Patent Description:
Mammography and tomosynthesis utilize x-ray radiation to visualize breast tissue. These techniques are often used to screen patients for potentially cancerous lesions. Traditional mammograms involve acquiring two-dimensional images of the breast from various angles. Tomosynthesis produces a plurality of x-ray images, each of discrete layers or slices of the breast, through the entire thickness thereof. Tomosynthesis pieces together a three-dimensional visualization of the breast. Mammography and tomosynthesis are typically performed while the patient is standing and the patient's breast tissue is under compression.

If a lesion is found, a diagnostic ultrasound may be the next step in determining whether the patient has a tumor. Ultrasound uses sound waves, typically produced by piezoelectric transducers, to image tissue in a patient. Ultrasound imaging provides a different view of tissue that can make it easier to identify solid masses. An ultrasound probe focuses the sound waves by producing an arc-shaped sound wave that travels into the body and is partially reflected from the layers between different tissues in the patient. The reflected sound wave is detected by the transducers and converted into electrical signals that can be processed by the ultrasound scanner to form an ultrasound image of the tissue. Ultrasound is typically performed while the patient is in a supine position and the patient's breast tissue is not under compression.

During diagnostic ultrasound imaging procedures, technologists and radiologists often have difficulty navigating to and locating a lesion previously identified during x-ray imaging. It is challenging to correlate the position of the lesion from x-ray imaging to ultrasound imaging because the former is performed while the patient is upright and the breast tissue is under compression while the latter is performed while the patient is lying down and the breast tissue is not under compression. Additionally, the ultrasound imaging has different levels of contrast and has a different appearance than x-ray imaging. Lesions detected in x-ray imaging procedures are becoming increasingly smaller as technology improves making it more difficult to locate the small lesions in an ultrasound image. <CIT> discloses a method for correlating lesions in images obtained from different imaging modalities using artificial intelligence.

It is against this background that the present disclosure is made. Techniques and improvements are provided herein.

Examples of the disclosure are directed to a method of locating a region of interest within a breast. An indication of a location of a target lesion within a breast is received at a computing device. The target lesion was identified during imaging of the breast using a first imaging modality. An image of the breast is obtained by a second imaging modality and a potential lesion is identified in the image. The first image including the target lesion is analyzed with a lesion matching engine operating on the computing system to compare it to a second image including a potential lesion using artificial intelligence. Analyzing the potential lesion comprises comparing form factors of breast tissue surrounding the target lesion and the potential lesion. A probability that the potential lesion corresponds to the target lesion is determined and an indicator of the level of confidence is output for display on a graphical user interface.

In another aspect, a lesion identification system includes a processing device and a memory storing instructions that, when executed by the processing device, facilitate performance of operations. The operations include: accessing an x-ray image of a breast, the x-ray image including an identified lesion indicated with a visual marker; receiving an ultrasound image of the breast, the ultrasound image including an indication of a potential lesion; analyzing the potential lesion and the identified lesion using an artificial intelligence lesion classifier, where the operations further comprise analyzing form factors of the breast tissue surrounding the identified lesion and the potential lesion to generate the confidence score; generating a confidence score indicating a likelihood that the potential lesion in the ultrasound image matches the identified lesion in the x-ray image; and displaying an output associated with the confidence score on a graphical user interface.

In yet another aspect, a non-transitory machine-readable storage medium stores executable instructions that, when executed by a processor, facilitate performance of operations. The operations include: obtaining data for a target lesion from a data store, wherein the data was obtained with x-ray imaging and includes at least an image of the target lesion and coordinates for a location of the target lesion within a breast; recording an image of the breast obtained by ultrasound imaging; identifying a general area of interest in the recorded image of the breast obtained by ultrasound based on the coordinates of the target lesion; identifying a potential lesion in the general area of interest; analyzing, using an artificial intelligence lesion classifier, the potential lesion to compare the potential lesion to the target lesion and determine a level of confidence that the potential lesion corresponds to the target lesion, wherein analyzing the potential lesion comprises comparing form factors of breast tissue surrounding the target lesion and the potential lesion; and outputting an indicator of the level of confidence on a graphical user interface.

In another aspect, a lesion identification system includes at least one optical camera, a projector, a processing device, and a memory storing instructions that, when executed by the processing device, facilitate performance of operations. The operations include capturing at least one optical image of a breast of a patient using the at least one optical camera; accessing at least one tomosynthesis image of the breast; receiving an indication of a target lesion on the at least one tomosynthesis image; co-registering the at least one optical image and the at least one tomosynthesis image of the breast by analyzing with artificial intelligence algorithms for region matching and a non-rigid deformable model; creating a probability map based on the co-registering and the indication of the target lesion, where the map indicates a likelihood that the target lesion is located at each of a plurality of points on the breast; and projecting, with the projector, the probability map onto the breast.

The details of one or more techniques are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of these techniques will be apparent from the description, drawings, and claims.

The present disclosure is directed to systems and methods for locating a lesion within breast tissue using an imaging device. In particular, a computing system utilizes machine learning to navigate to a potential lesion and provide a confidence level indicator for a correlation between a lesion identified in a breast with ultrasound imaging and a lesion identified with x-ray imaging.

An important step in evaluating breast health is a screening x-ray imaging procedure (e.g., mammography or tomosynthesis). In about <NUM>-<NUM>% of cases, a lesion is identified in the x-ray images that cause a patient to be recalled for additional imaging to determine if a lesion is potentially cancerous. Diagnostic imaging is then performed and this typically employs ultrasound technology. Ultrasound imaging can more accurately distinguish cysts from solid masses and ultrasound is the preferred imaging modality should a biopsy be needed.

Despite the benefits of ultrasound, clinicians may find it difficult to locate the same lesion identified during x-ray imaging while performing ultrasound imaging. This is due to three main factors. The first is that the position of the breast is different in an ultrasound procedure as compared to an x-ray imaging procedure (e.g., mammography or tomosynthesis). Typically the patient is in an upright position with the breast under compression during x-ray imaging while the patient is typically in a supine position and the breast is not under compression during ultrasound. This shift in position can make it difficult to correlate lesions found in the x-ray image with an image produced by ultrasound.

The second reason is that the ultrasound imaging modality looks different than the x-ray imaging with different contrasts. It can be difficult to be confident that the lesion identified in ultrasound is the same one previously identified with x-ray imaging.

Third, as technology continues to improve, lesions that can be detected with x-ray imaging procedures are becoming increasingly smaller. This makes it more difficult for a healthcare practitioner to find the lesions in an ultrasound image.

The computing system described herein operates to provide a confidence level indicator of a correlation between a lesion identified with ultrasound and a lesion identified with x-ray imaging. The computing system uses an artificial intelligence (AI) model trained on a library of digital breast tomosynthesis (DBT) cases and corresponding radiologist-correlated diagnostic ultrasound cases. The AI model analyzes new DBT and ultrasound images to determine if one lesion correlates to another. Additionally, the AI model can provide a confidence level indicator to a user to aid them in determining whether they have found the same lesion. In some cases, the AI model can be employed in conjunction with electromagnetic or optical tracking inputs to speed navigation to the target area during ultrasound and reduce the imaging set to be analyzed. The AI model utilizes form factors to calculate the confidence levels. Form factors include morphological features of breast tissue that can be used to identify a particular region of the breast. In some examples, the form factors are visual features on the surface of the breast such as moles, freckles, and tattoos.

<FIG> illustrates an example lesion identification system <NUM> for locating a region of interest within a breast. The system <NUM> includes a computing system <NUM>, an x-ray imaging system <NUM>, and an ultrasound imaging system <NUM>. In some examples, the lesion identification system <NUM> operates to guide a healthcare practitioner to a location of interest in a breast during ultrasound imaging based on data collected during an x-ray imaging procedure where the location of interest was first identified. In some examples, lesion identification system <NUM> provides a confidence level indicator to a healthcare practitioner on a display to aid the healthcare practitioner in confirming that a lesion visible on an ultrasound image is the same lesion identified previously in an x-ray image.

The computing system <NUM> operates to process and store information received from the x-ray imaging system <NUM> and ultrasound imaging system <NUM>. In the example of <FIG>, the computing system <NUM> includes a lesion matching engine <NUM> and a data store <NUM>. In some examples, the lesion matching engine <NUM> and data store <NUM> are housed within the memory of the computing system <NUM>. In some examples, the computing system <NUM> accesses the lesion matching engine <NUM> and data store <NUM> from a remote server such as a cloud computing environment. Though <FIG> shows the computing system <NUM> as standing alone from other components of the system <NUM>, it could also be incorporated into the x-ray computing device <NUM>, the ultrasound computing device <NUM>, or another computing device utilized in patient care. In some examples, the computing system <NUM> includes two or more computing devices.

The lesion matching engine <NUM> operates to analyze x-ray images of a target lesion and ultrasound images of a potential lesion to determine if the potential lesion is the same as the target lesion. In the example of <FIG>, a data store of DBT training data <NUM> is utilized to train an artificial intelligence unit <NUM>. The DBT training data store <NUM> stores multiple example cases of identified lesions with corresponding ultrasound images and x-ray images. The example cases are matches confirmed by healthcare professionals. The artificial intelligence unit <NUM> analyzes these example cases using a machine learning algorithm to identify features that can be used to match ultrasound images with x-ray images. These features are used to generate an image classifier.

Various machine learning techniques can be utilized to generate a lesion classifier. In some examples, the machine learning algorithm is a supervised machine learning algorithm. In other examples, the machine learning algorithm is an unsupervised machine learning algorithm. In some examples, the machine learning algorithm is based on an artificial neural network. In some examples, the neural network is a deep neural network (DNN). In some examples, the machine learning algorithm is a convolutional deep neural network (CNN). In some examples, a combination of two or more networks are utilized to generate the classifier. In some examples, two or more algorithms are utilizes to generate features from the example case data.

The resulting trained machine learning classifier is utilized by the image analyzer <NUM> to compare sets of x-ray images and ultrasound images. Various indicators are utilized to compare lesions including shape, color, margins, orientation, texture, pattern, density, stiffness, size, and depth within the breast. In some examples, the indicator is a numerical value. The confidence evaluator <NUM> operates in conjunction with the image analyzer <NUM> to determine a level of confidence that a potential lesion identified with ultrasound imaging is the same as a lesion identified with x-ray imaging. In some examples, a confidence score is generated by the confidence evaluator <NUM>. In some examples, the confidence level could indicate a category of confidence such as "high," "medium," or "low. " In alternative examples, the confidence level is provided as a percentage such as "<NUM>%," "<NUM>%," or "<NUM>%. " Finally, the graphical user interface (GUI) <NUM> operates to present information on a display of a computing device. In some examples, the GUI <NUM> display a confidence level indicator over one or more images of tissue being analyzed.

In some examples, the lesion matching engine <NUM> operates to perform region matching on two different types of images. The region matching is performed using artificial intelligence algorithms to use form factors and other features of breast tissue to match regions of a breast between two different imaging modalities. In some examples, the analysis provides a probabilistic value for a location at which the lesion is expected to be located on a breast using co-registration techniques. In some examples, the artificial intelligence models operate in conjunction with non-rigid deformable models to determine a likelihood that a target lesion and a potential lesion are the same.

The data store <NUM> operates to store information received from the x-ray imaging system <NUM>, ultrasound imaging system <NUM>, and lesion matching engine <NUM>. In some examples, the data store <NUM> is actually two or more separate data stores. For example, one data store could be a remote data store that stores images from x-ray imaging systems. Another data store could be housed locally within the computing system <NUM>. In some examples, the data store <NUM> could be part of an electronic medical record (EMR) system.

The x-ray imaging system <NUM> operates to take images of breast tissue using x-ray radiation. The x-ray imaging system <NUM> includes an x-ray imaging device <NUM> and an x-ray computing device <NUM> in communication with the x-ray imaging device <NUM>. In some examples, the x-ray imaging system <NUM> performs digital breast tomosynthesis (DBT). The x-ray imaging device <NUM> is described in further detail in relation to <FIG>. The x-ray computing device <NUM> operates to receive inputs from a healthcare provider H to operate the x-ray imaging device <NUM> and view images received from the x-ray imaging device <NUM>.

The ultrasound imaging system <NUM> operates to take images of breast tissue using ultrasonic sound waves. The ultrasound imaging system <NUM> is described in further detail in relation to <FIG>. The ultrasound imaging system <NUM> includes an ultrasound computing device <NUM> and an ultrasound imaging device <NUM>. The ultrasound computing device <NUM> operates to receive inputs from a healthcare provider H to operate the ultrasound imaging device <NUM> and view images received from the ultrasound imaging device <NUM>.

<FIG> illustrates how information obtained from an x-ray imaging system <NUM> could be utilized by an ultrasound imaging system <NUM>. A healthcare provider H operates the x-ray computing device <NUM> to capture x-ray images of the breast of a patient P using the x-ray imaging device <NUM>. The x-ray image may be taken as part of a routine health screening. During the screening, the healthcare provider H identifies one or more regions of interest in the patient P's breast that require additional analysis to determine if lesions within those regions of interest are potentially cancerous and require a biopsy.

In some examples, coordinates for the regions of interest can be recorded at the x-ray computing device <NUM> and communicated to the computing system <NUM>. The coordinates recorded by the x-ray computing device <NUM> are analyzed using as tissue deformation model, as described in co-pending US Patent Application <CIT>.

In some examples, a first set of coordinates identifies a location of a lesion identified while the breast is under compression. The first set of coordinates are translated into a second set of coordinates identifying a predicted location of the identified lesion while the breast is not under compression. A region of interest in the ultrasound image is identified that corresponds to the second set of coordinates. This enables a healthcare practitioner to identify the potential lesion in the ultrasound image.

The output of the analysis is a set of predicted coordinates that can be communicated to the ultrasound computing device <NUM> to be used at a later imaging exam, which may be in a location different than that where the imaging procedure was performed. A healthcare provider H operating the ultrasound computing device <NUM> uses the predicted coordinates to navigate to the region of interest on the patient P's breast using the ultrasound imaging device <NUM>.

In some examples, the x-ray images are displayed on a user interface of the ultrasound computing device <NUM> along with ultrasound images that are received from the ultrasound imaging device <NUM>. Additional information can be displayed on the ultrasound computing device <NUM> such as predicted coordinates of a region of interest and indications of biomarkers on an image of the patient's breast. In some examples, a visual marker is displayed on the image indicating the location of a target lesion. In some examples, a probability mapping can be displayed on the image indicating where the target lesion is most likely to be located.

The healthcare provider H operating the ultrasound computing device <NUM> locates a potential lesion in an ultrasound image that is potentially a match for a lesion previously identified in an x-ray image for the same patient P. The ultrasound image and an indication of the potential lesion are communicated to the computing system <NUM> for analysis. In some examples, a mammography image, a target region of interest, and B-mode imaging is displayed on the same GUI. The GUI <NUM> helps to visually guide an operator of an ultrasound system to the region of interest while also automating documentation of an ultrasound probe's position, orientation, and annotations.

In some examples, x-ray images including an identified lesion and ultrasound images including a potential lesion are analyzed by the lesion matching engine <NUM> of the computing system <NUM>. The lesion matching engine <NUM> outputs a confidence level indicator for the potential lesion and communicates that confidence level indicator to the ultrasound computing device <NUM>. The confidence level indicator could be a numeric value, a color, or a category that is displayed on a GUI on the ultrasound computing device <NUM>. An example GUI is described in <FIG>.

In some examples, as is described in <FIG>, the lesion matching engine <NUM> operates to generate a probability mapping. In some examples, optical cameras capture images of a breast being examined by ultrasound. Previously obtained x-ray images of the breast are accessed and analyzed using co-registration techniques and artificial intelligence region matching. The probability mapping generated from the analysis is visually projected onto the breast to aid a healthcare practitioner H in finding a target lesion. An example GUI <NUM> including a probability mapping is shown in <FIG>.

<FIG> illustrates a schematic diagram of an example system <NUM> for managing healthcare data including imaging data. The system <NUM> includes multiple computing components in communication with one another through a communication network <NUM>. The computing components can include a tracking system <NUM>, a navigation system <NUM>, an EMR system <NUM>, and a display system <NUM> in addition to the computing system <NUM>, x-ray imaging system <NUM>, and ultrasound imaging system <NUM> described in <FIG>.

It should be noted that, although the 'systems' are shown in <FIG> as functional blocks, different systems may be integrated into a common device, and the communication link may be coupled between fewer than all of the systems; for example, the tracking system <NUM>, navigation system <NUM> and display system <NUM> may be included in an acquisition work station or a technologist work station which may control the acquisition of the images in a radiology suite. Alternatively, the navigation system <NUM> and tracking system <NUM> may be integrated into the ultrasound imaging system <NUM>, or provided as standalone modules with separate communication links to the display <NUM>, x-ray imaging system <NUM> and ultrasound imaging system <NUM>. Similarly, skilled persons will additionally appreciate that communication network <NUM> can be a local area network, wide area network, wireless network, internet, intranet, or other similar communication network.

In one example, the x-ray imaging system <NUM> is a tomosynthesis acquisition system which captures a set of projection images of a patient's breast as an x-ray tube scans across a path over the breast. The set of projection images is subsequently reconstructed to a three-dimensional volume which may be viewed as slices or slabs along any plane. The three-dimensional volume may be stored locally at the x-ray imaging system <NUM> (either on the x-ray imaging device <NUM> or on the x-ray computing device <NUM>) or at a data store such as the data store <NUM> in communication with the x-ray imaging system <NUM> through the communication network <NUM>. In some examples, the three-dimensional volume could be stored in a patient's file within an electronic medical record (EMR) system <NUM>. Additional details regarding an example x-ray imaging system are described in reference to <FIG>.

The x-ray imaging system <NUM> may transmit the three-dimensional x-ray image volume to a navigation system <NUM> via the communication network <NUM>, where such x-ray image can be stored and viewed. The navigation system <NUM> displays the x-ray image obtained by the x-ray imaging system. Once reconstructed for display on navigation system <NUM> the x-ray image can be reformatted and repositioned to view the image at any plane and any slice position or orientation. In some examples, the navigation system <NUM> displays multiple frames or windows on the same screen showing alternative positions or orientations of the x-ray-image slice.

Skilled persons will understand that the x-ray image volume obtained by x-ray imaging system <NUM> can be transmitted to navigation system <NUM> at any point in time and is not necessarily transmitted immediately after obtaining the x-ray image volume, but instead can be transmitted on the request of navigation system <NUM>. In alternative examples, the x-ray image volume is transmitted to navigation system <NUM> by a transportable media device, such as a flash drive, CD-ROM, diskette, or other such transportable media device.

The ultrasound imaging system <NUM> obtains an ultrasound image of a tissue of a patient, typically using an ultrasound probe, which is used to image a portion of a tissue of a patient within the field of view of the ultrasound probe. For instance, the ultrasound imaging system <NUM> may be used to image a breast. The ultrasound imaging system <NUM> obtains and displays an ultrasound image of a patient's anatomy within the field of view of the ultrasound probe and typically displays the image in real-time as the patient is being imaged. In some examples, the ultrasound image can additionally be stored on a storage medium, such as a hard drive, CD-ROM, flash drive or diskette, for reconstruction or playback at a later time. Additional details regarding the ultrasound imaging system are described in reference to <FIG>.

In some examples, the navigation system <NUM> can access the ultrasound image, and in such examples the ultrasound imaging system <NUM> is further connected to the communication network <NUM> and a copy of the ultrasound image obtained by the ultrasound imaging system <NUM> can be transmitted to the navigation system <NUM> via communication network <NUM>. In other examples, the navigation system <NUM> can remotely access and copy the ultrasound image via the communication network <NUM>. In alternative examples, a copy of the ultrasound image can be stored on the data store <NUM> or EMR system <NUM> in communication with the navigation system <NUM> via the communication network <NUM> and accessed remotely by the navigation system <NUM>.

The tracking system <NUM> is in communication with the navigation system <NUM> via the communications network <NUM> and may track the physical position in which the ultrasound imaging system <NUM> is imaging the tissue of the patient. In some examples, the tracking system <NUM> can be connected directly to the navigation system <NUM> via a direct communication link or wireless communication link. The tracking system <NUM> tracks the position of transmitters connected to ultrasound imaging system <NUM> and provides the navigation system <NUM> with data representing their coordinates in a tracker coordinate space. In some examples, the tracking system <NUM> may be an optical tracking system comprising an optical camera and optical transmitters, however skilled persons will understand that any device or system capable of tracking the position of an object in space can be used. For example, skilled persons will understand that in some examples a radio frequency (RF) tracking system can be used, comprising an RF receiver and RF transmitters.

The ultrasound imaging system <NUM> may be configured for use with the navigation system <NUM> by a calibration process using the tracking system <NUM>. Transmitters that are connected to the ultrasound probe of ultrasound imaging system <NUM> may transmit their position to tracking system <NUM> in the tracker coordinate space, which in turn provides this information to navigation system <NUM>. For example, transmitters may be positioned on the probe of the ultrasound imaging system <NUM> so that the tracking system <NUM> can monitor the position and orientation of the ultrasound probe and provide this information to the navigation system <NUM> in the tracker coordinate space. The navigation system <NUM> may use this tracked position to determine the position and orientation of the ultrasound probe, relative to the tracked position of the transmitters. In some examples, the navigation system <NUM> and tracking system <NUM> operate to provide real time guidance to a healthcare practitioner H performing ultrasound imaging of a patient P.

In some examples, configuration occurs using a configuration tool. In such examples, the position and orientation of the configuration tool may be additionally tracked by tracking system <NUM>. During configuration the configuration tool contacts the transducer face of the ultrasound probe of the ultrasound imaging system <NUM> and the tracking system <NUM> transmits information representing the position and orientation of the configuration tool in the tracker coordinate space to the navigation system <NUM>. The navigation system <NUM> may determine a configuration matrix that can be used to determine the position and orientation of the field of view of the ultrasound probe in the tracker coordinate space, based on the tracked position of the transmitters connected to the ultrasound probe. In alternative examples, a database having configuration data of a plurality of brands or models of various ultrasound probes can be used to pre-load a field of view configuration into the navigation system <NUM> during configuration.

Once the ultrasound imaging system <NUM> is configured with the navigation system <NUM>, the tissue of a patient can be imaged with ultrasound imaging system <NUM>. During ultrasound imaging, the tracking system <NUM> monitors the position and orientation of the ultrasound probe of the ultrasound imaging system <NUM> and provides this information in the tracker coordinate space to the navigation system <NUM>. Since the ultrasound imaging system <NUM> has been configured for use with the navigation system <NUM>, the navigation system <NUM> is able to determine position and orientation of the field of view of the ultrasound probe of the ultrasound imaging system <NUM>.

The navigation system <NUM> can be configured to co-register an ultrasound image with an x-ray image. In some examples, the navigation system <NUM> can be configured to transform the position and orientation of the field of view of the ultrasound probe from the tracker coordinate space to a position and orientation in the x-ray image, for example, to x-ray system coordinates. This can be accomplished by tracking the position and orientation of the ultrasound probe and transmitting this positional information in the tracker coordinate space to navigation system <NUM> and relating this positional information to the x-ray coordinate system. In some examples, the co-registered images are displayed on the GUI <NUM>.

For example, a user can select an anatomical plane within the x-ray image, and the user can then manipulate the position and orientation of a tracked ultrasound probe to align the field of view of the ultrasound probe with the selected anatomical plane. Once alignment is achieved, the associated tracker space coordinates of the ultrasound image can be captured. Registration of the anatomic axes (superior-inferior (SI), left-right (LR) and anterior-posterior (AP)) between the x-ray image and the tracker coordinate space can be determined from the relative rotational differences between the tracked ultrasound field of view orientation and the selected anatomical plane using techniques known to those of skill in the art.

This configuration may further include the selection of landmarks within the x-ray image, for example, using an interface permitting a user to select an anatomical target. In some examples, the landmark can be an internal tissue landmark, such as veins or arteries, and in other examples, the landmark can be an external landmark, such as a fiducial skin marker or external landmark, such as a nipple. The same landmark selected in the x-ray image can be located with the ultrasound probe, and upon location, a mechanism can be provided for capturing coordinates of the representation of the target in the tracker coordinate space. The relative differences between the coordinates of the target in the x-ray image and the coordinates of the target in the tracker coordinate space are used to determine the translational parameters required to align the two co-ordinate spaces. The plane orientation information acquired previously can be combined with the translation parameters to provide a complete <NUM>×<NUM> transformation matrix capable of co-registering the two coordinate spaces.

The navigation system <NUM> can then use the transformation matrix to reformat the x-ray image being displayed so that the slice of tissue being displayed is in the same plane and in the same orientation as the field of view of the ultrasound probe of the ultrasound imaging system <NUM>. Matched ultrasound and x-ray images may then be displayed side by side, or directly overlaid in a single image viewing frame. In some examples, the navigation system <NUM> can display additional x-ray images in separate frames or positions on a display screen. For example, the x-ray image can be displayed with a graphical representation of the field of view of the ultrasound imaging system <NUM> wherein the graphical representation of the field of view is shown slicing through a 3D representation of the x-ray image. In other examples annotations can be additionally displayed, these annotations representing, for example, the position of instruments imaged by the ultrasound imaging system <NUM>, such as biopsy needles, guidance wires, imaging probes or other similar devices.

In other examples, the ultrasound image being displayed by the ultrasound imaging system <NUM> can be superimposed on the slice of the x-ray image being displayed by the navigation system <NUM> so that a user can view both the x-ray and ultrasound images simultaneously, overlaid on the same display. In some examples, the navigation system <NUM> can enhance certain aspects of the super imposed ultrasound or x-ray images to increase the quality of the resulting combined image.

As described in <FIG>, the computing system <NUM> operating a lesion matching engine <NUM> analyzes sets of x-ray images and ultrasound images to determine a confidence level that a lesion identified in an ultrasound image is the same lesion that was identified in an x-ray image. A confidence level indicator can be displayed on a computing device to aid a user operating the ultrasound imaging system <NUM> in determining whether a previously identified lesion in an x-ray image has been found in a corresponding ultrasound image.

The electronic medical record system <NUM> stores a plurality of electronic medical records (EMRs). Each EMR contains the medical and treatment history of a patient. Examples of electronic medical records systems <NUM> include those developed and managed by Epic Systems Corporation, Cerner Corporation, Allscripts, and Medical Information Technology, Inc. (Meditech).

<FIG> is a schematic view of an exemplary x-ray imaging system <NUM>. <FIG> is a perspective view of the x-ray imaging system <NUM>. Referring concurrently to <FIG> and <FIG>, the x-ray imaging system <NUM> immobilizes a patient's breast <NUM> for x-ray imaging (either or both of mammography and tomosynthesis) via a breast compression immobilizer unit <NUM> that includes a static breast support platform <NUM> and a moveable compression paddle <NUM>. The breast support platform <NUM> and the compression paddle <NUM> each have a compression surface <NUM> and <NUM>, respectively, that move towards each other to compress and immobilize the breast <NUM>. In known systems, the compression surface <NUM>, <NUM> is exposed so as to directly contact the breast <NUM>. The platform <NUM> also houses an image receptor <NUM> and, optionally, a tilting mechanism <NUM>, and optionally an anti-scatter grid. The immobilizer unit <NUM> is in a path of an imaging beam <NUM> emanating from x-ray source <NUM>, such that the beam <NUM> impinges on the image receptor <NUM>.

The immobilizer unit <NUM> is supported on a first support arm <NUM> and the x-ray source <NUM> is supported on a second support arm <NUM>. For mammography, support arms <NUM> and <NUM> can rotate as a unit about an axis <NUM> between different imaging orientations such as CC and MLO, so that the system <NUM> can take a mammogram projection image at each orientation. In operation, the image receptor <NUM> remains in place relative to the platform <NUM> while an image is taken. The immobilizer unit <NUM> releases the breast <NUM> for movement of arms <NUM>, <NUM> to a different imaging orientation. For tomosynthesis, the support arm <NUM> stays in place, with the breast <NUM> immobilized and remaining in place, while at least the second support arm <NUM> rotates the x-ray source <NUM> relative to the immobilizer unit <NUM> and the compressed breast <NUM> about the axis <NUM>. The system <NUM> takes plural tomosynthesis projection images of the breast <NUM> at respective angles of the beam <NUM> relative to the breast <NUM>.

Concurrently and optionally, the image receptor <NUM> may be tilted relative to the breast support platform <NUM> and in sync with the rotation of the second support arm <NUM>. The tilting can be through the same angle as the rotation of the x-ray source <NUM>, but may also be through a different angle selected such that the beam <NUM> remains substantially in the same position on the image receptor <NUM> for each of the plural images. The tilting can be about an axis <NUM>, which can but need not be in the image plane of the image receptor <NUM>. The tilting mechanism <NUM> that is coupled to the image receptor <NUM> can drive the image receptor <NUM> in a tilting motion.

For tomosynthesis imaging and/or CT imaging, the breast support platform <NUM> can be horizontal or can be at an angle to the horizontal, e.g., at an orientation similar to that for conventional MLO imaging in mammography. The x-ray imaging system <NUM> can be solely a mammography system, a CT system, or solely a tomosynthesis system, or a "combo" system that can perform multiple forms of imaging. An example of such a combo system has been offered by the assignee hereof under the trade name Selenia Dimensions. In some examples, initial imaging is performed with magnetic resonance imaging (MRI).

When the system is operated, the image receptor <NUM> produces imaging information in response to illumination by the imaging beam <NUM>, and supplies it to an image processor <NUM> for processing and generating breast x-ray images. A system control and work station unit <NUM> including software controls the operation of the system and interacts with the operator to receive commands and deliver information including processed-ray images.

<FIG> depicts an exemplary x-ray imaging system <NUM> in a breast positioning state for left mediolateral oblique MLO (LMLO) imaging orientation. A tube head <NUM> of the system <NUM> is set in an orientation so as to be generally parallel to a gantry <NUM> of the system <NUM>, or otherwise not normal to the flat portion of a support arm <NUM> against which the breast is placed. In this position, the technologist may more easily position the breast without having to duck or crouch below the tube head <NUM>.

The x-ray imaging system <NUM> includes a floor mount or base <NUM> for supporting the x-ray imaging system <NUM> on a floor. The gantry <NUM> extends upwards from the floor mount <NUM> and rotatably supports both the tube head <NUM> and a support arm <NUM>. The tube head <NUM> and support arm <NUM> are configured to rotate discretely from each other and may also be raised and lowered along a face <NUM> of the gantry so as to accommodate patients of different heights. An x-ray source, described elsewhere herein and not shown here, is disposed within the tube head <NUM>. The support arm <NUM> includes a support platform <NUM> that includes therein an x-ray receptor and other components (not shown). A compression arm <NUM> extends from the support arm <NUM> and is configured to raise and lower linearly (relative to the support arm <NUM>) a compression paddle <NUM> for compression of a patient breast during imaging procedures. Together, the tube head <NUM> and support arm <NUM> may be referred to as a C-arm.

A number of interfaces and display screens are disposed on the x-ray imaging system <NUM>. These include a foot display screen <NUM>, a gantry interface <NUM>, a support arm interface <NUM>, and a compression arm interface <NUM>. In general the various interfaces <NUM>, <NUM>, and <NUM> may include one or more tactile buttons, knobs, switches, as well as one or more display screens, including capacitive touch screens with graphic user interfaces (GUIs) so as to enable user interaction with and control of the x-ray imaging system <NUM>. In examples, the interfaces <NUM>, <NUM>, <NUM> may include control functionality that may also be available on a system control and work station, such as the x-ray computing device <NUM> of <FIG>. Any individual interface <NUM>, <NUM>, <NUM> may include functionality available on other interfaces <NUM>, <NUM>, <NUM>, either continually or selectively, based at least in part on predetermined settings, user preferences, or operational requirements. In general, and as described below, the foot display screen <NUM> is primarily a display screen, though a capacitive touch screen might be utilized if required or desired.

In examples, the gantry interface <NUM> may enable functionality such as: selection of the imaging orientation, display of patient information, adjustment of the support arm elevation or support arm angles (tilt or rotation), safety features, etc. In examples, the support arm interface <NUM> may enable functionality such as adjustment of the support arm elevation or support arm angles (tilt or rotation), adjustment of the compression arm elevation, safety features, etc. In examples, the compression arm interface <NUM> may enable functionality such as adjustment of the compression arm elevation, safety features, etc. Further, one or more displays associated with the compression arm interface <NUM> may display more detailed information such as compression arm force applied, imaging orientation selected, patient information, support arm elevation or angle settings, etc. The foot display screen <NUM> may also display information such as displayed by the display(s) of the compression arm interface <NUM>, or additional or different information, as required or desired for a particular application.

<FIG> depicts an example of an ultrasound imaging system <NUM>. The ultrasound imaging system <NUM> includes an ultrasound probe <NUM> that includes an ultrasonic transducer <NUM>. The ultrasonic transducer <NUM> is configured to emit an array of ultrasonic sound waves <NUM>. The ultrasonic transducer <NUM> converts an electrical signal into ultrasonic sound waves <NUM>. The ultrasonic transducer <NUM> may also be configured to detect ultrasonic sound waves, such as ultrasonic sound waves that have been reflected from internal portions of a patient, such as lesions within a breast. In some examples, the ultrasonic transducer <NUM> may incorporate a capacitive transducer and/or a piezoelectric transducer, as well as other suitable transducing technology.

The ultrasonic transducer <NUM> is also operatively connected (e.g., wired or wirelessly) to a display <NUM>. The display <NUM> may be a part of a computing system, such as the ultrasound computing device <NUM> of <FIG>, which includes processors and memory configured to produce and analyze ultrasound images. The display <NUM> is configured to display ultrasound images based on an ultrasound imaging of a patient.

The ultrasound imaging performed in the ultrasound imaging system <NUM> is primarily B-mode imaging, which results in a two-dimensional ultrasound image of a cross-section of a portion of the interior of a patient. The brightness of the pixels in the resultant image generally corresponds to amplitude or strength of the reflected ultrasound waves.

Other ultrasound imaging modes may also be utilized. For example, the ultrasound probe may operate in a 3D ultrasound mode that acquires ultrasound image data from a plurality of angles relative to the breast to build a 3D model of the breast.

In some examples, ultrasound images may not be displayed during the acquisition process. Rather, the ultrasound data is acquired and a 3D model of the breast is generated without B-mode images being displayed.

The ultrasound probe <NUM> may also include a probe localization transceiver <NUM>. The probe localization transceiver <NUM> is a transceiver that emits a signal providing localization information for the ultrasound probe <NUM>. The probe localization transceiver <NUM> may include a radio frequency identification (RFID) chip or device for sending and receiving information as well as accelerometers, gyroscopic devices, or other sensors that are able to provide orientation information. For instance, the signal emitted by the probe localization transceiver <NUM> may be processed to determine the orientation or location of the ultrasound probe <NUM>. The orientation and location of the ultrasound probe <NUM> may be determined or provided in three-dimensional components, such as Cartesian coordinates or spherical coordinates. The orientation and location of the ultrasound probe <NUM> may also be determined or provided relative to other items, such as an incision instrument, a marker, a magnetic direction, a normal to gravity, etc. With the orientation and location of the ultrasound probe <NUM>, additional information can be generated and provided to the surgeon to assist in guiding the surgeon to a lesion within the patient, as described further below. While the term transceiver is used herein, the term is intended to cover both transmitters, receivers, and transceivers, along with any combination thereof.

<FIG> depicts an example of the ultrasound imaging system <NUM> in use with a breast <NUM> of a patient. The ultrasound probe <NUM> is in contact with a portion of the breast <NUM>. In the position depicted in <FIG>, the ultrasound probe <NUM> is being used to image a lesion <NUM> of the breast <NUM>. To image the lesion <NUM>, the ultrasonic transducer <NUM> emits an array of ultrasonic sound waves <NUM> into the interior of the breast <NUM>. A portion of the ultrasonic sound waves <NUM> are reflected off internal components of the breast, such as the lesion <NUM> when the lesion is in the field of view, and return to the ultrasound probe <NUM> as reflected ultrasonic sound waves <NUM>. The reflected ultrasonic sound waves <NUM> may be detected by the ultrasonic transducer <NUM>. For instance, the ultrasonic transducer <NUM> receives the reflected ultrasonic sound waves <NUM> and converts the reflected ultrasonic sound waves <NUM> into an electric signal that can be processed and analyzed to generate ultrasound image data on display <NUM>.

The depth of the lesion <NUM> or other objects in an imaging plane may be determined from the time between a pulse of ultrasonic waves <NUM> being emitted from the ultrasound prove <NUM> and the reflected ultrasonic waves <NUM> being detected by the ultrasonic probe <NUM>. For instance, the speed of sound is well-known and the effects of the speed of sound based on soft tissue are also determinable. Accordingly, based on the time of flight of the ultrasonic waves <NUM> (more specifically, half the time of flight), the depth of the object within an ultrasound image may be determined. Other corrections or methods for determining object depth, such as compensating for refraction and variant speed of waves through tissue, may also be implemented. Those having skill in the art will understand further details of depth measurements in medical ultrasound imaging technology. Such depth measurements and determinations may be used to build a 3D model of the breast <NUM>.

In addition, multiple frequencies or modes of ultrasound techniques may be utilized. For instance, real time and concurrent transmit and receive multiplexing of localization frequencies as well as imaging frequencies and capture frequencies may be implemented. Utilization of these capabilities provide information to co-register or fuse multiple data sets from the ultrasound techniques to allow for visualization of lesions and other medical images on the display <NUM>. The imaging frequencies and capture sequences may include B-mode imaging (with or without compounding), Doppler modes (e.g., color, duplex), harmonic mode, shearwave and other elastography modes, and contrast-enhanced ultrasound, among other imaging modes and techniques.

<FIG> is a block diagram illustrating an example of the physical components of a computing device <NUM>. The computing device <NUM> could be any computing device utilized in conjunction with the lesion identification system <NUM> or the system <NUM> for managing imaging data such as the computing system <NUM>, x-ray computing device <NUM>, and ultrasound computing device <NUM>.

In the example shown in <FIG>, the computing device <NUM> includes at least one central processing unit ("CPU") <NUM>, a system memory <NUM>, and a system bus <NUM> that couples the system memory <NUM> to the CPU <NUM>. The system memory <NUM> includes a random access memory ("RAM") <NUM> and a read-only memory ("ROM") <NUM>. A basic input/output system that contains the basic routines that help to transfer information between elements within the computing device <NUM>, such as during startup, is stored in the ROM <NUM>. The computing system <NUM> further includes a mass storage device <NUM>. The mass storage device <NUM> is able to store software instructions and data.

The mass storage device <NUM> is connected to the CPU <NUM> through a mass storage controller (not shown) connected to the system bus <NUM>. The mass storage device <NUM> and its associated computer-readable storage media provide non-volatile, non-transitory data storage for the computing device <NUM>. Although the description of computer-readable storage media contained herein refers to a mass storage device, such as a hard disk or solid state disk, it should be appreciated by those skilled in the art that computer-readable data storage media can include any available tangible, physical device or article of manufacture from which the CPU <NUM> can read data and/or instructions. In certain examples, the computer-readable storage media includes entirely non-transitory media.

Computer-readable storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable software instructions, data structures, program modules or other data. Example types of computer-readable data storage media include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROMs, digital versatile discs ("DVDs"), other optical storage media, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing device <NUM>.

According to some examples, the computing device <NUM> can operate in a networked environment using logical connections to remote network devices through a network <NUM>, such as a wireless network, the Internet, or another type of network. The computing device <NUM> may connect to the network <NUM> through a network interface unit <NUM> connected to the system bus <NUM>. It should be appreciated that the network interface unit <NUM> may also be utilized to connect to other types of networks and remote computing systems. The computing device <NUM> also includes an input/output controller <NUM> for receiving and processing input from a number of other devices, including a touch user interface display screen, or another type of input device. Similarly, the input/output controller <NUM> may provide output to a touch user interface display screen or other type of output device.

As mentioned briefly above, the mass storage device <NUM> and the RAM <NUM> of the computing device <NUM> can store software instructions and data. The software instructions include an operating system <NUM> suitable for controlling the operation of the computing device <NUM>. The mass storage device <NUM> and/or the RAM <NUM> also store software instructions, that when executed by the CPU <NUM>, cause the computing device <NUM> to provide the functionality discussed in this document.

Referring now to <FIG>, an example method <NUM> of locating a region of interest within a breast is described. In some examples, the systems and devices described in <FIG> are usable to implement the method <NUM>. In particular, the computing system <NUM> of <FIG> operates to implement the steps of the method <NUM> to aid a healthcare provider in locating a region of interest within a breast during an imaging procedure.

At operation <NUM>, a first image obtained by an x-ray imaging modality is received. In some examples, the x-ray imaging device <NUM> of the x-ray imaging system <NUM> of <FIG> operates to record the x-ray image as the result of inputs provided by a healthcare provider H at an x-ray computing device <NUM>. In some examples, the x-ray image is acquired using digital breast tomosynthesis. In some examples, the x-ray image could be obtained from a remote data store. In such examples, the x-ray image may be have recorded at a different time and place and then stored in an EMR or other data store. In some examples, the first image is received at the computing system <NUM>.

At operation <NUM>, an indication of a target lesion on the x-ray image is received. In some examples, the indication is received from the healthcare provider H at the x-ray computing device <NUM>. The computing device <NUM> may operate to display a user interface that allows the healthcare provider H to easily interact with x-ray images to highlight a target lesion by means of inputs provided with an input device in communication with the x-ray computing device <NUM> such as a mouse, a touchscreen, or a stylus. In some examples, the target lesion can be indicated with a visual marker. The target lesion is identified by the healthcare provider H as requiring additional analysis. In some examples, the target lesion is later identified by a clinician after the x-ray image is taken. In some examples, target lesions can be identified in real-time as the x-ray image is being recorded using an artificial intelligence system.

At operation <NUM>, location coordinates of the target lesion are recorded. Coordinates of the target lesion are recorded during the x-ray imaging process using the x-ray imaging system <NUM>. In some examples, the coordinates can be Cartesian coordinates or polar coordinates. In some examples, a region of interest may be identified within a particular slice within a tomosynthesis image stack (z coordinate) and its position can be further identified by x and y coordinates within that image slice.

At operation <NUM>, a second image of the breast tissue is obtained by ultrasound imaging. The ultrasound image includes an area of the breast tissue corresponding to the location coordinates of the target lesion. In some examples, the ultrasound imaging device <NUM> of the ultrasound imaging system <NUM> of <FIG> operates to record the ultrasound image as the result of inputs provided by a healthcare provider H at an ultrasound computing device <NUM>. In some examples, the ultrasound image could be obtained from a remote data store. In such examples, ultrasound image may be have recorded at a different time and place and then stored in an EMR or other data store. In some examples, the second image is received at the computing system <NUM> for processing with the first image.

At operation <NUM>, a potential lesion is identified within the area of the breast tissue corresponding to the location coordinates of the target lesion. In some examples, the area is identified based on coordinates that were converted for ultrasound from the coordinates saved for the target lesion during x-ray imaging. In some examples, the location coordinates include at least two of a clock position relative to the nipple, a depth from the surface of the breast, and a distance from the nipple. In some examples, the potential lesion can be highlighted by a healthcare provider H at the ultrasound computing device <NUM>. The computing device <NUM> may operate to display a user interface that allows the healthcare provider H to easily interact with ultrasound images to highlight a potential lesion by means of inputs provided with an input device in communication with the ultrasound computing device <NUM> such as a mouse, a touchscreen, or a stylus. In some embodiments, a GUI <NUM> displays a DBT image and an ultrasound image side by side. An example of this GUI <NUM> is shown in <FIG>. The potential lesion is identified by the healthcare provider H as potentially being the same as the target lesion identified in the x-ray image. In some examples, a real-time artificial intelligence system can analyze DBT images as they are recorded to identify potential lesions. One example of such a system is described in co-pending application <CIT> entitled "Real-time AI for Physical Biopsy Marker Detection,".

At operation <NUM>, the potential lesion is analyzed using artificial intelligence to determine a level of confidence that the potential lesion in the second image corresponds to the target lesion in the first image. In some examples, the lesion matching engine <NUM> operates to analyze the potential lesion and target lesion to determine if the two lesions match. As is described above, a machine learning lesion classifier analyzes various aspects of the lesions such as size, shape, and texture to match ultrasound images and x-ray images of lesions. In some examples, stiffness and density can also be compared to determine a match.

At operation <NUM>, an indicator of the level of confidence is output. In some examples, a confidence level indicator is generated on the GUI <NUM>. In some examples, the GUI <NUM> includes ultrasound images and x-ray images along with the confidence level indicator. In some examples, the indicator could be displayed as text, graphics, colors, or symbols. More details regarding an example of the GUI <NUM> are provided in <FIG>.

<FIG> shows an example of the GUI <NUM> of <FIG>. In some examples, the GUI <NUM> is displayed on a computing device such as the ultrasound computing device <NUM> of <FIG>. In the example of <FIG>, the GUI <NUM> displays an x-ray image <NUM> and an ultrasound image <NUM> of a breast <NUM> side-by-side. A target lesion <NUM> previously identified during x-ray imaging is indicated in the x-ray image <NUM> with a visual marker. The corresponding ultrasound image <NUM> of the breast <NUM> shows an indication of a potential lesion <NUM>. A confidence level indicator <NUM> is displayed providing the likelihood that the target lesion <NUM> and potential lesion <NUM> are a match as a percentage. In this example, there is a <NUM>% match.

In some examples, other indicators of the confidence level could be provided such as a colored circle around the potential lesion <NUM>. Different colors could represent different levels of confidence. For example, a high level of confidence could be indicated with a green circle. A medium level of confidence could be indicated with a yellow circle. A low level of confidence could be indicated with a red circle. In some examples, both a visual indicator on the ultrasound image <NUM> and a text confidence level indicator <NUM> could be used.

The GUI <NUM> also includes a diagram <NUM> indicating the location on the breast <NUM> where the ultrasound image <NUM> is being taken as well as an arrow indicating the location of the target lesion <NUM>. Additionally, coordinates <NUM> are displayed. In this example, the coordinates <NUM> indicate the location of a potential lesion in the right breast at the <NUM>:<NUM> clock position, <NUM> from the nipple. The diagram <NUM> shows a corresponding visual representation of the potential lesion <NUM>.

<FIG> illustrates another embodiment of a lesion identification system <NUM>. The lesion correlator <NUM> operates on the computing device <NUM>, with similar functionality to the lesion matching engine <NUM> of <FIG>. However, in this example, lesions are correlated using probability mapping. Real time ultrasound imaging guidance is provided by using at least two optical cameras <NUM> and a projector <NUM>. The optical cameras <NUM> operate to capture multiple images of a patient's torso. Multiple stereotactic optical images are analyzed in combination with previously acquired x-ray images at the computing device <NUM>.

In this example the artificial intelligence image analyzer <NUM> is configured to match regions of a breast between two different imaging modalities. In some examples, deep learning models are utilized to generate probabilities that a target lesion identified in one type of image is located at any given location on another type of image. For example, the target lesion <NUM> shown in the tomosynthesis view <NUM> is analyzed to determine the probability of its location on the ultrasound image <NUM>.

In some examples a probability mapper <NUM> generates a probability mapping for the breast that indicates where the potential lesion <NUM> is most likely to be located in a different type of image. In some examples, this could be optical images obtained by the optical cameras <NUM>. The potential lesion <NUM> is indicated on the ultrasound image with a color gradient, with the center representing the highest likelihood of the target lesion being located there. In some examples, the probability mapping is a visual map and is laid over an ultrasound image or tomosynthesis image, as shown in the GUI <NUM> of <FIG>. In other examples, the probability mapping is a visual map projected onto the patient's actual breast during an ultrasound examination using the projector <NUM>. The potential lesion <NUM> is indicated by the colored regions of the probability map. This visual probability map is used to guide a healthcare practitioner H in obtaining ultrasound images using the ultrasound probe <NUM>.

In some examples, the tracking system <NUM> and navigation system <NUM> operate in conjunction with the lesion correlator <NUM> to guide a healthcare practitioner H during an ultrasound imaging session. The current location of the ultrasound probe <NUM> is communicated to the computing device <NUM> and the current location of the probe is indicated visually on the images presented on the GUI <NUM> in real time.

Referring now to <FIG>, an example method <NUM> of locating a region of interest within a breast is described. In some examples, the system of <FIG> operates to perform this method <NUM>.

At operation <NUM>, a series of stereo optical images of at least one breast are captured. This is typically performed as the patient is lying prone on an imaging table or other support. Images are captured with two or more optical cameras <NUM> positioned over the patient.

At operation <NUM>, at least one tomosynthesis image of the breast is accessed. In some embodiments, the tomosynthesis image(s) are accessed at a computing device <NUM> in response to receiving input from a user. In some examples, the tomosynthesis image(s) are accessed from an electronic medical record associated with the patient being imaged. In some embodiments, the tomosynthesis image(s) are then presented on a display of the computing device <NUM>.

At operation <NUM>, an indication of a target lesion on the tomosynthesis image is received. In some examples, the indication is received from the healthcare provider H at the x-ray computing device of <FIG>. An example of the indication of the target lesion <NUM> is shown in the GUI <NUM> of <FIG>. As described above with respect to <FIG>, there are other ways in which the target lesion can be indicated.

At operation <NUM>, a co-registration image analysis of the optical images and tomosynthesis images is performed. In some embodiments, artificial intelligence algorithms for region matching are used to generate a virtual deformable of the breast that both the optical images and tomosynthesis images can be co-registered into. In some examples, the artificial intelligence algorithm is a deep learning based region matching method.

At operation <NUM>, a probability mapping is created based on the image analysis. The probability mapping indicates a likelihood that the indicated lesion is located at a particular point of the breast.

In the examples shown in <FIG>, the visual probability map uses color indicators to indicate higher or lower probabilities at various locations on the breast. For example, red could indicate the highest probability and blue could indicate the lowest probability. In other examples, grayscale is used where black indicates highest probability and white indicates lowest probability. As can be seen in <FIG>, the resulting visual of the probability map will likely include a region of highest probability, indicating where the lesion is most likely to be. This region is surrounded by areas of decreasing probability that extend outward. For example, the region might be red and the colors around it extend from orange to yellow to green to blue. In other examples, the visual probability map is displayed as different types of hashing or shading. In some examples, the probability mapping provides different numerical values for the various probabilities. In some examples, a single target is projected at the point of highest probability.

At operation <NUM>, the probability map is projected onto the patient P. In some examples, the map is only projected onto one breast. In some examples, the map is projected over both breasts of the patient. This provides a healthcare provider H performing ultrasound imaging with a visual guide to a location where the target lesion is most likely to be located.

In some examples, additional feedback can be provided to the healthcare provider H to indicate that the ultrasound probe <NUM> is nearing the location of the target lesion. In some instances, the ultrasound probe <NUM> blocks the path of the projection of the probability map onto the patient, creating a shadow. To compensate for this interference with the visual guidance, feedback such as haptic feedback or audio feedback could be used to help a healthcare practitioner H determine when the ultrasound probe <NUM> is aligned with the target lesion.

In some examples, additional guidance is provided to a healthcare practitioner in the form of real-time navigation assistance. A real time position of an ultrasound probe is tracked during imaging and information regarding the location is provided on a display for the healthcare practitioner. In some examples, the display shows an indication of a present location and orientation of an ultrasound probe in relation to an image of the patient's breast.

The methods and systems described herein provide navigation and lesion matching technology that helps healthcare professionals to quickly and accurately locate mammography lesions under ultrasound. The system enables a healthcare professional to identify a region of interest during mammography. During a subsequence ultrasound examination, the mammogram, target region of interest, and b-mode imaging are displayed simultaneously. This guides the professional to the region of interest while simultaneously automating documentation of the probe's position, orientation, and annotations. Once the professional has navigated to the region of interest, the system automatically analyzes the images, matches the lesion, and provides a visual confidence indicator.

The systems and methods provided herein allow a healthcare professional to navigate within <NUM> of a target lesion using ultrasound. The artificial intelligence based system is built on thousands of confirmed cases. Lesions can be matched with greater accuracy than healthcare professionals can accomplish on their own. Additionally, it is faster and easier to locate lesions using ultrasound that were originally identified using x-ray imaging.

Claim 1:
A method of locating of a lesion within a breast, the method comprising:
receiving, at a computing system (<NUM>) an indication of a location of a target lesion within the breast on a first image of the breast obtained by a first imaging modality (<NUM>);
receiving, at the computing system, a second image of the breast obtained by a second imaging modality (<NUM>);
analyzing, with a lesion matching engine (<NUM>) operating on the computing system, the first image including the target lesion and the second image including a potential lesion to correlate the first image and the second image using artificial intelligence to determine a probability that the potential lesion corresponds to the target lesion, wherein analyzing the potential lesion comprises comparing form factors of breast tissue surrounding the target lesion and the potential lesion; and
outputting an indicator of said probability for display on a graphical user interface (<NUM>).