WO2005106795A1 - Method and system for mesh-to-image registration using raycasting - Google Patents

Method and system for mesh-to-image registration using raycasting Download PDF

Info

Publication number
WO2005106795A1
WO2005106795A1 PCT/US2005/013917 US2005013917W WO2005106795A1 WO 2005106795 A1 WO2005106795 A1 WO 2005106795A1 US 2005013917 W US2005013917 W US 2005013917W WO 2005106795 A1 WO2005106795 A1 WO 2005106795A1
Authority
WO
WIPO (PCT)
Prior art keywords
ray
image
point
vertices
points
Prior art date
Application number
PCT/US2005/013917
Other languages
French (fr)
Inventor
Jens Guhring
Original Assignee
Siemens Medical Solutions Usa, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Medical Solutions Usa, Inc. filed Critical Siemens Medical Solutions Usa, Inc.
Priority to DE112005000927T priority Critical patent/DE112005000927B4/en
Publication of WO2005106795A1 publication Critical patent/WO2005106795A1/en

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/30Determination of transform parameters for the alignment of images, i.e. image registration
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing

Definitions

  • Three-dimensional digital images can be constructed from stacked slice sections through various construction techniques known in the art.
  • the 3-D images are made up of discrete volume elements, also referred to as voxels, composed of pixels from the 2-D images.
  • the pixel or voxel properties can be processed to ascertain various properties about the anatomy of a patient associated with such pixels or voxels.
  • Image registration is an optimization problem that finds a geometric transformation that maps points from a source dataset space into homologous points in a target dataset space.
  • the objective of image registration is to describe a geometric mapping between two images with some kind of a model, which usually is dependent on parameters, known as registration parameters.
  • the model determines the type of registration, whether it is a rigid, affine, deformable, regitration, etc., and therefore the specifics of the registration parameters.
  • Registration of datasets is an important aspect for many imaging applications.
  • one dataset to be registered is provided with a polygonal representation, that is, represented as a triangular mesh, while the other dataset is provided as a volumetric image.
  • One widely used family of registration algorithms are based on the iterative closest point (ICP) algorithm, introduced by Besl and MacKay. The idea behind these algorithms is to find, for a set of points in one dataset, the set of closest compatible points in the other dataset, and compute a transformation that minimizes a cost function penalizing the difference/distance between the established correspondences.
  • volumetric datasets can be visualized using so-called ray-casting techniques, wherein for each pixel of the display device, a ray is cast into the volumetric dataset, and the intensity and opacity values along the ray are integrated.
  • the opacity values are obtained by passing the stored intensity data values through a look-up table. The integration along the ray can be stopped if a threshold value for the opacity is reached.
  • Visualization applications can perform ray-casting very efficiently.
  • Exemplary embodiments of the invention as described herein generally include methods and systems for casting rays through points on the surface of a polygonal dataset along the surface normal direction in order to compute the closest point on a surface defined in a volumetric dataset, and iteratively minimizing the discrepancy between the point pairs.
  • Applications of the embodiments of the invention include, but are not limited to, registering models of implants (e.g. stents), polygonal surfaces derived from the segmentation of volumetric datasets, and oriented point maps generated during electrophysiological mapping procedures.
  • a method of registering a digital image with a polygonal mesh comprising the steps of providing a digital image comprising a plurality of intensities corresponding to a domain of points in a D -dimensional space, providing a polygonal mesh comprising a plurality of vertices that approximates an object in the digital image, propagating a ray from a vertex of the polygonal mesh in a direction into the digital image and calculating a line integral of opacity values wherein each opacity value corresponds to the intensity of a pixel of the image through which said ray passes, terminating the ray at a point in the image when an opacity threshold is reached, and adding the termination point to a set of closest points.
  • the steps of propagating a ray from a vertex of the polygonal mesh into the digital image, terminating the ray at a point in the image, and adding the termination point to a set of closest points are repeated for each vertex in the polygonal mesh.
  • the method further comprises the steps of computing a registration between the vertices of the polygonal mesh and the set of closest points, applying the registration to the polygonal mesh to obtain a new set of vertices, and calculating an error between the set of closest points and the new set of vertices.
  • the method further comprises, if the error is greater than a predefined threshold, repeating the steps of propagating rays from the mesh vertices into the image, terminating each ray at a point in the image, adding each termination point to a set of closest points, computing a registration between the vertices of the polygonal mesh and the set of closest points, applying the registration to the polygonal mesh to obtain a new set of vertices, and calculating an error between the set of closest points and the new set of vertices.
  • said ray is terminated if a predefined distance threshold is reached before said opacity threshold is reached.
  • the starting point of said ray is offset in a negative direction by said distance threshold.
  • the direction of said ray is determined by a vector normal to a plane defined by those vertices nearest to the starting vertex of said ray.
  • the method further comprises the step of imposing an angular threshold on the direction of the ray, wherein if the angle between the ray and a normal to the termination point is greater than a predefined threshold, said termination point is excluded from the set of closest points.
  • the direction of said ray is determined by taking an average of normals of triangles neighboring said vertex.
  • the direction of said ray is determined from a stored normal vector associated with the vertex.
  • a program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for registering a digital image with a polygonal mesh.
  • Exemplary embodiments of the invention as described herein generally include systems and methods for performing a polygonal surface to image registration in medical images using ray-casting to find corresponding points.
  • image refers to multi-dimensional data composed of discrete image elements (e.g., pixels for 2-D images and voxels for 3-D images).
  • the image may be, for example, a medical image of a subject collected by computer tomography, magnetic resonance imaging, ultrasound, or any other medical imaging system known to one of skill in the art.
  • the image may also be provided from non- medical contexts, such as, for example, remote sensing systems, electron microscopy, etc.
  • an image can be thought of as a function from R 3 to R, the methods of the inventions are not limited to such images, and can be applied to images of any dimension, e.g. a 2-D picture or a 3-D volume.
  • the domain of the image is typically a 2- or 3-dimensional rectangular array, wherein each pixel or voxel can be addressed with reference to a set of 2 or 3 mutually orthogonal axes.
  • digital and “digitized” as used herein will refer to images or volumes, as appropriate, in a digital or digitized format acquired via a digital acquisition system or via conversion from an analog image.
  • the resulting intensity values or ranges of values can be correlated with specific types of tissue, enabling one to discriminate, for example, bone, muscle, flesh, and fat tissue, nerve fibers, blood vessels, organ walls, etc., based on the intensity ranges within the image.
  • the raw intensity values in the image can serve as input to a transfer function whose output is an opacity value that can characterize the type of tissue.
  • These opacity values can be used to define a look-up table where an opacity value that characterizes a particular type of tissue is associated with each pixel point.
  • opacity values to classify tissue also enables a user to select an object or tissue type to be displayed and only integrate opacity values corresponding to the selected object or tissue.
  • a previously obtained volumetric image is used to guide a medical procedure, such as an examination of an organ.
  • a medical procedure such as an examination of an organ.
  • new readings of the organ are frequently acquired that need to be correlated with the volumetric image guiding the examination.
  • These new readings need not be from the same imaging modality used to create the volumetric image.
  • a map of the electric properties of the heart wall is acquired.
  • the measured data includes a point in three dimensions and a direction approximately normal to the heart wall, and can form a polygonal mesh.
  • a 3D CT or MR scan is often acquired before the procedure. These scans typically show quite well the boundary between the inside of the heart chambers and the heart wall, as determined by lookup tables that map pixels in the inside of the chamber to transparent opacity values.
  • Another example of a procedure where a previously obtained volumetric image would need to be registered with a polygonal mesh obtained in the course of the procedure is a hip replacement surgery.
  • the replacement hip can be represented by polygonal mesh, which needs to be registered with a volumetric image of the hip to be replaced.
  • the origins of the rays to be cast would be the vertices of the mesh.
  • a ray casting algorithm can perform the registration.
  • Ray casting is a commonly used volume rendering technique.
  • Ray casting is a process that, for each pixel in an image volume to be rendered, casts a single ray from the eye through the center of the pixel and into the volume, integrating optical properties obtained from the encountered volume densities along the ray path.
  • the optical properties can be obtained for the pixel density from a look-up table, and typically include the degree to which a pixel emits and absorbs light.
  • the emission can include RGB values, if the image is a color image.
  • RGBA value The combination of RGB and absorption is frequently referred to as an RGBA value.
  • the emission can be regarded as a single, gray-scale value.
  • a ray cast into a volume can be denoted by x(t), where the ray has been parameterized by the distance t to the eye.
  • the scalar intensity value corresponding to this position on the ray is denoted by s(x(t)) .
  • the position of the ray is a pixel or voxel in the image volume.
  • the volume density is re-sampled, using bilinear interpolation or tri-linear interpolation to reconstruct the original sample.
  • the scalar data value is mapped to the optical properties via a look-up table, which yields an RGBA value for this location within the volume.
  • the volume rendering integral integrates along the ray absorption coefficients ⁇ (s(x(t))) , accounting for the absorption of light, and colors c(s(x(t))), accounting for the emission of light.
  • the absorption and emission coefficients can be obtained from look-up tables.
  • the volume rendering integral can be used to obtain the integrated output C, that subsumes the corresponding color (emission) and opacity (absorption) contributions along the ray up to a certain distance D into the volume:
  • C c(s(x(t)))expi - ⁇ (s(x(f)))df )dt ,
  • exp - ⁇ (s(x(t')))d ] represents the cumulative absorption at that point.
  • the ray-casting integral can be terminated if the integrated output C reaches a threshold value, for example, a value the represents opacity, or complete absorption.
  • a threshold value for example, a value the represents opacity, or complete absorption.
  • the emission term for the f h segment can then be approximated as , while the cumulative absorption can be approximated as ex ⁇ [- ⁇ ⁇ (s(x(id)))d).
  • the summation in the exponent can be replaced by a multiplication of exponentiation terms:
  • rays can be cast from the measured sample points outwards in a direction normal to the heart wall. These rays would reach opacity at the point on the heart wall that is closest to the sample point, and the ray-casting integral would be terminated at that point. These two points can form a corresponding point pair in the sense of the ICP algorithm.
  • the ICP algorithm is a procedure for registering a geometric shape whose internal representation is known with model shape.
  • the first stage of the ICP algorithm is to find a set of points in the model shape that are closest to the points in the geometric shape, according to a distance metric.
  • a ray can be propagated form each point in the measured sample set towards a point in the volumetric dataset.
  • the ray propagation integrates along the ray path in pixel space an opacity (or transparency) value in the look table corresponding to the volumetric dataset, until an opacity threshold is reached, at which point the ray propagation is terminated.
  • the termination point can then be taken as the point in the volumetric dataset that is closest to the point in the sample set.
  • the determination of the closest points can be controlled by modifying the look-up tables, which map image data values to opacity values, and which can be performed interactively by the user.
  • a registration between the sample set and the volumetric dataset can then be determined, and applied to the sample set to form an updated sample set.
  • the registration is based on a translation and a rotation, and thus the updated sample will typically not be perfectly aligned with the volumetric dataset.
  • a mean square error can be calculated between the points in the updated sample set and the points in the volumetric dataset identified as closest point, and if this error falls below a preset threshold, the registration can be terminated.
  • a flow chart of a mesh-to-image registration scheme according to an embodiment of the invention is presented in FIG. 1.
  • a pre-operation image volume is provided at step 11 , including opacity look-up tables for correlating pixel density to tissue type.
  • a polygonal mesh of an object to be registered with the pre- op image volume is obtained.
  • a ray is propagated from a vertex of the polygonal mesh, and the volume rendering line integral is calculated along the ray, using opacity values contained in the look-up tables.
  • the direction of the ray can be determined by a vector normal to a plane defined by those vertices nearest to the starting point of the ray.
  • Other method of determining the initial direction include taking the average of the normals of neighboring triangles or using a stored normal vector associated with the vertex. It is to be understood, however, that these examples are non-limiting and other methods of determining the initial ray direction are within the scope of an embodiment of the invention.
  • the look-up tables used are those containing opacity values that correspond to the object approximated by the polygonal mesh. The ray is terminated at a point where an opacity threshold is reached at step 14, and the termination point is added to a set of "closest" points.
  • the ray propagation is repeated for all vertices in the mesh at step 15, to obtain a set of closest points.
  • a registration is computed between the polygonal mesh and the set of closest points. There are many registration techniques known in the art. This registration is applied to the mesh at step 17, to obtain an updated mesh.
  • an error function typically a mean square error, is calculated between the points in the updated mesh and the set of closest points, and if the error is less than a predefined threshold, the registration is terminated at step 19. Otherwise, the process returns to step 13 and the ray casting is repeated.
  • the sample set can be a set of point/direction pairs of arbitrary origin, not only vertices on a polygonal mesh.
  • point/direction pair set could be derived from an acquisition mode that generates oriented points, such as a navigation catheter in an electrophysiology procedure, or points with associated normals derived from a volumetric dataset.
  • a distance threshold can be used to terminate ray propagation if a maximum distance has been reached from the starting point.
  • a distance threshold is useful for situations where there is a gap or missing feature in the volumetric dataset, for in such as case the line integral of opacity values along the ray may never reach the maximum opacity value.
  • the starting point of a ray propagation can be offset by the distance threshold in a negative ray direction.
  • an angular threshold can be used to check for compatibility between the ray direction and the normal direction at the destination point. This normal can be computed from the local gradient in the volumetric dataset. If the angle between the ray direction and the gradient normal is less than a predetermined value, the corresponding points can be regarding as matching points, while if the angle is greater than the value, the points can be regarded as not belonging to matching features. It is to be understood that the present invention can be implemented in various forms of hardware, software, firmware, special purpose processes, or a combination thereof.
  • a computer system 21 for implementing the present invention can comprise, inter alia, a central processing unit (CPU) 22, a memory 23 and an input output (I/O) interface 24.
  • the computer system 21 is generally coupled through the I/O interface 24 to a display 25 and various input devices 26 such as a mouse and a keyboard.
  • the support circuits can include circuits such as cache, power supplies, clock circuits, and a communication bus.
  • the memory 23 can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combinations thereof.
  • the present invention can be implemented as a routine 27 that is stored in memory 23 and executed by the CPU 22 to process the signal from the signal source 28.
  • the computer system 21 is a general purpose computer system that becomes a specific purpose computer system when executing the routine 27 of the present invention.
  • the computer system 21 also includes an operating system and micro instruction code.
  • the various processes and functions described herein can either be part of the micro instruction code or part of the application program (or combination thereof) which is executed via the operating system.
  • various other peripheral devices can be connected to the computer platform such as an additional data storage device and a printing device.

Abstract

A method of registering a digital image with a polygonal mesh comprising the steps of providing (11) a digital image, providing (12) a polygonal mesh comprising a plurality of vertices that approximates an object in the digital image, propagating (13) a ray from a vertex of the polygonal mesh into the digital image and calculating a line integral of opacity values wherein each opacity value corresponds to the intensity of a pixel of the image through which said ray passes, terminating (14) the ray at a point in the image when an opacity threshold is reached, and adding the termination point to a set of closest points. A registration is computed (16) between the vertices of the polygonal mesh and the set of closest points, and the registration is applied (17) to the polygonal mesh to obtain a new set of vertices.

Description

METHOD AND SYSTEM FOR MESH-TO-IMAGE REGISTRATION USING RAYCASTING
Cross Reference to Related United States Applications This application claims priority from "MESH-TO-IMAGE REGISTRATION USING RAYCASTING", U.S. Provisional Application No. 60/564,841 of Jens Guehring, filed April 23, 2004, the contents of which are incorporated herein by reference. Technical Field This invention is directed to registering datasets in a digital medical image by casting rays. Discussion of the Related Art Digital images are created from an array of numerical values representing a property (such as a grey scale value or magnetic field strength) associable with an anatomical location points referenced by a particular array location. The set of anatomical location points comprises the domain of the image. In 2-D digital images, or slice sections, the discrete array locations are termed pixels. Three-dimensional digital images can be constructed from stacked slice sections through various construction techniques known in the art. The 3-D images are made up of discrete volume elements, also referred to as voxels, composed of pixels from the 2-D images. The pixel or voxel properties can be processed to ascertain various properties about the anatomy of a patient associated with such pixels or voxels. Image registration is an optimization problem that finds a geometric transformation that maps points from a source dataset space into homologous points in a target dataset space. The objective of image registration is to describe a geometric mapping between two images with some kind of a model, which usually is dependent on parameters, known as registration parameters. The model determines the type of registration, whether it is a rigid, affine, deformable, regitration, etc., and therefore the specifics of the registration parameters. Registration of datasets is an important aspect for many imaging applications. In some applications, one dataset to be registered is provided with a polygonal representation, that is, represented as a triangular mesh, while the other dataset is provided as a volumetric image. One widely used family of registration algorithms are based on the iterative closest point (ICP) algorithm, introduced by Besl and MacKay. The idea behind these algorithms is to find, for a set of points in one dataset, the set of closest compatible points in the other dataset, and compute a transformation that minimizes a cost function penalizing the difference/distance between the established correspondences. Since the established correspondences do not necessarily reflect the true correspondences, the procedure is iterated until a termination criteria is fulfilled. The computation of the corresponding points is one of the most time consuming steps of the algorithm. In the visualization of medical images, volumetric datasets can be visualized using so-called ray-casting techniques, wherein for each pixel of the display device, a ray is cast into the volumetric dataset, and the intensity and opacity values along the ray are integrated. Typically, the opacity values are obtained by passing the stored intensity data values through a look-up table. The integration along the ray can be stopped if a threshold value for the opacity is reached. Visualization applications can perform ray-casting very efficiently. Summary of the Invention Exemplary embodiments of the invention as described herein generally include methods and systems for casting rays through points on the surface of a polygonal dataset along the surface normal direction in order to compute the closest point on a surface defined in a volumetric dataset, and iteratively minimizing the discrepancy between the point pairs. Applications of the embodiments of the invention include, but are not limited to, registering models of implants (e.g. stents), polygonal surfaces derived from the segmentation of volumetric datasets, and oriented point maps generated during electrophysiological mapping procedures. According to an aspect of the invention, there is provided a method of registering a digital image with a polygonal mesh comprising the steps of providing a digital image comprising a plurality of intensities corresponding to a domain of points in a D -dimensional space, providing a polygonal mesh comprising a plurality of vertices that approximates an object in the digital image, propagating a ray from a vertex of the polygonal mesh in a direction into the digital image and calculating a line integral of opacity values wherein each opacity value corresponds to the intensity of a pixel of the image through which said ray passes, terminating the ray at a point in the image when an opacity threshold is reached, and adding the termination point to a set of closest points. According to a further aspect of the invention, the steps of propagating a ray from a vertex of the polygonal mesh into the digital image, terminating the ray at a point in the image, and adding the termination point to a set of closest points are repeated for each vertex in the polygonal mesh. The method further comprises the steps of computing a registration between the vertices of the polygonal mesh and the set of closest points, applying the registration to the polygonal mesh to obtain a new set of vertices, and calculating an error between the set of closest points and the new set of vertices. According to a another aspect of the invention, the method further comprises, if the error is greater than a predefined threshold, repeating the steps of propagating rays from the mesh vertices into the image, terminating each ray at a point in the image, adding each termination point to a set of closest points, computing a registration between the vertices of the polygonal mesh and the set of closest points, applying the registration to the polygonal mesh to obtain a new set of vertices, and calculating an error between the set of closest points and the new set of vertices. According to a further aspect of the invention, the ray can be subdivided into n segments of length d, and the line integral of opacity values can be defined by n i—\ ∑ c{s{x{id )))dγ[ exp(- τ{s{x{j d )))d ) , <=0 j=0 wherein i,j are ray segment counters, x(kd) represents the position of the ray at a distance kd of the k"1 segment, the scalar intensity value corresponding to this position on the ray is denoted by s(x(kd)) , τ(s(x(kd))) represents an absorption look-up table value at said point, and c(s(x(kd))) represents an emission look-up table at said point. According to a further aspect of the invention, said ray is terminated if a predefined distance threshold is reached before said opacity threshold is reached. According to a further aspect of the invention, the starting point of said ray is offset in a negative direction by said distance threshold. According to a further aspect of the invention, the direction of said ray is determined by a vector normal to a plane defined by those vertices nearest to the starting vertex of said ray. According to a another aspect of the invention, the method further comprises the step of imposing an angular threshold on the direction of the ray, wherein if the angle between the ray and a normal to the termination point is greater than a predefined threshold, said termination point is excluded from the set of closest points. According to a further aspect of the invention, the direction of said ray is determined by taking an average of normals of triangles neighboring said vertex. According to a further aspect of the invention, the direction of said ray is determined from a stored normal vector associated with the vertex. According to another aspect of the invention, there is provided a program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for registering a digital image with a polygonal mesh. Brief Description of the Drawings FIG. 1 depicts a flow chart of a registration method according to an embodiment of the invention. FIG. 2 is a block diagram of an exemplary computer system for implementing a registration scheme according to an embodiment of the invention. Detailed Description of the Preferred Embodiments Exemplary embodiments of the invention as described herein generally include systems and methods for performing a polygonal surface to image registration in medical images using ray-casting to find corresponding points. As used herein, the term "image" refers to multi-dimensional data composed of discrete image elements (e.g., pixels for 2-D images and voxels for 3-D images). The image may be, for example, a medical image of a subject collected by computer tomography, magnetic resonance imaging, ultrasound, or any other medical imaging system known to one of skill in the art. The image may also be provided from non- medical contexts, such as, for example, remote sensing systems, electron microscopy, etc. Although an image can be thought of as a function from R3 to R, the methods of the inventions are not limited to such images, and can be applied to images of any dimension, e.g. a 2-D picture or a 3-D volume. For a 2- or 3- dimensional image, the domain of the image is typically a 2- or 3-dimensional rectangular array, wherein each pixel or voxel can be addressed with reference to a set of 2 or 3 mutually orthogonal axes. The terms "digital" and "digitized" as used herein will refer to images or volumes, as appropriate, in a digital or digitized format acquired via a digital acquisition system or via conversion from an analog image. In many imaging modalities, such as CT or MRI, the resulting intensity values or ranges of values can be correlated with specific types of tissue, enabling one to discriminate, for example, bone, muscle, flesh, and fat tissue, nerve fibers, blood vessels, organ walls, etc., based on the intensity ranges within the image. The raw intensity values in the image can serve as input to a transfer function whose output is an opacity value that can characterize the type of tissue. These opacity values can be used to define a look-up table where an opacity value that characterizes a particular type of tissue is associated with each pixel point. The use of opacity values to classify tissue also enables a user to select an object or tissue type to be displayed and only integrate opacity values corresponding to the selected object or tissue. In many diagnostic settings, a previously obtained volumetric image is used to guide a medical procedure, such as an examination of an organ. During the course of this procedure, new readings of the organ are frequently acquired that need to be correlated with the volumetric image guiding the examination. These new readings need not be from the same imaging modality used to create the volumetric image. For example, during an electrophysiological examination of the heart, a map of the electric properties of the heart wall is acquired. The measured data includes a point in three dimensions and a direction approximately normal to the heart wall, and can form a polygonal mesh. To support the diagnosis, a 3D CT or MR scan is often acquired before the procedure. These scans typically show quite well the boundary between the inside of the heart chambers and the heart wall, as determined by lookup tables that map pixels in the inside of the chamber to transparent opacity values. Another example of a procedure where a previously obtained volumetric image would need to be registered with a polygonal mesh obtained in the course of the procedure is a hip replacement surgery. In this case, the replacement hip can be represented by polygonal mesh, which needs to be registered with a volumetric image of the hip to be replaced. The origins of the rays to be cast would be the vertices of the mesh. It is helpful is this kind of diagnostic setting to be able to register the previously obtained volumetric image with the polygonal mesh acquired during the procedure. According to an embodiment of the invention, a ray casting algorithm can perform the registration. Ray casting is a commonly used volume rendering technique. Ray casting is a process that, for each pixel in an image volume to be rendered, casts a single ray from the eye through the center of the pixel and into the volume, integrating optical properties obtained from the encountered volume densities along the ray path. The optical properties can be obtained for the pixel density from a look-up table, and typically include the degree to which a pixel emits and absorbs light. The emission can include RGB values, if the image is a color image. The combination of RGB and absorption is frequently referred to as an RGBA value. For the purposes of the registering methods presented herein, the emission can be regarded as a single, gray-scale value. A ray cast into a volume can be denoted by x(t), where the ray has been parameterized by the distance t to the eye. The scalar intensity value corresponding to this position on the ray is denoted by s(x(t)) . Note that the position of the ray is a pixel or voxel in the image volume. At equispaced intervals along the ray, the volume density is re-sampled, using bilinear interpolation or tri-linear interpolation to reconstruct the original sample. After re-sampling, the scalar data value is mapped to the optical properties via a look-up table, which yields an RGBA value for this location within the volume. The volume rendering integral integrates along the ray absorption coefficients τ(s(x(t))) , accounting for the absorption of light, and colors c(s(x(t))), accounting for the emission of light. The absorption and emission coefficients can be obtained from look-up tables. The volume rendering integral can be used to obtain the integrated output C, that subsumes the corresponding color (emission) and opacity (absorption) contributions along the ray up to a certain distance D into the volume: C = c(s(x(t)))expi - τ(s(x(f)))df )dt ,
where c(s(x(t))) represents the color emitted at the point being evaluated, and
exp - τ(s(x(t')))d ] represents the cumulative absorption at that point. In some
cases, the ray-casting integral can be terminated if the integrated output C reaches a threshold value, for example, a value the represents opacity, or complete absorption. In practice, the integral can be approximated as a summation along ray- segments, where the ray is considered as a succession of ray segments of equal length of, with the number of samples being equal to n=D/d. The emission term for the fh segment can then be approximated as
Figure imgf000008_0001
, while the cumulative absorption can be approximated as exρ[-^ τ(s(x(id)))d). The summation in the exponent can be replaced by a multiplication of exponentiation terms:
Thus, the volume rendering integral can be approximated as n i—\ Capprøx=∑C(.J^ex (-τ(^( (y ))) ). ι=0 =0 According to an embodiment of the invention, rays can be cast from the measured sample points outwards in a direction normal to the heart wall. These rays would reach opacity at the point on the heart wall that is closest to the sample point, and the ray-casting integral would be terminated at that point. These two points can form a corresponding point pair in the sense of the ICP algorithm. As stated previously, the ICP algorithm is a procedure for registering a geometric shape whose internal representation is known with model shape. The first stage of the ICP algorithm is to find a set of points in the model shape that are closest to the points in the geometric shape, according to a distance metric. According to an embodiment of the invention, a ray can be propagated form each point in the measured sample set towards a point in the volumetric dataset. The ray propagation integrates along the ray path in pixel space an opacity (or transparency) value in the look table corresponding to the volumetric dataset, until an opacity threshold is reached, at which point the ray propagation is terminated. The termination point can then be taken as the point in the volumetric dataset that is closest to the point in the sample set. According to an embodiment of the invention, the determination of the closest points can be controlled by modifying the look-up tables, which map image data values to opacity values, and which can be performed interactively by the user. A registration between the sample set and the volumetric dataset can then be determined, and applied to the sample set to form an updated sample set. In the ICP, the registration is based on a translation and a rotation, and thus the updated sample will typically not be perfectly aligned with the volumetric dataset. A mean square error can be calculated between the points in the updated sample set and the points in the volumetric dataset identified as closest point, and if this error falls below a preset threshold, the registration can be terminated. If the registration is not being terminated, the points in the updated sample set can be taken as the starting points for another iteration of ray casting. "^ A flow chart of a mesh-to-image registration scheme according to an embodiment of the invention is presented in FIG. 1. A pre-operation image volume is provided at step 11 , including opacity look-up tables for correlating pixel density to tissue type. At step 12, a polygonal mesh of an object to be registered with the pre- op image volume is obtained. At step 13, a ray is propagated from a vertex of the polygonal mesh, and the volume rendering line integral is calculated along the ray, using opacity values contained in the look-up tables. According to one embodiment of the invention, the direction of the ray can be determined by a vector normal to a plane defined by those vertices nearest to the starting point of the ray. Other method of determining the initial direction include taking the average of the normals of neighboring triangles or using a stored normal vector associated with the vertex. It is to be understood, however, that these examples are non-limiting and other methods of determining the initial ray direction are within the scope of an embodiment of the invention. The look-up tables used are those containing opacity values that correspond to the object approximated by the polygonal mesh. The ray is terminated at a point where an opacity threshold is reached at step 14, and the termination point is added to a set of "closest" points. The ray propagation is repeated for all vertices in the mesh at step 15, to obtain a set of closest points. At step 16, a registration is computed between the polygonal mesh and the set of closest points. There are many registration techniques known in the art. This registration is applied to the mesh at step 17, to obtain an updated mesh. At step 18, an error function, typically a mean square error, is calculated between the points in the updated mesh and the set of closest points, and if the error is less than a predefined threshold, the registration is terminated at step 19. Otherwise, the process returns to step 13 and the ray casting is repeated. According to another embodiment of the invention, the sample set can be a set of point/direction pairs of arbitrary origin, not only vertices on a polygonal mesh. For example, point/direction pair set could be derived from an acquisition mode that generates oriented points, such as a navigation catheter in an electrophysiology procedure, or points with associated normals derived from a volumetric dataset. According to another embodiment of the invention, a distance threshold can be used to terminate ray propagation if a maximum distance has been reached from the starting point. A distance threshold is useful for situations where there is a gap or missing feature in the volumetric dataset, for in such as case the line integral of opacity values along the ray may never reach the maximum opacity value. In a further variation of this embodiment, the starting point of a ray propagation can be offset by the distance threshold in a negative ray direction. This can ensure that the ray-casting will find the surface implicitly represented in the volumetric dataset, assuming that the surface lies within the specified distance maximum. According to a further embodiment of the invention, an angular threshold can be used to check for compatibility between the ray direction and the normal direction at the destination point. This normal can be computed from the local gradient in the volumetric dataset. If the angle between the ray direction and the gradient normal is less than a predetermined value, the corresponding points can be regarding as matching points, while if the angle is greater than the value, the points can be regarded as not belonging to matching features. It is to be understood that the present invention can be implemented in various forms of hardware, software, firmware, special purpose processes, or a combination thereof. In one embodiment, the present invention can be implemented in software as an application program tangible embodied on a computer readable program storage device. The application program can be uploaded to, and executed by, a machine comprising any suitable architecture. Referring now to FIG. 2, according to an embodiment of the present invention, a computer system 21 for implementing the present invention can comprise, inter alia, a central processing unit (CPU) 22, a memory 23 and an input output (I/O) interface 24. The computer system 21 is generally coupled through the I/O interface 24 to a display 25 and various input devices 26 such as a mouse and a keyboard. The support circuits can include circuits such as cache, power supplies, clock circuits, and a communication bus. The memory 23 can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combinations thereof. The present invention can be implemented as a routine 27 that is stored in memory 23 and executed by the CPU 22 to process the signal from the signal source 28. As such, the computer system 21 is a general purpose computer system that becomes a specific purpose computer system when executing the routine 27 of the present invention. The computer system 21 also includes an operating system and micro instruction code. The various processes and functions described herein can either be part of the micro instruction code or part of the application program (or combination thereof) which is executed via the operating system. In addition, various other peripheral devices can be connected to the computer platform such as an additional data storage device and a printing device. It is to be further understood that, because some of the constituent system components and method steps depicted in the accompanying figures can be implemented in software, the actual connections between the systems components (or the process steps) may differ depending upon the manner in which the present invention is programmed. Given the teachings of the present invention provided herein, one of ordinary skill in the related art will be able to contemplate these and similar implementations or configurations of the present invention. The particular embodiments disclosed above are illustrative only, as the invention may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. It is therefore evident that the particular embodiments disclosed above may be altered or modified and all such variations are considered within the scope and spirit of the invention. Accordingly, the protection sought herein is as set forth in the claims below.

Claims

WHAT IS CLAIMED IS:
1. A method of registering a digital image with a polygonal mesh comprising the steps of: providing a digital image comprising a plurality of intensities corresponding to a domain of points in a D -dimensional space; providing a polygonal mesh comprising a plurality of vertices that approximates an object in the digital image; propagating a ray from a vertex of the polygonal mesh in a direction into the digital image and calculating a line integral of opacity values wherein each opacity value corresponds to the intensity of a pixel of the image through which said ray passes; terminating the ray at a point in the image when an opacity threshold is reached; and adding the termination point to a set of closest points.
2. The method of claim 1 , wherein the steps of propagating a ray from a vertex of the polygonal mesh into the digital image, terminating the ray at a point in the image, and adding the termination point to a set of closest points are repeated for each vertex in the polygonal mesh, and wherein the method further comprises the steps of: computing a registration between the vertices of the polygonal mesh and the set of closest points; applying the registration to the polygonal mesh to obtain a new set of vertices; and calculating an error between the set of closest points and the new set of vertices.
3. The method of claim 2, further comprising, if the error is greater than a predefined threshold, repeating the steps of propagating rays from the mesh vertices into the image, terminating each ray at a point in the image, adding each termination point to a set of closest points, computing a registration between the vertices of the polygonal mesh and the set of closest points, applying the registration to the polygonal mesh to obtain a new set of vertices, and calculating an error between the set of closest points and the new set of vertices.
4. The method of claim 1 , wherein the ray can be subdivided into n segments of length d, and the line integral of opacity values can be defined by i-l ∑c{s{x{ti)))dγiex -τ(s(x{jd)))d),
wherein /',;' are ray segment counters, x(kd) represents the position of the ray at a distance kd the ?h segment, the scalar intensity value corresponding to this position on the ray is denoted by s(x(kd)) , τ(s(x(kd))) represents an absorption look-up table value at said point, and c(s(x(kd))) represents an emission look-up table at said point.
5. The method of claim 1 , wherein said ray is terminated if a predefined distance threshold is reached before said opacity threshold is reached.
6. The method of claim 5, wherein the starting point of said ray is offset in a negative direction by said distance threshold.
7. The method of claim 1 , wherein the direction of said ray is determined by a vector normal to a plane defined by those vertices nearest to the starting vertex of said ray.
8. The method of claim 1 , wherein the direction of said ray is determined by taking an average of normals of triangles neighboring said vertex.
9. The method of claim 1 , wherein the direction of said ray is determined from a stored normal vector associated with the vertex.
10. The method of claim 1 , further comprising the step of imposing an angular threshold on the direction of the ray, wherein if the angle between the ray and a normal to the termination point is greater than a predefined threshold, said termination point is excluded from the set of closest points.
11. A method of registering an object in a pair of digital images, said method comprising the steps of: providing a first digital image and a second digital image, each digital image comprising a plurality of intensities corresponding to a domain of points in a D -dimensional space; extracting an object from said first image, and representing said object by a set of oriented points, wherein each oriented point has an associated direction vector; for each oriented point in the first image, propagating a ray from said oriented point of the first image in the direction of said direction vector into the second digital image and calculating a line integral of opacity values wherein each opacity value corresponds to the intensity of a pixel of the second image through which said ray passes; terminating the ray at a point in the second image when an opacity threshold is reached; adding the termination point to a set of closest points; computing a registration between the set of oriented points of the first image and the set of closest points; and applying the registration to the set of oriented points to obtain a new set of oriented points.
12. The method of claim 11 , wherein said object comprises a subset of points in said first image, and wherein the intensities of the points of said object are in a predetermined range.
13. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for registering a digital image with a polygonal mesh, said method comprising the steps of: providing a digital image comprising a plurality of intensities corresponding to a domain of points in a D -dimensional space; providing a polygonal mesh comprising a plurality of vertices that approximates an object in the digital image; propagating a ray from a vertex of the polygonal mesh in a direction into the digital image and calculating a line integral of opacity values wherein each opacity value corresponds to the intensity of a pixel of the image through which said ray passes; terminating the ray at a point in the image when an opacity threshold is reached; and adding the termination point to a set of closest points.
14. The computer readable program storage device of claim 13, wherein the steps of propagating a ray from a vertex of the polygonal mesh into the digital image, terminating the ray at a point in the image, and adding the termination point to a set of closest points are repeated for each vertex in the polygonal mesh, and wherein the method further comprises the steps of: computing a registration between the vertices of the polygonal mesh and the set of closest points; applying the registration to the polygonal mesh to obtain a new set of vertices; and calculating an error between the set of closest points and the new set of vertices.
15. The computer readable program storage device of claim 14, said method further comprising the step of, if the error is greater than a predefined threshold, repeating the steps of propagating rays from the mesh vertices into the image, terminating each ray at a point in the image, adding each termination point to a set of closest points, computing a registration between the vertices of the polygonal mesh and the set of closest points, applying the registration to the polygonal mesh to obtain a new set of vertices, and calculating an error between the set of closest points and the new set of vertices.
16. The computer readable program storage device of claim 13, wherein the ray can be subdivided into n segments of length d, and the line integral of opacity values can be defined by ∑c(s{x{id)))dflexV{-τ{s{x{jd)))d), 1=0 =0 wherein /,/are ray segment counters, x(kd) represents the position of the ray at a distance kdoi the kϊh segment, the scalar intensity value corresponding to this position on the ray is denoted by s(x(kd)) , τ(s(x(kd))) represents an absorption look-up table value at said point, and c(s(x(kd))) represents an emission look-up table at said point.
17. The computer readable program storage device of claim 13, wherein said ray is terminated if a predefined distance threshold is reached before said opacity threshold is reached.
18. The computer readable program storage device of claim 17, wherein the starting point of said ray is offset in a negative direction by said distance threshold.
19. The computer readable program storage device of claim 13, wherein the direction of said ray is determined by a vector normal to a plane defined by those vertices nearest to the starting vertex of said ray.
20. The computer readable program storage device of claim 13, said method further comprising the step of imposing an angular threshold on the direction of the ray, wherein if the angle between the ray and a normal to the termination point is greater than a predefined threshold, said termination point is excluded from the set of closest points.
21. The computer readable program storage device of claim 13, wherein the direction of said ray is determined by taking an average of normals of triangles neighboring said vertex.
22. The computer readable program storage device of claim 13, wherein the direction of said ray is determined from a stored normal vector associated with the vertex.
PCT/US2005/013917 2004-04-23 2005-04-22 Method and system for mesh-to-image registration using raycasting WO2005106795A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
DE112005000927T DE112005000927B4 (en) 2004-04-23 2005-04-22 Method for network-to-image registration using raycasting

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US56484104P 2004-04-23 2004-04-23
US60/564,841 2004-04-23
US11/111,397 US7339586B2 (en) 2004-04-23 2005-04-21 Method and system for mesh-to-image registration using raycasting
US11/111,397 2005-04-21

Publications (1)

Publication Number Publication Date
WO2005106795A1 true WO2005106795A1 (en) 2005-11-10

Family

ID=34967049

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2005/013917 WO2005106795A1 (en) 2004-04-23 2005-04-22 Method and system for mesh-to-image registration using raycasting

Country Status (3)

Country Link
US (1) US7339586B2 (en)
DE (1) DE112005000927B4 (en)
WO (1) WO2005106795A1 (en)

Families Citing this family (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050110793A1 (en) * 2003-11-21 2005-05-26 Steen Erik N. Methods and systems for graphics processing in a medical imaging system
US7333107B2 (en) * 2005-08-18 2008-02-19 Voxar Limited Volume rendering apparatus and process
EP1959391A1 (en) * 2007-02-13 2008-08-20 BrainLAB AG Determination of the three dimensional contour path of an anatomical structure
US8169435B2 (en) * 2007-12-06 2012-05-01 Esko Ip Nv Generating and rendering three dimensional models of flexible packaging
KR101194605B1 (en) * 2008-12-22 2012-10-25 한국전자통신연구원 Apparatus and method for synthesizing time-coherent texture
US8406497B2 (en) * 2009-02-25 2013-03-26 Dongwook YOON Method for population-driven identification of body landmarks
US20120082354A1 (en) * 2009-06-24 2012-04-05 Koninklijke Philips Electronics N.V. Establishing a contour of a structure based on image information
US9730776B2 (en) 2010-02-24 2017-08-15 D4D Technologies, Llc Display method and system for enabling an operator to visualize and correct alignment errors in imaged data sets
US9436868B2 (en) * 2010-09-10 2016-09-06 Dimensional Photonics International, Inc. Object classification for measured three-dimensional object scenes
US8922547B2 (en) * 2010-12-22 2014-12-30 Electronics And Telecommunications Research Institute 3D model shape transformation method and apparatus
US8941643B1 (en) * 2010-12-28 2015-01-27 Lucasfilm Entertainment Company Ltd. Quality assurance testing of virtual environments
US9489764B2 (en) * 2012-04-17 2016-11-08 Samsung Electronics Co., Ltd. Method of generating three-dimensional (3D) volumetric data
KR102068251B1 (en) * 2012-04-17 2020-01-20 삼성전자주식회사 Method of generating 3d volumetric data
CN104103083A (en) * 2013-04-03 2014-10-15 株式会社东芝 Image processing device, method and medical imaging device
EP3072111A4 (en) * 2013-11-20 2017-05-31 Fovia, Inc. Volume rendering polygons for 3-d printing
US20150178425A1 (en) * 2013-12-20 2015-06-25 The Procter & Gamble Company Method for modeling graphics on a flexible form
US10636184B2 (en) 2015-10-14 2020-04-28 Fovia, Inc. Methods and systems for interactive 3D segmentation
US10573200B2 (en) * 2017-03-30 2020-02-25 Cae Healthcare Canada Inc. System and method for determining a position on an external surface of an object

Family Cites Families (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7110587B1 (en) * 1995-05-31 2006-09-19 Ge Medical Systems Israel Ltd. Registration of nuclear medicine images
US6151404A (en) * 1995-06-01 2000-11-21 Medical Media Systems Anatomical visualization system
IL127314A0 (en) * 1998-11-27 1999-09-22 Algotec Systems Ltd A method for forming a perspective rendering from a voxel space
US20020190984A1 (en) * 1999-10-01 2002-12-19 Larry D. Seiler Voxel and sample pruning in a parallel pipelined volume rendering system
US6654012B1 (en) * 1999-10-01 2003-11-25 Terarecon, Inc. Early ray termination in a parallel pipelined volume rendering system
AU2001239926A1 (en) * 2000-02-25 2001-09-03 The Research Foundation Of State University Of New York Apparatus and method for volume processing and rendering
US6807290B2 (en) * 2000-03-09 2004-10-19 Microsoft Corporation Rapid computer modeling of faces for animation
US20050275652A1 (en) * 2000-06-19 2005-12-15 Alexander Keller Computer graphics system and computer-implemented method for simulating participating media using sample points determined using elements of a low-discrepancy sequence
US6750873B1 (en) * 2000-06-27 2004-06-15 International Business Machines Corporation High quality texture reconstruction from multiple scans
US6760024B1 (en) * 2000-07-19 2004-07-06 Pixar Method and apparatus for rendering shadows
US7348977B2 (en) * 2000-07-19 2008-03-25 Pixar Subsurface scattering approximation methods and apparatus
US20030028090A1 (en) * 2000-12-20 2003-02-06 Image-Guided Neurologics, Inc. Method for dynamic characterization of density fields in a compound structure
US6856324B2 (en) * 2001-03-27 2005-02-15 Siemens Corporate Research, Inc. Augmented reality guided instrument positioning with guiding graphics
US6744435B2 (en) * 2001-04-26 2004-06-01 Mitsubishi Electric Research Laboratories, Inc. Rendering discrete sample points projected to a screen space with a continuous resampling filter
US20050018885A1 (en) * 2001-05-31 2005-01-27 Xuesong Chen System and method of anatomical modeling
US7262770B2 (en) * 2002-03-21 2007-08-28 Microsoft Corporation Graphics image rendering with radiance self-transfer for low-frequency lighting environments
US7301538B2 (en) * 2003-08-18 2007-11-27 Fovia, Inc. Method and system for adaptive direct volume rendering
US8090164B2 (en) * 2003-08-25 2012-01-03 The University Of North Carolina At Chapel Hill Systems, methods, and computer program products for analysis of vessel attributes for diagnosis, disease staging, and surgical planning
US7197170B2 (en) * 2003-11-10 2007-03-27 M2S, Inc. Anatomical visualization and measurement system
US20050251029A1 (en) * 2004-04-21 2005-11-10 Ali Khamene Radiation therapy treatment plan
US7522163B2 (en) * 2004-08-28 2009-04-21 David Holmes Method and apparatus for determining offsets of a part from a digital image

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
J. GUEHRING: "Reliable 3D Surface Acquisition, Registration and Validation Using Statistical Error Models", THIRD INTERNATIONAL CONFERENCE ON 3-D DIGITAL IMAGING AND MODELING (3DIM '01), 2001, pages 224, XP002346323 *
KAUFFMANN C ET AL: "Computer-aided method for quantification of cartilage thickness and volume changes using MRI: validation study using a synthetic model", IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING IEEE USA, vol. 50, no. 8, August 2003 (2003-08-01), pages 978 - 988, XP002346322, ISSN: 0018-9294 *
MALLADI R ET AL: "SHAPE MODELING WITH FRONT PROPAGATION: A LEVEL SET APPROACH", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE INC. NEW YORK, US, vol. 17, no. 2, 1 February 1995 (1995-02-01), pages 158 - 174, XP000491540, ISSN: 0162-8828 *
MONTAGNAT J ET AL: "Anisotropic filtering for model-based segmentation of 4D cylindrical echocardiographic images", PATTERN RECOGNITION LETTERS, NORTH-HOLLAND PUBL. AMSTERDAM, NL, vol. 24, no. 4-5, February 2003 (2003-02-01), pages 815 - 828, XP004391219, ISSN: 0167-8655 *
RUSINKIEWICZ S ET AL: "Efficient variants of the ICP algorithm", 3-D DIGITAL IMAGING AND MODELING, 2001. PROCEEDINGS. THIRD INTERNATIONAL CONFERENCE ON 28 MAY - 1 JUNE 2001, PISCATAWAY, NJ, USA,IEEE, 28 May 2001 (2001-05-28), pages 145 - 152, XP010542858, ISBN: 0-7695-0984-3 *

Also Published As

Publication number Publication date
DE112005000927B4 (en) 2010-07-08
US20050237328A1 (en) 2005-10-27
DE112005000927T5 (en) 2007-04-26
US7339586B2 (en) 2008-03-04

Similar Documents

Publication Publication Date Title
US7339586B2 (en) Method and system for mesh-to-image registration using raycasting
US8423124B2 (en) Method and system for spine visualization in 3D medical images
Fischer et al. Ill-posed medicine—an introduction to image registration
EP2048617A2 (en) Method, system and software product for providing efficient registration of volumetric images
JP2007537770A (en) A dynamic crop box determination method for display optimization of luminal structures in endoscopic images
US9697600B2 (en) Multi-modal segmentatin of image data
EP2827301A1 (en) Image generation device, method, and program
JP7214434B2 (en) MEDICAL IMAGE PROCESSING APPARATUS AND MEDICAL IMAGE PROCESSING PROGRAM
CN110807770A (en) Medical image processing, recognizing and displaying method and storage medium
KR102537214B1 (en) Method and apparatus for determining mid-sagittal plane in magnetic resonance images
WO2022219631A1 (en) Systems and methods for reconstruction of 3d images from ultrasound and camera images
WO2011041475A1 (en) Medical image analysis system using n-way belief propagation for anatomical images subject to deformation and related methods
Bennani et al. Three-dimensional reconstruction of In Vivo human lumbar spine from biplanar radiographs
JP6257949B2 (en) Image processing apparatus and medical image diagnostic apparatus
CN107705350B (en) Medical image generation method, device and equipment
JP5122650B2 (en) Path neighborhood rendering
Turlington et al. New techniques for efficient sliding thin-slab volume visualization
CN108876783B (en) Image fusion method and system, medical equipment and image fusion terminal
Jung et al. Occlusion and slice-based volume rendering augmentation for PET-CT
Levin et al. Acceleration of 3D, nonlinear warping using standard video graphics hardware: implementation and initial validation
US10803645B2 (en) Visualization of anatomical cavities
Göcke et al. Fast volume rendering methods for voxel-based 2D/3D registration-A comparative study
Ruff et al. Volume rendering of multimodal images for the planning of skull base surgery
Hawkes et al. 3D multimodal imaging in image guided interventions
Liu et al. Symmetry identification using partial surface matching and tilt correction in 3D brain images

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BW BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE EG ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KM KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NA NI NO NZ OM PG PH PL PT RO RU SC SD SE SG SK SL SM SY TJ TM TN TR TT TZ UA UG US UZ VC VN YU ZA ZM ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): BW GH GM KE LS MW MZ NA SD SL SZ TZ UG ZM ZW AM AZ BY KG KZ MD RU TJ TM AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IS IT LT LU MC NL PL PT RO SE SI SK TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
WWE Wipo information: entry into national phase

Ref document number: 1120050009270

Country of ref document: DE

WWE Wipo information: entry into national phase

Ref document number: 6296/DELNP/2006

Country of ref document: IN

RET De translation (de og part 6b)

Ref document number: 112005000927

Country of ref document: DE

Date of ref document: 20070426

Kind code of ref document: P

WWE Wipo information: entry into national phase

Ref document number: 112005000927

Country of ref document: DE

122 Ep: pct application non-entry in european phase
REG Reference to national code

Ref country code: DE

Ref legal event code: 8607