WO2006134565A2 - Method of model-based elastic image registration for comparing a first and a second image - Google Patents
Method of model-based elastic image registration for comparing a first and a second image Download PDFInfo
- Publication number
- WO2006134565A2 WO2006134565A2 PCT/IB2006/051906 IB2006051906W WO2006134565A2 WO 2006134565 A2 WO2006134565 A2 WO 2006134565A2 IB 2006051906 W IB2006051906 W IB 2006051906W WO 2006134565 A2 WO2006134565 A2 WO 2006134565A2
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- image
- constraints
- restricted
- landmarks
- structures
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 239000000463 material Substances 0.000 claims abstract description 25
- 238000000638 solvent extraction Methods 0.000 claims abstract description 14
- 230000003044 adaptive effect Effects 0.000 claims description 16
- 230000009466 transformation Effects 0.000 claims description 12
- 230000005489 elastic deformation Effects 0.000 claims description 11
- 238000011524 similarity measure Methods 0.000 claims description 11
- 238000003384 imaging method Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 5
- 230000002452 interceptive effect Effects 0.000 claims description 5
- 230000011218 segmentation Effects 0.000 claims description 5
- 238000005457 optimization Methods 0.000 abstract description 17
- 238000013459 approach Methods 0.000 abstract description 7
- 238000006073 displacement reaction Methods 0.000 abstract description 6
- 210000004072 lung Anatomy 0.000 description 13
- 230000008569 process Effects 0.000 description 11
- 230000008901 benefit Effects 0.000 description 8
- 210000003484 anatomy Anatomy 0.000 description 7
- 239000000700 radioactive tracer Substances 0.000 description 7
- 210000001519 tissue Anatomy 0.000 description 6
- 238000000844 transformation Methods 0.000 description 6
- 239000013013 elastic material Substances 0.000 description 5
- 210000000056 organ Anatomy 0.000 description 5
- 206010028980 Neoplasm Diseases 0.000 description 3
- 210000003127 knee Anatomy 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 210000000988 bone and bone Anatomy 0.000 description 2
- 238000010968 computed tomography angiography Methods 0.000 description 2
- 230000008602 contraction Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000001959 radiotherapy Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 238000001356 surgical procedure Methods 0.000 description 2
- IVTMXOXVAHXCHI-YXLMWLKOSA-N (2s)-2-amino-3-(3,4-dihydroxyphenyl)propanoic acid;(2s)-3-(3,4-dihydroxyphenyl)-2-hydrazinyl-2-methylpropanoic acid Chemical compound OC(=O)[C@@H](N)CC1=CC=C(O)C(O)=C1.NN[C@@](C(O)=O)(C)CC1=CC=C(O)C(O)=C1 IVTMXOXVAHXCHI-YXLMWLKOSA-N 0.000 description 1
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 description 1
- 101000608154 Homo sapiens Peroxiredoxin-like 2A Proteins 0.000 description 1
- 102100039896 Peroxiredoxin-like 2A Human genes 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013152 interventional procedure Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000000135 prohibitive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000029058 respiratory gaseous exchange Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000002603 single-photon emission computed tomography Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000008685 targeting Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/14—Transformations for image registration, e.g. adjusting or mapping for alignment of images
- G06T3/153—Transformations for image registration, e.g. adjusting or mapping for alignment of images using elastic snapping
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
Definitions
- the invention relates to a method of model-based elastic image registration for comparing a first and a second image, in particular for a medical and/or a biomedical application.
- the invention also relates to a respective system for model-based elastic image registration for comparing a first and a second image, in particular for a medical and/or biomedical application.
- the invention relates to an image acquisition device, an image workstation and a computer program product and an information carrier.
- Image registration is an important procedure in medical image analysis, aiming at obtaining complementary information from different representations of the same anatomy.
- the goal of image registration is to find a transformation bringing the anatomy in the source and target image into best possible spatial correspondence.
- Point- based (landmark-based) elastic registration can be carried out by automatically finding corresponding point landmarks in both images and using the point landmark correspondences as constraints to interpolate or approximate the global displacement field as for instance described in "Medical Image Registration", J. V. Hajnal, D.L.G. Hill and DJ.
- An advantage of parametric methods is the ability to represent non- linear transformations with a moderate number of parameters.
- One example is deformable registration based on regular B-spline grids as for instance described in "Spline-Based Elastic Image Registration", Karl Rohr, PAMM, Vol. 3, Issue 1, pages 36-39, published online: November 28, 2000.
- the performance thereof is highly dependent on the grid resolution, with fine grids leading to a high-dimensional search space and coarse grids leading to improper registration of small structures.
- Desirable is a concept of increased computational efficiency and better results also for inhomogeneous materials.
- the object of which is to provide, in particular for a medical and/or biomedical application, a method and apparatus of model- based elastic image registration for comparing a first and a second image wherein even upon dealing with inhomogeneous materials computational efficiency and result quality is increased.
- a method of model-based elastic image registration for comparing a first and a second image comprising the steps of: determining an optimized elastic deformation field by optimizing a similarity measure between the first and the second image on basis of an adaptive elastic registration, wherein deformation field constraints are imposed by automatically providing corresponding control point landmarks in the first and second image applying adaptive Gaussian- shaped forces as a transformation module at the control point landmarks.
- the step of imposing deformation field constraints further comprises: partitioning one or more of corresponding, restricted structures in the first and the second image; providing additional constraints derived from a-priori-knowledge to the one or more restricted structures.
- a system for modul-based elastic image registration for comparing a first and a second image comprising: means for determining an optimized elastic deformation field by optimizing a similarity -measure between the first and the second image on basis of an adaptive elastic registration, means for imposing deformation field constraints comprising means for automatically providing corresponding control point landmarks in the first and second image means for applying adaptive Gaussian- shaped forces as a transformation module at the control point landmarks;
- the means for imposing deformation field constraints further comprises: means for partitioning one or more restricted structures corresponding in the first and the second image; - means for providing additional constraints derived from a-priori-knowledge to the one or more restricted structures.
- the present invention is directed to improve the elastic registration paradigm by combining the automated optimization procedure for adaptive Gaussian forces with a-priori-knowledge which is applied for restricted structures, correspondingly in the first and the second image, said restricted structures are identified by a proper process of partitioning.
- Such process may be applied manually, semi-automatically, for instance interactively, or automatically upon use of proper conditions and of a respective system by a user, in particular by a physician.
- tools for partitioning can be effectively implemented to identify restricted structures in an image and subsequently imposing additional constraints derived from a-priori-knowledge to the one or more restricted structures.
- A-priori-knowledge is available in various forms as will be described in developed configurations of the inventive concept.
- the concept leads to effective reduction of computational efforts, and therefore combines the advantages of non- linear model-based elastic image registration concepts based on adaptive Gaussian-shaped forces with practical approaches using a-priori-knowledge.
- the inventive concept allows to combine offline global optimization concepts with online local refinement concepts at interactive speed. This is of particular importance for online situations like in surgery.
- global and local registration accuracy requirement can be adapted to foci on clinical interest and registration can be improved to safely handle highly disparate modalities.
- it can be advantageous in certain situations to neglect an optimization of a global similarity measure to the advantage of an optimization of a local similarity measure.
- the partitioning step comprises segmentation of the first and second image.
- segmentation process may be accomplished by various techniques, for instance by a tresholding process or a seed-growing technique.
- the a-priori-knowledge constraints for the restricted structure are applied as geometrical constraints.
- Preferred geometrical constraints are imposed by defining a restricted structure in form of a point or a line or an area. Also the form or boundary of an area or a line or a point gives proper geometrical constraints to a restricted structure.
- the advantage is that geometrical constraints are easy to apply, in particular manually, semi- automatically or automatically if necessary.
- additional constraints are imposed as interactively defining corresponding point landmarks in the first and second image.
- additional constraints can be imposed as landmarks resulting from automatic, semi-automatic or interactive identification of corresponding areas or boundaries in the first and second image.
- Landmarks in form of a point, line or area can be easy applied for instance by manually selecting point landmarks or by applying automated deformable mesh adaptation in images to be registered wherein the vertices of meshes will provide a desired mapping.
- Meshes of the mentioned kind are preferably provided by a segmentation process mentioned above. This concept is advantageously applied even in the case that structures cannot clearly be identified automatically, e.g. on the basis of gray- values.
- the a-priori-knowledge constraints for the restricted structure are applied as physiological constraints.
- different material properties of corresponding structures may be applied to certain geometric structures in an image.
- the latter may be outlined by a point or a line or an area or a form or a boundary thereof or the like.
- the inhomogeneous properties of a material are accounted for. For instance different elastic material properties may be given to a first structure of an image as compared to further structures of the image.
- constraints which clearly transcend mere geometric reasoning.
- the latter kind of constraints are preferably applied when no geometric landmark can be identified clearly.
- spatial neighborhood relations can be established.
- time constraints based on, for instance, tracer dynamics, can be given to impose a-priori- knowledge constraints. Mathematically such schemes lead to more approximation- like concepts instead of strict interpolation- like concepts for clearly defined structures.
- a digital circuit processor of the mentioned kind may be implemented in one or more multi-processor systems.
- inventive concept also leads to an image acquisition device and image workstation each comprising the system as described above.
- the invention leads to a computer program product storable on a medium readable by a computing, imaging and/or printer system, comprising a software code section which induces the computing, imaging and/or printer system to execute the method as described above when the product is executed on the computing, imaging and/or system.
- the invention also leads to an information carrier comprising the computer program product as described above.
- the described embodiments are not mandatory.
- a person skilled in the art may change the order of steps or perform steps concurrently using threading models, multi-processor systems or multiple processes without departing from the concept as intended by the current invention.
- the invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer.
- several of these means can be embodied by one and the same item of computer readable software or hardware.
- imaging device in particular comprise systems for medical acquisition of data like 3D- RA, MR, PET, SPECT etc..
- Fig. 1 depicts a diagrammatic scheme of a system for model-based elastic image registration for comparing a first and a second image in a medical and/or biomedical application according to a preferred embodiment of the invention
- Fig. 2 depicts a source and a target image of a PET/CT registration of a lung wherein the partitioned restricted structure of the lungs are imposed to an additional constraint with regard to material properties and point landmarks are interactively defined, both kinds of constraints are derived from a-priori-knowledge according to a preferred embodiment of the invention
- Fig. 1 depicts a diagrammatic scheme of a system for model-based elastic image registration for comparing a first and a second image in a medical and/or biomedical application according to a preferred embodiment of the invention
- Fig. 2 depicts a source and a target image of a PET/CT registration of a lung wherein the partitioned restricted structure of the lungs are imposed to an additional constraint with regard to material properties and point landmarks are interactively defined
- FIG. 3 depicts a source and a target image of a CT registration with application to radiation therapy of an abdominopelvic as a further anatomical example using a boundary mapping as an additional constraint of a-priori-knowledge in a further preferred embodiment of the invention
- Fig. 4 depicts a source and target image and a respective deformed mesh image of a MRI registration of a knee example wherein a smooth deformation field is used for less compressible materials as an additional a-priori-knowledge constraint according to a further preferred embodiment of the invention
- Fig. 5 depicts images like those of Fig. 4 for a PET/CT registration of a lung wherein an inhomogeneous deformation field is used for more compressible materials as an additional constraint of a-priori-knowledge according to a further preferred embodiment of the invention
- Fig. 6 depicts a source and a target image of a PET/CT registration of a further anatomical example wherein an additional point landmark constraint is used as a- priori-knowledge according to a preferred embodiment of the invention.
- the preferred embodiment of the instant invention is directed to physics-based registration of images, i.e. a source image Is(x) indicated by reference mark 3 and a target image I ⁇ (x) indicated by reference mark 5.
- images i.e. a source image Is(x) indicated by reference mark 3 and a target image I ⁇ (x) indicated by reference mark 5.
- the elastic deformation or displacement field is indicated by reference mark 7.
- / : ⁇ — > IR 3 denotes the vector of the applied forces acting on the underlying physical medium.
- u : ⁇ — > IR 3 is the displacement field
- L is an operator defining the response of the material.
- the optimal elastic deformation is determined by optimizing, in particular maximizing a similarity measure M between a source image Is(x) and a target image Ij ⁇ x), which is indicated by reference mark 11.
- a respective system 1 for model based elastic image registration for comparing a first image 3 and a second image 5 is adapted for a medical and/or biomedical application.
- Said system 1 comprises means 11 for determining an optimal elastic deformation field 7 by maximizing a similarity measure 11 between the first image Is(x) and the second image Ij ⁇ x) on basis of an adaptive elastic registration.
- Deformation constraints are imposed by means 13 for automatically providing control point landmarks 2 in the first image 3 and correspondingly control point landmarks 4 in the second image 5.
- the system 1 provides means 15 for applying adaptive Gaussian- shaped forces f° as a transformation module at the control point landmarks 2, 4.
- the position of the landmarks 2, 4, the directions and magnitudes of the applied forces f° as well as their influence areas are optimized to reach a maximum of the similarity measure between the images 3 und 5, i.e. Is(x) and Ij ⁇ x).
- the preferred embodiment of the system 1 provides a means 17 for imposing additional deformation field constraints.
- the means 17 further comprises means 19 for partitioning one or more of restricted structures, corresponding in the first image 3 and the second image 5; and means 21 for providing additional constraints / Add pa r t derived from a-priori-knowledge to the one or more restricted structures, like for instance the structures indicated by landmarks 2, 4.
- the preferred embodiment of the instant invention as outlined in Fig. 1 reduces the complexity of the solution process and thereby limits computational costs for optimization and increases efficiency of the solution process. Furthermore, the embodiment of the instant invention achieves to perform the registration process also for inhomogeneous materials and still the computational effort is kept low.
- FIG. 2 shows on the left hand side a first image 3 in form of a CT image and on the right hand side a second image 5 in form of a PET transmission map each with segmented lung outlines 23 and additional point landmarks 25, both serving as additional deformation field constraints.
- lung and body outlines can be segmented automatically, for example by using deformable mesh adaptation and corresponding structures identified through a mapping between mesh vertices.
- point landmarks 25 which may be defined preferably interactively.
- point landmarks may be defined automatically.
- the instant embodiment has preferable use in the case point landmarks cannot be defined due to low contrast values or insufficient gray value parameters in an image 3, 5. Defining landmarks 23, 25 interactively, manually or semi-automatically is an essential help to reduce the complexity of the problem to be solved as outlined with regard to Fig. 1.
- a physiological constraint is imposed to the area 27 defined by the lung outlines 23.
- tissuel which is different from the elastic property assigned to the tissue2 outside the lung outlines 23. Consequently, different materials in the image are provided with different material properties of the corresponding structure upon registration of the images 3, 5.
- Interventional applications are a good example to illustrate a possible implementation of this technique.
- pre- operatively acquired images for instance CT, MR or nuclear images
- online acquired microscopic, endoscopic or ultra-sound images to monitor or plan the interventional procedure.
- Registration of such disparate modalities and of images that have been acquired under such different circumstances and patient positioning is hard due to the large non- linear deformations to be compensated and due to the lack of sufficient landmarks that can automatically and unambiguously identified in the respective image data.
- landmarks such as fiducial markers, surgical instruments, anatomical structures in conjunction with information extracted from the DICOM header accompanying the pre-operatively acquired images and providing information about patient position, details of the acquisition protocol, the anatomical domain imaged etc.
- a further example of point-to-point correspondences between the boundaries of organs in the images to be registered is given in Fig. 3.
- the corresponding anatomical structure in a primary image 3 and the follow up image 5 are denoted between regions of different gray- value appearance.
- Some boundaries 29 are indicated for example as boundaries between bone und tissue or as boundaries between organs.
- the boundaries 29 can be registered automatically adapting deformable models for instance triangular meshes to the anatomical structures of interest, where certain mesh vertices can also be used as landmarks. In the latter case also landmarks, which have not a reference mark in Fig. 3, can be registered automatically.
- the proposed concept of the invention can be used to achieve optimized global registration and thereafter, in a second step, to achieve optimized local registration of a certain body organ or another body feature of interest.
- a knee and to Fig. 5 the lungs are shown in a first image 3 as a source and a second image 5 as a target next to a respective deformation field 6.
- the underlying deformations can give a clue for assigning the material properties.
- incompressible elastic materials like those for the bones of a knee in the MRI registration of Fig. 4 are well suited for a modeling such deformation fields.
- highly compressible elastic materials are advantageous for registration of PET/CT lung images as shown in Fig. 5, where the breathing motion causes considerable local expansion and/or contraction in the images.
- An elasticity of less compressible material is favorable in cases where the deformation field is smooth like for instance inside the lungs. More compressible materials where the deformation field is inhomogeneous may be applied to an area outside the lung.
- This kind of assigning different material properties to individual anatomical structures based on the physical properties like compressibility etc. is a further developed embodiment and has advantages over an over-all assigning of a deformation elasticity for an image.
- a-priori-knowledge about elastic material properties can be derived by explicitly including the Poisson's ratio v into the optimization procedure.
- a smooth deformation field (of less compressible materials) can be achieved by v — > 0.49.
- An inhomogenous deformation field (of more compressible materials) can be achieved by v — > 0.0. It's optimal value for a specific registration application can be obtained by test runs and then fixed to reduce the dimensionality of the optimization problem.
- Fig. 6 depicts a structure where the position of the hot spot indicated by the arrow in the PET image was used as a point landmark to register a CT image with a PET image.
- MI molecular imaging
- CTAs may be based on geometric relations restricting CTAs to certain anatomical domains such as organ boundaries, areas of certain cell or tissue types etc.. They may alternatively or in addition be based on time constraints restricting the contrast ranges to match with respect to the tracer dynamics and the timing of the acquisition protocol. These relations will have to be inferred from the pharmaceutical description of the dynamic and the targeting behavior of the tracer substance at hand.
- the invention aims at improving the point-based elastic registration paradigm.
- Point-based elastic registration is typically carried out by finding corresponding point landmarks 2, 4 in both images and using the point correspondences as constraints to interpolate the global displacement field.
- a limitation of this approach is that it only ensures the correspondences between structures where point landmarks 2, 4 can be identified.
- Alternative concepts are limited by high computational costs for optimization.
- the concept of the invention provides a method and a system 1 wherein additional deformation fields constraints are imposed by: partitioning (PART (Is, I T )) one or more restricted structures corresponding in the first and the second image and providing additional constraints / Add pa r t derived from a-priori-knowledge to the one or more restricted structures.
- Preferred examples are i) pairs of interactively defined point landmarks 25, ii) landmarks resulting from automatic identification of corresponding structures in form of a line 23 or an area 27 or a form or a boundary (29, Fig. 3) thereof, iii) different material properties, like tissue 1 and tissue2, of corresponding structures, iv) physiological constraints establishing more general correspondences.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Processing Or Creating Images (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
Description
Claims
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/917,155 US20080205719A1 (en) | 2005-06-15 | 2006-06-14 | Method of Model-Based Elastic Image Registration For Comparing a First and a Second Image |
EP06765744A EP1894161A2 (en) | 2005-06-15 | 2006-06-14 | Method of model-based elastic image registration for comparing a first and a second image |
JP2008516489A JP2008546441A (en) | 2005-06-15 | 2006-06-14 | Elastic image registration method based on a model for comparing first and second images |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP05105238.9 | 2005-06-15 | ||
EP05105238 | 2005-06-15 |
Publications (2)
Publication Number | Publication Date |
---|---|
WO2006134565A2 true WO2006134565A2 (en) | 2006-12-21 |
WO2006134565A3 WO2006134565A3 (en) | 2007-02-22 |
Family
ID=37395855
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/IB2006/051906 WO2006134565A2 (en) | 2005-06-15 | 2006-06-14 | Method of model-based elastic image registration for comparing a first and a second image |
Country Status (5)
Country | Link |
---|---|
US (1) | US20080205719A1 (en) |
EP (1) | EP1894161A2 (en) |
JP (1) | JP2008546441A (en) |
CN (1) | CN101198981A (en) |
WO (1) | WO2006134565A2 (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2009019630A2 (en) * | 2007-08-03 | 2009-02-12 | Koninklijke Philips Electronics N.V. | Anatomically constrained image registration |
WO2010058854A1 (en) | 2008-11-20 | 2010-05-27 | Canon Kabushiki Kaisha | Information processing apparatus, information processing method, program, and storage medium |
WO2012172454A1 (en) * | 2011-06-16 | 2012-12-20 | Koninklijke Philips Electronics N.V. | Hybrid point-based registration |
EP2923262A4 (en) * | 2012-11-23 | 2016-07-13 | Cadens Medical Imaging Inc | Method and system for displaying to a user a transition between a first rendered projection and a second rendered projection |
Families Citing this family (21)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP2044884B1 (en) * | 2007-10-02 | 2015-12-09 | Brainlab AG | Detection and determination of changes in position of structural parts of a body |
JP2011512999A (en) * | 2008-03-04 | 2011-04-28 | トモセラピー・インコーポレーテッド | Improved image segmentation method and system |
US8811708B2 (en) * | 2009-04-15 | 2014-08-19 | Koninklijke Philips N.V. | Quantification of medical image data |
US9129360B2 (en) * | 2009-06-10 | 2015-09-08 | Koninklijke Philips N.V. | Visualization apparatus for visualizing an image data set |
EP2446416B1 (en) * | 2009-06-24 | 2014-04-30 | Koninklijke Philips N.V. | Establishing a contour of a structure based on image information |
JP5613235B2 (en) * | 2009-07-20 | 2014-10-22 | コーニンクレッカ フィリップス エヌ ヴェ | Tissue modeling for defining tumor areas of interest |
EP2617012B1 (en) * | 2010-09-16 | 2015-06-17 | Mor Research Applications Ltd. | Method and system for analyzing images |
EP2661228B1 (en) | 2011-01-05 | 2014-12-24 | Koninklijke Philips N.V. | Device and method for determining actual tissue layer boundaries of a body |
WO2012109641A2 (en) * | 2011-02-11 | 2012-08-16 | Emory University | Systems, methods and computer readable storage mediums storing instructions for 3d registration of medical images |
DE102011080905B4 (en) * | 2011-08-12 | 2014-03-27 | Siemens Aktiengesellschaft | Method for visualizing the registration quality of medical image data sets |
WO2014022480A1 (en) * | 2012-07-31 | 2014-02-06 | Henry Ford Health System | Deformable dosimetric phantom |
CN105027227B (en) | 2013-02-26 | 2017-09-08 | 安科锐公司 | Electromagnetically actuated multi-diaphragm collimator |
JP6131161B2 (en) * | 2013-09-27 | 2017-05-17 | 富士フイルム株式会社 | Image registration apparatus, method, program, and three-dimensional deformation model generation method |
EP3092618B1 (en) | 2014-01-06 | 2018-08-29 | Koninklijke Philips N.V. | Articulated structure registration in magnetic resonance images of the brain |
JP6873099B2 (en) * | 2015-07-17 | 2021-05-19 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | Alignment of histopathological images |
CN106056586B (en) * | 2016-05-25 | 2019-08-09 | 湖南拓视觉信息技术有限公司 | A kind of sub-pixel positioning method and device |
CN106691487B (en) * | 2017-01-05 | 2021-01-05 | 东软医疗系统股份有限公司 | Imaging method and imaging system |
US10635930B2 (en) * | 2017-02-24 | 2020-04-28 | Siemens Healthcare Gmbh | Patient position control for scanning |
US11010630B2 (en) | 2017-04-27 | 2021-05-18 | Washington University | Systems and methods for detecting landmark pairs in images |
CN113724307B (en) * | 2021-09-02 | 2023-04-28 | 深圳大学 | Image registration method and device based on characteristic self-calibration network and related components |
CN114187338B (en) * | 2021-12-08 | 2023-04-28 | 卡本(深圳)医疗器械有限公司 | Organ deformation registration method based on estimated 2d displacement field |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080279428A1 (en) * | 2003-12-08 | 2008-11-13 | Koninklijke Philips Electronic, N.V. | Adaptive Point-Based Elastic Image Registration |
WO2006036842A2 (en) * | 2004-09-24 | 2006-04-06 | The University Of North Carolina At Chapel Hill | Methods, systems, and computer program products for hierarchical registration between a blood vessel and tissue surface model for a subject and blood vessel and tissue surface image for the subject |
EP1815432B1 (en) * | 2004-11-17 | 2016-08-03 | Koninklijke Philips N.V. | Improved elastic image registration functionality |
EP1844446B1 (en) * | 2005-01-28 | 2011-03-16 | Koninklijke Philips Electronics N.V. | User interface for motion analysis in kinematic mr studies |
WO2007046047A1 (en) * | 2005-10-17 | 2007-04-26 | Koninklijke Philips Electronics N.V. | Motion estimation and compensation of image sequences |
WO2007102920A2 (en) * | 2005-12-20 | 2007-09-13 | University Of Maryland, Baltimore | Method and apparatus for accelerated elastic registration of multiple scans of internal properties of a body |
-
2006
- 2006-06-14 EP EP06765744A patent/EP1894161A2/en not_active Withdrawn
- 2006-06-14 US US11/917,155 patent/US20080205719A1/en not_active Abandoned
- 2006-06-14 JP JP2008516489A patent/JP2008546441A/en not_active Withdrawn
- 2006-06-14 WO PCT/IB2006/051906 patent/WO2006134565A2/en active Application Filing
- 2006-06-14 CN CNA2006800212615A patent/CN101198981A/en active Pending
Non-Patent Citations (4)
Title |
---|
B.FISCHER; J.MODESITZKI.: ""Fast image registration - a variational approach"", PROC. OF THE INT. CONF. ON NUMERICAL ANALYSES AND COMPUTATIONAL MATHEMATICS (NACOM'03), 2003, CAMBRIDGE, pages 69 - 74 |
J.V.HAJNAL; D.L.G.HILL; D.J.HAWKES., "MEDICAL IMAGE REGISTRATION", 2001, CRC PRESS |
M.BRO-NIELSEN; C.GRAMKOW.: ""Fast fluid registration of medical images"", PROC. VISUALIZATION IN BIOMEDICAL COMPUTING (VCB'96), 1996, HAMBURG, pages 267 - 276 |
V.PEKAR; E.GLADILIN.: ""Deformable Image Registation by Adaptive Gaussian Forces"", PROC. ECCV 2004, WORKSHOPS CVAMIA AND MMBIA., May 2004 (2004-05-01), PRAGUE, CZECH. REPUBLIC, LNCS 3117 SPRINGE., pages 317 - 328 |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2009019630A2 (en) * | 2007-08-03 | 2009-02-12 | Koninklijke Philips Electronics N.V. | Anatomically constrained image registration |
WO2009019630A3 (en) * | 2007-08-03 | 2009-08-13 | Koninkl Philips Electronics Nv | Anatomically constrained image registration |
WO2010058854A1 (en) | 2008-11-20 | 2010-05-27 | Canon Kabushiki Kaisha | Information processing apparatus, information processing method, program, and storage medium |
EP2358277A4 (en) * | 2008-11-20 | 2017-04-26 | Canon Kabushiki Kaisha | Information processing apparatus, information processing method, program, and storage medium |
WO2012172454A1 (en) * | 2011-06-16 | 2012-12-20 | Koninklijke Philips Electronics N.V. | Hybrid point-based registration |
US9600856B2 (en) | 2011-06-16 | 2017-03-21 | Koninklijke Philips N.V. | Hybrid point-based registration |
EP2923262A4 (en) * | 2012-11-23 | 2016-07-13 | Cadens Medical Imaging Inc | Method and system for displaying to a user a transition between a first rendered projection and a second rendered projection |
US10905391B2 (en) | 2012-11-23 | 2021-02-02 | Imagia Healthcare Inc. | Method and system for displaying to a user a transition between a first rendered projection and a second rendered projection |
Also Published As
Publication number | Publication date |
---|---|
EP1894161A2 (en) | 2008-03-05 |
US20080205719A1 (en) | 2008-08-28 |
CN101198981A (en) | 2008-06-11 |
JP2008546441A (en) | 2008-12-25 |
WO2006134565A3 (en) | 2007-02-22 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20080205719A1 (en) | Method of Model-Based Elastic Image Registration For Comparing a First and a Second Image | |
Ferrant et al. | Serial registration of intraoperative MR images of the brain | |
Oliveira et al. | Medical image registration: a review | |
Schnabel et al. | A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations | |
Cootes et al. | A unified framework for atlas matching using active appearance models | |
EP1695287B1 (en) | Elastic image registration | |
Maes et al. | Medical image registration using mutual information | |
EP1719078B1 (en) | Device and process for multimodal registration of images | |
Zheng | Statistical shape model‐based reconstruction of a scaled, patient‐specific surface model of the pelvis from a single standard AP x‐ray radiograph | |
US20080095422A1 (en) | Alignment method for registering medical images | |
WO2001043070A2 (en) | Method and apparatus for cross modality image registration | |
EP2052362B1 (en) | Registration of electroanatomical mapping points to corresponding image data | |
Rohr | Elastic registration of multimodal medical images: A survey | |
Zheng et al. | (i) Registration techniques for computer navigation | |
Fang et al. | 3D shape reconstruction of lumbar vertebra from two X-ray images and a CT model | |
Samir et al. | Elastic shape analysis of cylindrical surfaces for 3D/2D registration in endometrial tissue characterization | |
Papademetris et al. | Articulated rigid registration for serial lower-limb mouse imaging | |
Pekar et al. | An adaptive irregular grid approach for 3D deformable image registration | |
Sindhu Madhuri | Classification of image registration techniques and algorithms in digital image processing–a research survey | |
Morooka et al. | A survey on statistical modeling and machine learning approaches to computer assisted medical intervention: Intraoperative anatomy modeling and optimization of interventional procedures | |
EP1695289A1 (en) | Adaptive point-based elastic image registration | |
CN116612166A (en) | Registration fusion algorithm for multi-mode images | |
Wirth et al. | Point-to-point registration of non-rigid medical images using local elastic transformation methods | |
Bhattacharjee et al. | Non-rigid registration (computed tomography-ultrasound) of liver using B-splines and free form deformation | |
Perperidis et al. | Spatio-temporal free-form registration of cardiac MR image sequences |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
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: 2006765744 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 11917155 Country of ref document: US |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2008516489 Country of ref document: JP Ref document number: 200680021261.5 Country of ref document: CN |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
WWW | Wipo information: withdrawn in national office |
Ref document number: DE |
|
WWE | Wipo information: entry into national phase |
Ref document number: 251/CHENP/2008 Country of ref document: IN |
|
WWP | Wipo information: published in national office |
Ref document number: 2006765744 Country of ref document: EP |