Abstract:
An apparatus for measuring parameters preparatory to a stent replacement of an aneurytic blood vessel in a patient ( 26 ) includes a computed tomography (CT) scanner ( 10 ) that acquires image data ( 28 ) corresponding to multiple two-dimensional image slices. A reconstruction processor ( 32 ) reconstructs a three-dimensional image representation ( 34 ) from the image data ( 28 ). A tracking processor ( 40 ) produces a tracked vessel ( 92 ) including at least a centerline ( 80 ) and selected vessel boundaries ( 86 ). A user interface ( 44 ) displays a rendering ( 242 ) of the image representation to an associated user ( 42 ), measures selected vascular parameters corresponding to the stent parameters ( 276 ), and graphically superimposes the measured parameters on the rendering of the image representation ( 270, 272 ).

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates to the medical imaging arts. It particularly relates to the measurement of aneurysms and planning for surgical replacement thereof by synthetic stents using image data generated by multiple-slice computed tomography (CT), and will be described with particular reference thereto. However, the invention will also find application in conjunction with other imaging techniques such as magnetic resonance-based imaging (MRI) or nuclear medicine, as well as in acquiring and analyzing data which is useful for other types of medical procedure planning. 
     The development of multi-slice CT systems having increasingly improved resolution particularly in the slice-direction is making CT imaging of vascular systems attractive for clinical applications such as the discovery of potentially life-threatening aneurysms and the precise measurement of such an aneurysm in order to design a synthetic replacement stent and plan the surgical implantation thereof. However, for CT to gain clinical acceptance in this area, reconstruction and post-processing of the images should be automated to the greatest extent possible. Automation becomes increasingly important with multi-slice CT because of the much greater amount of data (i.e., number of slices) produced by the instrument. 
     Currently, imaging analyses for identifying and measuring aneurysms are cumbersome and laborious. Prior art systems typically employ maximum intensity projections (MIPS) which lose much of the valuable three-dimensional information available from a multi-slice CT scan. These methods are usually manual, and do not provide for efficient workflow, operator guidance, or means for verifying the stent measurements. 
     The present invention contemplates an improved method and apparatus for semi-automatic aneurysm measurement and stent planning using volume image data which overcomes the aforementioned limitations and others. 
     SUMMARY OF THE INVENTION 
     According to one aspect of the invention, a method for tracking a blood vessel containing an aneurysm in a three-dimensional image is disclosed. A blood vessel type is identified. Vascular landmarks are received from an associated user. An orthogonal vessel plane is extracted. A vessel center is located in the vessel plane. Vessel edges in the vessel plane are fitted. The extracting, locating, and fitting are recursively repeated a plurality of times to track the vessel. 
     According to another aspect of the invention, a method for assisting an associated user in planning a stent replacement of a blood vessel in an associated patient is disclosed. A three-dimensional vascular image is acquired that includes imaging of the vessel to be replaced. The vessel to be replaced is tracked in the three-dimensional vascular image. The vessel tracking includes at least extraction of a vessel centerline and vessel boundaries. Stent parameters are measured based on the vessel tracking. 
     According to yet another aspect of the invention, an apparatus is disclosed for measuring parameters preparatory to a stent replacement of an aneurytic blood vessel in an associated patient. A computed tomography (CT) scanner acquires image data corresponding to multiple two-dimensional image slices. A reconstruction processor reconstructs a three-dimensional image representation from the image data. A tracking processor produces a tracked vessel including at least a centerline and selected vessel boundaries. A user interface displays a rendering of the image representation to an associated user, measures selected vascular parameters corresponding to the stent parameters, and graphically superimposes the measured parameters on the rendering of the image representation. 
     According to still yet another aspect of the invention, an apparatus is disclosed for measuring stent parameters preparatory to a stent replacement operation. A means is provided for acquiring three-dimensional image data. A means is provided for reconstructing the image data into a three-dimensional image representation. A means is provided for tracking the blood vessel to be replaced. The tracking includes at least estimation of a vessel centerline and selected vessel boundaries in three-dimensions. A means is provided for displaying a rendering of the image representation to an associated user. A means is provided for measuring selected vascular parameters corresponding to the stent parameters. 
     One advantage of the present invention is that it operates directly on the three-dimensional data and performs the tracking in 3-D. 
     Another advantage of the present invention is that it provides for measurement of both the true and the false lumen of an aneurysm. 
     Another advantage of the present invention is that the vessel branches are identified and optionally tracked for a pre-selected distance to ascertain that adequate stent-anchoring branch portions are available. 
     Yet another advantage of the present invention is that it facilitates stent measurements in accordance with the stent manufacturer&#39;s specifications. 
     Still yet another advantage of the present invention is that it provides intuitive graphical feedback comparing the stent measurements and the stent structure with the acquired vascular images. 
     Numerous additional advantages and benefits of the present invention will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. 
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The invention may take form in various components and arrangements of components, and in various steps and arrangements of steps. The drawings are only for the purpose of illustrating preferred embodiments and are not to be construed as limiting the invention. 
     FIG. 1 schematically shows an exemplary multi-slice CT imaging system that suitably practices an embodiment of the invention; 
     FIG. 2 schematically shows an exemplary method embodiment of the invention; 
     FIG. 3 shows a schematic of a AAA aortic aneurysm with user-selected landmarks superimposed; 
     FIG. 4 shows an exemplary user interface window for user selection of the stent type; 
     FIG. 5 schematically shows a suitable embodiment of the vessel center finder of FIG. 2; 
     FIG. 6 schematically shows a selection of rays for the central measure map calculation at a point (i,j); 
     FIG. 7 shows an exemplary central measure map; 
     FIG. 8A shows an initial dynamic contour spline or snake to be used to fit the true lumen; 
     FIG. 8B shows the fitted dynamic contour spline or snake corresponding to FIG. 8A; 
     FIG. 9 shows an exemplary user interface for performing stent measurements and stent implantation planning in accordance with an embodiment of the invention; 
     FIG. 10 shows a suitable user interface for performing and verifying the stent measurements; and 
     FIG. 11 shows a suitable display of the stent structure superimposed on a vascular image. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     With reference to FIG. 1, a multiple-slice computed tomography (CT) scanner  10  includes a patient support  12  such as a patient bed which is linearly movable inside an examination region  14 . An x-ray tube assembly  16  mounted on a rotating gantry projects a cone beam or a plurality of parallel fan beams of radiation through the examination region  14 . A collimator  18  collimates the radiation beam or beams in two dimensions. In third generation scanners, a two-dimensional x-ray detector  20  is disposed on the rotating gantry across the examination region from the x-ray tube. In fourth generation scanners, an array of two-dimensional detector rings  22  is mounted on a stationary gantry around the rotating gantry. 
     Whether third or fourth generation, the x-ray detectors  20 ,  22  operate in known ways to convert x-rays that have traversed the examination region  14  into electrical signals indicative of x-ray absorption between the x-ray tube  16  and the detectors  20 ,  22 . The x-ray absorption signals, along with information on the angular position of the rotating gantry, are communicated to a data memory  28 . 
     An acquisition controller  30  communicates  32  with the CT scanner  10  to control CT scanning of a patient  26 . The data is reconstructed by a reconstruction processor  32  which reconstructs the x-ray absorption data into a plurality of CT image slices stored in a CT volume image memory  34 . The reconstruction processor  32  operates using the filtered back-projection technique known to the art or using other reconstruction techniques. 
     In another suitable embodiment (not shown), the patient couch advances continuously such that the data acquisition occurs over a spiral geometry. The resulting spiral data is reconstructed into a three-dimensional image again stored in image memory  34 . Those skilled in the art will also recognize that the invention is not limited to CT, but is also applicable to magnetic resonance imaging (MRI) and other methods capable of three-dimensionally imaging tubular biological structures. 
     In order to emphasize the vasculature, a contrast agent  36 , such as an iodine-based contrast agent, is administered to the patient  26 . 
     A vessel tracker  40  advantageously tracks the vessel containing the aneurysm based on landmarks supplied by an associated user  42  through a user interface  44 , which is preferably, interactive. As the vessel tracker tracks the vessel with the aneurysm, key vessel-defining characteristics such as the vessel center and periphery information are stored in a vessel memory  46 . A three-dimensional surface rendering processor  48  generates a 3-D representation, optionally rotatable, of the tracked vessel. The user interface  44  also permits selective viewing of the contents of the three-dimensional volume image memory  34 , the output of the tracker  40 , and the 3-D rendering. Further, the user interface  44  allows the user  42  to communicate with and direct the data acquisition controller  30  so that the user  42  can operate the CT scanner  10 . 
     The apparatus embodiment of FIG. 1 is exemplary only. Those skilled in the art will be able to make modifications to suit particular applications. For example, the user interface  44  can include other components, such as printers, network connections, storage units, and the like (not shown), to facilitate efficient manipulating of the CT scanner  10 . In another embodiment, the user interface  44  is a computer and vessel tracker  40  is a software component residing thereon. 
     With continuing reference to FIG.  1  and with further reference to FIG. 2, an exemplary vessel tracking method  70  embodiment is described. The user  42  is interrogated via the user interface  44  and supplies one or more starting landmarks  72  within the three-dimensional volume image memory  34 . Exemplary landmarks for a schematic AAA aortic aneurysm are shown in FIG.  3 . In a suitable embodiment, a first landmark serves as an initial vessel center estimate  74 , a second landmark serves in cooperation with the first landmark to define an initial vessel direction estimate, and the remaining landmarks serve as termination points. In an exemplary user interface  44  interactive window shown in FIG. 4, the user can select from among several standard types of stents, or can define a custom stent (e.g., “Dr. Smith&#39;s favorite protocol”). Based, on the stent type, the user interface  44  preferably prompts for appropriate landmarks which will enable efficient tracking. 
     With reference returning to FIG. 2, a vessel direction is estimated  76  by extrapolating from at least two vessel center points. The vessel center points are supplied recursively, i.e. corresponding to previously identified vessel centers. To initiate the tracking direction, two selected landmarks  72  can be used. In one embodiment, at least three points are used to extrapolate by fitting the points to a spline curve or other suitable curved linear function. In another embodiment, two points are taken at a time and the centerline is tracked between them. To avoid erroneously following a sharp turn corresponding to a branch, one of the landmarks  72  located approximately at the opposite end of the vessel from the starting landmark  72  optionally indicates the principle vessel direction. Another method for avoiding inadvertently following branches is to weight the points used to bias against sharp curving. In yet another suitable embodiment, the vessel direction is obtained by line filtering a finite volume, extracting an inertia matrix, and performing an Eigen analysis of the inertia matrix to yield a vessel direction. 
     With continuing reference to FIGS. 1 and 2, a normal planar image that is orthogonal to the vessel direction is extracted  78  from the 3-D volume image of memory  46 . The size of this plane can be varied based on the size of the vessel being tracked, but in any event it should be at least large enough to contain the entire vessel cross-section. The appropriate orthogonal plane is identified by transforming a plane parallel to the x-y plane into the orthogonal orientation according to:                γ   =     arctan        (       n   y       n   x       )         ,                φ   =     arctan        (       n   z           n   x   2     +     n   y   2           )         ,           (   1   )                                
     where (n x , n y , n z ) is the directional normal along the vessel direction  76 , γ is the angle of the plane with respect to the z-axis, and φ is the angle of the plane with respect to the y-axis. The transformed coordinates are used to tri-linearly interpolate the image voxels from the 3-D volume image  46 . 
     With the orthogonal plane found, the vessel center is identified within the plane  78 . This identification can be complicated by the low signal-to-noise ratio typically associated with multi-slice CT image data. To improve the boundaries&#39; detectability, an edge enhancement  80  is optionally performed. In one embodiment, a Gaussian function convolutional smoothing is applied prior to computing directional component magnitudes to determine the presence of edges. Optionally, these edges can be superimposed onto the image in the display portion of the user interface  44  and the user  42  prompted to visually verify the edge identification. The edge enhancement just described is exemplary only, and other edge enhancement algorithms known to the art can instead be employed. Additionally, especially in the usual case where the aneurysm is in a large vessel such as the aorta, it may be unnecessary to perform any edge enhancement, and the edge enhancement  80  is optionally omitted. 
     The planar image with optional edge enhancement is analyzed to find the vessel center  82 . In an exemplary embodiment, a central measure map is used for this analysis, as will be described in more detail later. 
     Those skilled in the art will recognize that the accuracy of the vessel center estimation  82  will depend upon the orthogonality of the planar image  80  relative to the actual vessel direction. Since the vessel direction  76  is only an estimate, in one preferred embodiment the an iterative loop  84  is included which uses the found vessel center  82  to update the vessel direction  76 . A new orthogonal plane extraction  78  is performed using the updated direction, optionally edge enhanced  80 , and the vessel center analysis  82  performed again. The looping  84  is preferably repeated until a convergence is reached. For the types of large vessels for which stent replacement is typically applied, the vessels are usually straight enough that such an iterative looping will converge in only a few iterations. 
     Once the vessel center is identified in an optimized orthogonal plane, the vessel boundaries are fitted  86 . In a suitable embodiment to be described later herein, parametric contour fitting is used to identify the vessel boundaries in the plane. In the course of the center and boundaries analysis  82 ,  86  vessel branching may be detected. If so, vessel branches are advantageously marked  88  for possible later tracking. In this way, the tracker can be applied recursively to track a pre-selected portion of the vessels branching off the vessel which is to be replaced by the stent. Such branch tracking is important for stent planning, because the stent is typically anchored to the branch vessels. Irregularities in these branches can adversely affect the stent implantation surgery. Prior knowledge of the these anatomical complications enables improved stent design and surgical planning. 
     Once the vessel center and boundaries are found, the process recursively iterates until a termination condition is satisfied  90 . Selected landmarks  72  optionally identify the termination points. The end result of the recursive tracking is the extracted vessel  92  including an accurate characterization of the aneurysm. 
     With continuing reference to FIG.  2  and with further reference to FIG. 5, a suitable embodiment of the vessel center analysis  82  is described. The analysis is performed in the planar image with optional edge enhancement  100 . A central measure map is computed as follows. 
     With continuing reference to FIG.  5  and with further reference to FIG. 6, for a pixel (i,j)  102 , a plurality of rays are generated  104  which pass through the point (i,j)  102  at a selected angular separation α  106  so that there are n  108  rays generated. In the exemplary FIG. 4 where α=30°, there are n=6 rays indexed by k which radially span about the point (i,j)  102 . 
     For each ray, the gradient is computed  110  by convolving with a gaussian derivative kernel according to: 
     
       
         ∇ R ( {right arrow over (r)} ,σ)= I ( {right arrow over (r)} )*σ 65   ∇G ( x ,σ)  (2), 
       
     
     where 
     {right arrow over (r)}=ray, I({right arrow over (r)})=image array, and 
     σ γ ∇G(x,σ)=the gaussian derivative kernel 
     where σ is the gaussian variance which serves as a scale factor  112  in the method and is selected based on the size or diameter of the vessel being tracked. Those skilled in the art will recognize that for larger scale factors only the dominant edges will be present after the gradient transform. The factor γ in equation (2) is a normalizing factor corresponding to Lindeberg&#39;s power constant which preserves the image characteristics across the gradient transform. G(x,σ) is the well known Gaussian function given by:                  G        (     x   ,   σ     )       =       1     2        πσ   2                     -     x   2         2        σ   2               ,           (   3   )                                
     and the derivative of the gaussian, defined as:                  ∇     G        (     x   ,   σ     )         =       ∂     ∂   x            G        (     x   ,   σ     )           ,           (   4   )                                
     is computed using a three-point difference operation, i.e. convolving with a {1, 0, −1} kernel. 
     A gradient magnitude is calculated  114  along the radial direction according to:                    ∂     R     1   ,   2           ∂       r   →       1   ,   2           =       (            ∇     R        (       r   →     ,   σ     )                     r   →            )           r   →       1   ,   2       =     first                 maximums           ,           (   5   )                                
     where the subscripts 1,2 refer to the positive and negative directions along the linear image array I(r) and the magnitude of the vector r reaches to the first maximum in the transformed radial array. The central measure map is then generated  116  by transforming each pixel (i,j)  102  according to the transformation:                CMQ        (     i   ,   j     )       =       1   n            ∑     k   =   1     n                       (       min        {         ∂     R   1         ∂       r   →     k         ,       ∂     R   2         ∂       r   →     k           }         max        {         ∂     R   1         ∂       r   →     k         ,       ∂     R   2         ∂       r   →     k           }         )     .                 (   6   )                                
     The CMQ function is indicative of how well centered the point (i,j) is within its surrounding edge boundaries. The calculation of CMQ(i,j) is repeated  118  for each pixel (i,j)  102  to produce the central measure map  120 . The vessel center is selected  122  as the point (i,j) having the largest central measure map value CMQ(i,j). Generally, the vessel center will have CMQ close to 1.0 for circular and elliptical borders, with decreases therefrom as vessel edge irregularities increase. 
     With reference to FIG. 7, an exemplary central measure map  140  is shown. The vessel center  144  is identified as the largest value of the central measure map. Additional, much smaller peaks are also seen in the central measure map, corresponding to smaller nearby vessels or other anatomical structures. Those skilled in the art will recognize that at a vessel branch there will be a merging of the central measure map peaks of the main vessel and a smaller branch coming off. Such a merging can be detected to facilitate marking of branches  88  (FIG.  2 ). The tracking system  70  can then be successively applied for a pre-selected distance to each marked branch to track the branch portion that will be used in anchoring the stent. 
     With reference returning to FIG. 2, once the vessel center is found  82 , an iterative looping  84  is optionally performed which iteratively improves the vessel direction estimate  76  using the found vessel center, extracts an iteratively improved orthogonal plane  78 , performs optional edge enhancement  80  of the improved plane, and finds an iteratively improved vessel center  82 . The iterative looping  84  corrects for any error in the initial direction estimate due to vessel curving. 
     With continuing reference to FIG. 2, once the orthogonal plane has been extracted  78  and the optimized vessel center has been located  82 , the vessel boundaries are identified  86  within the orthogonal plane. It will be recognized that the central measure map involves estimating vessel boundaries corresponding to the first maximum values. However, since the vessel being tracked contains an aneurysm, these values are insufficient to smoothly track the vessel lumen, especially in the vicinity of the aneurysm. Those skilled in the art will particularly recognize that an aneurysm will typically have a “true” lumen corresponding to the boundaries of the blood flow, as well as a “false” lumen which is larger than the true lumen and corresponds to the vessel boundaries. The distinction arises because plaque or other types of buildup generally occur on the vessel walls of the aneurysm which constrict blood flow. Furthermore, the first maxima used in calculating the central measure map can be inaccurate in regions where vessels branch off, and accurate tracking in these regions is also critical in designing a stent and planning the stent implantation surgery. 
     With continuing reference to FIG. 2, in a suitable embodiment of the true lumen extraction  86 , a dynamic contour spline or snake is arranged passing through the first maxima. The contour is then iteratively adjusted, i.e. the snake is slithered, by optimizing the edge strength with the internal spline energy. In this manner, the contour or snake is iteratively adjusted to match the true lumen boundary. In a suitable contour embodiment, the contour is parametrically defined as:                  E   snake   *     =       ∫   0   1          (         E   int          (     v        (   s   )       )       +       E   image          (     v        (   s   )       )       +       E   con          (     v        (   s   )       )         )         ,           (   7   )                                
     where E int  represents the internal energy of the spline due to bending, E image  gives rise to the image edge strength forces, and E con  gives rise to the external constraint forces. The internal energy E int  comprises a first order term controlled by a parameter α and a second order term controlled by a parameter β. The image forces are edge strength. In a preferred embodiment, the edge strength is computed in a manner similar to the scale-based gradient magnitude at any given image point. Those skilled in the art will recognize that this edge strength is different from conventional snake approaches. The constraint energy E con  in this dynamic contouring embodiment is 1/r where r is the radius of the cross-sectional vessel. 
     FIG. 8A shows an image slice of an aneurysm with a typical initial vessel contour  200  superimposed, with the maxima of the central measure map calculation shown as asterisks (*). It is seen that the initial contour  200  inadequately describes the true vessel lumen; for example, a portion  202  of the true vessel lumen is poorly fitted. FIG. 8B shows the fitted contour  204  which closely matches the true vessel lumen. Those skilled in the art will also recognize a false vessel lumen  206  in both FIG.  8 A and FIG. 8B, which is not fitted. However, it is contemplated that the dynamic contour spline or snake of equation (7) can also be employed to fit the false vessel lumen by merely adjusting the parameters of the snake to fit the lower intensity false lumen. In a suitable embodiment, the snake of FIG. 8B would be enlarged by a pre-selected amount, e.g. 30%, and the fitting with the adjusted snake parameters performed to iteratively fit the false lumen. 
     With reference to FIG. 9, a suitable work environment incorporating an embodiment of the invention is described. The type of stent to be employed, selected previously for example as shown in FIG. 4, is shown schematically  240  for the user&#39;s reference. In the exemplary FIG. 9, a AAA aortic aneurysm is selected. The tracked vessel and selected branch portions, suitably obtained using the apparatus and method of FIGS. 1 and 2, is rendered in 3-D  242 , optionally in a rotatable format. However, because many medical personnel are used to viewing in maximum intensity projection (MIP) format  244  or in multi-planar re-format (MPR)  246 , these images are also provided. Quantitative information is provided in other windows. A linear rendition of the vessel lumen diameter  250  and area  252  are shown with respect to the tracked vessel centerline. These plots are generated by plotting the vessel diameter or the vessel area versus a distance along the centerline from a reference vessel center. The plots  250 ,  252  allow accurate and intuitive identification of the thickest portion of the aneurysm  256 . Quantitative measurements are performed at selected slices  260 , and the vessel of the slice is displayed in enlarged format  262 . 
     With continuing reference to FIG.  9  and with further reference to FIG. 10, a suitable embodiment for calculating stent parameters is described. A 3-D rendering, optionally rotatable, of the tracked vessel containing the aneurysm and selected branch portions is shown  270 . The slice at which cross-sectional measurements are being performed is also shown  272 . Since the stent is schematically known  240 , the appropriate measurements used by the stent manufacturer are advantageously shown, e.g. in a measurements table  276 . The measurements are shown to scale superimposed on the images  270 ,  272 , allowing medical personnel to visually check the accuracy and reasonableness of the measurements. Optionally, the user can select a parameter to measure or view through the table  276 . This exemplary user interface ensures that all the standard measurements are supplied to the stent manufacturer, and facilitates easy and intuitive verification of the stent parameters. 
     With continuing reference to FIGS. 9 and 10, and with further reference to FIG. 11, once the stent measurements have been selected, the stent structure  280  is advantageously calculated and displayed superimposed on CT image data  282 . Although FIG. 11 shows a projection image, it is also contemplated that the stent structure  280  be superimposed on a 3-D tracked vessel rendition  242 ,  270  which is optionally rotatable. The graphical display of FIG. 11 further ensures through an intuitive graphical displaying that the stent structure which will be ordered is appropriate and correct. 
     The invention has been described with reference to the preferred embodiments obviously, modifications and alterations will occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.