point for generating simplicial surface model 41 since these facets 36 are known to be on the outside surface of the structure because, by definition, anything on the convex hull is necessarily on the outside surface. Thus, by identifying such facets, the algorithm knows that the identified facets are correctly oriented and on the outer surface of the model. Accordingly, in addition to being placed in the queue, the identified facets are also added to the final simplicial surface model 41 illustrated inFIG. 8 that the simplicial surface algorithm is computing/generating.  It should be noted, however, that the convex hull need not necessarily be computed separately in order to identify the shared points, edges, or facets. In an alternate embodiment, whether one or more facets 36 of alpha shape 34, for example, are shared by the convex hull can be determined by alpha shape 34 itself. More specifically, each facet 36 has two opposite vertices in the underlying triangulation that is performed by the alpha shape algorithm, which has essentially divided all of threedimensional space into tetrahedra. Alpha shape facets 36 that are also on the convex hull (i.e., shared by the convex hull) will have the “point at infinity” as one of their two opposite vertices. This theoretical point is part of all the exterior tetrahedra of the convex hull, and therefore, the simplicial surface algorithm can tell whether a particular facet 36 is shared by the convex hull by determining whether one of its opposite vertices is the “point at infinity.” Accordingly, the present invention is not limited to one singular way of identifying points, edges, and/or facets of alpha shape 34 that are shared by the convex hull.
 Whether the shared facets are determined by actually comparing the computed convex hull 43 and the alpha shape 34 or by evaluating alpha shape 34 alone, once one or more of the shared facets have been identified, a second substep 44 is performed on each facet in the queue (and those to be subsequently added to the queue), one at a time, to generate the final simplicial surface model. In this substep, the algorithm takes and analyzes a first facet, and for each edge thereof determines how many neighboring or adjacent facets share the particular edge. A facet that shares an edge with at least one other facet will hereinafter be referred to as an “adjacent facet.” If the edge is shared by no other facets, then, generally speaking, that edge is left as a boundary edge in model 41. If the edge is shared by only one other adjacent facet, then that adjacent facet is added to model 41 and also added to the queue. If, however, two or more facets of alpha shape 34 share the particular edge, the algorithm must choose which adjacent facet to include in model 41. To better illustrate this process,
FIG. 9 depicts a partial view of the surface of an alpha shape, though not necessarily alpha shape 34 illustrated inFIG. 6 , wherein one facet 36, facet 36 _{1}, has been identified as a “shared” facet, and includes edges 38 _{1}38 _{3}. With respect to edge 38 _{1}, since no other facets share this edge, edge 38 _{1 }would be left as a boundary of the simplicial surface model 41. With respect to edge 38 _{2}, since this edge is shared by only one adjacent facet, facet 36 _{2}, this facet would be added to the simplicial surface model 41 and placed in the queue for future analysis. With respect to edge 38 _{3}, since this edge is shared by two facets 36, facet 36 _{3 }and facet 36 _{4}, one of these facets must be selected by the algorithm to be added to the simplicial surface model 41 and the other one will be discarded.  In the instance wherein an edge 38 of a facet 36 is shared by two or more other adjacent facets 36, in one exemplary embodiment, the algorithm considers each adjacent facet 36 and then chooses the one that is the furthest outside or, in other words, closest to the convex hull. In order to determine which is the furthest outside, the algorithm looks at the angle between the facet being evaluated/analyzed (i.e., facet 36 _{1}, for example) and the respective adjacent facets 36 (i.e., facets 36 _{3 }and 36 _{4}, for example) and chooses the adjacent facet 36 making the smallest angle with facet 36, (i.e., the smallest dihedral angle).
 This methodology may be better understood with reference to
FIGS. 10 a10 d. With respect toFIG. 10 a, adjacent facets 36 _{1 }and 36 _{2 }form a flat surface. Accordingly, the dihedral angle between these two facets would be 180 degrees. Alternatively, with respect toFIG. 10 b, the adjacent facet 36 _{2}is disposed “interior” to facet 36 _{1}, and therefore, its dihedral angle will be greater than 180 degrees. Conversely, with respect toFIG. 10 c, adjacent facet 36 _{2}is disposed outside of facet 36 _{1}, and therefore, its dihedral angle is less than 180 degrees. In any ofFIGS. 10 a10 c, because facet 36 _{2}is the only other facet sharing the particular edge with facet 36 _{1}, facet 36 _{2 }would be added to the simplicial surface model 41 in each instance. However,FIG. 10 d illustrates an instance where two facets, facets 36 _{3 }and 36 _{4}, share a particular edge. Accordingly, only one of facets 36 _{3 }and 36 _{4 }may be selected to be part of the simplicial surface model 41. Based on the criteria described above, since facet 36 _{3 }makes a dihedral angle that is less than that made by facet 36 _{4}, facet 36 _{3 }is chosen to be part of the simplicial surface model 41. Therefore, facet 36 _{3 }(or the particular facet chosen) would be oriented to match facet 36 _{1 }(i.e., its vertices are reversed if they do not traverse the vertices of the common edge in the opposite order as the selected facet does), added to the simplicial surface model 41 being generated, and also added to the queue for later analysis. The other adjacent facets 36 that share the particular edge of facet 36 _{1 }and that were not chosen (i.e., facet 36 _{4}), would be discarded.  In an exemplary embodiment, it may be desirable to set a maximum threshold for an acceptable dihedral angle so that the algorithm leaves a boundary in the model instead of creating sharp creases and going into cavities of alpha shape 34. In one embodiment, this threshold angle is, for exemplary purposes only, 240 degrees. Therefore, if the angle between the facet being analyzed and any adjacent facet is greater than 240 degrees, that adjacent facet is not added to the model. If no other facets 36 share that particular edge or if other facets 36 share the edge but fail to make an angle that is within the predetermined threshold, the particular edge being shared by these two or more facets is left as a boundary in the model.
 Once each edge 38 of facet 36 _{1 }(or whichever facet 36 is being evaluated) has been analyzed as set forth above, the algorithm determines whether there are any other facets 36 in the queue. If there are, the algorithm takes the next facet 36 in the queue and performs the same analysis. Once the last facet 36 in the queue has been analyzed, the algorithm terminates, indicating that all allowable neighboring/adjacent facets 36 have been propagated and added to the simplicial surface model 41.
 If, after the simplicial surface model generation process is complete, the generated simplicial surface model 41 includes undesirable voids or holes, these voids or holes may be optionally filled in using various algorithms to generate a smoother or more complete model. One exemplary embodiment of such an algorithm has been described in great detail in U.S. patent application Ser. No. 11/715,922 entitled “Method and System for Repairing Triangulated Surface Meshes.” This exemplary algorithm generally includes the following steps. First, a void or hole in simplicial surface model 41 is identified. The hole is defined by a plurality of hole edges (i.e., boundary edges of facets 36 bordering the hole, for example). Second, a plurality of hole vertices that define the plurality of hole edges are determined. Third, the hole is filled in using one or more triangular facets, not unlike facets 36 described above. This “fillingin” step includes a series of substeps. For example, in one substep, a pair of hole vertices that are not connected by a hole edge are selected. In one embodiment, this substep includes selecting the closest pair of hole vertices. Alternatively, this substep includes locating the smallest interior angle of the plurality of hole edges, and selecting a pair of hole vertices that define a pair of the plurality of hole edges that intersect at the smallest interior angle of the plurality of hole edges. Once a pair of hole vertices are selected, in subsequent substep, a fabricated edge that connects the selected pair of hole vertices is defined. Next, the algorithm determines whether one or more triangles are formed using the defined fabricated edge. If one or more triangles are formed, the newly formed triangles are defined as new facets and are added to simplicial surface model 41. If, however, one or more triangles are not formed, the algorithm continues to define fabricated edges until one or more triangles are formed. This process is then repeated until the identified hole, and any other hole in model 41, has been filled in to create a voidfree simplicial surface model.
 Additionally, in the event the generated simplicial surface model has more than one component, the clinician/physician may be given choices or options as to what to do with the multiple components. These choices or options may include, without limitation, keeping all components as part of the model, keeping only the component having the largest surface area, or trying to connect the components using a stitching algorithm.
 In a final substep 46 of third step 40, the complete or final simplicial surface model 41 is displayed on display 22 and/or is saved to a storage medium resident in computer system 16 or elsewhere in system 10 for later use.
 It should be noted that while substeps 42 and 44 were described above with particular reference to facets 36 of alpha shape 34 that are shared by the convex hull, the present invention is not so limited. Rather, in certain instances, there may not be any facets 36 shared by the two models. Instead, an edge 38 of a facet 36 may be part of or shared by the convex hull. In that instance, and as briefly described above, the algorithm considers all of the shared edges 38 and chooses the facet 36 of alpha shape 34 sharing edge 38 that makes the smallest dihedral angle with the facets of the convex hull that also share edge 38. This facet is added to the queue and the simplicial surface model being created, and the process and methodology then continues as described above.
 In another instance, the convex hull and alpha shape 34 may not share any facets 36 or any edges 38, but rather one or more points or vertices 28 of alpha shape 34 may be part of or shared by the convex hull. In this situation, which was also briefly described above, the algorithm considers all of these points and then chooses the point 28 with an edge 38 making the smallest dihedral angle with an edge of the convex hull sharing that point 28. This edge 38 is then temporarily projected onto said convex hull edge, in order to compute dihedral angles, and the algorithm chooses the new facet 36 that makes the smallest dihedral angle with the facets of the convex hull that also share the projected edge 38. The original facet 36 is then added to the simplicial surface model being generated, and to the queue. The process and methodology then continues as described above.
 In still another instance, a particular facet 36 that is known to be part of the simplicial surface is chosen from which to start the above described process. More specifically, a facet 36 of alpha shape 34 may be chosen that may or may not be shared by the convex hull, and may be manually chosen by a clinician/physician (i.e., the alpha shape is displayed on display 22 and the clinician/physician may click on the facet using a computer mouse or the like). Alternatively, the facet may be chosen semiautomatically by the clinician/physician rotating the threedimensional viewing window within which alpha shape 34 is disposed and that is displayed on display 22, for example, to a desired angle, at which point the algorithm would find and choose one or more facets visible from the clinician/physician's viewpoint from which to begin the generating process. In either case, once a facet 36 is chosen, the process or methodologies described above simply continue in the same manner.
 In an exemplary embodiment, the methodologies described above may be used to generate a somewhat dynamic model of the heart by generating separate models for a plurality of different timepoints in the cardiac cycle of the heart. Accordingly, using the various steps of the process described above, surface points of the heart are collected during the different timepoints in the cardiac cycle, thereby creating point clouds 30 for each timepoint in the cardiac cycle. Using the methodologies described above, surface models (simplicial or otherwise) can be generated for each point cloud and then the models can be used together or separately for various purposes.
 Accordingly, once the various steps and substeps of the methodologies set forth above are performed, an orientable and locally manifold surface model (simplicial surface model 41) having improved detail is generated. This model can then be used to assist a clinician/physician in the performance of various diagnostic and therapeutic procedures, as well as to provide improved cardiac mapping functionality. Therefore, the methods and/or system described above may be employed in any number of visualization, navigation, and/or mapping systems, such as the EnSite NavX® system commercially offered by St. Jude Medical, Inc., and described in detail in U.S. Pat. No. 7,263,397 entitled Method and Apparatus for Catheter Navigation and Location and Mapping in the Heart, which was incorporated by reference above.
 Although only certain embodiments of this invention have been described above with a certain degree of particularity, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of this invention. For example, other processes may be used to collect points 28 that form point cloud 30. Further, the alpha shape acquired by the alpha shape algorithm may be subjected to intermediate filtering processes to extract a clean alpha shape. Still further, other processes or guidelines may be used to choose which neighboring/adjacent facet is ultimately added to the final output surface model. Yet still further, various algorithms not specifically identified above may be used to carry out or otherwise perform the described functionality. Additionally, all directional references (e.g., upper, lower, upward, downward, left, right, leftward, rightward, top, bottom, above, below, vertical, horizontal, clockwise, and counterclockwise) are only used for identification purposes to aid the reader's understanding of the present invention, and do not create limitations, particularly as to the position, orientation, or use of the invention. Joinder references (e.g., attached, coupled, connected, and the like) are to be construed broadly and may include intermediate members between a connection of elements and relative movement between elements. As such, joinder references do not necessarily infer that two elements are directly connected and in fixed relation to each other. It is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative only and not limiting. Changes in detail or structure may be made without departing from the spirit of the invention as defined in the appended claims.
Claims (44)
1. A method of generating a surface model of an anatomic structure, the method comprising
collecting a plurality of location data points from the surface of said anatomic structure; and
computing an alpha shape of said collection of location data points to generate a surface model of said anatomic structure.
2. The method of claim 1 , wherein said computing step includes the substep of choosing a value of alpha prior to computing said alpha shape.
3. The method of claim 1 , further comprising processing said computed alpha shape to generate a simplicial surface model of said structure.
4. The method of claim 3 wherein said processing step includes the substeps of
identifying at least a point of said alpha shape from which to generate a simplicial surface model; and
generating said simplicial surface model beginning with said at least a point of said alpha shape.
5. The method of claim 4 wherein said identifying step comprises computing the convex hull of said plurality of data points and comparing said alpha shape with said convex hull to identify at least a point of said alpha shape that is shared by said convex hull.
6. The method in accordance with claim 4 wherein said identifying step comprises identifying each facet of said alpha shape that is shared by the convex hull of said plurality of data points, and said generating step comprises
adding each identified facet to said simplicial surface model;
analyzing each edge of said identified facets to determine the number of adjacent facets sharing each of said edges;
selecting, for each of said edges, one adjacent facet to be added to said simplicial surface model; and
adding said selected adjacent facets to said simplicial surface model and discarding the remaining adjacent facets.
7. The method in accordance with claim 6 wherein said analyzing step comprises analyzing each facet one at a time.
8. The method in accordance with claim 6 wherein selecting step includes the substeps of calculating the dihedral angle between said facet being analyzed and each of said adjacent facets, and choosing said one adjacent facet forming the smallest dihedral angle.
9. The method in accordance with claim 8 , further comprising discarding an adjacent facet forming a dihedral angle that is greater than a predetermined threshold angle.
10. The method in accordance with claim 6 further comprising placing each of said identified facets and each of said selected adjacent facets in a queue, and said analyzing step further comprising analyzing each facet in said queue.
11. The method in accordance with claim 10 wherein said analyzing step includes selecting a first facet in said queue to be analyzed and analyzing said facet; said method further comprising
determining, after said first facet has been analyzed, whether additional facets are in said queue; and
if at least one additional facet is in said queue, selecting a second facet in said queue to be analyzed, otherwise, terminating said generating step if no other facets are in said queue.
12. The method in accordance with claim 4 wherein said identifying step comprises identifying an edge of said alpha shape that is shared by the convex hull of said plurality of data points, and said generating step comprises
choosing a facet of said alpha shape sharing said edge that makes the smallest dihedral angle with one of the facets of the convex hull also sharing said edge; and
adding said chosen facet to said simplicial surface model and discarding other facets of said alpha shape sharing said edge.
13. The method in accordance with claim 4 wherein said identifying step comprises identifying a point of said alpha shape that is shared by the convex hull of the plurality of data points, and said generating step comprises
choosing one of a facet and an edge of said alpha shape sharing said point and making the smallest dihedral angle with one of a facet and an edge of said convex hull also sharing said point; and
adding said chosen one of said facet and said edge to said simplicial surface model and discarding the other facets and edges of said alpha shape sharing said point.
14. The method in accordance with claim 4 wherein said identifying step comprises choosing a facet of said alpha shape known to be part of the simplicial surface that is also visible from a userspecified viewpoint, and said generating step comprises
adding said facet to said simplicial surface model;
analyzing each edge of said facet to determine the number of adjacent facets sharing each edge of said facet;
selecting, for each edge, one adjacent facet to be added to said simplicial surface model; and
adding said selected adjacent facets to said simplicial surface model and discarding the remaining adjacent facets.
15. The method of claim 3 wherein said processing step further comprises executing an algorithm to fillin voids in said simplicial surface model.
16. The method of claim 3 wherein said processing step results in the generation of a simplicial surface model having more than one component, the method further comprising one of the steps of keeping the entire generated simplicial surface model, retaining only the component having the largest surface area, and stitching said components together.
17. The method of claim 1 wherein said computing step includes computing a weighted alpha shape of said plurality of data points.
18. A system for generating a surface model of an anatomic structure comprising
a catheter configured to collect a plurality of surface data points from at least a portion of the anatomic structure;
a computer system electrically connected to said catheter and configured to receive said collected surface data points and to compute an alpha shape corresponding to said collected surface data points, said alpha shape representing a surface model of said portion of the anatomic structure.
19. The system in accordance with claim 18 wherein said computer system is further configured to process said computed alpha shape to generate a simplicial surface model of said anatomic structure.
20. The system in accordance with claim 18 wherein said computer system is configured to identify at least a point of said alpha shape and to generate said simplicial surface model beginning with said at least a point of said alpha shape.
21. The system in accordance with claim 20 wherein said computer system is configured to:
identify each facet of said alpha shape that is shared by the convex hull of said plurality of surface data points, each of said identified facets having a plurality of edges;
add each identified facet to said simplicial surface model;
analyze each edge of said identified facets to determine the number of adjacent facets sharing said edge;
select, for each of said edges, one adjacent facet to be added to said simplicial surface model; and
add said selected adjacent facets to said simplicial surface model and discard the other adjacent facets.
22. The system in accordance with claim 21 wherein for each edge of each of said identified facets, said computer system is configured to calculate the dihedral angle between said identified facet and each of said adjacent facets and to select said one adjacent facet forming the smallest dihedral angle with said identified facet.
23. The system in accordance with claim 22 wherein said computer system is configured to discard each adjacent facet that forms an angle that is greater than a predetermined threshold angle.
24. The system in accordance with claim 21 wherein said computer system is configured to analyze each of said facets one at a time.
25. The system in accordance with claim 21 wherein said computer system includes a queue in which each of said identified facets and each of said selected adjacent facets is stored prior to being analyzed.
26. The system in accordance with claim 25 wherein said computer system is configured to select a first facet in said queue to be analyzed and to analyze said facet;
determine, after said first facet has been analyzed, whether additional facets are in said queue; and
select a second facet in said queue to be analyzed if at least one facet remains in said queue.
27. The system in accordance with claim 20 wherein said computer system is configured to:
identify an edge of said alpha shape that is shared by the convex hull of sad plurality of surface data points;
choose a facet of said alpha shape sharing said edge that makes the smallest dihedral angle with one of the facets of said convex hull also sharing said edge; and
add said chosen facet to said simplicial surface model and discard the other facets of said alpha shape sharing said edge.
28. The system in accordance with claim 20 wherein said computer system is configured to:
identify a point of said alpha shape that is shared by the convex hull of said plurality of surface data points;
choose one of a facet and an edge of said alpha shape sharing said point and making the smallest dihedral angle with one of a facet and an edge of said convex hull also sharing said point; and
add said chosen one of said facet and said edge to said simplicial surface model and discard the other facets and edges of said alpha shape sharing said point.
29. The system in accordance with claim 20 , further comprising a display configured to display said alpha shape, said computer system further configured to:
choose a facet of said alpha shape known to be part of said surface model that is also visible on said display;
add said chosen facet to said simplicial surface model;
analyze each edge of said chosen facet to determine the number of adjacent facets sharing each respective edge;
select, for each edge, one adjacent facet to be added to said simplicial surface model; and
add said selected adjacent facets to said simplicial surface model and discard the other adjacent facets.
30. The system in accordance with claim 18 wherein said computer system is configured to compute a weighted alpha shape of said plurality of data points.
31. The system in accordance with claim 18 wherein said anatomic structure is at least a portion of a patient's heart, and said computer system is configured to generate a simplicial surface model for a plurality of different timepoints in the cardiac cycle of said heart to thereby create a substantially dynamic simplicial surface model of said heart.
32. A method of generating a simplicial surface model of an anatomic structure from an alpha shape model of said structure, the method comprising
identifying at least a point of said alpha shape from which to generate a simplicial surface model; and
generating a simplicial surface model of said structure beginning with said at least one point of said alpha shape.
33. The method of claim 32 wherein said identifying step comprises computing the convex hull of said structure and comparing said alpha shape with said convex hull to identify at least a point of said alpha shape that is shared by said convex hull.
34. The method in accordance with claim 32 wherein said identifying step comprises identifying each facet of said alpha shape that is shared by the convex hull of said structure, and said generating step comprises
adding each identified facet to said simplicial surface model;
analyzing each edge of said identified facets to determine the number of adjacent facets sharing each of said edges;
selecting, for each of said edges, one adjacent facet to be added to said simplicial surface model; and
adding said selected adjacent facets to said simplicial surface model and discarding the remaining adjacent facets.
35. The method in accordance with claim 34 wherein said analyzing step comprises analyzing each facet one at a time.
36. The method in accordance with claim 34 wherein selecting step includes the substeps of calculating the dihedral angle between said facet being analyzed and each of said adjacent facets, and choosing said one adjacent facet forming the smallest dihedral angle.
37. The method in accordance with claim 36 , further comprising discarding an adjacent facet forming a dihedral angle that is greater than a predetermined threshold angle.
38. The method in accordance with claim 34 further comprising placing each of said identified facets and each of said selected adjacent facets in a queue, and said analyzing step further comprising analyzing each facet in said queue.
39. The method in accordance with claim 38 wherein said analyzing step includes selecting a first facet in said queue to be analyzed and analyzing said facet; said method further comprising:
determining, after said first facet has been analyzed, whether additional facets are in said queue; and
if at least one addition facet is in said queue, selecting a second facet in said queue to be analyzed, otherwise, terminating said generating step if no other facets are in said queue.
40. The method in accordance with claim 32 wherein said identifying step comprises identifying an edge of said alpha shape that is shared by the convex hull of said structure, and said generating step comprises;
choosing a facet of said alpha shape sharing said edge that makes the smallest dihedral angle with one of the facets of the convex hull also sharing said edge; and
adding said chosen facet to said simplicial surface model and discarding other facets of said alpha shape sharing said edge.
41. The method in accordance with claim 32 wherein said identifying step comprises identifying a point of said alpha shape that is shared by the convex hull of said structure, and said generating step comprises
choosing one of a facet and an edge of said alpha shape sharing said point and making the smallest dihedral angle with one of a facet and an edge of said convex hull also sharing said point; and
adding said chosen one of said facet and said edge to said simplicial surface model and discarding the other facets and edges of said alpha shape sharing said point.
42. The method in accordance with claim 32 wherein said identifying step comprises choosing a facet of said alpha shape known to be part of the simplicial surface that is also visible from a userspecified viewpoint, and said generating step comprises
adding said facet to said simplicial surface model;
analyzing each edge of said facet to determine the number of adjacent facets sharing each edge of said facet;
selecting, for each edge, one adjacent facet to be added to said simplicial surface model; and
adding said selected adjacent facets to said simplicial surface model and discarding the remaining adjacent facets.
43. The method of claim 32 , further comprising processing said simplicial surface model to fillin voids in said simplicial surface model.
44. The method of claim 32 wherein said generating step results in the generation of a simplicial surface model having more than one component, the method further comprising one of the steps of keeping the entire simplicial surface model, retaining only the component having the largest surface area, and stitching said components together.
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Cited By (3)
Publication number  Priority date  Publication date  Assignee  Title 

US20130138404A1 (en) *  20111129  20130530  Carlos Carbonera  Method and system for generating and/or repairing a surface model of a geometric structure 
US8909502B2 (en)  20111229  20141209  St. Jude Medical, Atrial Fibrillation Division, Inc.  Method and system for constructing an electrophysiology map 
US9159162B2 (en)  20111228  20151013  St. Jude Medical, Atrial Fibrillation Division, Inc.  Method and system for generating a multidimensional surface model of a geometric structure 
Families Citing this family (2)
Publication number  Priority date  Publication date  Assignee  Title 

EP2956054B1 (en)  20130418  20200304  St. Jude Medical, Atrial Fibrillation Division, Inc.  Methods for visualizing and analyzing cardiac arrhythmias using 2d planar projection and partially unfolded surface mapping processes 
WO2020171998A2 (en)  20190221  20200827  St. Jude Medical, Cardiology Division, Inc.  Systems and methods for assessing ablation lesions 
Citations (68)
Publication number  Priority date  Publication date  Assignee  Title 

US558203A (en) *  18960414  Henry e  
US4341223A (en) *  19810204  19820727  Lutz Lauralee A  Fluoresceable composition and method of determining fluid flow 
US4741900A (en) *  19821116  19880503  Cytogen Corporation  Antibodymetal ion complexes 
US4894326A (en) *  19860409  19900116  Fred Hutchinson Cancer Research Center  Monoclonal antibody defining oncofetal structure of fibronectin 
US5120830A (en) *  19901025  19920609  Washington University  Inhibitory peptides against α2, β1 mediated mg++ dependent adhesion of platelets to collagen 
US5177015A (en) *  19880812  19930105  Fred Hutchinson Cancer Research Centre  Oncodevelopmentally regulated αNacetylgalactosaminyltransferase 
US5243029A (en) *  19860409  19930907  Fred Hutchinson Cancer Research Center  Oncofetal structure of fibronectin 
US5460785A (en) *  19890809  19951024  Rhomed Incorporated  Direct labeling of antibodies and other protein with metal ions 
US5523229A (en) *  19940322  19960604  Trustees Of The University Of Pennsylvania  Antibodies specific for oncofetal fibronectin 
US5534254A (en) *  19920206  19960709  Chiron Corporation  Biosynthetic binding proteins for immunotargeting 
USRE35500E (en) *  19890809  19970506  Aberlyn Capital Management Limited Partnership  Direct radiolabeling of antibodies and other proteins with technetium or rhenium 
US5648485A (en) *  19941026  19970715  University Of British Columbia  β, βdihydroxy mesosubstituted chlorins, isobacteriochlorins, and bacteriochlorins 
US5710134A (en) *  19940520  19980120  Behringwerke Aktiengesellschaft  Combination of necrosisinducing substances with substances which are activated by necroses for the selective therapy of tumors and inflammatory disorders 
US5734025A (en) *  19920925  19980331  Otsuka Pharnaceutical Factory, Inc.  Adsorbent for cellular fibronectin, a method for fractional purification of fibronectin and a method of hemocatharisis 
US5747452A (en) *  19920131  19980505  La Jolla Cancer Research Foundation  Method of modulating tumor cell migration using fibronectin type III peptides 
US5768156A (en) *  19951025  19980616  Sandia Corporation  Connectivitybased, allhexahedral mesh generation method and apparatus 
US5817776A (en) *  19951109  19981006  Emory University  Amino acid analogs for tumor imaging 
US5843156A (en) *  19880824  19981201  Endoluminal Therapeutics, Inc.  Local polymeric gel cellular therapy 
US5849701A (en) *  19921110  19981215  The United States Of America As Represented By The Department Of Health And Human Services  Peptide inhibitors of fibronectin and related collagenbinding proteins 
US5877786A (en) *  19920929  19990302  Ricoh Company, Ltd.  Ink jet recording method and head 
US5876691A (en) *  19931203  19990302  Cancer Research Campaign Technology Limited  Antibody against carcionembryonic antigen (CEA) 
US5877289A (en) *  19920305  19990302  The Scripps Research Institute  Tissue factor compositions and ligands for the specific coagulation of vasculature 
US5889524A (en) *  19950911  19990330  University Of Washington  Reconstruction of threedimensional objects using labeled piecewise smooth subdivision surfaces 
US5913884A (en) *  19960919  19990622  The General Hospital Corporation  Inhibition of fibrosis by photodynamic therapy 
US5965132A (en) *  19920305  19991012  Board Of Regents, The University Of Texas System  Methods and compositions for targeting the vasculature of solid tumors 
US5976535A (en) *  19920609  19991102  Neorx Corporation  Pretargeting protocols for the enhanced localization of cytotoxins to target sites and cytotoxic combinations useful therefore 
US5997842A (en) *  19980413  19991207  Light Sciences Limited Partnership  Radionuclide excited phosphorescent material for administering PDT 
US6004555A (en) *  19920305  19991221  Board Of Regents, The University Of Texas System  Methods for the specific coagulation of vasculature 
US6015897A (en) *  19931207  20000118  Neorx Corporation  Biotinamidonmethylglycylserylosuccinamidobenzyl dota 
US6016153A (en) *  19960730  20000118  International Business Machines Corporation  Method to convert nonmanifold polyhedral surfaces into manifold surfaces 
US6036955A (en) *  19920305  20000314  The Scripps Research Institute  Kits and methods for the specific coagulation of vasculature 
US6083485A (en) *  19941207  20000704  Institut Fur Diagnostikforschung Gmbh  Near infrared radiation invivo diagnostic methods and dyes 
US6093399A (en) *  19920305  20000725  Board Of Regents, The University Of Texas System  Methods and compositions for the specific coagulation of vasculature 
US6119069A (en) *  19990301  20000912  Case Corporation  System and method for deriving field boundaries using alpha shapes 
US6121426A (en) *  19881229  20000919  BioTechnology General Corp.  Fibrin binding domain polypeptides and uses and methods of producing same 
US6140470A (en) *  19950630  20001031  Yale University  Human monoclonal antitumor antibodies 
US6171578B1 (en) *  19990414  20010109  Diatide, Inc.  Benzodiazepine derivatives for imaging thrombi 
US6267722B1 (en) *  19980203  20010731  Adeza Biomedical Corporation  Point of care diagnostic systems 
US6278457B1 (en) *  19980115  20010821  International Business Machines Corporation  Methods and apparatus for performing sampling based synthesis of threedimensional geometric models 
US6296831B1 (en) *  19980410  20011002  Battelle Memorial Institute  Stimulus sensitive gel with radioisotope and methods of making 
US6301496B1 (en) *  19980724  20011009  Biosense, Inc.  Vector mapping of threedimensionally reconstructed intrabody organs and method of display 
US6342326B1 (en) *  20000510  20020129  Beckman Coulter, Inc.  Synthesis and use of acyl fluorides of cyanine dyes 
US6342219B1 (en) *  19990428  20020129  Board Of Regents, The University Of Texas System  Antibody compositions for selectively inhibiting VEGF 
US6377865B1 (en) *  19980211  20020423  Raindrop Geomagic, Inc.  Methods of generating threedimensional digital models of objects by wrapping point cloud data points 
US6384826B1 (en) *  19980814  20020507  Xerox Corporation  Method, apparatus and computer medium for surface reconstruction by Voronoi filtering 
US6394952B1 (en) *  19980203  20020528  Adeza Biomedical Corporation  Point of care diagnostic systems 
US6456867B2 (en) *  19980724  20020924  Biosense, Inc.  Threedimensional reconstruction of intrabody organs 
US20030045681A1 (en) *  19980511  20030306  Anthony J. Zelano  Specific binding molecules for scintigraphy, conjugates containing them and therapeutic method for treatment of angiogenesis 
US20030046714A1 (en) *  20010307  20030306  Simard John J.L.  Antineovasculature preparations for cancer 
US6545678B1 (en) *  19981105  20030408  Duke University  Methods, systems, and computer program products for generating tissue surfaces from volumetric data thereof using boundary traces 
US6606091B2 (en) *  20000207  20030812  Siemens Corporate Research, Inc.  System for interactive 3D object extraction from slicebased medical images 
US20030176663A1 (en) *  19980511  20030918  Eidgenossische Technische Hochscule  Specific binding molecules for scintigraphy 
US6630570B1 (en) *  19990409  20031007  Insitut für Diagnostikforschung GmbH  Shortchain peptidedye conjugates as contrast media for optical diagnosis 
US20040001790A1 (en) *  20020103  20040101  Schering Ag  Methods for diagnosis and treatment of tumours 
US20040013640A1 (en) *  20000224  20040122  Luciano Zardi  Compositions and methods for treatment of angiogenesis in pathological lesions 
US6696245B2 (en) *  19971020  20040224  Domantis Limited  Methods for selecting functional polypeptides 
US6749853B1 (en) *  19920305  20040615  Board Of Regents, The University Of Texas System  Combined methods and compositions for coagulation and tumor treatment 
US20040254437A1 (en) *  19980630  20041216  Hauck John A.  Method and apparatus for catheter navigation and location and mapping in the heart 
US20050142144A1 (en) *  20010406  20050630  Simard John J.L.  Epitope sequences 
US7023432B2 (en) *  20010924  20060404  Geomagic, Inc.  Methods, apparatus and computer program products that reconstruct surfaces from data point sets 
US20060088530A1 (en) *  20000112  20060427  Chen James C  Photodynamic therapy treatment for eye disease 
US7129254B2 (en) *  20020731  20061031  Schering Aktiengesellschaft  Effector conjugates, process for their production and their pharmaceutical use 
US20070047789A1 (en) *  20050830  20070301  AgfaGevaert N.V.  Method of Constructing Gray Value or Geometric Models of Anatomic Entity in Medical Image 
US20070179375A1 (en) *  20000127  20070802  Biosense Webster, Inc.  Method for mapping a tubular region near the heart 
US7273924B1 (en) *  19960524  20070925  Philogen S.P.A.  Antibodies to the EDB domain of fibronectin, their construction and uses 
US20070225558A1 (en) *  20040528  20070927  Hauck John A  Robotic surgical system and method for surface modeling 
US20070299353A1 (en) *  20060613  20071227  Doron Harlev  Noncontact cardiac mapping, including preprocessing 
US7916917B2 (en) *  20050830  20110329  Agfa Healthcare N.V.  Method of segmenting anatomic entities in digital medical images 

2007
 20071228 US US11/966,097 patent/US8253725B2/en active Active

2008
 20081217 WO PCT/US2008/087121 patent/WO2009085803A1/en active Application Filing
Patent Citations (77)
Publication number  Priority date  Publication date  Assignee  Title 

US558203A (en) *  18960414  Henry e  
US4341223A (en) *  19810204  19820727  Lutz Lauralee A  Fluoresceable composition and method of determining fluid flow 
US4741900A (en) *  19821116  19880503  Cytogen Corporation  Antibodymetal ion complexes 
US4894326A (en) *  19860409  19900116  Fred Hutchinson Cancer Research Center  Monoclonal antibody defining oncofetal structure of fibronectin 
US5243029A (en) *  19860409  19930907  Fred Hutchinson Cancer Research Center  Oncofetal structure of fibronectin 
US5177015A (en) *  19880812  19930105  Fred Hutchinson Cancer Research Centre  Oncodevelopmentally regulated αNacetylgalactosaminyltransferase 
US5843156A (en) *  19880824  19981201  Endoluminal Therapeutics, Inc.  Local polymeric gel cellular therapy 
US6121426A (en) *  19881229  20000919  BioTechnology General Corp.  Fibrin binding domain polypeptides and uses and methods of producing same 
US5460785A (en) *  19890809  19951024  Rhomed Incorporated  Direct labeling of antibodies and other protein with metal ions 
USRE35500E (en) *  19890809  19970506  Aberlyn Capital Management Limited Partnership  Direct radiolabeling of antibodies and other proteins with technetium or rhenium 
US5120830A (en) *  19901025  19920609  Washington University  Inhibitory peptides against α2, β1 mediated mg++ dependent adhesion of platelets to collagen 
US5837813A (en) *  19920131  19981117  La Jolla Cancer Research Foundation  Fibronectin Type III polypeptides 
US5747452A (en) *  19920131  19980505  La Jolla Cancer Research Foundation  Method of modulating tumor cell migration using fibronectin type III peptides 
US5534254A (en) *  19920206  19960709  Chiron Corporation  Biosynthetic binding proteins for immunotargeting 
US6004555A (en) *  19920305  19991221  Board Of Regents, The University Of Texas System  Methods for the specific coagulation of vasculature 
US7125541B2 (en) *  19920305  20061024  The University Of Texas System Board Of Regents  Combined methods for tumor vasculature targeting and tumor treatment with radiotherapy 
US6749853B1 (en) *  19920305  20040615  Board Of Regents, The University Of Texas System  Combined methods and compositions for coagulation and tumor treatment 
US6093399A (en) *  19920305  20000725  Board Of Regents, The University Of Texas System  Methods and compositions for the specific coagulation of vasculature 
US5965132A (en) *  19920305  19991012  Board Of Regents, The University Of Texas System  Methods and compositions for targeting the vasculature of solid tumors 
US5877289A (en) *  19920305  19990302  The Scripps Research Institute  Tissue factor compositions and ligands for the specific coagulation of vasculature 
US6051230A (en) *  19920305  20000418  Board Of Regents, The University Of Texas System  Compositions for targeting the vasculature of solid tumors 
US6036955A (en) *  19920305  20000314  The Scripps Research Institute  Kits and methods for the specific coagulation of vasculature 
US5976535A (en) *  19920609  19991102  Neorx Corporation  Pretargeting protocols for the enhanced localization of cytotoxins to target sites and cytotoxic combinations useful therefore 
US5734025A (en) *  19920925  19980331  Otsuka Pharnaceutical Factory, Inc.  Adsorbent for cellular fibronectin, a method for fractional purification of fibronectin and a method of hemocatharisis 
US5877786A (en) *  19920929  19990302  Ricoh Company, Ltd.  Ink jet recording method and head 
US5849701A (en) *  19921110  19981215  The United States Of America As Represented By The Department Of Health And Human Services  Peptide inhibitors of fibronectin and related collagenbinding proteins 
US5876691A (en) *  19931203  19990302  Cancer Research Campaign Technology Limited  Antibody against carcionembryonic antigen (CEA) 
US6015897A (en) *  19931207  20000118  Neorx Corporation  Biotinamidonmethylglycylserylosuccinamidobenzyl dota 
US5523229A (en) *  19940322  19960604  Trustees Of The University Of Pennsylvania  Antibodies specific for oncofetal fibronectin 
US5710134A (en) *  19940520  19980120  Behringwerke Aktiengesellschaft  Combination of necrosisinducing substances with substances which are activated by necroses for the selective therapy of tumors and inflammatory disorders 
US5648485A (en) *  19941026  19970715  University Of British Columbia  β, βdihydroxy mesosubstituted chlorins, isobacteriochlorins, and bacteriochlorins 
US5831088A (en) *  19941026  19981103  The University Of British Columbia  Methods to prepare ββ'dihydroxy mesosubstituted chlorins, isobacteriochlorins and bacteriochlorins 
US6083485A (en) *  19941207  20000704  Institut Fur Diagnostikforschung Gmbh  Near infrared radiation invivo diagnostic methods and dyes 
US6140470A (en) *  19950630  20001031  Yale University  Human monoclonal antitumor antibodies 
US5889524A (en) *  19950911  19990330  University Of Washington  Reconstruction of threedimensional objects using labeled piecewise smooth subdivision surfaces 
US5768156A (en) *  19951025  19980616  Sandia Corporation  Connectivitybased, allhexahedral mesh generation method and apparatus 
US5817776A (en) *  19951109  19981006  Emory University  Amino acid analogs for tumor imaging 
US7273924B1 (en) *  19960524  20070925  Philogen S.P.A.  Antibodies to the EDB domain of fibronectin, their construction and uses 
US6016153A (en) *  19960730  20000118  International Business Machines Corporation  Method to convert nonmanifold polyhedral surfaces into manifold surfaces 
US5913884A (en) *  19960919  19990622  The General Hospital Corporation  Inhibition of fibrosis by photodynamic therapy 
US6696245B2 (en) *  19971020  20040224  Domantis Limited  Methods for selecting functional polypeptides 
US6278457B1 (en) *  19980115  20010821  International Business Machines Corporation  Methods and apparatus for performing sampling based synthesis of threedimensional geometric models 
US6267722B1 (en) *  19980203  20010731  Adeza Biomedical Corporation  Point of care diagnostic systems 
US6394952B1 (en) *  19980203  20020528  Adeza Biomedical Corporation  Point of care diagnostic systems 
US6377865B1 (en) *  19980211  20020423  Raindrop Geomagic, Inc.  Methods of generating threedimensional digital models of objects by wrapping point cloud data points 
US6296831B1 (en) *  19980410  20011002  Battelle Memorial Institute  Stimulus sensitive gel with radioisotope and methods of making 
US5997842A (en) *  19980413  19991207  Light Sciences Limited Partnership  Radionuclide excited phosphorescent material for administering PDT 
US20030045681A1 (en) *  19980511  20030306  Anthony J. Zelano  Specific binding molecules for scintigraphy, conjugates containing them and therapeutic method for treatment of angiogenesis 
US20030176663A1 (en) *  19980511  20030918  Eidgenossische Technische Hochscule  Specific binding molecules for scintigraphy 
US7263397B2 (en) *  19980630  20070828  St. Jude Medical, Atrial Fibrillation Division, Inc.  Method and apparatus for catheter navigation and location and mapping in the heart 
US20040254437A1 (en) *  19980630  20041216  Hauck John A.  Method and apparatus for catheter navigation and location and mapping in the heart 
US6301496B1 (en) *  19980724  20011009  Biosense, Inc.  Vector mapping of threedimensionally reconstructed intrabody organs and method of display 
US6456867B2 (en) *  19980724  20020924  Biosense, Inc.  Threedimensional reconstruction of intrabody organs 
US6384826B1 (en) *  19980814  20020507  Xerox Corporation  Method, apparatus and computer medium for surface reconstruction by Voronoi filtering 
US6545678B1 (en) *  19981105  20030408  Duke University  Methods, systems, and computer program products for generating tissue surfaces from volumetric data thereof using boundary traces 
US6119069A (en) *  19990301  20000912  Case Corporation  System and method for deriving field boundaries using alpha shapes 
US6630570B1 (en) *  19990409  20031007  Insitut für Diagnostikforschung GmbH  Shortchain peptidedye conjugates as contrast media for optical diagnosis 
US6171578B1 (en) *  19990414  20010109  Diatide, Inc.  Benzodiazepine derivatives for imaging thrombi 
US6342219B1 (en) *  19990428  20020129  Board Of Regents, The University Of Texas System  Antibody compositions for selectively inhibiting VEGF 
US7320786B2 (en) *  20000112  20080122  Light Sciences Oncology, Inc.  Photodynamic therapy treatment for eye disease 
US20060088530A1 (en) *  20000112  20060427  Chen James C  Photodynamic therapy treatment for eye disease 
US20070179375A1 (en) *  20000127  20070802  Biosense Webster, Inc.  Method for mapping a tubular region near the heart 
US6606091B2 (en) *  20000207  20030812  Siemens Corporate Research, Inc.  System for interactive 3D object extraction from slicebased medical images 
US20040013640A1 (en) *  20000224  20040122  Luciano Zardi  Compositions and methods for treatment of angiogenesis in pathological lesions 
US6342326B1 (en) *  20000510  20020129  Beckman Coulter, Inc.  Synthesis and use of acyl fluorides of cyanine dyes 
US20050260234A1 (en) *  20010307  20051124  Simard John J L  Antineovasculature preparations for cancer 
US20030046714A1 (en) *  20010307  20030306  Simard John J.L.  Antineovasculature preparations for cancer 
US20050221440A1 (en) *  20010406  20051006  Simard John J L  Epitope sequences 
US20050142144A1 (en) *  20010406  20050630  Simard John J.L.  Epitope sequences 
US7023432B2 (en) *  20010924  20060404  Geomagic, Inc.  Methods, apparatus and computer program products that reconstruct surfaces from data point sets 
US20040001790A1 (en) *  20020103  20040101  Schering Ag  Methods for diagnosis and treatment of tumours 
US7129254B2 (en) *  20020731  20061031  Schering Aktiengesellschaft  Effector conjugates, process for their production and their pharmaceutical use 
US7335775B2 (en) *  20020731  20080226  Schering Aktiengesellschaft  Effector conjugates, process for their production and their pharmaceutical use 
US20070225558A1 (en) *  20040528  20070927  Hauck John A  Robotic surgical system and method for surface modeling 
US7916917B2 (en) *  20050830  20110329  Agfa Healthcare N.V.  Method of segmenting anatomic entities in digital medical images 
US20070047789A1 (en) *  20050830  20070301  AgfaGevaert N.V.  Method of Constructing Gray Value or Geometric Models of Anatomic Entity in Medical Image 
US20070299353A1 (en) *  20060613  20071227  Doron Harlev  Noncontact cardiac mapping, including preprocessing 
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Publication number  Priority date  Publication date  Assignee  Title 

US20130138404A1 (en) *  20111129  20130530  Carlos Carbonera  Method and system for generating and/or repairing a surface model of a geometric structure 
US9282915B2 (en) *  20111129  20160315  St. Jude Medical, Atrial Fibrillation Division, Inc.  Method and system for generating and/or repairing a surface model of a geometric structure 
US9159162B2 (en)  20111228  20151013  St. Jude Medical, Atrial Fibrillation Division, Inc.  Method and system for generating a multidimensional surface model of a geometric structure 
US20160012637A1 (en) *  20111228  20160114  St. Jude Medical, Atrial Fibrillation Division, Inc.  Method and system for generating a multidimensional surface model of a geometric structure 
US8909502B2 (en)  20111229  20141209  St. Jude Medical, Atrial Fibrillation Division, Inc.  Method and system for constructing an electrophysiology map 
EP2797502A4 (en) *  20111229  20151104  St Jude Medical Atrial Fibrill  Method and system for constructing an electrophysiology map 
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