Joint registration, display and visualization of volumetric specimen imaging data with preoperative imaging data

Abstract

Some embodiments are provided for improving imaging, analysis, and visualization of surgical explant samples or other types of tissue samples in association with image data depicting the samples prior to extraction from the body. These embodiments help analyze the sample relative to the pre-explant tissue, guide removal of additional tissue, determine diagnosis or prognosis, or perform some other task or analysis. The 3D image of the explant sample is registered with a target 2D or 3D image of a portion of the body including the sample prior to explant of the sample. The availability of 3D image data of a sample helps to improve registration with a pre-explant image, including by digitally projecting a 3D image to a target 2D image, in order to determine the translation and direction of the 3D sample required for alignment with the target 2D image.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 332,865, filed on April 20, 2022, the contents of which are incorporated herein by reference. The contents of U.S. Patent No. 8,605,975 filed on October 11, 2010, U.S. Application No. 2014 / 0161332 filed on December 2, 2013, U.S. Patent No. 9,189,871 filed on December 29, 2014, U.S. Patent No. 9,613.442 filed on November 5, 2015, International Application No. US18 / 52175 filed on September 21, 2018, U.S. Provisional Patent Application No. 62 / 562,138 filed on September 22, 2017, International Application PCT / US20 / 62462 filed on November 26, 2020, and International Application PCT / US21 / 20020 filed on February 26, 2021 are also incorporated herein by reference. Background Art

[0003] The treatment of various health conditions can include removing specific tissues from the body. For example, the treatment of certain cancers can include removing one or more tumor masses from the body by surgery. Other diseases can be treated by removing other types of tissues, foreign bodies, or other masses from the body. When performing such removal, it is desirable to ensure that the target tissue is completely removed while removing as little nearby healthy tissue as possible. In practice, surgeons often remove additional tissue around the target to ensure that the target is completely removed (e.g., to prevent recurrence due to continued growth of residual tumor tissue).

[0004] In order to improve the health outcomes of patients, pathologists can analyze the explanted tissue to determine whether the entire target has been removed, determine the type of explanted tissue (e.g., verify that the explanted target structure is a malignant tumor), perform DNA sequencing or other analysis on the explanted tissue (e.g., customize systemic anti-cancer therapy), or provide some other benefit for the general treatment of patients and / or diseases. This can include slicing the sample to examine the target within the tissue sample by visual, microscopic or other optical means. This inspection can allow the pathologist to identify the tissue type in the target (e.g., malignant cancer tissue, benign tumor tissue), the state of the target (e.g., may be before or after metastasis), and whether the target is completely removed as a result of the tissue sample explantation (e.g., by observing the distance that the target tissue extends to relative to the edge of the tissue sample). The pathologist can then make a final diagnosis of the success of the target removal surgery, which can then be used to decide whether to perform additional surgery to remove the target portion that may remain in the patient's body. Summary of the invention

[0005] One aspect of the present disclosure relates to a method comprising: (i) obtaining a target two-dimensional (2D) image of a part of a body; (ii) obtaining a three-dimensional (3D) image of a specimen explanted from the part of the body; (iii) determining a registered translation and orientation of the 3D image so that the 3D image is perspectively aligned with the part of the body represented in the target 2D image; (iv) projecting the 3D image to the plane of the target 2D image via a numerical method based on the registered translation and orientation to generate a projected 2D image; and (v) displaying an indication of the projected 2D image superimposed on the target 2D image.

[0006] Another aspect of the present disclosure relates to a method comprising: (i) obtaining a target three-dimensional (3D) image of a portion of a body; (ii) obtaining a sample 3D image of a sample explanted from the portion of the body; (iii) determining a registered translation and orientation of the sample 3D image such that the explanted tissue represented in the sample 3D image is aligned with tissue of the portion of the body represented in the target 3D image; and (iv) displaying an indication of the sample 3D image superimposed on the target 3D image, rotated and translated according to the registered translation and orientation.

[0007] Yet another aspect of the present disclosure relates to a transitory or non-transitory computer-readable medium configured to store at least computer-readable instructions that, when executed by one or more processors of a computing device, cause the computing device to perform controller operations to perform a method of any of the above aspects.

[0008] Yet another aspect of the present disclosure relates to a system comprising: (i) a controller comprising one or more processors; and (ii) a transient or non-transitory computer-readable medium storing computer-readable instructions which, when executed by one or more processors of the controller, cause the system to perform any of the methods of the above aspects.

[0009] These and other aspects, advantages and alternatives will become apparent to those skilled in the art by reading the following detailed description with reference to the accompanying drawings as appropriate. In addition, it should be understood that the descriptions provided in this abstract section and elsewhere in this document are intended to illustrate the claimed subject matter by way of example and not limitation. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1A Depicted are 2D images of portions of a body in accordance with example embodiments.

[0011] Figure 1B Depicted is a diagram of a Figure 1A A 3D image of an explanted sample of this part of the body is depicted.

[0012] Figure 1C Depicts a rotated Figure 1B 3D image.

[0013] Figure 1D Depicts a 2D image that has been projected onto a Figure 1C 3D image.

[0014] Figure 2 Depicts superimposed on each other according to an example embodiment Figure 1A and Figure 1D 2D image.

[0015] Figure 3A Depicted are 2D images of portions of a body in accordance with example embodiments.

[0016] Figure 3B Depicted is a diagram according to an example embodiment Figure 3A A 2D image, on which is superimposed Figure 3A The 2D projection range of the 3D image of the explanted sample in this part of the body is depicted.

[0017] Figure 4 is a simplified block diagram showing some components of an example system.

[0018] Figure 5 is a flowchart of a method according to an example embodiment.

[0019] Figure 6 is a flowchart of a method according to an example embodiment. DETAILED DESCRIPTION

[0020] Examples of methods and systems are described herein. It should be understood that the words "exemplary," "example," and "illustrative" are used herein to mean "serving as an example, instance, or illustration." Any embodiment or feature described herein as "exemplary," "example," or "illustrative" is not necessarily to be construed as being superior or more advantageous than other embodiments or features. Furthermore, the exemplary embodiments described herein are not intended to be limiting. It is readily understood that certain aspects of the disclosed systems and methods can be arranged and combined in a variety of different configurations.

[0021] I. Overview

[0022] Various clinical interventions involve removing tumors or other undesirable tissues or substances. Ideally, only unwanted tissues would be removed, leaving adjacent tissues. This is difficult to achieve in practice, so surgeons often remove more tissue than necessary to prevent any unwanted tissues that could, for example, cause recurrence from being left in the body. However, this must be balanced to retain healthy tissues, thereby improving postoperative results regarding tissue function and appearance. In order to facilitate this positioning, the surgical procedure can be guided by preoperative imaging of the tumor or other target tissue sites. For example, a mammogram can be performed on the breast and used to plan the removal of a tumor or other target tissue from the breast while minimizing the removal of other tissues, thereby avoiding blood vessels, nerves, ducts or other tissues, or considering other factors. Sutures, clips, staplers, biopsy markers, fiducials or other artifacts may also be inserted into the target tissue and / or surrounding tissues, which may be used to help surgeons locate preoperative images to anatomical structures during surgery. Such artifacts may be implanted for the explicit purpose of allowing surgeons to locate preoperative images to anatomical structures and / or for other purposes.

[0023] During surgery, explanted tissue is often analyzed by a pathologist, surgeon, or other medical professional to determine whether the process successfully completely removes the tumor or other target tissue / structure. The pathologist's diagnosis can then be used to determine whether to perform additional surgery (to remove additional tissue), where to remove such tissue (e.g., in a patient's body such a position: the tumor corresponding to the tissue sample is close to and / or significantly exceeds the position of the tissue sample boundary) or other medical decisions. It would be beneficial to quickly perform such imaging and analysis after the explanted sample to guide the removal of additional tissue during a single surgical procedure. This avoids the cost and risk of performing additional separate processes (e.g., avoiding the risks associated with anesthesia) and avoids the possibility of metastasis or other unnecessary disease progression due to the additional time that residual tumor cells stay in the body. Additionally, it is desirable to generate imaging data of the explanted tissue sample so that the same image data is compared with the preoperative image data. This can allow a surgeon, radiologist, or other medical professional to compare the removed tissue with the preoperative plan to determine whether all target areas of the anatomical structure have been removed, and if not, determine from which edge of the sample site to remove additional tissue so as to fully complete the surgical plan based on the preoperative image.

[0024] Embodiments described herein provide a variety of benefits, including benefits that address the above-mentioned problems. These embodiments include scanning an explanted tissue sample to generate 3D image data (e.g., volume density information generated by a micro-CT imager), which can then be registered with a preoperative image (e.g., a 2D mammogram, tomosynthesis image data, 3D image data). Such scanning can be quickly accomplished by an imager located in an operating room, allowing the sample image data to guide additional tissue removal during the same surgical procedure, thereby avoiding reoperation. In addition, such 3D sample image data can be quickly processed in a variety of ways to accurately register the 3D image with the pre-operative 2D (or 3D) image.

[0025] The registered 3D image data can then be processed and displayed along with the pre-operative image to allow the surgeon, radiologist, or other medical professional to make better decisions about the completeness of the surgical procedure, the necessity and location of additional tissue removal, and / or whether and how any removed tissue fits into the pre-operative plan. For example, the registered 3D image can be digitally projected onto the same plane as the pre-operative 2D image of the anatomical structure from which the sample was collected, and the displayed images can be superimposed on each other, allowing the surgeon to compare their concept of the extent of the tumor or other target tissue with the extent and content of the sample that has actually been removed from the patient. This can allow the surgeon to, for example, determine whether an area annotated on the pre-operative 2D image as containing a target tumor or other target tissue extends beyond the edge of the sample removed from the patient, and therefore requires additional tissue to be removed from the patient along a surgical margin area that corresponds to the location in the superimposed image where the annotated area extends beyond the image of the explanted sample.

[0026] The use of 3D explanted specimen image data helps improve the identification and positioning of sutures, clips, fiducials, or other artifacts within the specimen to help surgeons or other medical professionals determine the relationship of the specimen to a preoperative procedure (e.g., biopsy, palpation, etc.). The high-resolution 3D image data available when the specimen is imaged alone in vitro allows for more accurate determination of the position and orientation of such fiducials. This improved positioning accuracy can, for example, lead to improved registration of the 3D image data with a 2D (or 3D) preoperative image by determining the position and orientation of the fiducial within the preoperative image, and then registering the preoperative image and the specimen image based on the determined position and orientation of the fiducial within each image.

[0027] It should be understood that the above-described embodiments and other embodiments described herein are for illustration purposes only and are not intended to be limiting.

[0028] II. Example Embodiments of Image-Guided Specimen Explantation

[0029] As briefly noted above, it is desirable to be able to directly compare preoperative images of a surgical site (e.g., a breast containing a tumor or other tissue to be removed) with 3D images of any tissue samples removed therefrom. This can help improve the assessment of the completeness of the surgery in removing any target tissue identified in the preoperative images (and, if incomplete, help improve the determination of what additional residual tissue needs to be removed in a revision surgery in order to completely remove the target tissue). In embodiments where the sample image data is generated during a sample explantation surgery, this can also guide the surgeon in removing such additional tissue, thereby improving patient outcomes by ensuring more complete removal of the target tissue and reducing the chances of requiring additional tissue removal surgery.

[0030] Thus, embodiments described herein provide apparatus for generating high quality 3D images of explanted tissue samples (e.g., based on volume density information of the sample generated by a micro-CT imager) and accurately registering these 3D images to 2D (or 3D) preoperative images of target tissue within the body region from which the tissue sample was removed. The availability of 3D images of the explanted sample means that a variety of analyses and visualizations of the sample can be provided (e.g., digitally projected 2D views of the sample from various angles, slices through the sample at various angles / positions). Additionally, such 3D image data allows the 3D image to be registered to a 2D preoperative target image regardless of the angle / plane of the 2D image (e.g., compared to taking one or several 2D images through the sample, where the angle / plane of any one of the available images is unlikely to closely match the plane / angle of the 2D target image).

[0031] Note that various embodiments are described herein in which anatomical images (e.g., 2D preoperative images of a target surgical site, images or renderings of 3D images of a sample surgically removed from the target site) are displayed to a surgeon, radiologist, or other medical professional. In some examples, such a display is provided by a computing system (e.g., an imaging system) that operates an imager to generate scan data of the sample, reconstruct volume density information from the scan data, register a 3D image generated therefrom to a preoperative 2D (or 3D) image, and render a two-dimensional image and / or generate other analyses based on the image data. However, such a display can also be part of a pathologist workstation, a remote control and display unit of an imaging system, or some other interface system that is not used to reconstruct volume density information of the sample or render a two-dimensional or other type of image or image information therefrom.

[0032] Figure 1A An example target 2D image 100 depicts a part of a body (eg, a breast). Figure 1BVarious aspects of an example 3D image 110A (e.g., a 3D matrix of volume density values) of a sample explanted from the portion of the body depicted in the target 2D image 100 are depicted. Embodiments described herein facilitate registration of the 3D image 110A to the target 2D image 100 so that the contents of the images can be displayed together (e.g., superimposed). This combined display enables a surgeon or other healthcare professional to assess the success of the explantation procedure, plan the removal of additional tissue (e.g., during the same surgical procedure as the removal of the original sample), determine a disease prognosis, decide on a treatment plan (e.g., whether to provide chemotherapy, the chemotherapy dosage), or perform some other analysis.

[0033] This combined display of the preoperative target 2D image and the 3D specimen image or other image data derived therefrom can take a variety of forms. For example, the 3D image data can be represented by a semi-transparent rendering surrounding the target 2D image (e.g., of the boundaries of the specimen and optionally one or more regions of interest therein, volume density, or other 3D information represented by simulated fog or other occlusions), thereby allowing the combination to be rotated to view the combined image data from different directions and / or to adjust the position of the target 2D image relative to the 3D image perpendicular to the 2D image plane. Additionally or alternatively, the 3D image can be projected (e.g., via numerical methods) onto the plane of the target 2D image, and the two 2D images displayed side by side, or superimposed on each other (e.g., with different images depicted in different colors, allowing similarities and differences between the images to be easily understood).

[0034] A variety of different methods can be used to generate volume density information or other 3D image data of tissue samples. For example, a micro CT imager can be used to generate X-ray non-transmissive density information, and an MRI imager can be used to generate hydrogen atoms or MRI contrast density information, etc. In order to use such 3D images to compare with preoperative 2D images, for example, to determine whether repair surgery is needed, etc., it is generally advantageous to render one or more two-dimensional images of the sample from the 3D image. Such two-dimensional images can include high-resolution cross-sectional images of sample slices (for example, slices through the sample parallel to the standard coronal plane, sagittal plane and / or transverse plane of the sample and / or slices through the sample parallel to the plane of the preoperative target 2D image, according to the direction of the sample in the patient's body). The two-dimensional image can also include a perspective view of the sample. Such a perspective view can be used to illustrate the direction and position of the high-resolution cross-sectional image relative to the sample. Additionally, such a perspective view can display the position of tumors, staplers, lines or other substances or structures of interest in the tissue sample in three-dimensional space. In yet another example, such a 2D image may be a digitally generated 2D image that projects the 3D image into a designated plane (eg, the same plane as the target 2D image) as if the sample had been imaged 2D in the designated plane.

[0035] Figure 2 An example of a display of such a 2D digital projection 120 of a registered 3D image 110B superimposed on a target 2D image 100 is depicted. If annotations are available for the target image and / or the 3D image, an indication of such annotations can also be provided on the display. For example, a target annotation 105 of a target region within the target 2D image 110 is provided as a 3D region of interest 115 segmentation map within the 3D image 110A. The 3D region of interest 115 is also projected as a two-dimensional region of interest 125 within the projected 2D image 120. Both annotations 105, 125 are also indicated on the display. Figure 2 The superimposed 2D display depicted in FIG.

[0036] Such annotations (2D or 3D) can be obtained in a variety of ways. In some examples, such annotations can be manually generated by a radiologist or other medical professional. For example, a radiologist can manually mark a certain area of ​​a mammogram or other preoperative 2D image (e.g., 100) as interesting (e.g., because it contains a tumor to be removed from the breast). Additionally or alternatively, such annotations can be determined in an automatic or semi-automatic manner based on a 2D or 3D image. For example, a 3D image (e.g., rendered from volumetric imaging data of a tissue sample) can be used to identify regions of interest within the 3D image (e.g., regions of increased density that may be tumors, calcifications, or other unwanted tissues). Indications of these identified regions of interest (e.g., segmentation maps indicating the location, extent, and / or geometry of such regions) can then be provided to a radiologist's workstation to, for example, identify the location, extent, or other information of the regions of interest within the 2D or 3D image.

[0037] Segmentation maps of tumors, staplers, or other regions of interest within a sample can be generated in a variety of ways. In some examples, an automatic algorithm can generate a segmentation map. This can include applying a density threshold to volume density information (e.g., to segment staplers, lines, calcifications, or other high-density content within a sample), applying a trained neural network, or performing some other process on volume density information or other 3D or 2D image information. In some examples, a segmentation map can be generated by a radiologist or other healthcare professional (e.g., a pathologist). For example, a radiologist can annotate the extent of a tumor or other structure of interest within a tissue sample by, for example, indicating the extent of the structure of interest in one or more two-dimensional cross-sectional images of the sample. The capabilities of a pathologist can be enhanced by one or more automatic segmentation methods. For example, an automatic method can generate an estimated segmentation map, which can then be edited by a pathologist (e.g., by dragging the edge of the segmentation map to expand or reduce the volume of the sample contained in the segmentation map). In another example, an automatic method can generate multiple possible segmentations, and the pathologist can select the "best" segmentation.

[0038] A plurality of different tumors, staplers, lines or other objects or sets of objects within a sample may be associated with correspondingly different segmentation maps and / or corresponding regions of interest. The user interface may then provide the user with the ability to selectively hide certain contents of the sample from view by selecting or deselecting the corresponding segmentation map. The user selecting or deselecting a single object or other content in the sample for display in this manner may include clicking or otherwise interacting with a button associated with the corresponding content of the sample in the user interface, clicking or otherwise interacting with a portion of a display displaying the sample content, or otherwise interacting with the user interface.

[0039] The 3D image of the explanted specimen may be registered with a preoperative target 2D image of the portion of the body from which the specimen was explanted in a variety of ways. The three-dimensional nature of the 3D image of the explanted specimen allows for various transformations and projections to be applied thereto to aid in registration and alignment with the target 2D image, or to aid in some other analysis or visualization. For example, the 3D image 110A may be rotated according to a candidate orientation to generate a rotated 3D image 110B, such as Figure 1CAs depicted. The rotated 3D image 110B can then be projected via a numerical method to generate a candidate projected 2D image 120. The candidate projected 2D image 120 can then be aligned with the target 2D image 100 by determining a candidate translation of the candidate projected 2D image 120 using a similarity measure between the target 2D image and versions of the candidate projected 2D image 120 translated in various ways (e.g., searching an enumerated set of vertical and horizontal translations, performing gradient descent on the translation based on the similarity measure). The value of the similarity measure of the determined candidate translation can then be used as a similarity score for the candidate orientation in order to compare different candidate orientations. Such projections and translations can be determined for a plurality of different candidate orientations (e.g., for a regularly spaced grid of candidate orientations), and the corresponding similarity score for each candidate orientation can be used to determine the correct orientation (and corresponding translation) to register the 3D image 110A to the target 2D image 100.

[0040] A variety of different numerical methods can be used to project the 3D image onto the plane of the target 2D image (or some other plane). This can include performing multiple numerical integrations (e.g., line integrals, volume integrals along a cylinder, cone, or other geometric shape) through the 3D image (e.g., integrating density information represented by the 3D image along a line, cone, or other path through the 3D image) to determine the intensity or other value of the pixels of the numerically projected 2D image. The specific path / line / position / geometry of such integration can be based on information about the device used to generate the target 2D image so as to generate a projected 2D image that can accurately simulate how the device used to generate the target 2D image images the explanted sample represented in the 3D image. For example, the geometry of the line integral or other integration geometry for each pixel of the projected 2D image of the 3D image can be specified based on the relative positions of the corresponding pixels of the X-ray emitter and X-ray sensor array of the device used to generate the target 2D image.

[0041] Various similarity metrics may be used to align the candidate projected 2D image 210 with the target 2D image 100, thereby determining similarity scores for their corresponding candidate orientations and allowing determination of a final orientation to register the 3D image 110A with the target 2D image 100. For example, mutual information, Kullbeck-Leibler divergence, L2 norm, L1 norm, or some other metric may be used. The mutual information between the two images may be represented as

[0042]

[0043] Where MI() is the mutual information operator, X and Y are the images whose mutual information is to be calculated, and P (X,Y) (x, y) is the joint probability mass function of the two images with respect to their pixel values ​​(e.g., intensities), P(X) (x) is the marginal probability mass function of the first image with respect to its pixel values, P (Y) (y) is the marginal probability mass function of the second image with respect to its pixel values.

[0044] Using mutual information as a similarity measure for image comparison provides a variety of benefits. For example, the histogram boundaries and "bucket" assignments for each pixel of the target 2D image 100 can be pre-computed once and reused to determine the mutual information similarity measure for various different candidate projected 2D images 120 and their translations. In addition, the histogram boundaries and "bucket" assignments for each pixel of each candidate projected 2D image 120 can also be pre-computed once and reused for various attempted transformations of the candidate projected 2D image 120 relative to the target 2D image 100. In addition, determining the mutual information in this manner only requires calculating the membership of any particular pair of pixels in the target 2D image 100 and the transformation of the candidate projected 2D image 120, which saves computation relative to L2 distance or other similarity measures, which may require determining non-linear functions (e.g., power functions), sums of floating point numbers, or other more complex computational tasks, and are therefore more computationally expensive than calculating the membership of various two-dimensional "buckets" to generate the mutual information.

[0045] As described above, it is possible to determine a direction to register a 3D image (e.g., 110A) with a target 2D image (e.g., 100) by evaluating multiple candidate directions (e.g., a pre-specified set of candidate directions spanning all 4 pi of a solid angle), and then selecting a direction that results in a maximum value of a similarity measure between the target 2D image and the 3D image rotated according to the candidate directions, projected into the 2D image, and then translated relative to the target 2D image in a manner that maximizes or otherwise results in an increase in the value of the similarity measure. This method of determining the registration direction provides various computational advantages because the computations for evaluating any one candidate direction are independent of the computations for any other direction. Therefore, the process of evaluating all candidate directions can be extensively parallelized to reduce the total time used to evaluate the entire space of possible candidate directions, and allows the amount of computational resources devoted to the task (e.g., the number of GPUs or other processors, clock speed, processor power) to be limited in order to obtain the desired total time for evaluating the set of candidate directions. For example, fewer computing resources may be devoted to performing such registration for postoperative evaluation, whereas more resources may be allocated in examples where the results of the registration will be used intraoperatively to guide additional tissue removal and / or termination of the surgical procedure.

[0046] The process of determining the registration direction can be improved by searching a coarser "grid" of candidate directions, and then refining the direction estimate determined at that core resolution by performing subsequent direction searches at higher angular resolutions in the neighborhood of the direction determined by the coarse search. This can allow a higher resolution registration direction to be determined using fewer computational resources. Such a process can be performed at three or more progressively finer search angular resolution levels.

[0047] Another way to reduce the computational cost of registering a 3D image to a target 2D (or 3D) image is to restrict the similarity evaluation to only the content of the high-density (or otherwise high-intensity) image. For example, if the image contains clips, sutures, fiducials, or other artificial objects (e.g., objects placed in the tissue to assist the surgeon in locating the preoperative image relative to the anatomical structure during surgery), the registration process can be based only on pixels of the target 2D image and / or 3D image whose values ​​(e.g., intensity values) exceed a threshold for selecting such content. Such a threshold can be specified so that pixels of the target 2D (or 3D) image and 3D image (or the candidate projection 2D image determined therefrom) that do not depict metallic substances, ceramic substances, synthetic polymer substances, or radiopaque substances are discarded. Additionally or alternatively, the threshold can be set to include pixels representing calcification or other high-intensity biological content of the image. A threshold may be specified such that pixels of the target 2D (or 3D) image and 3D image (or candidate projected 2D images determined therefrom) that do not depict metallic material, ceramic material, synthetic polymer material, radiopaque material or calcification are discarded.

[0048] Pixels in the image that do not exceed the threshold may then be discarded, and the above-described registration process (or some other registration process) may be performed based only on the non-discarded pixels (treating the discarded pixels as "empty" and / or zero-valued, and thus avoiding calculations associated with them as part of the registration process). The computational cost of the registration process may be further reduced by determining the location of the centroid or other measure of the location of the non-discarded pixels, and starting the registration process for a given direction by starting with a candidate translation corresponding to the centroid of the projected 2D image to the centroid of the target 2D image.

[0049] After determining the rotation and translation of the 3D image to correspond to the target 2D (or 3D) image, the registration can be further improved by deforming the rotated and translated 3D image (or the projected 2D image determined therefrom). Such a deformation process can be performed to account for deformation of the specimen after explantation and / or to account for deformation of the anatomical structure from which the specimen was obtained during generation of the target 2D (or 3D) image (e.g., to account for compression or other manipulation of the breast to generate a 2D, tomographic, and / or 3D preoperative mammographic image).

[0050] Additionally or alternatively, the position and orientation of the fiducials in the image may be determined directly, and the registration of the 3D image to the target 2D image determined as the orientation and translation required to align the fiducials depicted in the 3D image with the fiducials depicted in the target 2D image. This approach may be computationally cheaper, particularly in examples where the fiducials within the image are significantly denser or more intense, thereby allowing low density (e.g., below a threshold) pixels of the image to be discarded prior to determining the orientation and position of the fiducials within the image.

[0051] Such a fiducial may be an artificial radiopaque element or other feature to facilitate imaging via micro-CT, MRI, or some other volumetric device. Thus, such a fiducial will be represented in the volume density information of the tissue sample, allowing the volume density information to be aligned with the physical location and extent of the sample container. To assist in determining the orientation of the fiducial, the fiducial may have a non-degenerate geometry, for example, the fiducial may have a geometry that does not exhibit any rotational symmetry axes and / or symmetry planes. For example, the fiducial may have a tetrahedron whose sides are of varying lengths. Alternatively, the fiducial may have a relatively non-degenerate geometry (e.g., no rotational symmetry axes, but only one symmetry plane), wherein any ambiguity in the determined orientation of the fiducial is determined based on the similarity between the overall images when evaluated according to different fuzzy options for orientation or relies on manual selection between different orientation options.

[0052] Regardless of the registration approach (e.g., evaluating candidate orientations and translations, determining the location and orientation of a fiducial), the registration process can include human input or feedback to improve the registration of the 3D image to the target 2D (or 3D) image. This can include determining multiple candidate registrations between the 3D image and the target 2D (or 3D) image, and displaying an indication of each candidate registration to the user (e.g., displaying a projected 2D image of the 3D image superimposed on the target 2D image according to each candidate registration). The user can then select which candidate registration to use as the "true" registration. A candidate registration set can be selected by, for example, determining a candidate registration set that represents a local maximum of a similarity metric with respect to the candidate orientations. Such a candidate registration set can be a set number (e.g., always providing three candidate registrations) or selected based on some other consideration (e.g., only local maximum candidate registrations with similarity metric scores within a set percentage of the absolute maximum similarity metric score). Additionally or alternatively, user input to the registration process may include user modifications to the orientation and translation of a candidate registration (eg, a user may "fine tune" an automatically generated registration determined using the methods described herein).

[0053] As described above, the display of the target 2D image, the projection 2D image determined by projecting the 3D image onto the plane of the target 2D image after 3D image registration, and / or the display of such images superimposed on each other can include an indication of annotations associated with the image. It is also possible to further analyze such annotations and indicate the results of such analysis on the display. For example, the overlapping area between the annotation area in the projection 2D image of the target 2D image and the 3D image (e.g., the annotation area determined as the projection of the segmentation map within the 3D image) can be indicated on the display (e.g., on a display that also indicates one or both of the target 2D image and the projection 2D image). Additionally or alternatively, the annotation area in the target 2D image (which can indicate an area that a radiologist has identified as a part of a tumor to be removed) can be indicated on the display (e.g., to indicate the target tumor portion that is not removed when the sample is explanted), and these annotation areas do not overlap with the range of the explanted sample in the projection 2D image.

[0054] Figure 3A and 3B Such a display is illustrated. Figure 3A A target 2D image 100 is depicted with a target annotation area 105 indicated thereon. Figure 3B Depicted is a superimposed display 200 of a target 2D image 100 and a projected 2D image that has been derived from a 3D image of a sample explanted from a body region depicted in the target 2D image. The extent of the explanted sample in the projected 2D image can be determined (e.g., by comparing the pixel intensity of the projected 2D image to a threshold value) and used to generate an extent annotation 210 that can be indicated on the superimposed display 200. The area of ​​the target annotation region 105 that does not overlap with the extent annotation 210 can be determined (e.g., to indicate the portion of the target represented by the target annotation region 105 that was not removed during the process used to explant the sample) and provided on the display as a non-overlapping annotation 220. The surgeon or other healthcare professional can then use the indicated non-overlapping annotation 220 to decide whether to remove additional tissue from the patient, and if so, to decide along which edge of the explantation site from which the tissue is to be removed.

[0055] Additional or alternative analysis and related instructions can be provided based on a set of target 2D images and 3D images and / or projected 2D images registered therewith. For example, features of interest can be identified and located within the target 2D image and the associated 3D image and / or projected 2D image, and instructions related thereto can be provided. This can include identifying the number, location, or other information about calcifications or other tumor-related features of interest within the image data, and then providing instructions related thereto. Such instructions may include instructions for multiple calcifications identified in the preoperative target 2D image and instructions for multiple calcifications identified in the 3D image of the explanted sample (or the projected 2D image determined therefrom). The surgeon can use such instructions to determine whether to remove additional tissue (e.g., if the number of calcifications identified in the preoperative target 2D image is less than the number of calcifications identified in the explanted sample). In some examples, image data can be used to determine the correspondence between individual calcifications identified in the preoperative target 2D image and individual calcifications identified in the explanted sample image. An indication relating to such correspondence may then be provided, such as a line or other indication on a display linking corresponding calcifications in the preoperative and explanted specimen images, an indication of calcifications in a preoperative image for which no corresponding calcifications are identified in the explanted specimen image, or some other indication.

[0056] As described herein, the specific perspective view of the provided sample provided in the display can be controlled by the user in various ways. For example, the user can click and drag to rotate the perspective view around the axis, or use a two-finger gesture to zoom in or out. Alternatively, buttons or some other user interface elements (not shown) arranged as four direction arrows can be used to realize this change. The type of perspective view (for example, according to the surface coloring of the direction, the projection density view with the internal structure of indication, etc.) can be modified by pressing a button on the user interface, using a drop-down menu or by some other device. For example, a user interface can be used (for example, by clicking a button or otherwise interacting with a button, not shown) to switch between a simulated surface rendering view and a simulated slice view.

[0057] An imaging device for generating volume density information or other 3D image data as described herein may include various components to facilitate various different volume imaging modalities. In some examples, the imager may include a high-power magnet (e.g., a superconducting magnet), a bias coil, a radio frequency scanning coil, and other elements configured to perform magnetic resonance imaging (MRI) on a sample. Such an MRI imager can generate volume density information of a target sample related to the density of hydrogen atoms, MRI contrast agent atoms (e.g., gadolinium), or the density of some other magnetic particles. In some examples, the imager may include a micro-CT imager configured to generate volume density information of a target sample related to the X-ray radiodensity or radiopacity of the sample.

[0058] Such a micro-CT imager includes at least one X-ray source capable of generating X-rays, and at least one X-ray imager capable of generating an image after the emitted X-rays have passed through a target sample. Higher density regions of the target sample (which may alternatively be referred to as regions with higher X-ray radiation density or radiopacity) will absorb and / or scatter the emitted X-rays to a greater extent, resulting in corresponding regions of the X-ray imager being exposed to lower intensity X-rays. The micro-CT imager operates to generate scan data in the form of multiple X-ray images of the target sample, each image being taken at a corresponding angle and / or position relative to the target sample. The multiple X-ray images of the target sample can then be reconstructed to generate volume density information of the target sample.

[0059] The X-ray source may include an X-ray tube, a cyclotron, a synchrotron, a radioactive X-ray source, or some other X-ray source. The X-ray source may include a plurality of different X-ray sources, for example to allow modulation of the beam power, beam width, direction of the X-ray beam relative to the target sample, focus or divergence of the X-ray beam at the target sample location, or to allow control of some other characteristic of the emitted X-rays to facilitate imaging of the target sample.

[0060] The x-ray imager may include a photoexcited phosphor plate, a scintillator, an x-ray intensifier, or other element for converting x-rays into visible light that is coupled to a charge coupled device, a photodetector array, a flat panel detector, or other visible light imaging element. Additionally or alternatively, the x-ray imager may include amorphous selenium or some other element that is configured to convert x-rays directly into electron-hole pairs or other electronically detectable phenomena. The x-ray imager and the x-ray source together define a field of view, which is the area that the micro-CT imager can image. Thus, the micro-CT imager is able to generate x-ray images of portions of a target sample (or other material or structure) that are within the field of view.

[0061] Compared with the possibility of imaging the body part of a living patient, micro CT imaging of samples removed from the body allows the use of higher intensity and longer duration scans. Additionally, the X-ray source and the X-ray imager can be positioned closer to the sample. Compared with imaging the tissue located in the patient's body, these factors help improve image resolution and contrast. In addition, the position and direction of the explanted tissue sample can be arbitrarily rotated and / or translated by the actuating gantry, thereby allowing the accurate position and direction of the sample relative to the imaging device to be arbitrarily and accurately controlled. For example, an X-ray image can be taken of the sample at a non-uniform angle or some other reduced or sparse angle set. Additionally, when the entire sample is small enough to fit completely within the field of view of the imaging device, the actuating gantry can be operated to ensure that the sample is actually completely within the field of view. In some examples, the sample container configured to contain the sample can have a size roughly the same as the field of view, thereby ensuring that any sample placed therein will remain completely within the field of view. Alternatively, when the sample is too large to fit completely within the field of view, the position and orientation of the sample can be controlled to obtain X-ray images specific to the relative position and orientation that are sufficient to reconstruct volume density information for the entire sample.

[0062] Imaging of implanted tissue samples also allows the X-ray source to be completely enclosed within an X-ray shielding material (e.g., lead sheet) while the X-ray source is operated to emit X-rays. For example, after the sample has been placed within the micro-CT imager, a door composed of X-ray shielding material can be translated and / or rotated into position, thereby reducing the amount of X-ray exposure experienced by the surgeon, nurse, or other persons near the imager. This can also allow the intensity of X-rays emitted by the X-ray source to be increased while maintaining the environmental exposure limit below a specified safety level, thereby potentially improving image resolution and / or contrast.

[0063] The micro-CT imager for generating volume density information described herein can be operated in a variety of ways to generate sample X-ray scan data sufficient to generate accurate reconstruction of the volume density information of the sample. The reconstruction methods described in U.S. Patent No. 8,605,975, U.S. Application No. 2014 / 0161332, U.S. Patent No. 9,189,871, U.S. Patent No. 9,613.442, PCT Application No. US18 / 52175, and U.S. Provisional Patent Application No. 62 / 562,138 allow such volume density information to be accurately reconstructed using a reduced number of X-ray images of the sample relative to other methods. Specifically, the reduced view and sparse view reconstruction methods described in these patents and patent applications allow clinical quality volume density information of explanted breast tissue or other target tissue samples to be generated using less than 300 single X-ray images of the sample or less than 100 single X-ray images of the sample. The reduction in the number of X-ray images required for reconstruction can result in a reduction in the total scan time to less than 10 minutes or less than 5 minutes.

[0064] Such an imaging system can be configured to create volume density information of a sample using a micro-CT imager or other X-ray-based tomography techniques. However, such an imaging system may include additional or alternative imaging techniques, such as magnetic resonance imaging, volume fluorescence imaging, ultrasound imaging, far ultraviolet imaging, spontaneous emission imaging (e.g., positron emission imaging), or some other form of volume imaging or some combination of modalities. In practice, the precise automated specimen handling described herein (e.g., using standardized sample containers with registration features and / or imaging references) can facilitate automatic imaging of samples using a variety of imaging modalities. The lack of human intervention in sample handling between imaging modalities can improve the registration of data from a variety of different imaging modalities by reducing the amount of sample motion or deformation that may occur between the execution of a variety of different imaging modalities.

[0065] III. Example System

[0066] The computing functions described herein may be performed by one or more computing systems. Such computing functions may include functions to operate an imager to generate scan data of a target sample, functions to reconstruct volume density information from such scan data, functions to render cross-sections, perspectives, digital projections, or other two-dimensional views from the volume density data, functions to register or otherwise align such three-dimensional density data (or two-dimensional projections thereof) with two-dimensional or three-dimensional image data generated by some other system (e.g., by a mammography imaging system), and / or user interface functions. Such computing systems may be integrated into or take the form of computing devices such as portable medical imaging systems, remote interfaces for such imaging systems, pathologist workstations, tissue analysis and / or slicing tables or workstations, tablet computers, laptop computers, servers, cloud computing networks, and / or programmable logic controllers.

[0067] For example purposes, Figure 4 4 is a simplified block diagram illustrating some components of an example computing device 400, which may include components for providing an indication of scan-related data on a screen or other display device. Alternatively, the example computing device may lack such components and provide an indication of imaging data via some other means (e.g., via the Internet or some other network or other communication interface).

[0068] The computing device 400 may also include an imaging component 424 for obtaining imaging data of such tissue samples. The imaging component 424 may include a micro-CT imager, an MRI imager, and / or some other component configured to provide information indicating volume density information or other types of 3D image data (e.g., a 3D tensor indicating diffusion patterns throughout an organ) of the sample. Alternatively, the example computing device may lack such components and receive scan information via some other means (e.g., via the Internet or some other network or other communication interface).

[0069] like Figure 4 As shown, computing device 400 may include a communication interface 402 , a user interface 404 , a processor 406 , a data storage 408 , and an imaging component 424 , all of which may be communicatively linked together via a system bus, network, or other connection mechanism 410 .

[0070] The communication interface 402 may operate to allow the computing device 400 to communicate with other devices, access networks, and / or transport networks using analog or digital modulation of electrical, magnetic, electromagnetic, optical, or other signals. Thus, the communication interface 402 may facilitate circuit-switched and / or packet-switched communications, such as plain old telephone service (POTS) communications and / or Internet Protocol (IP) or other packet communications. For example, the communication interface 402 may include a chipset and an antenna that are arranged to wirelessly communicate with a wireless access network or access point. In addition, the communication interface 402 may take the form of or include a wired interface, such as an Ethernet, universal serial bus (USB), or high-definition multimedia interface (HDMI) port. The communication interface 402 may also take the form of or include a wireless interface, such as Wi-Fi, A global positioning system (GPS) or a wide area wireless interface (such as WiMAX or 3GPP Long Term Evolution (LTE)). However, other forms of physical layer interfaces and other types of standard or proprietary communication protocols may be used on the communication interface 402. In addition, the communication interface 402 may include multiple physical communication interfaces (such as Wi-Fi interfaces, interface and wide area wireless interface).

[0071] In some embodiments, the communication interface 402 can operate to allow the computing device 400 to communicate with other devices, remote servers, access networks, and / or transmission networks. For example, the communication interface 402 can operate to send and / or receive an indication of image information, an indication of imaging-related data that can be subsequently displayed, an indication of the relative direction and / or translation of 3D image data relative to target 2D and / or 3D image data, or some other information. For example, the computing device 400 can be a pathologist workstation located in a pathologist's office, away from one or more operating rooms where sample explantation and imaging are performed, and the remote system can be a display or other system configured to display the analysis results described herein to facilitate diagnosis and treatment of diseases by surgeons in the operating room.

[0072] In some examples, the computing device 400 may include a volume imaging system (e.g., a micro-CT imager) and computing resources for reconstructing volume density information or other types of 3D images from scan data, for identifying regions of interest from the volume density information, for registering the 3D image to a target 2D and / or 3D image (e.g., a 2D mammogram image), for rendering an image of a tissue sample based on the volume density information (e.g., a perspective view, a simulated two-dimensional slice through the sample, a numerically generated simulated 2D image through the sample projected onto a specified 2D plane, etc.), or for performing some other computing task. Such computing resources may include one or more GPUs or other processors dedicated to reconstruction, rendering, or other image processing tasks as described herein. Such a computing device 400 may communicate with a terminal device (e.g., a workstation, a tablet computer, a head-mounted display, an automatic slicing tool, a thin client), and may provide a rendered image to such a terminal in response to user input indicating such a rendered image. For example, user input to a user interface (e.g., a keyboard, a touch screen, a mouse, a head tracker of a head mounted display) can cause the terminal device to send a request to the computing device 400 for imaging data related to the user input (e.g., a request for a two-dimensional numerical projection of a 3D density information image updated based on the user input to update the 3D density information relative to the registered relative orientation and / or position of the target 2D or 3D image). The computing device 400 can then send some information indicating the requested data to the terminal device in response to the request (e.g., one or more two-dimensional images, wireframe / segmentation maps, or other simplified representations of volumetric density information or other 3D image data). Such operation can allow the terminal device to be less expensive, lighter, smaller, or otherwise improved to facilitate interaction therewith by a pathologist or other healthcare professional while maintaining access to the imaging and processing resources of the computing device 400.

[0073] User interface 404 can operate to allow computing device 400 to interact with a user, for example to receive input from a user and / or provide output to the user. Therefore, user interface 404 can include input components, such as keypad, keyboard, touch sensing or presence sensing panel, computer mouse, trackball, joystick, microphone etc. User interface 404 can also include one or more output components, such as display screen, which for example can be combined with the presence sensing panel. Display screen can be based on CRT, LCD and / or LED technology or other technologies known now or developed later. User interface 404 can also be configured to generate audible output via loudspeaker, speaker jack, audio output port, audio output device, earphone and / or other similar devices.

[0074] In some embodiments, user interface 404 may include a display for providing a user with an indication of potentially overlapping 2D and / or 3D images (e.g., a numerically generated 2D projection of a 3D image that has been aligned with a target 2D image), regions of interest within such images, or other imaging-related information. Additionally, user interface 404 may include one or more buttons, switches, knobs, and / or dials that facilitate configuration and operation of imaging assembly 424 or configure some other operation of computing device 400. Some or all of these buttons, switches, knobs, and / or dials may be implemented as functions on a touch or presence sensitive panel.

[0075] Processor 406 may include one or more general purpose processors (e.g., microprocessors) and / or one or more special purpose processors (e.g., digital signal processors (DSPs), graphics processing units (GPUs), floating point units (FPUs), network processors, or application specific integrated circuits (ASICs)). In some cases, the special purpose processors may be capable of image processing, image registration and / or scaling, tomographic reconstruction, numerical simulation of 2D projection images from 3D image data, and other applications or functions. Data storage 408 may include one or more volatile and / or non-volatile storage components, such as magnetic, optical, flash, or organic memory, and may be integrated in whole or in part with processor 406. Data storage 408 may include removable and / or non-removable components.

[0076] The processor 406 may be capable of executing program instructions 418 (e.g., compiled or uncompiled program logic and / or machine code) stored in the data storage 408 to perform various functions described herein. Thus, the data storage 408 may include a non-transitory computer-readable medium having program instructions stored thereon that, when executed by the computing device 400, cause the computing device 400 to perform any method, process, or function disclosed in this specification and / or the accompanying drawings.

[0077] For example, program instructions 418 may include an operating system 422 (eg, an operating system kernel, device drivers, and / or other modules) and one or more application programs 420 installed on computing device 400 (eg, sample scanning functionality, reconstruction or rendering functionality).

[0078] Application programs 420 may take the form of "apps" that may be downloaded to computing device 400 through one or more online application stores or application marketplaces (via, for example, communication interface 402). However, applications may also be installed on computing device 400 in other ways, such as via a web browser or through a physical interface of computing device 400 (e.g., a USB port).

[0079] In some examples, parts of the methods described herein may be performed by different devices depending on the application. For example, different devices of the system may have different amounts of computing resources (e.g., memory, processor cycles) and different information bandwidths for communication between devices. For example, a first device may be a pathologist's workstation or remote interface that may send commands and / or requests for imaging data to another device or server with the necessary computing resources to perform, for example, reconstruction and / or rendering methods required to generate the requested imaging data from CT scan data of a tissue sample. Different parts of the methods described herein may be allocated based on these considerations.

[0080] IV. Example Method

[0081] Figure 5 is a flow chart of method 500. Method 500 includes obtaining a target two-dimensional (2D) image of a portion of a body (510). Method 500 also includes obtaining a three-dimensional (3D) image of a sample explanted from the portion of the body (520). Method 500 also includes determining a registered translation and orientation of the 3D image so that the 3D image is aligned with the perspective of the portion of the body represented in the target 2D image (530). Method 500 also includes projecting the 3D image to the plane of the target 2D image via a numerical method based on the registered translation and orientation, thereby generating a projected 2D image (540). Method 500 also includes displaying an indication of the projected 2D image superimposed on the target 2D image (550). Method 500 may include additional elements or features.

[0082] Figure 6 is a flow chart of method 600. Method 600 includes obtaining a target three-dimensional (3D) image of a portion of a body (610). Method 600 also includes obtaining a sample 3D image of a sample explanted from the portion of the body (620). Method 600 also includes determining a registered translation and orientation of the sample 3D image such that the explanted tissue represented in the sample 3D image is aligned with tissue of the portion of the body represented in the target 3D image (630). Method 600 also includes displaying an indication of the sample 3D image superimposed on the target 3D image, rotated and translated according to the registered translation and orientation (640). Method 600 may include additional elements or features.

[0083] In any of the methods described herein (e.g., methods 500, 600, or other embodiments described herein), the process of obtaining (e.g., "receiving") volume density information or other 2D and / or 3D image information about a target sample and / or body region may include a variety of different processes and / or devices. In some examples, the image information may be stored on a hard drive that is accessed and used according to the embodiments described herein. Such stored image information may be generated near the time and / or space of its use to help guide a surgical procedure (e.g., explanting a tissue sample to, for example, remove a tumor or other target), or may be generated at a longer time period and / or a longer distance before the time and place where the information is used to help diagnose a condition, plan or provide treatment (e.g., a subsequent tissue removal surgery), or some other purpose. For example, image data may be generated by operating an X-ray scanner or other volume imaging device located in an operating room where a tissue sample is removed from a patient. Such volume density information may be used by a surgeon and / or radiologist to decide whether additional tissue should be removed from a patient during tissue removal, and if removed, to decide from which location(s) in the patient's body to remove it.

[0084] V. in conclusion

[0085] The above detailed description describes various features and functions of the disclosed systems, devices, and methods with reference to the accompanying drawings. In the drawings, similar symbols generally identify similar components unless the context indicates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not intended to be limiting. Other embodiments can be utilized, and other changes can be made, without departing from the scope of the subject matter presented herein. It is readily understood that the various aspects of the present disclosure as generally described herein and illustrated in the accompanying drawings can be arranged, replaced, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein.

[0086] The embodiments herein are described as being used by pathologists, radiologists, surgeons, and other healthcare professionals to facilitate slicing or other manipulation or analysis of tissue samples in an image-guided manner, and to visualize such image data to select a plane through which to segment such samples or otherwise for further manipulation and / or analysis of such samples. However, these are merely illustrative example applications. The embodiments described herein may be used to image, slice, or otherwise manipulate other objects or substances of interest (e.g., plant or animal tissue), and to visualize such image data.

[0087] With regard to any or all message flow diagrams, scenarios and flow charts discussed in the figure and herein, each step, frame and / or communication can represent information processing and / or information transmission according to the example embodiments. Alternative embodiments are included in the scope of these example embodiments. In these alternative embodiments, for example, the functions described as steps, frames, transmissions, communications, requests, responses and / or messages may not be performed in the order shown or discussed, including performing in substantially simultaneous or opposite order, depending on the functions involved. In addition, more or less steps, frames and / or functions may be used for any message flow diagrams, scenarios and flow charts discussed herein, and these message flow diagrams, scenarios and flow charts may be combined in part or in whole with each other.

[0088] The steps or boxes representing information processing may correspond to circuits that can be configured to perform specific logical functions of the methods or techniques described herein. Additionally or alternatively, the steps or boxes representing information processing may correspond to a module, segment, or part of a program code (including associated data). The program code may include one or more instructions that can be executed by a processor to implement specific logical functions or actions in a method or technique. The program code and / or associated data may be stored on any type of computer-readable medium, such as a storage device, including a disk drive, a hard drive, or other storage medium.

[0089] Computer readable media may also include non-transitory computer readable media, such as computer readable media that stores data in a short period of time, such as register memory, processor cache and / or random access memory (RAM). Computer readable media may also include non-transitory computer readable media that stores program code and / or data over a longer period of time, such as secondary or persistent long-term memory, such as read-only memory (ROM), optical or magnetic disks and / or compact disk read-only memory (CD-ROM). Computer readable media may also be any other volatile or non-volatile storage system. Computer readable media may be considered to be, for example, a computer readable storage medium or a tangible storage device.

[0090] In addition, the steps or blocks representing one or more information transfers may correspond to information transfers between software and / or hardware modules in the same physical device. However, other information transfers may be between software modules and / or hardware modules in different physical devices.

[0091] Although various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for illustrative purposes only and are not intended to be limiting, with the true scope being indicated by the appended claims.

[0092] VI. Listed Example Embodiments

[0093] Therefore, embodiments of the present disclosure may be directed to one of the Enumerated Example Embodiments (EEEs) listed below. It will be understood that features indicated for one EEE may be combined with other EEEs.

[0094] EEE 1 is a method comprising: (i) obtaining a target two-dimensional (2D) image of a part of a body; (ii) obtaining a three-dimensional (3D) image of a specimen explanted from the part of the body; (iii) determining a registered translation and orientation of the 3D image so that the 3D image is perspectively aligned with the part of the body represented in the target 2D image; (iv) projecting the 3D image onto the plane of the target 2D image via a numerical method based on the registered translation and orientation to generate a projected 2D image; and (v) displaying an indication of the projected 2D image superimposed on the target 2D image.

[0095] EEE 2 is a method according to EEE 1, wherein determining the registered translation and orientation includes: (i) for each of multiple candidate orientations of the 3D image, rotating the 3D image according to the candidate orientation, and projecting the rotated 3D image via a numerical method to generate a candidate projected 2D image; (ii) for each of the multiple candidate orientations, determining a candidate translation of the candidate projected 2D image to maximize a similarity measure between the target 2D image and the candidate projected 2D image, thereby generating a corresponding similarity score for the candidate orientation and translation; and (iii) determining a maximum similarity score among the similarity scores determined for the multiple candidate orientations and translations, and selecting the corresponding candidate orientation and translation as the registered translation and orientation.

[0096] EEE 3 is a method according to EEE 2, wherein the similarity measure is the mutual information between the target 2D image and the candidate projected 2D image.

[0097] EEE 4 is a method according to EEE 2, further comprising: discarding pixels of the target 2D image and the candidate projection 2D image whose intensity does not exceed a threshold, wherein determining a candidate translation of the candidate projection 2D image to maximize a similarity measure between the target 2D image and the candidate projection 2D image comprises: determining a candidate translation of the candidate projection 2D image to maximize a similarity measure between non-discarded pixels of the target 2D image and non-discarded pixels of the candidate projection 2D image.

[0098] EEE 5 is a method according to EEE 4, wherein the threshold intensity is specified so that pixels of the target 2D image and the candidate projection 2D image that do not depict metal substances, ceramic substances, synthetic polymer substances, or radiopaque substances are discarded.

[0099] EEE 6 is a method according to EEE 4, wherein the threshold intensity is specified so that pixels of the target 2D image and the candidate projection 2D image that do not depict metal materials, ceramic materials, synthetic polymer materials, radiopaque materials, or calcifications are discarded.

[0100] EEE 7 is a method according to any of the preceding EEEs, wherein determining the registered translation and orientation comprises: (i) determining an orientation and position of a reference member within the 3D image based on the 3D image, wherein the reference member has a rotationally non-degenerate geometry; (ii) determining an orientation and position of a reference member within a target 2D image based on the target 2D image; and (iii) determining the registered translation and orientation of the 3D image based on a difference between the orientation and position of the reference member within the 3D image and the orientation and position of the reference member within the target 2D image.

[0101] EEE 8 is a method according to any of the preceding EEEs, wherein determining the registered translation and orientation comprises: (i) determining a plurality of candidate translations and orientations of the 3D image based on the target 2D image and the 3D image to align the 3D image with the perspective of a portion of a body represented in the target 2D image; (ii) generating a candidate projected 2D image of the 3D image projected onto a plane of the target 2D image for each of the candidate translations and orientations via a numerical method; (iii) displaying an indication of the candidate projected 2D image superimposed on the target 2D image; (iv) receiving a user selection of one of the candidate projected 2D images; and (v) determining the registered translation and orientation of the 3D image based on the candidate translation and orientation corresponding to the selected candidate projected 2D image.

[0102] EEE 9 is a method according to any of the preceding EEEs, wherein determining a registered translation and orientation comprises: (i) determining a candidate translation and orientation of the 3D image based on the target 2D image and the 3D image to align the 3D image with the perspective of a portion of a body represented in the target 2D image; (ii) generating a candidate projected 2D image of the 3D image projected onto a plane of the target 2D image via a numerical method; (iii) displaying an indication of the candidate projected 2D image superimposed on the target 2D image; (iv) receiving user modifications to the candidate translation and orientation; and (v) determining a registered translation and orientation of the 3D image based on the user modifications to the candidate translation and orientation.

[0103] EEE 10 is a method according to any of the preceding EEEs, further comprising: (i) identifying and counting a plurality of calcifications within a target 2D image; (ii) identifying and counting a plurality of calcifications within a projection 2D image; and (iii) displaying an indication of at least one of: (a) a count of calcifications within the target 2D image and a count of calcifications within the target 2D image, or (b) a set of correspondences between individual calcifications within the target 2D image and individual calcifications within the projection 2D image.

[0104] EEE 11 is a method according to any of the aforementioned EEEs, further comprising: i) obtaining annotation information of at least one of a target 2D image, a 3D image and a projected 2D image, wherein displaying an indication of the projected 2D image superimposed on the target 2D image comprises displaying an indication of the annotation information superimposed on the projected 2D image and the target 2D image.

[0105] EEE 12 is a method according to EEE 11, wherein obtaining annotation information includes: determining a segmentation map of one or more volumes of interest within the 3D image based on the 3D image.

[0106] EEE 13 is a method according to EEE 11 or 12, wherein obtaining annotation information includes obtaining an indication of the extent of the region of interest within the target 2D image, and wherein the method further includes: (i) determining the position and extent of a remaining portion of the region of interest, the remaining portion extending beyond the extent of the explanted sample depicted in the projected 2D image; and (ii) wherein displaying an indication of the projected 2D image superimposed on the target 2D image includes: displaying an indication of the position and extent of the remaining portion superimposed on the projected 2D image and the target 2D image.

[0107] EEE 14 is a method according to any of the preceding EEEs, wherein determining the registered translation and orientation and projecting the 3D image to the plane of the target 2D image via a numerical method is performed by a controller of the system, the system also comprising an imager operable to image a sample of interest, and wherein obtaining a 3D image of the explanted sample comprises: the controller operating the imager to image the explanted sample, thereby generating a 3D image.

[0108] EEE 15 is a method according to EEE 14, further comprising: sending, by the controller, an instruction to project the 2D image to a remote system.

[0109] EEE 16 is a method according to EEE 14 or 15, wherein the displaying of the indication of the projected 2D image superimposed on the target 2D image is performed by a display of the system while the explanted sample is located within the imager.

[0110] EEE 17 is a method according to EEE 14, 15 or 16, wherein the imager is a micro-computed tomography (CT) imager, comprising an X-ray source, an X-ray imager, and a sample container configured to contain an explant sample, wherein the X-ray source and the X-ray imager define a field of view, and wherein operating the imager to generate scan data of the target sample includes: rotating the sample container and operating the X-ray source and the X-ray imager to generate multiple X-ray images of the target sample.

[0111] EEE 18 is a method comprising: (i) obtaining a target three-dimensional (3D) image of a part of a body; (ii) obtaining a sample 3D image of a sample explanted from the part of the body; (iii) determining a registered translation and orientation of the sample 3D image such that the explanted tissue represented in the sample 3D image is aligned with tissue of the part of the body represented in the target 3D image; and (iv) displaying an indication of the sample 3D image superimposed on the target 3D image, rotated and translated according to the registered translation and orientation.

[0112] EEE 19 is a method according to EEE 18, wherein determining the registered translation and orientation includes: (i) for each candidate direction of a plurality of candidate orientations of the sample 3D image, rotating the sample 3D image according to the candidate orientation, and determining a candidate translation of the rotated 3D image to maximize a similarity measure between the target 3D image and the rotated 3D image, thereby generating a corresponding similarity score for the candidate orientation and translation; and (ii) determining a maximum similarity score among the similarity scores determined for the plurality of candidate orientations and translations and selecting the corresponding candidate orientation and translation as the registered translation and orientation.

[0113] EEE 20 is a method according to EEE 19, wherein the similarity measure is mutual information between the rotated 3D image and the target 3D image.

[0114] EEE 21 is a method according to EEE 19, further comprising: discarding pixels in the target 3D image and the rotated 3D image that do not exceed a threshold intensity, wherein determining a candidate translation of the rotated 3D image to maximize a similarity measure between the target 3D image and the rotated 3D image comprises: determining a candidate translation of the rotated 3D image to maximize a similarity measure between non-discarded pixels of the target 3D image and non-discarded pixels of the rotated 3D image.

[0115] EEE 22 is a method according to EEE 21, wherein a threshold intensity is specified so that pixels of the target 3D image and the rotated 3D image that do not depict metal substances, ceramic substances, synthetic polymer substances, or radiopaque substances are discarded.

[0116] EEE 23 is a method according to EEE 21, wherein a threshold intensity is specified so that pixels of the target 3D image and the rotated 3D image that do not depict metal substances, ceramic substances, synthetic polymer substances, radiopaque substances, or calcifications are discarded.

[0117] EEE 24 is a method according to any item of EEE 18-23, wherein determining the registered translation and orientation includes: (i) determining the orientation and position of a reference part within the sample 3D image based on the sample 3D image, wherein the reference part has a rotationally non-degenerate geometry; (ii) determining the orientation and position of the reference part within the target 3D image based on the target 3D image; and (iii) determining the registered translation and orientation of the sample 3D image based on the difference between the orientation and position of the reference part within the sample 3D image and the orientation and position of the reference part within the target 3D image.

[0118] EEE 25 is a method according to any of EEEs 18-24, wherein determining the registered translation and orientation includes: (i) determining multiple candidate translations and orientations of the sample 3D image based on the target 3D image and the sample 3D image to align the sample 3D image with the perspective of the part of the body represented in the target 3D image; (ii) generating a candidate registered 3D image of the sample 3D image according to each rotation and translation of the candidate translations and orientations; (iii) displaying an indication of the candidate registered 3D image superimposed on the target 3D image; (iv) receiving a user selection of one of the candidate registered 3D images; and (v) determining the registered translation and orientation of the sample 3D image based on the candidate translation and orientation corresponding to the selected candidate registered 3D image.

[0119] EEE 26 is a method according to any of EEEs 18-25, wherein determining the registered translation and orientation includes: (i) determining a candidate translation and orientation of the sample 3D image based on the target 3D image and the sample 3D image to align the sample 3D image with the perspective of the part of the body represented in the target 3D image; (ii) generating a candidate registered 3D image of the sample 3D image rotated and translated according to the candidate rotation and translation; (iii) displaying an indication of the candidate registered 3D image superimposed on the target 3D image; (iv) receiving user modifications to the candidate translation and orientation; and (v) determining the registered translation and orientation of the sample 3D image based on the user modifications to the candidate translation and orientation.

[0120] EEE 27 is a method according to any of EEEs 18-26, further comprising: (i) identifying and counting multiple calcifications within a target 3D image; (ii) identifying and counting multiple calcifications within a sample 3D image; and (iii) displaying an indication of at least one of: (a) a count of calcifications within the target 3D image and a count of calcifications within the sample 3D image, or (b) a set of correspondences between individual calcifications within the target 3D image and individual calcifications within the sample 3D image.

[0121] EEE 28 is a method according to any item of EEE 18-27, further comprising: obtaining annotation information for at least one of the target 3D image or the sample 3D image, wherein displaying an indication of the sample 3D image superimposed on the target 3D image comprises: displaying an indication of the annotation information superimposed on the sample 3D image and the target 3D image.

[0122] EEE 29 is a method according to EEE 28, wherein obtaining the annotation information includes determining a segmentation map of one or more volumes of interest within the sample 3D image based on the sample 3D image.

[0123] EEE 30 is a method according to any of EEEs 28-29, wherein obtaining annotation information includes obtaining an indication of the extent of the region of interest within the target 3D image, and wherein the method further includes: (i) determining the position and extent of a remaining portion of the region of interest, the remaining portion extending beyond the extent of the explanted sample depicted in the sample 3D image; and (ii) wherein displaying an indication of the sample 3D image superimposed on the target 3D image includes: displaying an indication of the position and extent of the remaining portion superimposed on the sample 3D image and the target 3D image.

[0124] EEE 31 is a method according to any item of EEE 18-30, wherein determining the registered translation and orientation is performed by a controller of the system, the system also includes an imager operable to image a sample of interest, and wherein obtaining a sample 3D image of the explanted sample includes: the controller operates the imager to image the explanted sample to generate the sample 3D image.

[0125] EEE 32 is a method according to EEE 31, wherein the indicating of displaying the sample 3D image superimposed on the target 3D image is performed by a display of the system when the explanted sample is located within the imager.

[0126] EEE 33 is a method according to any of EEE 31-32, wherein the imager is a micro-computed tomography (CT) imager, comprising an X-ray source, an X-ray imager, and a sample container configured to contain an explant sample, wherein the X-ray source and the X-ray imager define a field of view, and wherein operating the imager to generate scan data of a target sample comprises: rotating the sample container and operating the X-ray source and the X-ray imager to generate multiple X-ray images of the target sample.

[0127] The EEE 34 is a non-transitory computer-readable medium configured to store at least computer-readable instructions that, when executed by one or more processors of a computing device, cause the computing device to perform controller operations to perform a method according to any of the aforementioned EEEs.

[0128] EEE 35 is a system comprising: (i) a controller comprising one or more processors; and (ii) a non-transitory readable medium storing computer readable instructions which, when executed by the one or more processors of the controller, cause the system to perform a method according to any one of EEE 1-33.

Claims

1. A method comprising: obtaining a targeted two-dimensional (2D) image of a body portion; obtaining a three-dimensional (3D) image of a sample explanted from the part of the body; determining a registered translation and orientation of the 3D image such that the 3D image is perspectively aligned with the portion of the body represented in the target 2D image; projecting the 3D image to the plane of the target 2D image via a numerical method based on the registered translation and orientation, thereby generating a projected 2D image; and An indication of the projected 2D image is displayed superimposed on the target 2D image.

2. The method according to claim 1, wherein: Determining the registered translation and orientation includes: For each candidate direction of a plurality of candidate directions of the 3D image, rotating the 3D image according to the candidate direction, and projecting the rotated 3D image via a numerical method to generate a candidate projected 2D image; For each candidate direction of the plurality of candidate directions, determining a candidate translation of the candidate projected 2D image to maximize a similarity measure between the target 2D image and the candidate projected 2D image, thereby generating a corresponding similarity score for the candidate direction and translation; and A maximum similarity score among the similarity scores determined for the plurality of candidate orientations and translations is determined, and a corresponding candidate orientation and translation is selected as the registered translation and orientation.

3. The method according to claim 2, wherein: The similarity measure is the mutual information between the target 2D image and the candidate projected 2D image.

4. The method according to claim 2, further comprising: discarding pixels of the target 2D image and the candidate projection 2D image whose intensity does not exceed a threshold, Wherein, determining the candidate translation of the candidate projected 2D image to maximize the similarity measure between the target 2D image and the candidate projected 2D image comprises: determining the candidate translation of the candidate projected 2D image to maximize the similarity measure between non-discarded pixels of the target 2D image and non-discarded pixels of the candidate projected 2D image.

5. The method according to claim 4, wherein: The threshold intensity is specified such that pixels of the target 2D image and the candidate projection 2D image that do not depict metallic materials, ceramic materials, synthetic polymer materials, or radiopaque materials are discarded.

6. The method according to claim 4, wherein: The threshold intensity is specified such that pixels of the target 2D image and the candidate projection 2D images that do not depict metallic material, ceramic material, synthetic polymer material, radiopaque material, or calcification are discarded.

7. The method according to claim 1, wherein: Determining the registered translation and orientation includes: determining an orientation and a position of a reference feature within the 3D image based on the 3D image, wherein the reference feature has a rotationally non-degenerate geometry; determining the orientation and position of the reference feature within the target 2D image based on the target 2D image; and The registered translation and orientation of the 3D image is determined based on a difference between an orientation and position of a fiducial within the 3D image and an orientation and position of a fiducial within the target 2D image.

8. The method according to claim 1, wherein: Determining the registered translation and orientation includes: determining a plurality of candidate translations and orientations of the 3D image based on the target 2D image and the 3D image to align the 3D image with the perspective of the portion of the body represented in the target 2D image; generating, via a numerical method, for each of the candidate translations and orientations, a candidate projected 2D image of the 3D image projected to the plane of the target 2D image; displaying an indication of the candidate projected 2D image superimposed on the target 2D image; receiving a user selection of one of the candidate projected 2D images; and The registered translation and orientation of the 3D image is determined based on the candidate translation and orientation corresponding to the selected candidate projected 2D image.

9. The method according to claim 1, wherein: Determining the registered translation and orientation includes: determining a candidate translation and orientation of the 3D image based on the target 2D image and the 3D image to align the 3D image with the perspective of the portion of the body represented in the target 2D image; generating, via a numerical method, candidate projected 2D images of the 3D image projected onto the plane of the target 2D image; displaying an indication of the candidate projected 2D image superimposed on the target 2D image; receiving user modifications to candidate translations and directions; and Based on the user modifications to the candidate translations and orientations, the registered translation and orientation of the 3D image is determined.

10. The method according to claim 1, further comprising: identifying and counting a plurality of calcifications within the target 2D image; identifying and counting a plurality of calcifications within the projected 2D image; as well as An indication of at least one of: (i) a count of calcifications within the target 2D image and a count of calcifications within the projected 2D image, or (ii) a set of correspondences between individual calcifications within the target 2D image and individual calcifications within the projected 2D image is displayed.

11. The method according to claim 1, further comprising: obtaining annotation information for at least one of the target 2D image, the 3D image, and the projected 2D image, The instruction to display the projected 2D image superimposed on the target 2D image includes: an instruction to display the annotation information superimposed on the projected 2D image and the target 2D image.

12. The method according to claim 11, wherein: Obtaining the annotation information includes: determining a segmentation map of one or more volumes of interest within the 3D image based on the 3D image.

13. The method according to claim 11, wherein: Obtaining the annotation information comprises obtaining an indication of an extent of a region of interest within the target 2D image, and wherein the method further comprises: determining the location and extent of a remaining portion of the region of interest, the remaining portion extending beyond the extent of the explanted specimen depicted in the projected 2D image; and Wherein, displaying the indication of the projected 2D image superimposed on the target 2D image includes: displaying an indication of the position and range of the remaining portion superimposed on the projected 2D image and the target 2D image.

14. The method according to claim 1, wherein: Determining the registered translation and orientation and projecting the 3D image to the plane of the target 2D image via a numerical method is performed by a controller of the system, the system also includes an imager operable to image a sample of interest, and wherein obtaining the 3D image of the explanted sample includes: the controller operating the imager to image the explanted sample, thereby generating the 3D image.

15. The method according to claim 14, further comprising: An indication of the projected 2D image is sent by the controller to a remote system.

16. The method according to claim 14, wherein: The indicating of displaying the projected 2D image superimposed on the target 2D image is performed by a display of the system while the explant sample is located within the imager.

17. The method according to claim 14, wherein: The imager is a micro-computed tomography (CT) imager, comprising an X-ray source, an X-ray imager, and a sample container configured to contain the explant sample, wherein the X-ray source and the X-ray imager define a field of view, and wherein operating the imager to generate scan data of the target sample comprises rotating the sample container and operating the X-ray source and the X-ray imager to generate a plurality of X-ray images of the target sample.

18. A method comprising: obtaining a targeted three-dimensional (3D) image of a body part; obtaining a 3D image of a sample explanted from the part of the body; determining a registered translation and orientation of the sample 3D image such that explanted tissue represented in the sample 3D image is aligned with tissue of the portion of the body represented in the target 3D image; as well as An indication of the sample 3D image rotated and translated according to the registered translation and orientation is displayed superimposed on the target 3D image.

19. The method according to claim 18, wherein: Determining the registered translation and orientation includes: For each candidate direction of a plurality of candidate directions of the sample 3D image, rotating the sample 3D image according to the candidate direction, and determining a candidate translation of the rotated 3D image to maximize a similarity measure between the target 3D image and the rotated 3D image, thereby generating a corresponding similarity score for the candidate direction and translation; and A maximum similarity score among the similarity scores determined for the plurality of candidate orientations and translations is determined, and a corresponding candidate orientation and translation is selected as the registered translation and orientation.

20. The method according to claim 19, wherein: The similarity measure is the mutual information between the rotated 3D image and the target 3D image.

21. The method of claim 19, further comprising: discarding pixels of the target 3D image and the rotated 3D image that do not exceed a threshold intensity, Wherein, determining the candidate translation of the rotated 3D image to maximize the similarity measure between the target 3D image and the rotated 3D image comprises: determining the candidate translation of the rotated 3D image to maximize the similarity measure between non-discarded pixels of the target 3D image and non-discarded pixels of the rotated 3D image.

22. The method according to claim 21, wherein: The threshold intensity is specified such that pixels of the target 3D image and the rotated 3D image that do not depict metallic materials, ceramic materials, synthetic polymer materials, or radiopaque materials are discarded.

23. The method according to claim 21, wherein: The threshold intensity is specified such that pixels of the target 3D image and the rotated 3D image that do not depict metallic material, ceramic material, synthetic polymer material, radiopaque material, or calcification are discarded.

24. The method according to claim 18, wherein: Determining the registered translation and orientation includes: determining an orientation and a position of a reference feature within the sample 3D image based on the sample 3D image, wherein the reference feature has a rotationally non-degenerate geometry; determining the orientation and position of the reference feature within the target 3D image based on the target 3D image; and The registered translation and orientation of the sample 3D image is determined based on a difference between an orientation and position of a fiducial within the sample 3D image and an orientation and position of a fiducial within the target 3D image.

25. The method of claim 18, wherein: Determining the registered translation and orientation includes: determining a plurality of candidate translations and orientations of the sample 3D image based on the target 3D image and the sample 3D image to align the sample 3D image with the perspective of the portion of the body represented in the target 3D image; generating a candidate registered 3D image of the sample 3D image for each rotation and translation of the candidate translation and orientation; displaying an indication of the candidate registered 3D image superimposed on the target 3D image; receiving a user selection of one of the candidate registered 3D images; and The registered translation and orientation of the sample 3D image is determined based on the candidate translation and orientation corresponding to the selected candidate registered 3D image.

26. The method of claim 18, wherein: Determining the registered translation and orientation includes: determining a candidate translation and orientation of the sample 3D image based on the target 3D image and the sample 3D image to align the sample 3D image with the perspective of the portion of the body represented in the target 3D image; generating a candidate registered 3D image of the sample 3D image rotated and translated according to the candidate rotation and translation; displaying an indication of the candidate registered 3D image superimposed on the target 3D image; receiving user modifications to the candidate translations and directions; and The registered translation and orientation of the sample 3D image is determined based on the user modification of the candidate translation and orientation.

27. The method of claim 18, further comprising: identifying and counting a plurality of calcifications within the target 3D image; identifying and counting a plurality of calcifications within the 3D image of the sample; as well as An indication of at least one of: (i) a count of calcifications within the target 3D image and a count of calcifications within the sample 3D image, or (ii) a set of correspondences between individual calcifications within the target 3D image and individual calcifications within the sample 3D image is displayed.

28. The method of claim 18, further comprising: obtaining annotation information for at least one of the target 3D image or the sample 3D image, The indicating of displaying the sample 3D image superimposed on the target 3D image comprises: displaying an indicating of the annotation information superimposed on the sample 3D image and the target 3D image.

29. The method according to claim 28, wherein: Obtaining the annotation information includes determining a segmentation map of one or more volumes of interest within the sample 3D image based on the sample 3D image.

30. The method of claim 28, wherein: Obtaining the annotation information comprises obtaining an indication of an extent of a region of interest within the target 3D image, and wherein the method further comprises: determining the location and extent of a remaining portion of the region of interest, the remaining portion extending beyond the extent of the explanted specimen depicted in the specimen 3D image; and Wherein, displaying the indication of the sample 3D image superimposed on the target 3D image includes: displaying an indication of the position and the range of the remaining portion superimposed on the sample 3D image and the target 3D image.

31. The method of claim 18, wherein: Determining the registered translation and orientation is performed by a controller of the system, the system also comprising an imager operable to image a sample of interest, and wherein obtaining the sample 3D image of the explanted sample comprises: the controller operating the imager to image the explanted sample, thereby generating the sample 3D image.

32. The method according to claim 31, wherein: The indicating of displaying the sample 3D image superimposed on the target 3D image is performed by a display of the system while the explant sample is located within the imager.

33. The method according to claim 31, wherein: The imager is a micro-computed tomography (CT) imager, comprising an X-ray source, an X-ray imager, and a sample container configured to contain the explant sample, wherein the X-ray source and the X-ray imager define a field of view, and wherein operating the imager to generate scan data of the target sample comprises rotating the sample container and operating the X-ray source and the X-ray imager to generate a plurality of X-ray images of the target sample.

34. A non-transitory computer readable medium configured to store at least computer readable instructions which, when executed by one or more processors of a computing device, cause the computing device to perform controller operations to perform the method of any preceding claim.

35. A system comprising: a controller including one or more processors; as well as A non-transitory readable medium having computer readable instructions stored therein, the computer readable instructions, when executed by the one or more processors of the controller, causing the system to perform the method according to any one of claims 1-33.

Images