Method and system for generating a biopsy plan

Abstract

A method for generating a biopsy plan includes retrieving a selected biopsy template from a plurality of stored biopsy templates. The selected biopsy template comprises a first 3-dimensional (3D) model of an organ and a plurality of biopsy core positions spatially distributed on the first 3D model. A a second 3D model of the organ is generated based on image data of the organ from a patient. A fusion algorithm is applied to map each of the plurality of biopsy core positions from the first 3D model to the second 3D model.

Description

METHOD AND SYSTEM FOR GENERATING A BIOPSY PLANTECHNICAL FIELD

[0001] The present disclosure relates broadly, but not exclusively, to methods and systems for generating a biopsy plan.BACKGROUND

[0002] A biopsy is a procedure to remove a piece of body tissue or a sample of cells for testing. For example, prostate biopsies are performed to collect tissue samples, or cores, of the prostate gland for histologic examination. Current practice requires histopathological confirmation of cancer achieved through biopsy for diagnosis. Based on current urological guidelines, for biopsy-naive patients who have a suspicious lesion on magnetic resonance imaging (MRI), clinicians should perform targeted biopsies of the suspicious lesion and may also perform a systematic template biopsy. Systematic biopsies involve taking systematically spaced samples across the prostate gland to find cancer. Transperineal systematic biopsies are based on a few commonly used biopsy templates. In these templates, the prostate is split into zones and a number of cores are assigned per zone.

[0003] Biopsies may be performed cognitively or with the assistance of devices which help with needle tracking and / or navigation. However, there is significant cognitive skill involved in mapping the biopsy locations in the template to the actual prostate. This is also a time-consuming process. Furthermore, there is no guarantee that the needle lands exactly where the user intends. This can affect how well spaced out the cores are throughout the prostate. While devices which allow for tracking of the needle and provide a simulated needle trajectory are available, they mostly lack the capability to plan biopsy core positions prospectively before the procedure that will be precisely adhered to.

[0004] While preset templates offer the potential to hasten the planning of biopsy cores, two significant complications emerge from this solution. Firstly, the inherent variability in prostate shapes and sizes among patients is a limitation of fixed templates, as they cannot universally accommodate these differences. Secondly,individual physicians possess unique preferences regarding various aspects of the biopsy core execution, such as core positions, nomenclature, sequencing, and more.

[0005] Robot-guided prostate biopsy systems enable doctors to plan the positions of biopsy cores prospectively before extracting tissue samples, thereby ensuring precise and swift needle insertions that match the intended positions. However, this planning phase can take time (up to 5 minutes) and necessitates the expertise of a trained physician to accurately determine core positions.

[0006] It may be desirable to provide methods and devices that can address at least some of the above problem and / or allow for quicker planning of biopsy positions for systematic biopsies.SUMMARY

[0007] An aspect of the present disclosure provides a method for generating a biopsy plan, the method comprising: retrieving a selected biopsy template from a plurality of stored biopsy templates, the selected biopsy template comprising a first 3-dimensional (3D) model of an organ and a plurality of biopsy core positions spatially distributed on the first 3D model; generating a second 3D model of the organ based on image data of the organ from a patient; and applying a fusion algorithm to map each of the plurality of biopsy core positions from the first 3D model to the second 3D model.

[0008] Applying the fusion algorithm may comprise: calculating a first transformation to align positions, orientations and sizes of the first and second 3D models in space; after calculating the first transformation, calculating a second transformation to compensate for differences between shapes of the first and second 3D models; and applying the first transformation to the plurality of biopsy core positions followed by the second transformation to obtain a corresponding plurality of biopsy core positions spatially distributed on the second 3D model.

[0009] The first transformation may comprise an affine transformation comprising a translation, a rotation and a scale.

[0010] Calculating the affine transformation may comprise: determining respective centers of the first and second 3D models; aligning the center of the first 3D model with the center of the second 3D model; and determining the affine transformationbased on two sets of points on the first and second 3D models using an iterative closest point algorithm.

[0011] The second transformation may comprise an elastic transformation, and calculating the elastic transformation may comprise: determining a set of source points on a third 3D model, the third 3D model comprising the first 3D model transformed by the first transformation, and a set of target points on the second 3D model, wherein the sets of source points and target points comprise corresponding landmarks on the third and second 3D models; and calculating a non-linear warp transform based on the sets of source points and target points, the non-linear warp transform stretching the third 3D model to match the second 3D model.

[0012] Calculating the non-linear warp transform may comprise building a 3D octree for the set of source points; using at most one point in each cell of the 3D octree to identify a corresponding target point from the set of target points on the second 3D model; and calculating the non-linear warp transform that match the set of source points to the set of target points.

[0013] The method may further comprise, before retrieving the selected biopsy template, adjusting at least one of a number of the biopsy core positions, coordinates of at least one of the biopsy core positions, and a sequence of the biopsy core positions, and a density of the biopsy core positions in a selected zone; and storing the adjusted biopsy template.

[0014] The method may further comprise storing the second 3D model and the plurality of mapped biopsy core positions as one of the plurality of stored biopsy templates.

[0015] Another aspect of the present disclosure provides a system for generating a biopsy plan, the system comprising: a processor; a computer-readable memory coupled to the processor and having instructions stored thereon that are executable by the processor to: retrieve a selected biopsy template from a plurality of stored biopsy templates, the selected biopsy template comprising a first 3-dimensional (3D) model of an organ and a plurality of biopsy core positions spatially distributed on the first 3D model; generate a second 3D model of the organ based on image data of the organ from a patient; and apply a fusion algorithm to map each of the plurality of biopsy core positions from the first 3D model to the second 3D model.

[0016] There is also disclosed a robotic system for biopsy comprising the system as described above.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Embodiments of the disclosure will be better understood and readily apparent to one of ordinary skill in the art from the following written description, by way of example only, and in conjunction with the drawings, in which:

[0018] Figure 1 shows a flow chart illustrating a method for generating a biopsy plan according to an example embodiment.

[0019] Figure 2 shows a flow chart illustrating an implementation for selecting and applying a biopsy plan based on the method of Figure 1.

[0020] Figure 3 shows a user interface for selecting a biopsy template from a plurality of stored templates according to an example embodiment.

[0021] Figure 4 shows a user interface for customizing a selected biopsy template according to an example embodiment.

[0022] Figure 5 shows various views of a current prostate model with the biopsy cores’ positions removed.

[0023] Figure 6(a) shows a transverse view of a current prostate model together with the biopsy cores’ positions. Figure 6(b) shows a corresponding 3D representation of the needle insertion paths based on the positions shown in Figure 6(a).

[0024] Figure 7 shows a block diagram of a computing device capable of implementing the method of Figure 1.

[0025] Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been depicted to scale. For example, the dimensions of some of the elements in the illustrations, block diagrams or flowcharts may be exaggerated in respect to other elements to help to improve understanding of the present embodiments.DETAILED DESCRIPTION

[0026] It has been recognized that there is a necessity to integrate template biopsy plans to expedite procedures, while simultaneously allowing for customization tailored to the specific characteristics of the patient’s prostate and the preferences of the attending physician. It has also been recognized that preparation of biopsy templates prior to the biopsy procedure has the potential to significantly reduce the time required for the biopsy. Moreover, this streamlined process may potentially enable less experienced professionals to perform the biopsy independently under the guidance of a doctor’s prescribed template. The method and system according to the example embodiments can provide both customization and automation when generating biopsy plans.

[0027] Embodiments will be described, by way of example only, with reference to the drawings. Like reference numerals and characters in the drawings refer to like elements or equivalents. Further, while the example embodiments are described in relation to prostate biopsies, it will be appreciated by a person skilled in the art that the present method and system are applicable to other types of biopsies, e.g. liver biopsy, kidney biopsy, with appropriate adaptations if required.

[0028] Some portions of the description which follows are explicitly or implicitly presented in terms of algorithms and functional or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to convey most effectively the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities, such as electrical, magnetic or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated.

[0029] Unless specifically stated otherwise, and as apparent from the following, it will be appreciated that throughout the present specification, discussions utilizing terms such as “retrieving”, “calculating”, “determining”, “applying”, “extracting”, “generating”, “initializing”, “outputting”, or the like, refer to the action and processes of a computer system, or similar electronic device, that manipulates and transforms data represented as physical quantities within the computer system into other data similarly representedas physical quantities within the computer system or other information storage, transmission or display devices.

[0030] The present specification also discloses apparatus for performing the operations of the methods. Such apparatus may be specially constructed for the required purposes, or may comprise a computer or other device selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various machines may be used with programs in accordance with the teachings herein. Alternatively, the construction of more specialized apparatus to perform the required method steps may be appropriate. The structure of a conventional computer will appear from the description below.

[0031] In addition, the present specification also implicitly discloses a computer program, in that it would be apparent to the person skilled in the art that the individual steps of the method described herein may be put into effect by computer code. The computer program is not intended to be limited to any particular programming language and implementation thereof. It will be appreciated that a variety of programming languages and coding thereof may be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. There are many other variants of the computer program, which can use different control flows without departing from the scope of the disclosure.

[0032] Furthermore, one or more of the steps of the computer program may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. The computer readable medium may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a computer. The computer readable medium may also include a hard-wired medium such as exemplified in the Internet system, or wireless medium such as exemplified in the GSM, GPRS, 3G, 4G or 5G mobile telephone systems, as well as other wireless systems such as Bluetooth, ZigBee, Wi-Fi. The computer program when loaded and executed on such a computer effectively results in an apparatus that implements the steps of the preferred method.

[0033] The present disclosure may also be implemented as hardware elements. More particularly, in the hardware sense, an element is a functional hardware unit designed for use with other components or elements. For example, an element may beimplemented using discrete electronic components, or it can form a portion of an entire electronic circuit such as an Application Specific Integrated Circuit (ASIC) or Field Programmable Gate Array (FPGA). Numerous other possibilities exist. Those skilled in the art will appreciate that the system can also be implemented as a combination of hardware and software elements.

[0034] According to various embodiments, a “circuit” may be understood as any kind of a logic implementing entity, which may be special purpose circuitry or a processor executing software stored in a memory, firmware, or any combination thereof. Thus, in an embodiment, a “circuit” may be a hard-wired logic circuit or a programmable logic circuit such as a programmable processor, e.g. a microprocessor (e.g. a Complex Instruction Set Computer (CISC) processor or a Reduced Instruction Set Computer (RISC) processor). A “circuit” may also be a processor executing software, e.g. any kind of computer program, e.g. a computer program using a virtual machine code such as e.g. Java. Any other kind of implementation of the respective functions which may be described in more detail herein may also be understood as a “circuit” in accordance with an alternative embodiment.

[0035] Figure 1 shows a flow chart 100 illustrating a method for generating a biopsy plan according to an example embodiment. At step 102, a selected biopsy template is retrieved from a plurality of stored biopsy templates. The selected biopsy template comprises a first 3-dimensional (3D) model of an organ (e.g. a prostate gland, a liver, etc.) and a plurality of biopsy core positions spatially distributed on the first 3D model. At step 104, a second 3D model of the organ is generated based on image data of the organ from a patient. At step 106, a fusion algorithm is applied to map each of the plurality of biopsy core positions from the first 3D model to the second 3D model.

[0036] Figure 2 shows a flow chart 200 illustrating an implementation for selecting and applying a biopsy plan based on the method as described above with reference to Figure 1. At step 202, the physician can first select his / her desired biopsy template from a plurality of stored (i.e. pre-loaded) templates. The template selection by the physician may be consistent with what pathologists typically use. Alternatively or in addition, the template selection can be computer-assisted where one or more relevant templates from a full list of templates may be presented for a quicker confirmation or selection. This can be prior to the procedure, or once-off (e.g. during installation). For example, this selection may be in the application settings, via a dropdown menu, as shown in Figure 3. The biopsy template includes a plurality of biopsy positions spaced out in a 3-dimensional model of an organ, e.g. a prostategland, or a liver. The software implementing the present method may be loaded with a plurality of common biopsy templates, such as the Modified Barzell, MUSIC, 12-core, 10-sector and Ginsburg templates. In these templates, the prostate is typically split into zones and a number of biopsy cores are assigned per zone. Alternatively, or in addition, the user may have the option to change the name or create his / her own customized template and store it as one of the pre-loaded templates.

[0037] For the pre-loaded templates, they may already have certain adaptations built in. For example, based on clinical literature, standard templates simply specify a biopsy core within a certain segment of the prostate. However, in a pre-loaded template according to the example embodiments, the position of the core may be more specific e.g. closer to the edge to allow for greater sampling of the peripheral zone.

[0038] The pre-loaded templates can be achieved by having a default 3D model of the prostate in a known coordinate system. The multiple biopsy core positions are then recorded as coordinate positions within this coordinate system, and relative to the default 3D model of the prostate. Other implementations are possible, such as having different 3D models of the prostate representing different shapes available for selection. The prostate model is then displayed on the user interface together with the biopsy core positions. The display can be in a 3D format, which the user can rotate, zoom in / out on, and move, etc. The biopsy cores can be represented as points at the center of the core, among other methods.

[0039] At step 204, the physician can customize the template plans. For example, they can change the name of the template, add new biopsy cores, delete existing ones, change the order of how the cores are executed, name of each core and the position of each core. This allows for alignment with how the cores are described in the pathology report. Figure 4 shows a user interface for the customization of the biopsy template according to an example embodiment. Here, the selected biopsy core C2 can be moved up and down in the sequence and its coordinates can be modified by modifying the values in the table on the right. The image on the left provides a corresponding graphical representation of the position of the biopsy core C2 on the prostate model based on the coordinates.

[0040] Such customized templates can be user-centric or patient-centric. For example, the physician may know from training and experience that a certain sequence may work better, or certain locations / zones on the organ are more likelyto have relevant samples. Also, one template may be more applicable to a certain patient profile (e.g. ethnicity, age group, body weight, etc.) or condition than other templates. In other words, it is possible to include a customized biopsy template in the stored templates and subsequently select it so that it can be more relevant and quickly used.

[0041] More advanced customizations may be possible. For example, the user may define a certain core density (i.e. number of cores per unit volume) for specific zones of the prostate such as the peripheral zone.

[0042] Steps 202 and 204 as described above belong to the pre-planning stage. In the procedure as shown in Figure 2, at an appropriate time, the user (e.g. a physician) then proceeds to the planning stage (step 206) and selects the relevant stored biopsy template (step 208). That is, after saving the customized biopsy template, the physician can select it in the planning stage. Using the template, the software implementing the present method can automatically position the biopsy cores onto a model of the patient’s prostate, fitted based on its size and shape (step 210 in Figure 2). This is achieved in example embodiments by a fusion algorithm, where the default prostate model with template biopsy cores is translated, rotated, and stretched to match the current prostate model.

[0043] In an implementation, the fusion algorithm takes two 3D prostate models, i.e. the current prostate model which is created by using image data of the prostate of the patient, and the default prostate model with the template biopsy cores (i.e. from the selected biopsy template), and finds two transformations, one followed the other, such that they register the default prostate model to the current prostate model. The image data may be received directly from an imaging device (e.g. an ultrasound scanner) or read from a data carrier (e.g. a storage device). The two transformations include an affine transformation and an elastic transformation which are calculated sequentially.

[0044] The affine transformation contains a translation (tx, ty, tz), a rotation (rx, ry, rz) and a scale (sx, sy, sz). The affine transformation helps to align the positions, orientations and sizes of the two models in 3D space. An example algorithm to calculate the affine transformation includes the followings steps:a. Find the center of the two prostate models.b. Translate (i.e. move) the default prostate model to the current prostate model so that their centers are the same.c. Use vtklterativeClosestPointTransform to find the affine transformation. The inputs for this algorithm are the 3D models of the current prostate model and the translated default prostate model in the format of vtkPolyData.

[0045] The elastic transformation attempts to match two sets of corresponding landmarks in the current prostate model and the affine-transformed default prostate model respectively and finds a nonlinear warp transform that stretches the affine-transformed default prostate model to match the current prostate model. This transformation can cater for differences in the shapes of the current prostate model and the default prostate model. An example algorithm to calculate the elastic transformation includes the following steps:a. Given the current prostate model and the affine-transformed default prostate model, calculate two sets of corresponding points, the target and source points respectively. The source points are the points of the affine-transformed default prostate model, while the target points are on the current prostate model.b. Build a 9x9x93D octree for the source points.c. For each source pointI. Use only the first point that lies in the octree, i.e. only at most one point in each cell is used. This may ensure that all the used points are evenly distributed and not concentrated on areas that have a higher density of points.II. For each source point that is used, form a line having an infinite length and projecting out of the center of the source model (i.e. the affine- transformed default prostate model) and intersecting the source point, find the first intersection of the line and the target model (i.e. the current prostate model). Use the first intersection point in the target model as the corresponding target point. The source point is discarded and not used if there is no intersection found.ill. Use vtkThinPlateSplineTransform to calculate a warp transform that matches the source points to the target points. The points in between are interpolated smoothly using Bookstein's Thin Plate Spline algorithm.

[0046] After the affine transformation and elastic transformation are calculated, the transformations are then applied to the template biopsy cores’ positions in the same sequence so that they will transform to the relevant positions in the current biopsy model. In an implementation, the user interface includes a graphical representation of the current biopsy model together with the transformed biopsy cores’ positions and the user can shift the position of the biopsy cores should they require any final adjustments (step 212 in Figure 2). Figure 5 shows an example graphical representation of the current prostate model with the biopsy cores removed.

[0047] In a typical prostate biopsy procedure, a positioning stage follows the planning stage. During the positioning stage, a robotic system can move a needle guide to each of these biopsy cores’ positions in turn. The user inserts the biopsy needle through the needle guide to each of these positions, allowing for accurate insertion of needles to the desired locations. Figure 6(a) shows a transverse view of the current prostate model together with the biopsy cores’ positions, and Figure 6(b) shows a corresponding 3D representation of the needle insertion paths to reach the biopsy cores’ positions.

[0048] Figure 7 depicts an exemplary computing device 700, hereinafter interchangeably referred to as a computer system 700, where one or more such computing devices 700 may be used to implement the present method and system. The following description of the computing device 700 is provided by way of example only and is not intended to be limiting.

[0049] As shown in Figure 7, the example computing device 700 includes a processor 704 for executing software routines. Although a single processor is shown for the sake of clarity, the computing device 700 may also include a multi-processor system. The processor 704 is connected to a communication infrastructure 706 for communication with other components of the computing device 700. The communication infrastructure 706 may include, for example, a communications bus, cross-bar, or network.

[0050] The computing device 700 further includes a main memory 708, such as a random access memory (RAM), and a secondary memory 710. The secondary memory 710 may include, for example, a hard disk drive 712 and / or a removable storage drive 714, which may include a floppy disk drive, a magnetic tape drive, an optical disk drive, or the like. The removable storage drive 714 reads from and / or writes to a removable storage unit 718 in a well-known manner. The removable storage unit 718 may include a floppy disk, magnetic tape, optical disk, or the like, which is read by and written to byremovable storage drive 714. As will be appreciated by persons skilled in the relevant art(s), the removable storage unit 718 includes a computer readable storage medium having stored therein computer executable program code instructions and / or data.

[0051] In an alternative implementation, the secondary memory 710 may additionally or alternatively include other similar means for allowing computer programs or other instructions to be loaded into the computing device 700. Such means can include, for example, a removable storage unit 722 and an interface 720. Examples of a removable storage unit 722 and interface 720 include a program cartridge and cartridge interface (such as that found in video game console devices), a removable memory chip (such as an EPROM or PROM) and associated socket, and other removable storage units 722 and interfaces 720 which allow software and data to be transferred from the removable storage unit 722 to the computer system 700.

[0052] The computing device 700 also includes at least one communication interface 724. The communication interface 724 allows software and data to be transferred between computing device 700 and external devices via a communication path 726. In various embodiments of the disclosure, the communication interface 724 permits data to be transferred between the computing device 700 and a data communication network, such as a public data or private data communication network. The communication interface 724 may be used to exchange data between different computing devices 700 which such computing devices 700 form part an interconnected computer network. Examples of a communication interface 724 can include a modem, a network interface (such as an Ethernet card), a communication port, an antenna with associated circuitry and the like. The communication interface 724 may be wired or may be wireless. Software and data transferred via the communication interface 724 are in the form of signals which can be electronic, electromagnetic, optical or other signals capable of being received by communication interface 724. These signals are provided to the communication interface via the communication path 726.

[0053] As shown in Figure 7, the computing device 700 further includes a display interface 702 which performs operations for rendering images to an associated display 730 and an audio interface 732 for performing operations for playing audio content via associated speaker(s) 734.

[0054] As used herein, the term "computer program product" may refer, in part, to removable storage unit 718, removable storage unit 722, a hard disk installed in harddisk drive 712, or a carrier wave carrying software over communication path 726 (wireless link or cable) to communication interface 724. Computer readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and / or data to the computing device 700 for execution and / or processing. Examples of such storage media include floppy disks, magnetic tape, CD-ROM, DVD, Blu-ray™ Disc, a hard disk drive, a ROM or integrated circuit, USB memory, a magnetooptical disk, or a computer readable card such as a PCMCIA card and the like, whether or not such devices are internal or external of the computing device 700. Examples of transitory or non-tangible computer readable transmission media that may also participate in the provision of software, application programs, instructions and / or data to the computing device 700 include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the Internet or Intranets including e-mail transmissions and information recorded on Websites and the like.

[0055] The computer programs (also called computer program code) are stored in main memory 708 and / or secondary memory 710. Computer programs can also be received via the communication interface 724. Such computer programs, when executed, enable the computing device 700 to perform one or more features of embodiments discussed herein. In various embodiments, the computer programs, when executed, enable the processor 704 to perform features of the above-described embodiments. Accordingly, such computer programs represent controllers of the computer system 700.

[0056] Software may be stored in a computer program product and loaded into the computing device 700 using the removable storage drive 714, the hard disk drive 712, or the interface 720. Alternatively, the computer program product may be downloaded to the computer system 700 over the communications path 726. The software, when executed by the processor 704, causes the computing device 700 to perform functions of embodiments described herein.

[0057] It is to be understood that the embodiment of Figure 7 is presented merely by way of example. Therefore, in some embodiments one or more features of the computing device 700 may be omitted. Also, in some embodiments, one or more features of the computing device 700 may be combined together. Additionally, in some embodiments, one or more features of the computing device 700 may be split into one or more component parts.

[0058] It will be appreciated that the elements illustrated in Figure 4 function to provide means for performing the various functions and operations of the servers as described in the above embodiments.

[0059] In an implementation, a server may be generally described as a physical device comprising at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the physical device to perform the requisite operations.

[0060] It will be appreciated by a person skilled in the art that numerous variations and / or modifications may be made to the present disclosure as shown in the specific embodiments without departing from the scope of the disclosure as broadly described. For example, alternative and / or additional transformations may be used. The biopsy plan is not limited to a prostate biopsy, but be one for a liver biopsy, a kidney biopsy, etc. The present embodiments are, therefore, to be considered in all respects to be illustrative and not restrictive.

Claims

CLAIMS1. A method for generating a biopsy plan, the method comprising:retrieving a selected biopsy template from a plurality of stored biopsy templates, the selected biopsy template comprising a first 3-dimensional (3D) model of an organ and a plurality of biopsy core positions spatially distributed on the first 3D model;generating a second 3D model of the organ based on image data of the organ from a patient; andapplying a fusion algorithm to map each of the plurality of biopsy core positions from the first 3D model to the second 3D model.

2. The method as claimed in claim 1 , wherein applying the fusion algorithm comprises:calculating a first transformation to align positions, orientations and sizes of the first and second 3D models in space;after calculating the first transformation, calculating a second transformation to compensate for differences between shapes of the first and second 3D models; andapplying the first transformation to the plurality of biopsy core positions followed by the second transformation to obtain a corresponding plurality of biopsy core positions spatially distributed on the second 3D model.

3. The method as claimed in claim 2, wherein the first transformation comprises an affine transformation comprising a translation, a rotation and a scale.

4. The method as claimed in claim 3, wherein calculating the affine transformation comprises:determining respective centers of the first and second 3D models; aligning the center of the first 3D model with the center of the second 3D model; anddetermining the affine transformation based on two sets of points on the first and second 3D models using an iterative closest point algorithm.

5. The method as claimed in any one of claims 2 to 4, wherein the second transformation comprises an elastic transformation, and wherein calculating the elastic transformation comprises:determining a set of source points on a third 3D model, the third 3D model comprising the first 3D model transformed by the first transformation, and a set of target points on the second 3D model, wherein the sets of source points and target points comprise corresponding landmarks on the third and second 3D models; and calculating a non-linear warp transform based on the sets of source points and target points, the non-linear warp transform stretching the third 3D model to match the second 3D model.

6. The method as claimed in claim 5, wherein calculating the non-linear warp transform comprises:building a 3D octree for the set of source points;using at most one point in each cell of the 3D octree to identify a corresponding target point from the set of target points on the second 3D model; and calculating the non-linear warp transform that match the set of source points to the set of target points.

7. The method as claimed in any one of the preceding claims, further comprising, before retrieving the selected biopsy template, adjusting at least one of a number of the biopsy core positions, coordinates of at least one of the biopsy core positions, and a sequence of the biopsy core positions, and a density of the biopsy core positions in a selected zone; and storing the adjusted biopsy template.

8. The method as claimed in any one of the preceding claims, further comprising storing the second 3D model and the plurality of mapped biopsy core positions as one of the plurality of stored biopsy templates.

9. A system for generating a biopsy plan, the system comprising:a processor;a computer-readable memory coupled to the processor and having instructions stored thereon that are executable by the processor to:retrieve a selected biopsy template from a plurality of stored biopsy templates, the selected biopsy template comprising a first 3-dimensional (3D) model of an organ and a plurality of biopsy core positions spatially distributed on the first 3D model;generate a second 3D model of the organ based on image data of the organ from a patient; andapply a fusion algorithm to map each of the plurality of biopsy core positions from the first 3D model to the second 3D model.

10. A robotic system for biopsy comprising the system as claimed in claim 9.

11. The robotic system as claimed in claim 10, wherein the system is configured to perform biopsies according to the biopsy plan comprising the second 3D model and the plurality of mapped biopsy core positions.

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