Scanning dental impressions

Information

  • Patent Grant
  • 11559378
  • Patent Number
    11,559,378
  • Date Filed
    Friday, November 17, 2017
    6 years ago
  • Date Issued
    Tuesday, January 24, 2023
    a year ago
Abstract
Systems and methods are provided for scanning a dental impression to obtain a digital model of a patient's dentition as an input to computer aided design (CAD) and computer aided manufacturing (CAM) methods for producing dental prostheses.
Description
FIELD

The present disclosure relates to systems and methods of scanning a dental impression to obtain a digital model of a patient's dentition as an input to computer aided design (CAD) and computer aided manufacturing (CAM) methods for producing dental prostheses.


BACKGROUND

Dental prostheses are typically manufactured at specialized dental laboratories that employ computer-aided design (CAD) and computer-aided manufacturing (CAM) milling systems to produce dental prostheses according to patient-specific specifications provided by dentists. In a typical work flow, information about the oral situation of a patient is received from a dentist, the dental laboratory designs the dental prosthesis, and the prosthesis is manufactured using a mill or other fabrication system. When making use of CAD design and CAM manufacturing in dentistry, a digital model of the patient's dentition is required as an input to the process. Despite the rise of intraoral scanning technology, the prevalent method of acquisition of digital model data is still scanning a stone model cast from an impression. Even in more technically advanced markets it is estimated that only 10% of clinicians own an intraoral scanner, therefore any improvements to the conventional process are likely to remain relevant and benefit patients and clinicians alike for some time. Accordingly, improvements to methods of acquiring digital models of patients' dentition are desirable.


SUMMARY

Certain embodiments of the disclosure concern systems and methods for scanning a physical impression of a patient's dentition and constructing a virtual surface image of the patient's dentition from the scan data thereby obtained. In some embodiments, the virtual surface image of the patient's dentition is constructed using isosurfaces and density gradients of a volumetric image and then directly creating a surface image based upon void spaces that correspond to the patient's dentition. This avoids an unnecessary step of first creating a surface image of the impression and then digitally or virtually reversing the surface image of the impression to obtain a surface image intended to correspond with the patient's dentition. Instead, the isosurfaces and vector gradients are selected and oriented directly to define the patient's dentition, thereby providing a surface image that is suitable for use in a dental restoration design program.


In some embodiments, the physical impression of a patient's dentition comprises a three-way dental impression tray that is adapted to obtain a physical impression containing information relating to a patient's upper jaw, lower jaw, and bite registration for at least a portion of the patient's dentition. In some embodiments, a three-way dental impression tray is scanned and the data from a single scan is used to generate virtual models of at least a portion of the patient's upper jaw, lower jaw, and bite registration. The virtual models thereby obtained are suitable for use in designing a dental restoration using known digital design products.


The foregoing and other objects, features, and advantages of the disclosed embodiments will become more apparent from the following detailed description, which proceeds with reference to the accompanying figures.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a perspective view of a three-way dental impression tray.



FIG. 2 is a cross-sectional view of a three-way dental impression tray containing impression material.



FIG. 3 is a schematic diagram of a computed tomography (CT) scanning system.



FIG. 4 is a 2-dimensional (2D) radiographic image of a dental impression tray containing a dental impression.



FIG. 5 is a cross-section of a 3-dimensional (3D) volumetric image.



FIG. 6 is a 3-dimensional (3D) surface image representation of a portion of a patient's dentition.



FIG. 7 is an illustration of a 3-dimensional (3D) object in the form of a cylinder.



FIG. 8A is an illustration of a cross-section of the cylinder of FIG. 7.



FIG. 8B is an illustration of isosurfaces derived from the 3D image of the cylinder of FIG. 7.



FIG. 8C is an illustration of gradient vectors derived from the isosurfaces shown in FIG. 8B.



FIG. 9 is a perspective view of a surface image representation of a portion of a patient's dentition including portions of the dentition of an upper jaw and portions of the dentition of a lower jaw.



FIG. 10 is an overhead view of the surface image representation of the lower jaw dentition shown in FIG. 9.





DETAILED DESCRIPTION

For purposes of this description, certain aspects, advantages, and novel features of the embodiments of this disclosure are described herein. The disclosed methods, apparatus, and systems should not be construed as being limiting in any way. Instead, the present disclosure is directed toward all novel and nonobvious features and aspects of the various disclosed embodiments, alone and in various combinations and sub-combinations with one another. The methods, apparatus, and systems are not limited to any specific aspect or feature or combination thereof, nor do the disclosed embodiments require that any one or more specific advantages be present or problems be solved.


Although the operations of some of the disclosed embodiments are described in a particular, sequential order for convenient presentation, it should be understood that this manner of description encompasses rearrangement, unless a particular ordering is required by specific language set forth below. For example, operations described sequentially may in some cases be rearranged or performed concurrently. Moreover, for the sake of simplicity, the attached figures may not show the various ways in which the disclosed methods can be used in conjunction with other methods. Additionally, the description sometimes uses terms like “provide” or “achieve” to describe the disclosed methods. These terms are high-level abstractions of the actual operations that are performed. The actual operations that correspond to these terms may vary depending on the particular implementation and are readily discernible by one of ordinary skill in the art.


As used in this application and in the claims, the singular forms “a,” “an,” and “the” include the plural forms unless the context clearly dictates otherwise. Additionally, the term “includes” means “comprises.” Further, the terms “coupled” and “associated” generally mean electrically, electromagnetically, and/or physically (e.g., mechanically or chemically) coupled or linked and does not exclude the presence of intermediate elements between the coupled or associated items absent specific contrary language.


In some examples, values, procedures, or apparatus may be referred to as “lowest,” “best,” “minimum,” or the like. It will be appreciated that such descriptions are intended to indicate that a selection among many alternatives can be made, and such selections need not be better, smaller, or otherwise preferable to other selections.


In the following description, certain terms may be used such as “up,” “down,” “upper,” “lower,” “horizontal,” “vertical,” “left,” “right,” and the like. These terms are used, where applicable, to provide some clarity of description when dealing with relative relationships. But, these terms are not intended to imply absolute relationships, positions, and/or orientations. For example, with respect to an object, an “upper” surface can become a “lower” surface simply by turning the object over. Nevertheless, it is still the same object.


As noted above, in a typical work flow, information about the oral situation of a patient is received from a dentist, the dental laboratory designs the dental prosthesis, and the prosthesis is manufactured using a mill or other fabrication system. When making use of CAD design and CAM manufacturing in dentistry, a digital model of the patient's dentition is required as an input to the process. Despite the rise of intraoral scanning technology, the prevalent method of acquisition of digital model data is still scanning a stone model cast from a physical negative impression of the patient's dentition.


A physical negative impression of the patient's dentition is typically obtained by the use of a dental impression tray containing impression material. An example of an impression tray is shown in FIG. 1 in the form of a three-way impression tray or “triple tray” 100. The triple tray 100 includes a generally rigid frame 102 within which a mesh 104 is retained. The rigid frame defines a handle 106 configured to be gripped by the user, a buccal side wall 108, and a lingual side wall 110. In use, impression material is loaded onto the upper and lower surfaces of the mesh 104 by the clinician. The triple tray 100 is then inserted into the mouth of a patient and the patient is instructed to bite down onto the triple tray 100 and impression material, causing the impression material to conform to the patient's dentition as the impression material cures. Because the triple tray 100 is situated between the upper and lower jaws of the patient, the impression obtained via the triple tray 100 includes information about the dental situation of the patient's upper jaw, lower jaw, and bite registration in the area of the patient's dentition covered by the triple tray.


For example, in FIG. 2, there is shown a sectional view of the triple tray 100 containing impression material 120 after the taking of a physical impression of a patient. An upper impression 122 is formed on the upper side of the mesh 104, and a lower impression 124 is formed on the lower side of the mesh 104. As noted above, after deformation, the impression material 122 defines a physical negative impression of the patient's dentition. Accordingly, the upper void space 126 defined by the upper impression 122 defines the space occupied by the patient's teeth and gingiva in the patient's upper jaw, and the lower void space 128 defined by the lower impression 124 defines the space occupied by the patient's teeth and gingiva in the patient's lower jaw. Moreover, the location and orientation of the upper void space 126 relative to the lower void space 128 defines the bite registration of the patient's dentition in the subject area, including the occlusal spacing and registration.


As noted above, in a conventional workflow, a physical dental impression formed in the manner described above would be used to cast a model of the patient's dentition formed of stone, polymeric, or other suitable material. The cast model would then be scanned using a laser scanner in order to obtain a digital model. The digital model would then be used to design one or more restorations, or for other purposes. This conventional workflow creates potential sources of error or inaccuracy that would be avoided by alternative methods or alternative workflows that avoided the step of forming the case model and, instead, proceeded directly from the physical impression to a digital model.


In one embodiment of the present method, a computed tomography (CT) scanner uses x-rays to make a detailed image of a physical impression. A plurality of such images are then combined to form a 3D model of the patient's dentition. A schematic diagram of an example of a CT scanning system 140 is shown in FIG. 3. The CT scanning system 140 includes a source of x-ray radiation 142 that emits an x-ray beam 144. An object being scanned—in the present case, a triple tray containing a physical impression 146—is placed between the source 142 and an x-ray detector 148. The x-ray detector 148, in turn, is connected to a processor 150 that is configured to receive the information from the detector 148 and to convert the information into a digital image file. Those skilled in the art will recognize that the processor 150 may comprise one or more computers that may be directly connected to the detector, wirelessly connected, connected via a network, or otherwise in direct or indirect communication with the detector 148.


An example of a suitable scanning system 140 includes a Nikon Model XTH 255 CT Scanner which is commercially available from Nikon Corporation. The example scanning system includes a 225 kV microfocus x-ray source with a 3 μm focal spot size to provide high performance image acquisition and volume processing. The processor 150 may include a storage medium that is configured with instructions to manage the data collected by the scanning system.


As noted above, during operation of the scanning system 140, the impression 146 is located between the x-ray source 142 and the x-ray detector 148. A series of images of the impression 146 are collected by the processor 150 as the impression 146 is rotated in place between the source 142 and the detector 146. An example of a single image 160 is shown in FIG. 4. The image 160 may be a radiograph, a projection, or other form of digital image. In one embodiment, a series of 720 images are collected as the impression 146 is rotated in place between the source 142 and the detector 148. In other embodiments, more images or fewer images may be collected as will be understood by those skilled in the art.


The plurality of images 160 of the impression 146 are generated by and stored within a storage medium contained within the processor 150 of the scanning system 140, where they may be used by software contained within the processor to perform additional operations. For example, in an embodiment, the plurality of images 160 undergo tomographic reconstruction in order to generate a 3D virtual image 170 (see FIG. 5) from the plurality of 2D images 160 generated by the scanning system 140. In the embodiment shown in FIG. 5, the 3D virtual image 170 is in the form of a volumetric image or volumetric density file (shown in cross-section in FIG. 5) that is generated from the plurality of radiographs 160 by way of a reconstruction algorithm associated with the scanning system 140.


In one embodiment, the volumetric image 170 is converted into a surface image 180 (see, e.g., FIG. 6) using a surface imaging algorithm. In the embodiment shown, the volumetric image 170 is converted into a surface image 180 having a format (e.g., an .STL file format) that is suitable for use with a dental restoration design software, such as the FastDesign™ dental design software provided by Glidewell Laboratories of Newport Beach, Calif.


In one embodiment, the surface imaging algorithm used to convert the volumetric image 170 into a surface image 180 is configured to construct the surface image of the dentition 180 directly from the volumetric image 170 without including an intermediate step of constructing a surface image of the impression. For example, FIGS. 7 and 8A-C do not show images of a dental impression or of a patient's dentition, but are instead presented to illustrate by way of example the direct surface imaging method employed in the illustrated embodiment. In FIG. 7, a volumetric image 190 of an object having a boundary 192 that is not precisely defined. A small patch of an isosurface 194 is illustrated within the boundary 192, with the isosurface 194 representing a collection of points within the volumetric image 190 that have the same volumetric density value. It is a mathematical property that a gradient vector 196 at a given position will always point perpendicular to an isosurface 194 at position. Accordingly, the gradient vector 196 is used to determine the direction which passes perpendicularly through the object boundary 192. These properties are further illustrated in FIGS. 8A-C, which show the non-precisely defined boundary 192 of the object (FIG. 8A), a plurality of isosurfaces 194 of the object (FIG. 8B), and a plurality of gradient vectors 196 that are perpendicular to the isosurfaces at given positions with in the object (FIG. 8C).


In one embodiment, the surface imaging algorithm used to convert the volumetric image 170 of the dental impression into a surface image 180 of the patient's dentition relies on defining isosurfaces and density gradients and then directly creating a surface image based upon the void spaces 126 and 128 (see, e.g., FIG. 2) that correspond to the patient's dentition. This avoids an unnecessary step of first creating a surface image of the impression 122, 124 and then digitally or virtually reversing the surface image of the impression to obtain a surface image intended to correspond with the patient's dentition. Instead, the isosurfaces and vector gradients are selected and oriented directly to define the patient's dentition, thereby providing a surface image 180 that is suitable for use in a dental restoration design program.


In the embodiment shown, as described above, a dental impression is collected using a triple tray 100 dental impression tray, thereby collecting an upper impression 122, a lower impression 124, and a bite registration in a single step. As a result, after scanning, reconstruction, and generation of a volumetric image of the triple tray and impression 146 (see FIG. 3), the resulting surface image 200 (see FIG. 9) includes a surface image of the upper dentition 202, a surface image of the lower dentition 204, and their relative positions and orientation providing information about the bite registration between the upper and lower dentition. These surface images 202, 204 and bite registration information are obtained using a single scan of a single object (the triple tray and impression 146).


For example, in FIG. 9, the surface image of the lower dentition 204 includes a representation of the tooth preparation 206 (i.e., the tooth that has been prepared by the dentist for a restoration to be designed using the surface image). The surface image of the upper dentition 202 includes a representation of the opposing tooth 208 (i.e., the tooth or teeth that are in the opposite jaw from the jaw containing the tooth preparation 206 and that are in opposition to the tooth preparation 204 when the jaws are closed in a bite). The orientation and relative positions of the teeth in the lower jaw and upper jaw, including the tooth preparation 206 and the opposing tooth 208, provide the bite registration. The bite registration information is used by the dental restoration design software to determine, for example, limitations on the height, shape, and position of the occlusal surface of the restoration being designed. Additional information is provided by the size, shape, and positions of the teeth 210 and 212 adjacent to the tooth preparation 206, as shown in FIG. 10.


The above descriptions of the scanning system and the algorithms used to perform the scanning, imaging, reconstruction, and surface imaging functions are not intended to suggest any limitation as to scope of use or functionality, as the innovations may be implemented in diverse general-purpose or special-purpose computing systems. For example, the computing environment used to perform these functions can be any of a variety of computing devices (e.g., desktop computer, laptop computer, server computer, tablet computer, gaming system, mobile device, programmable automation controller, etc.) that can be incorporated into a computing system comprising one or more computing devices.


For example, a computing environment may include one or more processing units and memory. The processing units execute computer-executable instructions. A processing unit can be a central processing unit (CPU), a processor in an application-specific integrated circuit (ASIC), or any other type of processor. In a multi-processing system, multiple processing units execute computer-executable instructions to increase processing power. For example, a representative computing environment may include a central processing unit as well as a graphics processing unit or co-processing unit. The tangible memory may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two, accessible by the processing unit(s). The memory stores software implementing one or more innovations described herein, in the form of computer-executable instructions suitable for execution by the processing unit(s).


A computing system may have additional features. For example, in some embodiments, the computing environment includes storage, one or more input devices, one or more output devices, and one or more communication connections. An interconnection mechanism such as a bus, controller, or network, interconnects the components of the computing environment. Typically, operating system software provides an operating environment for other software executing in the computing environment, and coordinates activities of the components of the computing environment.


The tangible storage may be removable or non-removable, and includes magnetic or optical media such as magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other medium that can be used to store information in a non-transitory way and can be accessed within the computing environment. The storage stores instructions for the software implementing one or more innovations described herein.


The input device(s) may be, for example: a touch input device, such as a keyboard, mouse, pen, or trackball; a voice input device; a scanning device; any of various sensors; another device that provides input to the computing environment; or combinations thereof. For video encoding, the input device(s) may be a camera, video card, TV tuner card, or similar device that accepts video input in analog or digital form, or a CD-ROM or CD-RW that reads video samples into the computing environment. The output device(s) may be a display, printer, speaker, CD-writer, or another device that provides output from the computing environment.


The communication connection(s) enable communication over a communication medium to another computing entity. The communication medium conveys information, such as computer-executable instructions, audio or video input or output, or other data in a modulated data signal. A modulated data signal is a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media can use an electrical, optical, RF, or other carrier.


Any of the disclosed methods can be implemented as computer-executable instructions stored on one or more computer-readable storage media (e.g., one or more optical media discs, volatile memory components (such as DRAM or SRAM), or nonvolatile memory components (such as flash memory or hard drives)) and executed on a computer (e.g., any commercially available computer, including smart phones, other mobile devices that include computing hardware, or programmable automation controllers) (e.g., the computer-executable instructions cause one or more processors of a computer system to perform the method). The term computer-readable storage media does not include communication connections, such as signals and carrier waves. Any of the computer-executable instructions for implementing the disclosed techniques as well as any data created and used during implementation of the disclosed embodiments can be stored on one or more computer-readable storage media. The computer-executable instructions can be part of, for example, a dedicated software application or a software application that is accessed or downloaded via a web browser or other software application (such as a remote computing application). Such software can be executed, for example, on a single local computer (e.g., any suitable commercially available computer) or in a network environment (e.g., via the Internet, a wide-area network, a local-area network, a client-server network (such as a cloud computing network), or other such network) using one or more network computers.


For clarity, only certain selected aspects of the software-based implementations are described. Other details that are well known in the art are omitted. For example, it should be understood that the disclosed technology is not limited to any specific computer language or program. For instance, the disclosed technology can be implemented by software written in C++, Java, Perl, Python, JavaScript, Adobe Flash, or any other suitable programming language. Likewise, the disclosed technology is not limited to any particular computer or type of hardware. Certain details of suitable computers and hardware are well known and need not be set forth in detail in this disclosure.


It should also be well understood that any functionality described herein can be performed, at least in part, by one or more hardware logic components, instead of software. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.


Furthermore, any of the software-based embodiments (comprising, for example, computer-executable instructions for causing a computer to perform any of the disclosed methods) can be uploaded, downloaded, or remotely accessed through a suitable communication means. Such suitable communication means include, for example, the Internet, the World Wide Web, an intranet, software applications, cable (including fiber optic cable), magnetic communications, electromagnetic communications (including RF, microwave, and infrared communications), electronic communications, or other such communication means.


In view of the many possible embodiments to which the principles of the disclosure may be applied, it should be recognized that the illustrated embodiments are only preferred examples and should not be taken as limiting the scope of the disclosure. Rather, the scope of the invention is defined by all that comes within the scope and spirit of the following claims.

Claims
  • 1. A computer-implemented method of designing a dental restoration for a patient, comprising: providing a physical negative impression of at least a portion of the patient's dentition, the physical negative impression comprising an impression tray containing impression material previously molded to the at least a portion of the patient's dentition;using a computed tomography (CT) scanner system, scanning the physical negative impression to create a three dimensional (3D) virtual image corresponding to the physical negative impression, the 3D virtual image comprising a volumetric density file;using the 3D virtual image of the physical negative impression, directly constructing a surface image of the at least a portion of the patient's dentition from the 3D virtual image of the physical negative impression without constructing a surface image of the physical negative impression; anddesigning a dental restoration for the patient based upon the surface image of the at least a portion of the patient's dentition,wherein the step of directly constructing a surface image of the at least a portion of the patient's dentition from the 3D virtual image of the physical negative impression further includes:defining isosurfaces having the same volumetric density value within the 3D virtual image; andidentifying density gradient vectors from the defined isosurfaces.
  • 2. The computer-implemented method of claim 1, wherein the CT scanner system includes a source of x-ray radiation, an x-ray detector, and a processor connected to the detector, and wherein the step of scanning the physical negative impression further includes: locating the physical negative impression between the source of x-ray radiation and the x-ray detector; androtating the physical negative impression in place as a series of images is captured by the CT scanner system.
  • 3. The computer-implemented method of claim 2, further including a step of reconstructing the series of images to create the 3D virtual image.
  • 4. The computer-implemented method of claim 1, wherein the step of directly constructing a surface image further includes constructing a surface image that corresponds with void spaces of the physical negative impression.
  • 5. The computer-implemented method of claim 4, wherein the surface image includes an upper dentition surface image, a lower dentition surface image, and bite registration information between the upper dentition and lower dentition.
  • 6. The computer-implemented method of claim 5, wherein the surface image is in .STL file format.
  • 7. A system for designing a dental restoration for a patient, the system comprising: a source of x-ray radiation;an x-ray detector;a processor; anda non-transitory computer-readable storage medium comprising instructions executable by the processor to perform steps comprising:reconstructing a series of two dimensional (2D) images collected by the x-ray detector to create a three dimensional (3D) virtual image comprising a volumetric density file corresponding to a physical negative impression of at least a portion of the patient's dentition;using the 3D virtual image of the physical negative impression, directly constructing a surface image of the at least a portion of the patient's dentition from the 3D virtual image of the physical negative impression without constructing a surface image of the physical negative impression; anddesigning a dental restoration for the patient based upon the surface image of the at least a portion of the patient's dentition,wherein the step of directly constructing a surface image of the at least a portion of the patient's dentition from the 3D virtual image of the physical negative impression further comprises:defining isosurfaces having the same volumetric density value within the 3D virtual image; andidentifying density gradient vectors from the defined isosurfaces.
  • 8. The system of claim 7, wherein the step of directly constructing a surface image further includes constructing a surface image that corresponds with void spaces of the physical negative impression.
  • 9. The system of claim 8, wherein the surface image includes an upper dentition surface image, a lower dentition surface image, and bite registration information between the upper dentition and lower dentition.
  • 10. The system of claim 9, wherein the surface image is in .STL file format.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. provisional patent application No. 62/423,460, filed Nov. 17, 2016 which is hereby incorporated by reference in its entirety.

US Referenced Citations (219)
Number Name Date Kind
D302683 Iwasaki et al. Aug 1989 S
5023895 McCroskey et al. Jun 1991 A
5270827 Kobyayashi et al. Dec 1993 A
5368478 Andreiko et al. Nov 1994 A
5431562 Andreiko et al. Jul 1995 A
5447432 Andreiko et al. Sep 1995 A
5454717 Andreiko et al. Oct 1995 A
5605459 Kuroda et al. Feb 1997 A
D394316 Kodama et al. May 1998 S
5879158 Doyle et al. Mar 1999 A
6068482 Snow May 2000 A
6081739 Lemchen Jun 2000 A
6091412 Simonoff et al. Jul 2000 A
6152731 Jordan et al. Nov 2000 A
6198552 Nagae Mar 2001 B1
6217334 Hultgren Apr 2001 B1
6227850 Chishti et al. May 2001 B1
6244861 Andreiko et al. Jun 2001 B1
6318994 Chishti et al. Nov 2001 B1
6322359 Jordan et al. Nov 2001 B1
6350120 Sachdeva et al. Feb 2002 B1
6371761 Cheang et al. Apr 2002 B1
6386867 Durbin et al. May 2002 B1
6386878 Pavlovskaia et al. May 2002 B1
6406292 Chishti et al. Jun 2002 B1
6409504 Jones et al. Jun 2002 B1
6450807 Chishti et al. Sep 2002 B1
6463344 Pavloskaia et al. Oct 2002 B1
6512994 Sachdeva Jan 2003 B1
6554611 Chishti et al. Apr 2003 B2
6582225 Bergersen Jun 2003 B1
D476658 Machi et al. Jul 2003 S
6602070 Miller et al. Aug 2003 B2
6621491 Baumrind et al. Sep 2003 B1
6632089 Rubbert et al. Oct 2003 B2
6633789 Nikolskiy et al. Oct 2003 B1
6648640 Rubbert et al. Nov 2003 B2
6688886 Hughes et al. Feb 2004 B2
6726478 Isiderio et al. Apr 2004 B1
6767208 Kaza Jul 2004 B2
6783360 Chishti Aug 2004 B2
7013191 Rubbert et al. Mar 2006 B2
7027642 Rubbert et al. Apr 2006 B2
7029275 Rubbert et al. Apr 2006 B2
7040896 Pavlovskaia et al. May 2006 B2
7068825 Rubbert et al. Jun 2006 B2
7080979 Rubbert et al. Jul 2006 B2
7134874 Chishti et al. Nov 2006 B2
7140877 Kaza Nov 2006 B2
D533555 Odhe et al. Dec 2006 S
7156655 Sachdeva et al. Jan 2007 B2
7234937 Sachdeva et al. Jun 2007 B2
7292716 Kim Nov 2007 B2
7361018 Imgrund et al. Apr 2008 B2
7361020 Abolfathi et al. Apr 2008 B2
7373286 Nikolskiy et al. May 2008 B2
D573146 Sukenari et al. Jul 2008 S
D580962 Sukenari et al. Nov 2008 S
7476100 Kuo Jan 2009 B2
7545372 Kopelman et al. Jun 2009 B2
7609875 Liu et al. Oct 2009 B2
D612851 Maruyama et al. Mar 2010 S
7717708 Sachdeva et al. May 2010 B2
7740476 Rubbert et al. Jun 2010 B2
7805003 Cohen et al. Sep 2010 B1
8013853 Douglas et al. Sep 2011 B1
8045180 Friemel Oct 2011 B2
8075306 Kitching et al. Dec 2011 B2
8229180 Baloch et al. Jul 2012 B2
8308481 DiAngelo et al. Nov 2012 B2
8332061 Baloch et al. Dec 2012 B2
8342843 Perot et al. Jan 2013 B2
8380644 Zouhar et al. Feb 2013 B2
D678383 Park et al. Mar 2013 S
D714940 Kim Oct 2014 S
8855375 Macciola et al. Oct 2014 B2
8995732 Kaza et al. Mar 2015 B2
9055988 Galgut et al. Jun 2015 B2
9135498 Andreiko et al. Sep 2015 B2
D742010 Metcalf Oct 2015 S
9421074 Sachdeva et al. Aug 2016 B2
D776818 Metcalf Jan 2017 S
9629698 Lior et al. Apr 2017 B2
9737381 Lee Aug 2017 B2
9888983 Sachdeva et al. Feb 2018 B2
10149744 Lior et al. Dec 2018 B2
10624717 Wen Apr 2020 B2
20020006217 Rubbert et al. Jan 2002 A1
20020028418 Farag et al. Mar 2002 A1
20020141626 Caspi Oct 2002 A1
20020150859 Imgrund et al. Oct 2002 A1
20030198377 Ng Oct 2003 A1
20030198378 Ng Oct 2003 A1
20030207227 Abolfathi Nov 2003 A1
20030207235 Van der Zel Nov 2003 A1
20030224314 Bergersen Dec 2003 A1
20040072120 Lauren Apr 2004 A1
20040146198 Herley Jul 2004 A1
20040152036 Abolfathi Aug 2004 A1
20040175671 Jones et al. Sep 2004 A1
20040197728 Abolfathi et al. Oct 2004 A1
20040214128 Sachdeva et al. Oct 2004 A1
20050018901 Kaufmann et al. Jan 2005 A1
20050019732 Kaufmann et al. Jan 2005 A1
20050030368 Morrison Feb 2005 A1
20050043837 Rubbert et al. Feb 2005 A1
20050089213 Geng Apr 2005 A1
20050089822 Geng Apr 2005 A1
20050191593 Knopp Sep 2005 A1
20050192835 Kuo et al. Sep 2005 A1
20050208449 Abolfathi et al. Sep 2005 A1
20050271996 Sporbert et al. Dec 2005 A1
20060127859 Wen Jun 2006 A1
20060147872 Andreiko Jul 2006 A1
20060154198 Durbin Jul 2006 A1
20060263739 Sporbert et al. Nov 2006 A1
20060263741 Imgrund et al. Nov 2006 A1
20060275736 Wen et al. Dec 2006 A1
20070003900 Miller Jan 2007 A1
20070031790 Raby et al. Feb 2007 A1
20070031791 Cinader et al. Feb 2007 A1
20070065768 Nadav Mar 2007 A1
20070128573 Kuo Jun 2007 A1
20070128574 Kuo et al. Jun 2007 A1
20070129991 Kuo Jun 2007 A1
20070134613 Kuo et al. Jun 2007 A1
20070141527 Kuo et al. Jun 2007 A1
20070167784 Shekhar et al. Jul 2007 A1
20070168152 Matov et al. Jul 2007 A1
20070190481 Schmitt Aug 2007 A1
20070207441 Lauren Sep 2007 A1
20070238065 Sherwood et al. Oct 2007 A1
20080020350 Matov et al. Jan 2008 A1
20080048979 Ruttenberg Feb 2008 A1
20080057466 Jordan et al. Mar 2008 A1
20080064008 Schmitt Mar 2008 A1
20080182220 Chishti et al. Jul 2008 A1
20080248443 Chishti et al. Oct 2008 A1
20080261165 Steingart et al. Oct 2008 A1
20080305458 Lemchen Dec 2008 A1
20090080746 Ku et al. Mar 2009 A1
20090087817 Jansen et al. Apr 2009 A1
20090162813 Glor et al. Jun 2009 A1
20090191503 Matov et al. Jul 2009 A1
20090220916 Fisker Sep 2009 A1
20090246726 Chelnokov et al. Oct 2009 A1
20090248184 Steingart Oct 2009 A1
20090298017 Boerjes et al. Dec 2009 A1
20090311647 Fang et al. Dec 2009 A1
20100009308 Wen Jan 2010 A1
20100100362 Zouhar et al. Apr 2010 A1
20100105009 Karkar Apr 2010 A1
20100111386 El-Baz May 2010 A1
20100138025 Morton et al. Jun 2010 A1
20100145898 Malfliet et al. Jun 2010 A1
20100217567 Marshall Aug 2010 A1
20100260405 Cinader, Jr. Oct 2010 A1
20100297572 Kim Nov 2010 A1
20110004331 Cinader, Jr. et al. Jan 2011 A1
20110045428 Boltunov et al. Feb 2011 A1
20110059413 Schutyser et al. Mar 2011 A1
20110060438 Stoddard et al. Mar 2011 A1
20110090513 Seidl et al. Apr 2011 A1
20110184762 Chishti et al. Jul 2011 A1
20110206247 Dachille et al. Aug 2011 A1
20110207072 Schiemann Aug 2011 A1
20110244415 Batesole Oct 2011 A1
20110268326 Kuo et al. Nov 2011 A1
20110292047 Chang et al. Dec 2011 A1
20120015316 Sachdeva et al. Jan 2012 A1
20120065756 Rubbert Mar 2012 A1
20120088208 Schulter et al. Apr 2012 A1
20120139142 Van der Zel Jun 2012 A1
20120214121 Greenberg Aug 2012 A1
20130172731 Gole Jul 2013 A1
20130218531 Deichmann et al. Aug 2013 A1
20130226534 Fisker et al. Aug 2013 A1
20130275107 Alpern et al. Oct 2013 A1
20130325431 See et al. Dec 2013 A1
20130329020 Kriveshko et al. Dec 2013 A1
20130335417 McQueston et al. Dec 2013 A1
20140003695 Dean Jan 2014 A1
20140055135 Nielsen et al. Feb 2014 A1
20140067334 Kuo Mar 2014 A1
20140067337 Kopleman Mar 2014 A1
20140185742 Chen Jul 2014 A1
20140272772 Andreiko et al. Sep 2014 A1
20140278278 Nikolskiy et al. Sep 2014 A1
20140278279 Azernikov et al. Sep 2014 A1
20140308624 Lee et al. Oct 2014 A1
20140329194 Sachdeva et al. Nov 2014 A1
20140379356 Sachdeva et al. Dec 2014 A1
20150049081 Coffey et al. Feb 2015 A1
20150056576 Nikolskiy et al. Feb 2015 A1
20150111168 Vogel Apr 2015 A1
20150154678 Fonte et al. Jun 2015 A1
20150182316 Morales Jul 2015 A1
20150320320 Kopelman et al. Nov 2015 A1
20150347682 Chen et al. Dec 2015 A1
20160135924 Choi et al. May 2016 A1
20160148370 Maury et al. May 2016 A1
20160239631 Wu et al. Aug 2016 A1
20160256035 Kopelman et al. Aug 2016 A1
20160256246 Stapleton et al. Sep 2016 A1
20160367336 Lv et al. Dec 2016 A1
20170100214 Wen Apr 2017 A1
20170135655 Wang et al. May 2017 A1
20170231721 Akeel et al. Aug 2017 A1
20170340418 Raanan Nov 2017 A1
20180028063 Elbaz Feb 2018 A1
20180028064 Elbaz et al. Feb 2018 A1
20180028065 Elbaz et al. Feb 2018 A1
20180055600 Matov et al. Mar 2018 A1
20180132982 Nikolskiy et al. May 2018 A1
20180146934 Ripoche et al. May 2018 A1
20180165818 Tsai et al. Jun 2018 A1
20180189420 Fisker Jul 2018 A1
20180303581 Martz et al. Oct 2018 A1
20200121429 Pesach et al. Apr 2020 A1
Foreign Referenced Citations (12)
Number Date Country
108024841 May 2018 CN
108665533 Oct 2018 CN
2345387 Jul 2011 EP
2886077 Jun 2015 EP
3180761 Nov 2001 WO
2001080763 Nov 2001 WO
2013180423 May 2013 WO
2016097033 Jun 2016 WO
2017178908 Oct 2017 WO
2018022054 Feb 2018 WO
2018038748 Mar 2018 WO
2018101923 Jun 2018 WO
Non-Patent Literature Citations (26)
Entry
Hollt et al, “GPU-Based Direct vol. Rendering of Industrial CT Data ” (Year: 2007).
Kilic et al. “GPU Supported Haptic Device Integrated Dental Simulation Environment” (Year: 2006).
Zeng et al. “Finite Difference Error Analysis of Geometry Properties of Implicit Surfaces” (Year: 2011).
Ibraheem, “Reduction of artifacts in dental cone beam CT images to improve the three dimensional image reconstruction” (Year: 2012).
Changhwan Kim, Scientific Reports, “Efficient digitalization method for dental restorations using micro-CT data”, www.nature.com/scientificreports, 7:44577|DOI:10.1038/srep44577 (dated Mar. 15, 2017).
Emiliano Perez et al., A Comparison of Hole-Filing Methods In 3D, Int. J. Appl. Math. Comput. Sci., 2016, vol. 26, No. 4, 885-903, in 19 pages.
Yokesh Kumar et al., Automatic Feature Identification in Dental Meshes, ResearchGate, Article in Computer-Aided Design and Applications, Aug. 2013, in 24 pages.
Andrew W. Fitzgibbon et al., Direct Least Squares Fitting of Ellipses, Department of Artificial Intelligence, The University of Edinburgh, dated Jan. 4, 1996, in 15 pages.
Oscar Sebio Cajaraville, Four Ways to Create a Mesh for a Sphere, Dec. 7, 2015, in 9 pages.
Shuai Yang et al., Interactive Tooth Segmentation Method of Dental Model based on Geodesic, ResearchGate, Conference paper, Jan. 2017, in 6 pages.
Changhwan Kim et al., Efficient digitalization method for dental restorations using micro-CT data, nature.com/scientificreports, published Mar. 15, 2017, in 8 pages.
Dexter C. Kozen, The Design and Analysis of Algorithms, Texts and Monographs in Computer Science, (c) 1992, See Whole book.
Bob Sedgewick et al., Algorithms and Data Structures Fall 2007, Department of Computer Science, Princeton University, https://www.cs.princeton.edu/˜rs/AlgsDS07/, downloaded Oct. 28, 2021, in 41 pages.
Alban Pages et al., Generation of Computational Meshes from MRI and CT-Scan data, ResearchGate, ESAIM Proceedings, Sep. 2005, vol. 14, 213-223 in 12 pages.
William E. Lorensen et al., Marching Cubes: A High Resolution 3D Surface Construction Algorithm, Computer Graphics, vol. 21, No. 4, Jul. 1987 in 7 pages.
Alfred V. Aho et al., The Design and Analysis of Computer Algorithms, Addison-Wesley Publishing Company, Jun. 1974, pp. 124-155.
Sheng-hui Liao et al., Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields, Hindawi Publishing Corporation, BioMed Research International, vol. 2015, Article ID 187173, in 11 pages.
Bribiesca, E. “3D-Curve Representation by Means of a Binary Chain Code”, Mathematical and computer modelling 10.3(2004):285-295; p. 292, paragraph 2; p. 293, paragraph 1.
Kiattisin, S. et al. “A Match of X-Ray Teeth Films Using Image Processing Based on Special Features of Teeth”, SICE Annual Conference, 2008. IEEE: Aug. 22, 2008; p. 97; col. 2, paragraph 2; a 98, col. 1-2.
Cui, M, Femiani, J., Hu, J., Wondka, Razada A. “Curve Matching for Open 2D Curves”, Pattern Recognition Letters 30 (2009): pp. 1-10.
Gumhold, S., Wang, X., MacLeod R. “Feature Extraction From Point Clouds”, Scientific Computing and Imaging Institute: pp. 1-13 Proceedings, 10th International Meshing Roundtable, Sandia National Laboratores, pp. 293-305, Oct. 7-10, 2001.
Wolfson, H. “On Curve Matching”, Robotics Research Technical Report, Technical Report No. 256, Robotic Report No. 86 (Nov. 1986) New York University, Dept, of Computer Science, New York, New York 10012.
Rietzel et al., “Moving targets: detection and tracking of internal organ motion for treatment planning and patient set up”, Radiotherapy and Oncology, vol. 73, supplement 2, Dec. 2004, pp. S68-S72.
Murat Arikan et al., O-Snap: Optimization-Based Snapping for Modeling Architecture, ACM Transactions on Grphics, vol. 32, No. 1, Article 6, Publication date: Jan. 2013, in 15 pages.
Brian Amberg et al., Optimal Step Nonrigid ICP Algorithms for Surface Registration, Proceedings/CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun. 2007, in 9 pages.
T. Rabbani et al., Segmentation Of Point Clouds Using Smoothness Constraint, ISPRS vol. XXXVI, Part 5, Dresden Sep. 25-27, 2006, in 6 pages.
Related Publications (1)
Number Date Country
20180132982 A1 May 2018 US
Provisional Applications (1)
Number Date Country
62423460 Nov 2016 US