Patent Description:
A number of X-ray imaging systems of various designs are known and are presently in use. Such systems are generally based upon generation of X-rays that are directed from an X-ray source toward a subject of interest. The X-rays traverse the subject and impinge on a detector, for example, a film, an imaging plate, or a portable cassette. The detector detects the X-rays, which are attenuated, scattered or absorbed by the intervening structures of the subject.

In medical imaging contexts, for example, such systems may be used to visualize the internal structures, tissues and organs of a subject for the purpose screening or diagnosing ailments.

With regard to the X-ray images produced by the X-ray systems, it is desirable for a radiologist or other medical practitioner to obtain various angular and length measurements of the anatomical components illustrated in the X-ray image for diagnosis and other purposes. In order to obtain these measurements, it is necessary for the radiologist to be able to calculate the distances between different points and/or areas of the anatomy presented in the X-ray image. This process is normally accomplished utilizing a measurement algorithm formed as a part of or separate from the X-ray system that can provide these measurements.

In calculating the desired measurements, as shown in <FIG> a prior art AI model/algorithm <NUM> reviews the anatomy present within the X-ray image <NUM> input to the model <NUM> and performs an anatomy landmark segmentation on the anatomy. The anatomy landmark segmentation <NUM> identifies a number of particular and important areas, points or structures <NUM> of the anatomy in the X-ray image <NUM> that function as specified landmarks known by the measurement algorithm. Once identified within the X-ray image <NUM>, the areas <NUM> are run through a complex geometric/trigonometry post-processing algorithm/computation <NUM> that provides the desired measurements <NUM> between the areas <NUM> shown in the X-ray image <NUM>.

As shown in <FIG>, prior art methods and systems for providing measurements of this type from X-ray images <NUM> include systems <NUM> incorporating a single AI model <NUM> (<FIG>) that performs a multiple landmark segmentations on the X-image <NUM>, and alternative systems <NUM> that incorporate multiple AI models <NUM> (<FIG>), each AI model <NUM> configured to provide an anatomy segmentation <NUM> of a single area <NUM> of the anatomy illustrated in the X-ray image <NUM> that are subsequently stitched together prior to the post-processing/measurement calculation.

Looking now at <FIG>, in an exemplary prior art segmentation process performed on an X-ray image <NUM> of a pelvis, the AI model(s) <NUM> analyzes the image <NUM> to locate various landmarks/structures known to be present in an image of a pelvis, such as the femur heads, the femur shafts, the pelvic teardrops, the acetabular sourcils and the acetabular lateral edges, among others. The AI model <NUM> proceeds to identify and locate the landmarks within the image <NUM>, and segments the image <NUM> into various representations <NUM> of the important and/or desired structures of the pelvis. As shown in <FIG>, These representations <NUM> are subsequently combined in correspondence with their location in the image <NUM> to form the composite structure <NUM> of the pelvis from the image <NUM> on which the measurements are to be based. The geometric/trigonometric post-processing <NUM> is performed on this structure <NUM> in order to provide the desired angular and length measurements from the structure <NUM> as represented/illustrated in measurement image <NUM>.

This measurement process results in highly accurate measurements for the anatomy represented in the X-ray image <NUM>. However, the complexity of the computations for the image segmentation and for the post-processing requires a significant amount of memory and processing capability for the system performing the segmentation and post-processing, in addition to requiring a significant amount of time to complete.

Therefore, it is desirable to develop a system and method for automatically calculating various measurements on an anatomical structure present in an X-ray image that minimizes the computational complexity and time limitations of the prior art.

<CIT> describes a computer implemented system for adjusting the placement of an implant in a patient through the use of a dimensioned grid template placed relative to patient anatomy on a fluoroscopic machine and a method to digitally quantify alignment parameters. This system can be used for determining leg length, offset, and cup position during arthroplasty replacement surgery.

<CIT> describes systems, methods, computer programs, and circuits that can perform automated spinal assessments using a <NUM>-D model and a <NUM>-D patient image data set of the spine which do not rely on vertebral symmetry.

According to one aspect of an exemplary embodiment of the disclosure, an artificial intelligence (AI) measurement system for an X-ray image is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to automatically scan post-exposure X-ray images to detect and locate various landmarks of the anatomy presented within the X-ray image. A set of key image features approximating the locations of the landmarks having known distance relationships to one another is overlaid onto the X-ray image. The positions of the key image features are then adjusted to correspond to the landmarks within the X-ray image. These adjustments are made relative to the prior known distance relationships between the key features, which enables the measurement system to readily calculate desired angular and length measurements between landmarks as a result.

According to another aspect of an exemplary embodiment of the disclosure, the AI measurement system can utilize key points, key lines or key areas alone or in combination with one another as the key features to calculate various angular and length measurements for an anatomy illustrated in an X-ray image.

According to another aspect of an exemplary embodiment of the disclosure, an X-ray system includes an X-ray source, an X-ray detector positionable in alignment with the X-ray ray source, and a processing unit operably connected to the X-ray source and the X-ray detector to produce X-ray images from data transmitted from the X-ray detector, wherein the processing unit includes an X-ray measurement system configured to provide an overlay of one or more key features corresponding to one or more landmarks of an anatomy within the X-ray images, and to calculate a measurement of the anatomy based on the positions of the key features within the overlay.

According to another aspect of an exemplary embodiment of the disclosure, a method of determining measurements between landmarks of an anatomy within an X-ray image includes the steps of providing an X-ray system having an X-ray source, an X-ray detector positionable in alignment with the X-ray ray source, and a processing unit operably connected to the X-ray source and the X-ray detector to produce X-ray images from data transmitted from the X-ray detector, wherein the processing unit includes an X-ray measurement system configured to provide an overlay of one or more key features corresponding to one or more landmarks of an anatomy within the X-ray images, and to calculate measurements of the anatomy based on the positions of the one or more key features in the overlay, applying the overlay to the X-ray image and calculating a measurement of the anatomy in the X-ray image based on the position of the one or more key features in the overlay applied X-ray image.

These and other exemplary aspects, features and advantages of the invention will be made apparent from the following detailed description taken together with the drawing figures.

The drawings illustrate the best mode currently contemplated of practicing the present invention.

Furthermore, any numerical examples in the following discussion are intended to be non-limiting, and thus additional numerical values, ranges, and percentages are within the scope of the disclosed embodiments. As used herein, the terms "substantially," "generally," and "about" indicate conditions within reasonably achievable manufacturing and assembly tolerances, relative to ideal desired conditions suitable for achieving the functional purpose of a component or assembly. Also, as used herein, "electrically coupled", "electrically connected", and "electrical communication" mean that the referenced elements are directly or indirectly connected such that an electrical current may flow from one to the other. The connection may include a direct conductive connection, i.e., without an intervening capacitive, inductive or active element, an inductive connection, a capacitive connection, and/or any other suitable electrical connection. Intervening components may be present. The term "real-time," as used herein, means a level of processing responsiveness that a user senses as sufficiently immediate or that enables the processor to keep up with an external process.

Referring to <FIG>, a block diagram of an x-ray imaging system <NUM> in accordance with an embodiment is shown. The x-ray imaging system <NUM> includes an x-ray source <NUM> which radiates x-rays, a stand <NUM> upon which the subject <NUM> stands during an examination, and an x-ray detector <NUM> for detecting x-rays radiated by the x-ray source <NUM> and attenuated by the subject <NUM>. The x-ray detector <NUM> may comprise, as non-limiting examples, a scintillator, one or more ion chamber(s), a light detector array, an x-ray exposure monitor, an electric substrate, and so on. The x-ray detector <NUM> is mounted on a stand <NUM> and is configured so as to be vertically moveable according to an imaged region of the subject.

The operation console <NUM> comprises a processor <NUM>, a memory <NUM>, a user interface <NUM>, a motor drive <NUM> for controlling one or more motors <NUM>, an x-ray power unit <NUM>, an x-ray controller <NUM>, a camera data acquisition unit <NUM>, an x-ray data acquisition unit <NUM>, and an image processor <NUM>. X-ray image data transmitted from the x-ray detector <NUM> is received by the x-ray data acquisition unit <NUM>. The collected x-ray image data are image-processed by the image processor <NUM>. A display device <NUM> communicatively coupled to the operating console <NUM> displays an image-processed x-ray image thereon.

The x-ray source <NUM> is supported by a support post <NUM> which may be mounted to a ceiling (e.g., as depicted) or mounted on a moveable stand for positioning within an imaging room. The x-ray source <NUM> is vertically moveable relative to the subject or patient <NUM>. For example, one of the one or more motors <NUM> may be integrated into the support post <NUM> and may be configured to adjust a vertical position of the x-ray source <NUM> by increasing or decreasing the distance of the x-ray source <NUM> from the ceiling or floor, for example. To that end, the motor drive <NUM> of the operation console <NUM> may be communicatively coupled to the one or more motors <NUM> and configured to control the one or more motors <NUM>. The one or more motors <NUM> may further be configured to adjust an angular position of the x-ray source <NUM> to change a field-of-view of the x-ray source <NUM>, as described further herein.

The x-ray power unit <NUM> and the x-ray controller <NUM> supply power of a suitable voltage current to the x-ray source <NUM>. A collimator (not shown) may be fixed to the x-ray source <NUM> for designating an irradiated field-of-view of an x-ray beam. The x-ray beam radiated from the x-ray source <NUM> is applied onto the subject via the collimator.

The x-ray source <NUM> and the camera <NUM> may pivot or rotate relative to the support post <NUM> in an angular direction <NUM> to image different portions of the subject <NUM>.

Memory <NUM> is a suitable electronic storage medium and/or computer-readable medium that stores x-ray images <NUM> and executable instructions <NUM> that when executed cause one or more of the processor <NUM> and the image processor <NUM> to perform one or more actions. Example methods that may be stored as the executable instructions <NUM> are described further herein with regard to the X-ray measurement system <NUM> and AI application <NUM> of <FIG>.

The processor <NUM> additionally includes an automatic X-ray measurement system <NUM>, optionally stored in memory <NUM> as a part of the executable instructions <NUM> employed by the processor <NUM> to perform the functions of the measurement system <NUM>. While the measurement system <NUM> is illustrated and described as being employed in conjunction with an X-ray system <NUM>, the measurement system <NUM> is also contemplated as being used with other types of imaging systems, including, but not limited to, computed tomography (CT) systems, magnetic resonance (MRI) imaging systems, and ultrasound (US) systems, among other compatible imaging systems. The X-ray measurement system <NUM> is formed by an artificial intelligence (AI) application <NUM> that can scan and detect various types of information associated with a post-exposure X-ray image <NUM> (<FIG>). The AI application <NUM>, which can be a deep learning neural network, for example, is an image-based object detection application that is configured for the detection various attributes of the post-exposure X-ray image <NUM>, such as information regarding various landmarks located in the anatomy presented within the X-ray image <NUM>.

Referring now to <FIG>, in an exemplary embodiment of a method <NUM> of operation of the X-ray measurement system <NUM>, initially in block <NUM> the AI application <NUM> creates an overlay <NUM> (<FIG>,<FIG>) of a number of key features <NUM> that correspond to an estimated or approximated location of each of a corresponding number of landmarks <NUM> found within a selected anatomy <NUM> that is presented in the X-ray image <NUM>. The estimation of the locations of the key features <NUM> can be done in any suitable manner, such as a through the use of a training set of X-ray images to enable the system <NUM>/AI application <NUM> to determine the locations of each landmark <NUM> in the training images and determine an average position of the landmarks <NUM> in the set of training images to be used for the location of the key feature(s) <NUM> in the overlay <NUM> for the anatomy <NUM> or for one or more different anatomies <NUM> present within the X-ray image <NUM>. In the overlay <NUM>, the distances, angles and other measurements between each of the key features <NUM> is known, such that any desired measurement can be readily and directly determined from the known relationships of the positions of the key features <NUM> relative to one another.

In block <NUM>, an X-ray image <NUM> is supplied or input to the system <NUM> and the system <NUM> positions the overlay <NUM> onto the anatomy <NUM> within the X-ray image <NUM>, such as on the display <NUM>, to provide a visual indication of the overlay <NUM> with respect to the X-ray image <NUM>. In block <NUM>, the system <NUM> analyzes the X-ray image <NUM> utilizing any suitable image review process or method employed by the AI application <NUM> in order to determine the exact locations of the landmarks <NUM> associated with each of the key features <NUM> forming the overlay <NUM>.

After determining the exact location of each landmark <NUM> within the X-ray image <NUM>, in block <NUM> the AI application <NUM> proceeds to adjust or edit the position of each associated key feature <NUM> for the landmarks <NUM>, if necessary, such that position of the key feature <NUM> in the overlay <NUM> corresponds exactly to the location of the associated landmark <NUM> in the X-ray image <NUM>. The adjustment of the key features <NUM> can alternatively or in association with the AI application <NUM>, be conducted by the user, such as by moving the position of one or more of the key features <NUM> in the overly <NUM> on the X-ray image <NUM> via the user interface <NUM>.

With this adjustment and the corresponding known amount of the adjustment to the known distance, angle, etc. or other measurement parameter(s) between each of the key features <NUM> in the overlay <NUM>, in block <NUM> can determine and provide a direct calculation of the desired measurements between any combination of the key features <NUM> and associated landmarks <NUM> within the X-ray image <NUM>. The complexity of the geometric/trigonometric calculations required in prior art measurement systems is replaced in the system <NUM> by a relatively simple adjustment to the existing and known measurement parameters between the various key features <NUM> in the overlay <NUM> based on the alterations of the relative positions of the key features <NUM> in the overlay <NUM> corresponding the locations of the landmarks <NUM> in the X-ray image <NUM>. In this manner, the measurement system <NUM> and AI application <NUM> enable a fast and direct determination of various desired measurements between landmarks in an X-ray image <NUM>.

Further, in alternative embodiments for the method <NUM>, the steps in block <NUM>-<NUM> can be reversed or altered in order, such that, for example, the AI application <NUM> can determine the exact position of the landmarks <NUM> prior to without positioning the overlay <NUM> on the anatomy <NUM> in the X-ray image <NUM>, and/or the adjustment of the positions of the key features <NUM> in the overlay <NUM> can be performed prior to or without positioning the overlay <NUM> on the X-ray image <NUM>. Additionally, the step in block <NUM> of positioning the overlay <NUM> on the X-ray image <NUM> can be moved to after the adjustment of the positions of the key features <NUM>, in order to provide a visual representation of the overlay <NUM> on the anatomy <NUM> of the X-ray image only after all adjustments have been performed. Alternatively, the entire process of the method <NUM> can be maintained as an internal process within the system <NUM>, with no visual representation of the overlay <NUM> being presented, e.g., on the display <NUM>.

With regard to the key feature(s) <NUM>, the form of the features <NUM> can be selected by the user and/or by the AI application <NUM> as desired, such as depending upon the anatomy <NUM> to be presented within the X-ray image <NUM>, with different anatomies <NUM> having different key feature(s) <NUM>. In an exemplary embodiment, the one or more key feature(s) <NUM> can take the form of one or more key points <NUM>, one or more key lines <NUM> and/or one or more key areas <NUM>, and combinations thereof. The various types of key features <NUM> can be illustrated on the display <NUM> in various manners, such as by changing types (e.g., points, crosses, etc.), color, size, flashing or other attributes of a key feature(s) <NUM> currently being analyzed and/or reviewed by the AI application <NUM>.

As a first example of the method <NUM>, in <FIG> an anatomy <NUM> of an X-ray image <NUM> including anatomical landmarks <NUM> is shown positioned next to an overlay <NUM> containing a number of key features <NUM> in the form of key points <NUM>. The overlay <NUM> is also shown disposed over the anatomy <NUM> in the X-ray image <NUM>, with the positions of the key points <NUM> adjusted from the positions in the stand-alone overlay <NUM> to be disposed directly over the landmarks <NUM> detected in the X-ray image <NUM> associated with each of the key points <NUM>. This X-ray image <NUM> is juxtaposed in <FIG> with a similar X-ray image <NUM> that has undergone a prior art segmentation to identify landmarks <NUM> in the image <NUM> and form the segmented structure <NUM> (also overlaid onto the image <NUM>) on which the complex post-processing computations must be performed to provide the required measurements from the structure <NUM>.

A second example of the process of the method <NUM> is illustrated in <FIG> where the overlay <NUM> in <FIG> includes a smaller number key features <NUM> in the form of key points <NUM> within the overlay <NUM>, which are positioned over the landmarks <NUM> in the X-ray image <NUM>. The overlay <NUM> on the image <NUM> is again compared with <FIG> showing the segmented structure <NUM> produced in the prior art method. Further, <FIG> each illustrate an exemplary measurement line, with the line being a distance measurement line <NUM> (<FIG>) taken between two (<NUM>) landmarks <NUM> on the segmented structure <NUM> via the prior art method, in contrast to the distance measurement line <NUM> (<FIG>) obtained by the system <NUM> in the current method <NUM> corresponding to the known distance between two (<NUM>) key points <NUM> associated with landmarks <NUM> on the anatomy <NUM> in the X-ray image <NUM>.

In addition, <FIG> illustrate an alternative embodiment of the X-ray image <NUM> output by the system <NUM>/AI application <NUM> where the overlay <NUM> provided on the image <NUM> includes key features <NUM> in the form of key points <NUM>, key lines <NUM> and key areas <NUM> corresponding to the various landmarks <NUM> in the X-ray image <NUM>. As described previously, the representations of the key points <NUM>, key lines <NUM> and key areas <NUM> corresponding to the various landmarks <NUM> in the X-ray image <NUM> have determined in a suitable training for the system <NUM>/AI application <NUM> where the average locations of the key points <NUM>, key lines <NUM> and key areas <NUM> corresponding to the various landmarks <NUM> in a particular anatomy <NUM> are determined from a dataset including a number of X-ray images <NUM> of the anatomy <NUM> supplied to the system <NUM>/AI application <NUM>. The various key points <NUM>, key lines <NUM> and key areas <NUM> in the anatomy <NUM> are subsequently adjusted automatically by the system <NUM>/AI application <NUM> and/or manually by the user, to position the key points <NUM>, key lines <NUM> and key areas <NUM> directly over the corresponding landmarks in the anatomy <NUM> to form the overlay <NUM>. In this configuration, the system <NUM>/AI application <NUM> can readily calculate the required measurements within the anatomy <NUM> presented in the X-ray image <NUM> based on the prior know relationships between the various key points <NUM>, key lines <NUM> and key areas <NUM> and the adjustments made to these relationships in the formation of the overlay <NUM>. Further, the system <NUM>/AI application <NUM> can be trained to.

Finally, it is also to be understood that the system 1000AI application <NUM> may include the necessary computer, electronics, software, memory, storage, databases, firmware, logic/state machines, microprocessors, communication links, displays or other visual or audio user interfaces, printing devices, and any other input/output interfaces to perform the functions described herein and/or to achieve the results described herein. For example, as previously mentioned, the system may include at least one processor/processing unit/computer and system memory/data storage structures, which may include random access memory (RAM) and read-only memory (ROM). The at least one processor of the system may include one or more conventional microprocessors and one or more supplementary co-processors such as math co-processors or the like. The data storage structures discussed herein may include an appropriate combination of magnetic, optical and/or semiconductor memory, and may include, for example, RAM, ROM, flash drive, an optical disc such as a compact disc and/or a hard disk or drive.

Additionally, a software application(s)/algorithm(s) that adapts the computer/controller to perform the methods disclosed herein may be read into a main memory of the at least one processor from a computer-readable medium. The term "computer-readable medium", as used herein, refers to any medium that provides or participates in providing instructions to the at least one processor of the system <NUM>,<NUM> (or any other processor of a device described herein) for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media include, for example, optical, magnetic, or opto-magnetic disks, such as memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes the main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, a RAM, a PROM, an EPROM or EEPROM (electronically erasable programmable read-only memory), a FLASH-EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read.

While in embodiments, the execution of sequences of instructions in the software application causes at least one processor to perform the methods/processes described herein, hard-wired circuitry may be used in place of, or in combination with, software instructions for implementation of the methods/processes of the present invention. Therefore, embodiments of the present invention are not limited to any specific combination of hardware and/or software.

Claim 1:
A method of determining measurements between landmarks (<NUM>) of an anatomy (<NUM>) within an X-ray image (<NUM>,<NUM>) comprising the steps of:
- providing an X-ray system (<NUM>) comprising:
- an X-ray source (<NUM>);
- an X-ray detector (<NUM>) positionable in alignment with the X-ray source (<NUM>); and
- a processing unit (<NUM>) operably connected to the X-ray source (<NUM>) and the X-ray detector (<NUM>) to produce X-ray images (<NUM>,<NUM>) from data transmitted from the X-ray detector (<NUM>), wherein the processing unit (<NUM>) includes an X-ray measurement system (<NUM>) configured to provide an overlay (<NUM>) of one or more key features (<NUM>) corresponding to one or more landmarks (<NUM>) of an anatomy (<NUM>) within the X-ray images (<NUM>,<NUM>), and to calculate measurements (<NUM>) of the anatomy (<NUM>) based on the positions of the one or more key features (<NUM>) in the overlay (<NUM>);
- applying the overlay (<NUM>) to the X-ray image (<NUM>,<NUM>);
- adjusting the position of the one or more key features (<NUM>) within the overlay (<NUM>) into alignment with the associated one or more landmarks (<NUM>) in the X-ray image (<NUM>,<NUM>); and
- calculating a measurement (<NUM>) of the anatomy (<NUM>) in the X-ray image (<NUM>,<NUM>) based on the position of the one or more key features (<NUM>) in the overlay (<NUM>) applied to the X-ray image (<NUM>,<NUM>).