Method and system for implementing and using integration of structured report (SR) objects in digital imaging and communications in medicine (DICOM) - Patents.com

Abstract

A system and method for implementing and using consolidation of Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) objects. The consolidation process can be applied to multiple objects generated based on the same medical imaging data. The consolidation process includes evaluating each object of the multiple objects, the evaluating including determining whether the object is a parent of another object of the multiple objects, and discarding the object if the object is a parent of another object. A composite object is then generated based on the multiple objects. The generating includes copying the one object into a consolidated object if only one object remains after the evaluating, and processing the multiple remaining objects from newest to oldest if multiple remaining objects remain after the evaluating, the processing including copying each data element of each remaining object into the composite object.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of and priority to U.S. patent application Ser. No. 17 / 459,542, filed Aug. 27, 2021, the disclosure of which is incorporated herein by reference in its entirety.

[0002] Aspects of the present disclosure relate to medical imaging solutions. More specifically, certain embodiments relate to methods and systems for implementing and using integration of Structured Report (SR) objects in Digital Imaging and Communications in Medicine (DICOM). [Background technology]

[0003] Various medical imaging techniques can be used to image the organs and soft tissues of the human body. Examples of medical imaging techniques include ultrasound imaging, computed tomography (CT) scanning, and magnetic resonance imaging (MRI). The method by which images are generated during medical imaging varies depending on the individual technique.

[0004] For example, ultrasound imaging uses non-invasive, real-time, high frequency sound waves to generate ultrasound images of organs, tissues, and objects (e.g., a fetus), typically within the human body. Images generated during medical imaging can be two-dimensional (2D), three-dimensional (3D), and / or four-dimensional (4D) images (essentially real-time / continuous 3D images). During medical imaging, imaging datasets (including, for example, volumetric imaging datasets during 3D / 4D imaging) are acquired and the imaging datasets are used to generate corresponding images in real-time and displayed (e.g., via a display).

[0005] In some cases, imaging data generated during and / or based on medical imaging needs to be managed (especially when managed by various users) (especially with respect to analysis and evaluation of the imaging data). This can cause certain problems, especially with respect to ensuring the reliability and integrity of the imaging data and / or information obtained based on the imaging data. The limitations and shortcomings, if any, of conventional approaches for handling such situations will become apparent to one skilled in the art by comparing the approaches with certain aspects of the present disclosure, as described in the remainder of this application with reference to the drawings. Summary of the Invention

[0006] Systems and methods are provided for implementing and using integration of Structured Report (SR) objects in Digital Imaging and Communications in Medicine (DICOM), as illustrated and / or described in connection with at least one of the figures, and as more fully set forth in the claims.

[0007] These and other advantages, aspects, and novel features of the present disclosure, as well as details of one or more illustrated exemplary embodiments thereof, can be more fully understood from the following description and drawings. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram showing an example of a medical imaging device. [Diagram 2] 1 is a block diagram illustrating an exemplary ultrasound imaging system. [Diagram 3] FIG. 1 is a block diagram illustrating a use case for integrating multiple Structured Report (SR) objects in Digital Imaging and Communications in Medicine (DICOM). [Figure 4]1 shows a flowchart of an example process for integration of Structured Report (SR) objects in Digital Imaging and Communications in Medicine (DICOM). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Certain implementations according to the present disclosure implement and use the integration of Structured Report (SR) objects in Digital Imaging and Communications in Medicine (DICOM). In particular, the following detailed description of certain embodiments will be better understood when read in conjunction with the drawings. Where the figures show diagrams of functional blocks of various embodiments, the functional blocks do not necessarily indicate a division between hardware circuits. Thus, for example, one or more of the functional blocks (e.g., a processor or memory) may be implemented in a single piece of hardware (e.g., a block of a general-purpose signal processor, or random access memory, hard disk, etc.) or in multiple pieces of hardware. Similarly, a program may be a stand-alone program, or may be incorporated as a subroutine of an operating system, or may be a function of an installed software package, etc. It should be understood that the various embodiments are not limited to the arrangements and instrumentalities shown in the drawings. It should also be understood that the embodiments may be combined, other embodiments may be utilized, and structural, logical, and electrical changes may be made without departing from the scope of the various embodiments. Thus, the following detailed description is not to be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims and their equivalents.

[0010] As used herein, an element or step described in the singular preceding "a" or "an" should be understood not to exclude the presence of a plurality of such elements or steps, unless expressly stated to the contrary. Furthermore, references to "exemplary embodiments," "various embodiments," "specific embodiments," "representative embodiments," and the like are not intended to be interpreted as excluding the presence of additional embodiments that also incorporate the recited features. Furthermore, unless expressly stated to the contrary, embodiments "comprising," "including," or "having" an element or elements having a particular characteristic can include additional elements that do not have that characteristic.

[0011] Also, in this specification, the term "image" broadly refers to both a displayable image and data representing a displayable image. However, in many embodiments, at least one displayable image is generated (or configured to be generated). Furthermore, the term "image" used in the context of ultrasound imaging is used herein to refer to an ultrasound mode in which an "image" and / or a "plane" is generated with a single beam or multiple beams. Ultrasound modes include, for example, B-mode (2D mode), M-mode, three-dimensional (3D) mode, CF mode, PW Doppler, CW Doppler, MGD, and / or sub-modes of B-mode and / or CF, such as shear wave elasticity imaging (SWEI), TVI, Angio, B-flow, BMI, BMI_Angio, and possibly MM, CM, TVD.

[0012] Additionally, as used herein, the term "pixel" also includes embodiments in which data is represented by a "voxel." Accordingly, the terms "pixel" and "voxel" may be used interchangeably throughout this specification.

[0013] Further, in this specification, the term processor or processing unit refers to any type of processing unit (CPU, Accelerated Processing Unit (APU), graphics board, DSP, FPGA, ASIC, or combinations thereof) capable of performing the required calculations necessary in various embodiments, such as single-core or multi-core.

[0014] It should be noted that various embodiments described herein that generate or form an image include a process for forming an image that includes beamforming in some embodiments and does not include beamforming in other embodiments. For example, an image can be formed without beamforming, such as by multiplying a matrix of demodulated data by a matrix of coefficients and taking the product as an image, in which case the process does not form any "beams." Additionally, image formation can be performed using a combination of channels (e.g., synthetic aperture techniques) derived from multiple transmit events.

[0015] In various embodiments, the processing to form the image is performed in software, firmware, hardware, or a combination thereof. The processing may include the use of beamforming. As shown in FIG. 2, an example implementation of an ultrasound system having a software beamformer architecture formed in accordance with various embodiments is shown.

[0016] Figure 1 is a block diagram illustrating an example medical imaging facility. Shown in Figure 1 is an example medical imaging facility 100 that includes one or more medical imaging systems 110 and one or more computing systems 120. The medical imaging facility 100 (including its various components) can be configured to support implementation and use of the Integration of Structured Report (SR) Objects in Digital Imaging and Communications in Medicine (DICOM) in accordance with the present disclosure.

[0017] The medical imaging system 110 includes suitable hardware, software, or combinations thereof to support medical imaging that allows for acquisition of data used in generating and / or displaying an image during a medical imaging examination. Examples of medical imaging include ultrasound imaging, computed tomography (CT) scanning, magnetic resonance imaging (MRI), etc., in which certain types of data are acquired in a particular manner, and the certain types of data can be used in generating data for an image. For example, the medical imaging system 110 can be an ultrasound imaging system configured to generate and / or display ultrasound images. An example of an ultrasound system corresponding to the medical imaging system 110 is described in more detail in FIG. 2.

[0018] As shown in FIG. 1, the medical imaging system 110 may include a portable and mobile scanner device 112 and a display / control unit 114. The scanner device 112 may be configured to generate and / or acquire a particular type of image signal (and / or corresponding data), such as by being moved over a patient's body (or a portion of the body), and may include appropriate circuitry to perform and / or support such functionality. The scanner device 112 may be an ultrasound probe, an MRI scanner, a CT scanner, or any suitable imaging device. For example, if the medical imaging system 110 is an ultrasound system, the scanner device 112 may emit ultrasound signals and acquire echo ultrasound images.

[0019] The display / control unit 114 can be configured to display images (e.g., via screen 116). In some implementations, the display / control unit 114 can be further configured to at least partially generate the display image. Additionally, the display / control unit 114 can also support user input / user output. For example, the display / control unit 114 may display user feedback (e.g., information related to the system, system functionality, system settings, etc.) in addition to the image (e.g., via screen 116). The display / control unit 114 can also support user input (e.g., via user controls 118), for example, to allow control of medical imaging. User input can be used to control the display of images, select settings, specify user preferences, request feedback, etc.

[0020] In some implementations, the medical imaging facility 100 may also incorporate additional dedicated computing resources (such as one or more computing systems 120). In this regard, each computing system 120 may include appropriate circuitry, interfaces, logic, and / or code for processing, storing, and / or communicating data. The computing systems 120 may be dedicated equipment configured specifically for use with medical imaging, or may be general-purpose computing systems (e.g., personal computers, servers, etc.) configured and / or arranged to perform the operations described below with respect to the computing systems 120. The computing systems 120 may be configured to support the operation of the medical imaging system 110, as described below. In this regard, various functions and / or operations may be offloaded from the imaging system. This may be done to streamline and / or centralize certain aspects of processing, for example, to reduce costs by avoiding the need to increase the processing resources of the imaging system.

[0021] The multiple computing systems 120 can be set up and / or configured to be used in different ways. For example, in some implementations, a single computing system 120 can be used. In other implementations, the multiple computing systems 120 are configured to operate together or separately (e.g., based on a distributed processing configuration), with each computing system 120 configured to handle specific aspects and / or functions and / or process data only for a particular medical imaging system 110. Furthermore, in some implementations, the computing system 120 can be local (e.g., co-located with one or more medical imaging systems 110, such as within the same facility and / or within the same local network). In other implementations, the computing system 120 can be remote and thus accessible only through a remote connection (e.g., through the Internet or other available remote access technology). In certain implementations, the computing system 120 can be configured in a cloud-based manner, and the computing system 120 can be accessed and / or used in a manner substantially similar to the manner in which other cloud-based systems are accessed and used.

[0022] Once data is generated and / or created in the computing system 120, the data can be copied and / or loaded into the medical imaging system 110. This can be done in various ways. For example, the data can be loaded through a direct connection or link between the medical imaging system 110 and the computing system 120. In this regard, communication between the different components of the medical imaging equipment 100 can be performed using available wired and / or wireless connections and / or according to any suitable communication (and / or networking) standard or communication protocol. Alternatively or additionally, the data may be loaded into the medical imaging system 110 indirectly. For example, the data can be stored on a suitable machine-readable medium (e.g., a flash card, etc.) which can be used to load the data into the medical imaging system 110 (e.g., by a user of the system (e.g., an imaging specialist) or an authorized user at the facility), or the data can be downloaded to a local communication capable electronic device (e.g., a laptop, etc.) which can be used at the facility (e.g., by a user of the system or an authorized user) to upload the data to the medical imaging system 110 via a direct connection (e.g., a USB connector, etc.).

[0023] In operation, the medical imaging system 110 can be used to generate and present (e.g., render or display) images during a medical examination and / or support user input / output associated with the generation and presentation of images. The images can be 2D, 3D, and / or 4D images. The specific operations or functions performed by the medical imaging system 110 to assist in the generation and / or presentation of images depend on the type of system, i.e., the manner in which data corresponding to the images is acquired and / or generated. For example, in imaging based on computed tomography (CT) scans, the data is based on emitted and captured x-ray signals. In ultrasound imaging, the data is based on emitted and reflected ultrasound signals as echoes, as described in more detail in FIG. 2.

[0024] In various implementations according to the present disclosure, a medical imaging system and / or medical imaging architecture (e.g., the medical imaging system 110 and / or the entire medical imaging facility 100) can be configured to support an enhanced solution for medical imaging data storage and management. In particular, the medical imaging solution may be configured and / or modified to incorporate functionality using Digital Imaging and Communications in Medicine (DICOM), such as integration of Structured Report (SR) objects. The integration scheme / methodology according to the present disclosure can be used in any application that utilizes DICOM SR objects, such as reporting and analysis packages.

[0025] DICOM is an international standard for the communication and management of medical imaging information and related data. The DICOM standard describes how medical data are represented in files and how they are exchanged, for example by defining both file formats and network transfer protocols. In this regard, the DICOM standard defines various structures used in the storage, management and communication of image data. For example, the DICOM 3.0 standard defines several object types, called Structured Report (SR) objects, which can be used to support the exchange of medical findings between software applications. In the standard, an SR (report) does not necessarily mean a report in the "clinical" sense, but may simply be or may correspond to an observation record based on the corresponding image data. An SR is created and accompanied by the corresponding image files (including information about those image files or images related to the image files (e.g., information related to the structures or features captured therein, measurements, etc.)).

[0026] When multiple SR objects exist for a single exam, applications that use these sets of SR objects are challenged with integrating the content. In this regard, there are different kinds of SR objects: 1) the "final" SR, which contains the "final" information related to the image file, and 2) the "intermediate" or "incomplete" SR. The "intermediate" or "incomplete" SR records the details of the observations. However, problems can arise since multiple observations may be made. For example, in cardiac imaging, there may be observations about the anatomical structures, observations about the blood flow, etc. Then, multiple observations are calculated based on these observations and a conclusion is drawn. Furthermore, another human (clinician, doctor, etc.) may review the images and measurements related to the images and may make new measurements and therefore new calculations.

[0027] Thus, in some cases, multiple reports need to be generated and summarized. The DICOM 3.0 standard does not provide a mechanism to consolidate multiple SR objects (e.g., all the different intermediate / incomplete SR objects) related to a particular study. In this regard, existing solutions typically focus on creating SR objects with findings (e.g., measurements) and little (if any) functionality to enable data consumers to reconcile and resolve conflicting data elements from multiple SR objects. The solution according to the present disclosure addresses such issues by incorporating a mechanism for consolidating multiple SR objects (e.g., automatically consolidating data while resolving data conflicts).

[0028] These mechanisms are configured, for example, to automatically identify anomalies and / or discrepancies between SR objects and resolve / align these anomalies and / or discrepancies. This can be done, for example, by using an integration module that can be deployed and used to manage multiple SR objects as they are created. Such an integration means may be adaptively deployed, for example, at the medical imaging device, at a local dedicated system, or at a remote entity (e.g., a cloud-based system), or in a distributed manner where different functions or components of the integration means are deployed and / or performed at different components within the imaging environment. In some examples, advanced processing techniques can be used to further improve the handling of multiple SR objects. For example, in some exemplary implementations, a learning mode using artificial intelligence (AI) can be used to recognize common manual alignment anomalies and automatically perform alignment.

[0029] The solution according to the present disclosure can achieve various technical and commercial advantages over existing solutions. In this regard, having independent applications prepared to utilize SR data has the following advantages: For example, the use of such a dedicated integration facility relieves DICOM-using applications from needing specialized knowledge of the DICOM SR format; the use of such a dedicated integration facility also avoids errors resulting from data inconsistencies or anomalies; and the use of such a dedicated integration facility resolves differences between vendors that create DICOM SR objects.

[0030] Exemplary implementations and use cases / use scenarios based on the solutions according to the present disclosure are described in more detail below, particularly in conjunction with the exemplary use case scenario illustrated in FIG.

[0031] 2 is a block diagram illustrating an example ultrasound imaging system. Shown in FIG. 2 is an ultrasound imaging system 200 that can be configured to support and use implementation of the Integration of Structured Report (SR) Objects in Digital Imaging and Communications in Medicine (DICOM) in accordance with the present disclosure.

[0032] The ultrasound imaging system 200 can be configured to provide ultrasound imaging and can include, for example, appropriate circuits, interfaces, logic, and / or code for performing and / or supporting related functions of ultrasound imaging. The ultrasound imaging system 200 can correspond to the medical imaging system 110 of FIG. 1. The ultrasound imaging system 200 includes, for example, a transmitter 202, an ultrasound probe 204, a transmit beamformer 210, a receiver 218, a receive beamformer 220, an RF processor 224, an RF / IQ buffer 226, a user input module 230, a signal processor 240, an image buffer 250, a display system 260, an archive 270, and a training engine 280.

[0033] The transmitter 202 may include appropriate circuitry, interfaces, logic, and / or code operable to drive an ultrasound probe 204. The ultrasound probe 204 may include a two-dimensional (2D) array of piezoelectric elements. The ultrasound probe 204 includes a group of transmit transducer elements 206 and a group of receive transducer elements 208, which may typically be composed of the same elements. In certain embodiments, the ultrasound probe 204 may be operable to acquire ultrasound image data covering at least a substantial portion of an anatomical structure (such as a heart, a blood vessel, or any suitable anatomical structure).

[0034] The transmit beamformer 210 may include suitable circuitry, interfaces, logic, and / or code operable to control the transmitter 202, via the transmit sub-aperture beamformer 214, to drive groups of transmit transducer elements 206 to emit ultrasonic transmit signals into an area of ​​interest (e.g., a human, an animal, an underground cavity, a physical structure, etc.). The transmitted ultrasonic signals may backscatter off tissue within the area of ​​interest, such as blood cells or tissue, to generate echoes that are received by the receive transducer elements 208.

[0035] A group of receive transducer elements 208 of the ultrasound probe 204 are operable to convert the received echoes into analog signals that are sub-aperture beamformed by a receive sub-aperture beamformer 216 and then transmitted to a receiver 218. The receiver 218 may be comprised of suitable circuitry, interfaces, logic, and / or code operable to receive signals from the receive sub-aperture beamformer 216. The analog signals are transmitted to one or more A / D converters of a plurality of A / D converters 222.

[0036] The multiple A / D converters 222 may be comprised of suitable circuitry, interfaces, logic, and / or code operable to convert analog signals from the receiver 218 to corresponding digital signals. The multiple A / D converters 222 are disposed between the receiver 218 and the RF processor 224. This disclosure is not limited in this respect. Thus, in some embodiments, the multiple A / D converters 222 may be incorporated within the receiver 218.

[0037] The RF processor 224 may include appropriate circuitry, interfaces, logic, and / or code operable to demodulate the digital signals output by the plurality of A / D converters 222. According to one embodiment, the RF processor 224 may include a complex demodulator (not shown) operable to demodulate the digital signals to form I / Q data pairs representative of corresponding echo signals. The RF or I / Q signal data may then be transmitted to an RF / IQ buffer 226. The RF / IQ buffer 226 may include appropriate circuitry, interfaces, logic, and / or code operable to temporarily store the RF or I / Q signal data generated by the RF processor 224.

[0038] The receive beamformer 220 includes appropriate circuitry, interfaces, logic, and / or code operable to perform digital beamforming processing, for example, to sum delayed channel signals received from the RF processor 224 through the RF / IQ buffer 226 and output a summed beam signal. The resulting processed information may be a summed beam signal output from the receive beamformer 220 and transmitted to the signal processor 240. In some embodiments, the receiver 218, the multiple A / D converters 222, the RF processor 224, and the beamformer 220 may be combined into a single digital beamformer. In various embodiments, the ultrasound imaging system 200 includes multiple receive beamformers 220.

[0039] The user input device 230 may be used to input patient data, scan parameters, settings, select protocols and / or templates, interact with an artificial intelligence segmentation processor to select tracking targets, etc. In an exemplary embodiment, the user input device 230 is operable to configure, manage, and / or control the operation of one or more components and / or modules of the ultrasound imaging system 200. In this regard, the user input device 230 is operable to configure, manage, and / or control the operation of the transmitter 202, the ultrasound probe 204, the transmit beamformer 210, the receiver 218, the receive beamformer 220, the RF processor 224, the RF / IQ buffer 226, the user input device 230, the signal processor 240, the image buffer 250, the display system 260, and / or the archive 270.

[0040] For example, the user input devices 230 may include buttons, rotary encoders, touch screens, motion tracking, voice recognition, mouse devices, keyboards, cameras, and / or any other devices capable of receiving user commands. In certain embodiments, one or more of the user input devices 230 may be integrated into other components (such as, for example, the display system 260 or the ultrasound probe 204).

[0041] As an example, the user input device 230 may include a touch screen display. As another example, the user input device 230 may include an accelerometer, gyroscope, and / or magnetometer attached to and / or integrated with the probe 204 to recognize the movement of the probe 204 (e.g., to identify compression of the patient's body by one or more probes, predefined movement or tilting of the probe, etc.). In some embodiments, the user input device 230 may additionally or alternatively include performing an image analysis process to identify the movement of the probe by analyzing the acquired image data. In accordance with the present disclosure, the user input and functions associated with the user input may be configured to support the use of new data storage schemes, as described herein. For example, the user input device 230 may be configured to support receiving user input to trigger and (if necessary) manage the application of a separation process, as described herein, and / or to provide or set parameters used in performing such a process. Similarly, user input device 230 may be configured to support receiving user input for triggering and managing the application of a restoration process (as desired) and / or to provide or set parameters used in executing such a process, as described herein.

[0042] The signal processor 240 may include suitable circuitry, interfaces, logic, and / or code operable to process the ultrasound scan data (i.e., the summed IQ signals) to generate an ultrasound image for display on the display system 260. The signal processor 240 is operable to perform one or more processing operations on the acquired ultrasound scan data according to a number of selectable ultrasound modalities. In an exemplary embodiment, the signal processor 240 is operable to perform display and / or control operations, among others. The acquired ultrasound scan data may be processed in real-time during a scan period as echo signals are received. Additionally or alternatively, the ultrasound scan data may be temporarily stored in the RF / IQ buffer 226 during a scan period and processed at a time slower than real-time in live or offline operation. In various embodiments, the processed image data may be displayed on the display system 260 and / or stored in the archive 270.

[0043] Archive 270 may be a local archive, a Picture Archiving and Communication System (PACS), or any suitable device for storing images and related information, and archive 270 may be coupled to such a device or system to facilitate storing and / or retrieving image-related data. In an exemplary embodiment, archive 270 is further coupled to a remote system (such as a radiology information system, a hospital information system, etc.) and / or an internal or external network (not shown) to allow operators at different locations to provide commands and parameters and / or access image data.

[0044] The signal processor 240 may be one or more central processing units, microprocessors, microcontrollers, and / or the like. The signal processor 240 may be, for example, an integrated component or may be distributed across various locations. The signal processor 240 may be configured to, among other things, receive input information from the user input device 230 and / or the archive 270, generate output displayable by the display system 260, and manipulate the output in response to the input information from the user input device 230. The signal processor 240 may, for example, execute any of the methods and / or sets of instructions described herein according to various embodiments.

[0045] The ultrasound imaging system 200 is operable to continuously acquire ultrasound scan data at a frame rate appropriate for the imaging situation. Typical frame rates are in the range of 20-220, but may be lower or higher. The acquired ultrasound scan data may be displayed on a display system 260 at a display rate equal to the frame rate, or at a display rate slower or faster than the frame rate. An image buffer 250 is included to store processed frames of the acquired ultrasound scan data that are not scheduled for immediate display. Preferably, the image buffer 250 is a buffer of sufficient capacity to store at least several minutes' worth of frames of ultrasound scan data. The frames of ultrasound scan data are stored according to frame order or acquisition time in a manner that facilitates retrieval of the frames. The image buffer 250 may be embodied as any known data storage medium.

[0046] In an exemplary embodiment, the signal processor 240 can include a data management module 242 that includes appropriate circuitry, interfaces, logic, and / or code that can be configured to perform and / or support various functions or operations associated with or supporting new data storage and management schemes for medical imaging solutions, as described in this disclosure.

[0047] In some implementations, the signal processor 240 (and / or components of the signal processor, such as the data management module 242) can be configured to implement and / or use artificial intelligence and / or machine learning techniques to improve and / or optimize imaging-related functions or operations. For example, the signal processor 240 (and / or components of the signal processor, such as the data management module 242) can be configured to implement and / or use deep learning techniques and / or algorithms (such as using deep neural networks (e.g., convolutional neural networks (CNNs))) and / or can utilize any suitable form of artificial intelligence processing techniques or machine learning processing capabilities (e.g., for image analysis). Such artificial intelligence image analysis can be configured, for example, to analyze acquired ultrasound images (such as to identify, segment, label, and track structures (or tissues of those structures) that meet certain criteria and / or have certain characteristics).

[0048] In an example implementation, the signal processor 240 (and / or components of the signal processor, such as the data management module 242) may be provided as a deep neural network, which may be comprised of, for example, an input layer, an output layer, and one or more hidden layers between the input layer and the output layer. Each of these layers may be comprised of multiple processing nodes, called neurons.

[0049] For example, a deep neural network may include an input layer having a neuron for each pixel or group of pixels from a scan plane of an anatomical structure, and an output layer may have neurons corresponding to a predefined number of structures or types of structures (tissues contained in the structures). Each neuron in each layer may perform a processing function and pass the processed ultrasound image information to one of a number of neurons in a downstream layer for further processing. As an example, a first layer neuron may learn to recognize edges of structures in the ultrasound image data. A second layer neuron may learn to recognize shapes based on detected edges from the first layer. A third layer neuron may learn the location of the recognized shapes relative to landmarks in the ultrasound image data. A fourth layer neuron may learn features of a particular tissue type present in a particular structure, and so on. Thus, the processing performed by the deep neural network (e.g., a convolutional neural network (CNN)) may identify biological and / or artificial structures in the ultrasound image data with a high probability.

[0050] In some implementations, the signal processor 240 (and / or components of the signal processor 240, such as the data management module 242) may be configured to execute or control at least some of the functions performed by the signal processor based on user instructions via the user input device 230. As an example, a user may provide a voice command, a probe gesture, a button press, etc. to issue specific instructions (such as to initiate and / or control various aspects of the new data management scheme, including operations using artificial intelligence (AI), and / or to provide or specify various parameters or settings associated therewith) as described in this disclosure.

[0051] The training engine 280 may include appropriate circuitry, interfaces, logic, and / or code operable to train neurons of a deep neural network of the signal processor 240 (and / or components of the signal processor, such as the data management module 242). For example, the signal processor 240 may be trained to identify particular structures and / or tissues (or types of structures and tissues) within an ultrasound scan plane, and the training engine 280 may train the signal processor's deep neural network to perform some of the required functions (such as using a database of various classified structures in ultrasound images).

[0052] As an example, the training engine 280 can be configured to utilize ultrasound images of particular structures to train the signal processor 240 (and / or components of the signal processor, such as the data management module 242) on characteristics of particular structures (such as the appearance of the edges of the structure, the appearance of the shape of the structure based on the edges, the location of the shape relative to landmarks in the ultrasound image data, etc.) and / or on characteristics of particular tissues (e.g., the softness of the particular tissue). In various embodiments, the database of training images may be stored in the archive 270 or in any suitable data storage medium. In certain embodiments, the training engine 280 and / or the training image database may be an external system communicatively coupled to the ultrasound imaging system 200 via a wired or wireless connection.

[0053] In operation, the ultrasound imaging system 200 may be used in generating ultrasound images, such as two-dimensional (2D), three-dimensional (3D), and / or four-dimensional (4D) images. In this regard, the ultrasound imaging system 200 is operable to continuously acquire ultrasound scan data at a particular frame rate appropriate for the imaging situation. For example, the frame rate may range from 30 to 70, but may also be lower or higher frame rates. The acquired ultrasound scan data may be displayed on the display system 260 at a display rate equal to the frame rate or at a slower or faster display rate than the frame rate. An image buffer 250 is included to store processed frames of the acquired ultrasound scan data that are not scheduled for immediate display. The image buffer 250 is preferably large enough to store at least several seconds' worth of frames of ultrasound scan data. The frames of ultrasound scan data are stored in a manner that allows for easy retrieval of the ultrasound scan data according to the order or time of acquisition of the ultrasound scan data. The image buffer 250 may be any known data storage medium.

[0054] In some embodiments, the ultrasound imaging system 200 can be configured to support grayscale and color-based operation. For example, the signal processor 240 is operable to perform grayscale B-mode processing and / or color processing. Grayscale B-mode processing includes processing B-mode RF signal data or IQ data pairs. For example, grayscale B-mode processing includes processing the quantity (I 2 +Q 2 ) 1 / 2 By calculating x, the envelope of the beamsummed received signal can be formed. The envelope can be subjected to additional B-mode processing (such as logarithmic compression to form the display data).

[0055] The display data may be converted to an XY format for video display. The scan converted frames may be mapped to grayscale and displayed. The B-mode frames are provided to the image buffer 250 and / or the display system 260. Color processing includes processing color based RF signal data or IQ data pairs to form frames to overlay on the B-mode frames provided to the image buffer 250 and / or the display system 260. The grayscale and / or color processing may be adaptively adjusted based on user input (selection from the user input device 230) to, for example, improve the grayscale and / or color quality of a particular region.

[0056] In some cases, ultrasound imaging produces and / or displays volumetric ultrasound images, i.e., a volumetric ultrasound image provides a three-dimensional (3D) display of an object (e.g., an organ, tissue, etc.). In this regard, in 3D (and similarly 4D) imaging, a volumetric ultrasound data set is acquired, which includes voxels corresponding to the imaged object. This can be done, for example, by transmitting sound waves at different angles and acquiring the returning sound waves, rather than simply transmitting sound waves in one direction (e.g., straight down). The reflected echoes (transmitted at different angles) are then acquired and processed (e.g., by signal processor 240) to generate a corresponding volumetric data set. The volumetric data set can be used, for example, by display 250, to create and / or display a volumetric (e.g., 3D) image. In this case, certain processing techniques must be used to provide the desired 3D perception.

[0057] For example, volume rendering techniques can be used in displaying projections (e.g., 3D projections) of volumetric (e.g., 3D) datasets. In this regard, rendering a 3D projection of a 3D dataset involves setting or defining a perceived angle in space for the object to be displayed, and then defining or calculating the necessary information (e.g., opacity and color) for every voxel of the dataset. This can be done, for example, using an appropriate transfer function to define the RGBA (red, green, blue, alpha) values ​​of each voxel.

[0058] In some embodiments, the ultrasound imaging system 200 may be configured to support implementing and using integration of Structured Report (SR) objects in Digital Imaging and Communications in Medicine (DICOM) in accordance with the present disclosure. In this regard, as described in this disclosure, a medical imaging system and / or environment may be configured to support implementing and using improved solutions for storage and management of medical image data, particularly when supporting integration of multiple SR objects based on image files, as illustrated in the exemplary use cases described in FIG. 1 and shown and described in FIG. 4.

[0059] For example, as imaging data is acquired or generated, the signal processor 240 (and / or components of the signal processor, such as the data management module 242) can store the processed image files in the archive 270, which can be configured, independently or under the control of the signal processor 240 (and / or components of the signal processor, such as the data management module 242), to apply archive-based encoding (e.g., DICOM-based encoding) to the data and then perform storage and management functions (e.g., based on the DICOM standard) (e.g., perform necessary communication functions to send the resulting encoded data objects to a corresponding storage location (local or remote)).

[0060] The archive 270 may also be configured to obtain the encoded data and therefore perform a restoration process. In this regard, the archive 270 may be configured to apply the restoration process to the previously archived data (including performing necessary communication functions to request and receive data files from a storage location (local or remote)), decode the data, and generate corresponding images for display, for example, by the display system 260. These functions may be controlled or managed by the signal processor 240 (and / or components of the signal processor, such as the data management module 242). Alternatively, the archive 270 may be configured to perform at least some of these functions independently, in which case the processor 240 may not even be aware that the data has been separated.

[0061] Further, ultrasound imaging system 200 (e.g., by processor 240 and / or components of the processor, such as data management module 242, among others) may be configured to handle multiple SR objects, and may be used to handle the integration of SR objects, among others, according to an integration scheme / methodology in accordance with the present disclosure. In this regard, as described in some embodiments, image files generated based on ultrasound imaging may be processed according to the DICOM standard for storage, management, and / or communication. As a result, when these image files are reviewed and / or analyzed, corresponding SR objects may be generated. Thus, multiple SR objects may be generated, and an integration scheme / methodology may be used to handle the integration of the SR objects, as described herein. An exemplary use case for multiple SR objects and their handling is described in detail in FIG. 4.

[0062] In one embodiment, at least a portion of the integration scheme / methodology may be performed within the ultrasound imaging system 200 by the processor 240 (and / or components of the signal processor, such as the data management module 242), which may be configured to execute applications that process or manipulate DICOM SR objects, among other things. Alternatively or additionally, at least a portion of the integration scheme / methodology may be offloaded to an external system (e.g., an example of a computer system 120 as described in FIG. 1).

[0063] Additionally, in some examples, the integration scheme / methodology and the implementation or execution of the integration scheme / methodology may require the use of advanced processing techniques (such as artificial intelligence (AI) or other machine learning techniques). In this regard, the ultrasound imaging system 200 may be configured, particularly by the processor 240 (and / or signal processor components, such as the data management module 242), to implement and / or support the use of an artificial intelligence (AI)-based learning mode in conjunction with the integration scheme / methodology. For example, the data management module 242 (and the learning engine 280) may be configured to support and use an artificial intelligence (AI)-based learning mode when executing or using the integration scheme / methodology to recognize anomalies and / or automatically perform alignment of common manual anomalies. Alternatively or additionally, at least a portion of the functionality associated with the artificial intelligence (AI)-based learning mode may be offloaded to an external system (e.g., a local dedicated computing system, a remote (e.g., cloud-based) server, etc.).

[0064] Additionally, the ultrasound imaging system 200 may be configured to support the use and handling of composite SR objects that may result from the integration schemes / methodologies as described herein. For example, the archive 270 may be configured to handle such composite SR objects when applying a restoration process as described above.

[0065] 3 is a block diagram illustrating an example use case scenario of integration of multiple Structured Report (SR) objects in Digital Imaging and Communications in Medicine (DICOM). Diagram 300 shown in FIG. 3 illustrates the relationships and handling involved in integration of multiple DICOM SR objects (SR1-SR6).

[0066] In this regard, as mentioned above, a DICOM SR object can store information related to an image file (such as findings from a medical procedure (examination) including measurements, calculations, interpretations, etc.). An SR object typically contains two required status tags, namely a "Complete" flag and a "Verified" flag. The "Complete" flag can have a value of "COMPLETE" or "PARTIAL". The "Verified" flag can have a value of "VERIFIED" or "UNVERIFIED". These tags can be used to convey information related to the content (such as responsibility for content completion). SR objects that are "COMPLETE" and "VERIFIED" can be used as a "source of truth" (e.g., for findings of an examination). However, if only "PARTIAL" SR objects exist, it is difficult for a consumer of a set of SR objects to integrate individual data elements and resolve conflicts. In this regard, since a "data element" may represent both a label and a value, there may be multiple data elements with the same label. The label includes any fixed "qualifiers" that describe the context of the value. The difficulty of integrating SR objects is especially true for specialized examinations such as echocardiography, which involve many measurements and calculations across multiple measurement sessions.

[0067] In one example of a cardiac usage scenario / examination, the SR object can be used to store findings regarding the aortic valve cardiovascular orifice area. In this regard, the aortic valve cardiovascular orifice area is encoded as follows: Measurement type: cardiovascular orifice area, Location: aortic valve, Image mode: 2D, Measurement method: planar area method, Flow direction: antegrade flow, Value: 1.391677163142, Unit: square centimeters (cm 2 ). However, such encoding is not mandatory and is not fixed, and other parties (e.g., other vendors) may measure the aortic valve cardiovascular opening area differently (e.g., omit the measurement method and add the selection state: selected average value). In the DICOM SR object, "templates" are defined by the DICOM 3.0 standard for various clinical use cases (e.g., "Adult Echocardiography").

[0068] Individual SR objects of a study may be independent of previous DICOM SR objects or may be aggregated to previous DICOM SR objects. When aggregation is used, the DICOM standard defines an optional field called "predecessor document sequence" that lists "parent" DICOM SR objects, whose contents are inherited by the new "child" DICOM SR object. However, when creating SR objects in the "predecessor document sequence", it is possible for the SR objects to "branch" if, for example, two users simultaneously create new SR objects from the same SR object. Such new objects are called "branch SR objects". However, when multiple SR objects exist in one study (the same study), several possible problem scenarios can be faced. The following table lists possible problem scenarios in such a study.

[0069] [Table 1] According to the present disclosure, a consolidation scheme / methodology can be used to handle the consolidation of multiple SR objects. In this regard, the consolidation scheme / methodology described herein can be adaptively applied (e.g., only SR objects with completion flag "PARTIAL" and the same template). The following consolidation scheme / methodology can be used to handle various possible problem scenarios detailed in the table above into a composite SR object: 1) Examine the antecedent document sequence of an SR object and discard SR objects that are parents of other SR objects. In this regard, "discarding" SR objects does not necessarily include deleting such objects, but rather these objects are simply ignored without actually being deleted. This is especially true for DICOM objects, since such objects are part of the permanent medical record and therefore it is common to avoid deleting objects from the archive. If only one SR object remains, copy it into a consolidated SR object; 2) otherwise, for each remaining SR object, process from newest to oldest and sort using the content date and content time fields. 3) For each SR object, copy each data element found into a composite SR object, and delete each data element from any raw SR objects to avoid copying them again later. Copy all duplicate findings that exist in a single SR object, since there is clinical value in knowing that the same finding has been created twice.

[0070] In the consolidation scheme, a new composite SR object is created every time a new DICOM SR object is added to a study. In this way, a composite SR object is always present in the study. Any consumer of a DICOM SR object can easily find the correct unique composite SR by inspecting the preceding document sequence.

[0071] For example, diagram 300 in Figure 3 shows an example use case where all four problem scenarios listed in the table above are present. The integration scheme processes the set of SR objects in the following steps: 1) discard SR1 and SR2, 2) process SR6, 3) process SR5, 4) process SR3 (in the case of branching, SR3 and SR2 branch off from each other), 5) process SR4 (predecessor tag was not saved in SR5), and 6) create a new composite SR object that lists all six SR objects as predecessor objects so that the SR consumer can ignore the six SR objects.

[0072] In some cases, examining an object may involve encountering and handling special use cases. For example, a special use case may involve the presence of deleted data elements, and the need to address the deleted data elements. In this regard, if a predecessor document sequence exists, the deletion can be handled automatically, since it is not present in the child SR object and the parent SR object is ignored. However, if a predecessor document sequence does not exist, the new SR object may encounter an old SR object that contains the deleted data element. There is no way to distinguish between a true deletion and the case where the SR object is obtained from different sources and content was added by both sources.

[0073] Thus, measures can be used to handle such situations. For example, the following options can be used to handle such potential deletions: 1) provide settings to control whether the potential deletion should be retained or not, 2) provide a reconciliation tool for administrators to resolve the potential deletions, 3) the reconciliation tool can incorporate a learning mechanism (e.g., an AI "learning mode") to recognize common reconciliation patterns that allow deletions to be made automatically upon certain findings (e.g., retain or remove).

[0074] In another special use case, a user may wish to keep only the most recent instance of a finding. In this regard, if a predecessor document sequence exists, the old SR object is discarded and therefore the old finding is ignored. However, if a predecessor document sequence does not exist, a means may be used to handle such a condition. For example, the following options may be used to handle multiple instances of a finding: 1) provide a setting to control whether multiple instances of a finding should be kept or only the most recent instance should be kept, 2) use an optional modifier (e.g., Maximum, Minimum, First, Last, Average) that indicates how to handle multiple finding instances if a predecessor document sequence exists, 3) provide a reconciliation tool for administrators to manage multiple instances of a finding, and 4) the reconciliation tool may incorporate a learning mechanism (e.g., an AI "learning mode") to recognize common reconciliation patterns that allow deletion to be done automatically by a particular finding (e.g., keep all or keep last).

[0075] In another specialized use case, understanding the semantics of findings may be necessary for the execution of the methodology. In this regard, it is possible that two SR objects represent the same finding instance with different sets of qualifiers. The methodology may be configured to adopt a semantic interpretation that allows recognizing these findings as the same instance. However, the methodology may also work without a semantic interpretation, resulting in some finding instances being duplicated. Furthermore, a learning mechanism (e.g., an AI "learning mode") may be configured to detect patterns of findings with identical values ​​but slightly different expressions for different vendors, allowing the findings to be automatically detected as duplicates.

[0076] The AI ​​"learning mode" may be implemented in and / or provided by appropriate components of the system, such as the signal processor 240 of the ultrasound system 200 (particularly the components of the signal processor 240 that cooperate with the learning engine 280, such as the data management module 242).

[0077] In an example implementation, an audit log can be maintained to track actions taken by an administrator or by the AI ​​mode for the special cases listed above.

[0078] Figure 4 illustrates a flow chart of an exemplary process for integration of Structured Reporting (SR) objects in Digital Imaging and Communications in Medicine (DICOM). Flow chart 400 illustrated in Figure 4 includes a number of exemplary steps (represented by blocks 402-416) that may be performed in a suitable system for integration of Structured Reporting (SR) objects in Digital Imaging and Communications in Medicine (DICOM) (e.g., medical imaging system 110 of Figure 1 or ultrasound imaging system 200 of Figure 2).

[0079] In a start step 402, the system is set up and allowed to begin operation.

[0080] In step 404, image signals may be acquired during a medical imaging examination. This may be performed by transmitting certain signals and then receiving echoes of these signals. For example, in an ultrasound imaging system (e.g., ultrasound system 200 of FIG. 2), acquiring image signals may include transmitting ultrasound signals and receiving corresponding echoes of the ultrasound signals.

[0081] In step 406, the image signals (e.g., received echoes of ultrasound signals) may be processed (e.g., by the display / control unit 114 of the medical imaging system 110 or the signal processor 240 of the ultrasound system 200) to generate corresponding imaging data for use in generating a corresponding medical image (e.g., an ultrasound image). In some cases, at least a portion of the data generation may be performed in a system different from the system in which the image signals are captured.

[0082] In step 408, the generated image data (e.g., image files) may be processed and archived, among other things, according to a particular standard (such as DICOM), which may include encoding the image data (e.g., DICOM-based encoding). In some embodiments, at least a portion of the archiving may be performed on a system different from the system where the image signals are captured and / or the image data is generated.

[0083] In step 410, multiple objects (eg, DICOM SR objects) may be generated based on imaging data from, for example, multiple users and / or multiple runs / reviews (including multiple runs / reviews by the same user).

[0084] Once such a plurality of objects has been created, object consolidation can be performed. Object consolidation begins at step 412 where each object of the plurality of DICOM SR objects is evaluated. This evaluation includes determining whether the object is a parent of another object of the plurality of objects, and if it is determined that the object is a parent of another object, the object is discarded. At step 414, a check is made to determine whether all objects have been evaluated, proceeding to step 416 if all objects have been processed (i.e., "yes"), or returning to step 412 if not all objects have been processed (i.e., "no").

[0085] In step 416, a composite object (e.g., a DICOM SR composite object) may be created. In this regard, creating a composite object includes copying one object into an integrated object if only one object remains after evaluation, and processing the remaining objects if multiple remaining objects remain after evaluation, said processing being performed in order from newest to oldest, and said processing including, for each remaining object, copying each data element found into a composite object and discarding (from the processing list) the remaining object.

[0086] An exemplary method for managing medical data, according to the present disclosure, includes applying, by a processor, an integration process for integrating a plurality of objects, the plurality of objects being generated based on the same medical imaging data, the integration process including evaluating each object of the plurality of objects, the evaluating including determining whether the object is a parent of another object of the plurality of objects, and discarding the object if the object is a parent of another object, the evaluating, and generating a composite object based on the plurality of objects, the generating including, if only one object remains after the evaluating, copying the one object into an integrated object, and if multiple remaining objects remain after the evaluating, processing the multiple remaining objects, the processing being performed in order from newest object to oldest object, and the processing including, for each remaining object, copying each data element found into a composite object and discarding the remaining object.

[0087] In an exemplary embodiment, the medical dataset includes a Digital Imaging and Communications in Medicine (DICOM) based dataset.

[0088] In an exemplary embodiment, each of the plurality of objects includes a Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) object.

[0089] In an exemplary embodiment, the method further includes sorting the remaining objects in the plurality of remaining objects from newest object to oldest object based on the content date field and the content time field of the DICOM SR object.

[0090] In an exemplary embodiment, evaluating the object includes determining a leading unique identifier (UID) sequence for the object and determining whether the object is a parent of another object based on a matching of a leading document sequence.

[0091] In an exemplary embodiment, processing the plurality of remaining objects further includes copying overlapping findings that exist in a single object.

[0092] In an exemplary embodiment, the method further includes processing the plurality of remaining objects including discarding findings that overlap with another remaining object already processed.

[0093] In an exemplary embodiment, the method further includes utilizing artificial intelligence when applying said integration process.

[0094] In an exemplary embodiment, the method further includes applying artificial intelligence based learning during processing of the plurality of remaining objects to recognize matching patterns common to findings.

[0095] In an exemplary embodiment, the method further includes configuring at least a portion of the integration process based on user input.

[0096] In an exemplary embodiment, the method further includes maintaining an audit log, the audit log including data tracking actions taken in connection with the consolidation of the plurality of objects.

[0097] An exemplary system for managing medical data, according to the present disclosure, includes at least one processing circuitry calibrated to apply an integration process to integrate a plurality of objects, the plurality of objects being generated based on the same medical imaging data, the at least one processing circuitry, upon application of the integration process, includes: evaluating each object of the plurality of objects, the evaluating including determining whether the object is a parent of another object of the plurality of objects, and discarding the object if the object is a parent of another object; and generating a composite object based on the plurality of objects, the generating including copying the one object into an integrated object if only one object remains after the evaluating, and processing the plurality of remaining objects if multiple remaining objects remain after the evaluating, the processing being performed in order from newest object to oldest object, and the processing including, for each remaining object, copying each data element found into a composite object and discarding the remaining object.

[0098] In an exemplary embodiment, each of the plurality of objects comprises a Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) object; The at least one processing circuit is configured to sort the remaining objects in the plurality of remaining objects from newest to oldest based on a content date field and a content time field based on the DICOM SR object.

[0099] In an exemplary embodiment, each of the plurality of objects includes a Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) object, and the at least one processing circuit, when evaluating the object, determines a DICOM-based leading unique identifier (UID) sequence of the object; and The method is configured to perform a determination of whether the object is a parent of another object based on a match of the antecedent document sequence.

[0100] In an exemplary embodiment, the at least one processing circuit is configured to copy overlapping findings present in a single object when processing the plurality of remaining objects.

[0101] In an exemplary embodiment, the at least one processing circuit is configured, when processing the plurality of remaining objects, to discard findings that overlap with another remaining object already processed.

[0102] In an exemplary embodiment, the at least one processing circuit is configured to utilize artificial intelligence when applying the integration process.

[0103] In an exemplary embodiment, the at least one processing circuit is configured to utilize and / or apply artificial intelligence based learning to recognize common matching patterns in findings during processing of the plurality of remaining objects.

[0104] In an exemplary embodiment, the at least one processing circuit is configured to set or adjust at least a portion of the integration process based on user input.

[0105] In an exemplary embodiment, the at least one processing circuit is configured to maintain an audit log, the audit log including data tracking actions taken in connection with the consolidation of the plurality of objects.

[0106] As used herein, the terms "circuit" and "circuitry" refer to physical electronic components (e.g., hardware) and any software and / or firmware ("code") that may comprise, be executed by, or otherwise be associated with hardware. As used herein, for example, a particular processor and memory may comprise a first "circuit" when executing one or more lines of a first code, and a second "circuit" when executing one or more lines of a second code. As used herein, "and / or" means any one or more of the items in the list joined by "and / or". As an example, "x and / or y" means any element of the three element set {(x), (y), (x,y)}. In other words, "x and / or y" means "one or both of x and y". As another example, "x, y, and / or z" means any element of the seven element set {(x), (y), (z), (x,y), (x,z), (y,z), (x,y,z)}. In other words, "x, y and / or z" means "one or more of x, y and z." As used herein, the terms "block" and "module" represent functions that can be performed by one or more circuits. As used herein, the term "exemplary" is used in the sense of a non-limiting example, instance, or illustration. As used herein, the term "for example" refers to a list of one or more non-limiting examples, instances, or illustrations. As used herein, a circuit is "operable" to perform a function whenever the circuit includes the necessary hardware (and code, if necessary) to perform the function, regardless of whether performance of the function is disabled or enabled (e.g., by some user-adjustable setting, factory trim, etc.).

[0107] Other embodiments of the invention may provide a non-transitory computer readable medium and / or storage medium, and / or a non-transitory machine readable medium and / or storage medium storing machine code and / or computer programs having at least one code section executable by a machine and / or computer to cause the machine and / or computer to perform the processes described herein.

[0108] Thus, the present disclosure can be realized in hardware, software, or a combination of hardware and software. The present invention can be realized in a centralized manner in at least one computing system, or in a distributed manner where different elements are distributed across multiple interconnected computing systems. Any kind of computing system or other device is suitable for performing the methods described herein. A typical combination of hardware and software may be a general-purpose computing system having a program or other code that, when loaded and executed, controls the computing system to perform the methods described herein. Another typical implementation may be configured in an application specific integrated circuit or chip.

[0109] Various embodiments according to the present disclosure may also be incorporated into a computer program product, which contains all the functions enabling the implementation of the methods described herein and is capable of implementing these methods when loaded into a computer system. A computer program in this specification means a set of instructions expressed in any language, code or notation, intended to cause a system having information processing capabilities to perform a specific function directly, or to perform a specific function after a) being converted into another language, code or notation, b) being reproduced in a different material form, or both.

[0110] Although the invention has been described with reference to specific embodiments, those skilled in the art will recognize that various modifications can be made and equivalents substituted without departing from the scope of the invention. In addition, many modifications can be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is not intended that the invention be limited to the particular embodiments disclosed, but rather, it is intended to include all embodiments falling within the scope of the appended claims.

Claims

1. 1. A method for managing medical data, the method comprising: applying, by a processor, an integration process to integrate the plurality of objects; the plurality of objects are generated based on the same medical imaging data; The integration process comprises: evaluating each object of the plurality of objects, said evaluating comprising: determining whether the object is a parent of another object of the plurality of objects; and said evaluating including destroying said object if said object is a parent of another object; and generating a composite object based on the plurality of objects, said generating comprising: if only one object remains after said evaluating, copying said one object into the unified object; and if a plurality of remaining objects remain after said evaluating, processing said plurality of remaining objects; Including, said processing being performed in order from newest object to oldest object; The processing step comprises: copying each data element found into a composite object; and Discarding said remaining objects. A method comprising:

2. The method of claim 1, wherein the medical dataset includes a Digital Imaging and Communications in Medicine (DICOM)-based dataset.

3. The method of claim 1 , wherein each of the plurality of objects comprises a Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) object.

4. 4. The method of claim 3, further comprising sorting the remaining objects in the plurality of remaining objects from newest object to oldest object based on a content date field and a content time field of the DICOM SR object.

5. 4. The method of claim 3, wherein evaluating the object includes determining a leading unique identifier (UID) sequence of the object and determining whether the object is a parent of another object based on a match of a leading document sequence.

6. The method of claim 1 , wherein processing the plurality of remaining objects further comprises copying overlapping findings that exist in a single object.

7. The method of claim 1 , wherein processing the plurality of remaining objects further comprises discarding findings that overlap with another remaining object already processed.

8. The method of claim 1 , further comprising utilizing artificial intelligence when applying the integration process.

9. The method of claim 8 , further comprising applying artificial intelligence based learning during processing of the plurality of remaining objects to recognize common matching patterns in findings.

10. The method of claim 1 , further comprising configuring at least a portion of the integration process based on user input.

11. The method of claim 1 , further comprising maintaining an audit log, the audit log including data tracking actions taken in connection with consolidating the plurality of objects.

12. A system for managing medical data, comprising: at least one processing circuit configured to apply a merging process for merging a plurality of objects; the plurality of objects are generated based on the same medical imaging data; The at least one processing circuit, when the integration process is applied, evaluating each object of the plurality of objects, said evaluating comprising: determining whether the object is a parent of another object of the plurality of objects; and said evaluating including destroying said object if said object is a parent of another object; and generating a composite object based on the plurality of objects, said generating comprising: if only one object remains after said evaluating, copying said one object into the unified object; and if a plurality of remaining objects remain after said evaluating, processing said plurality of remaining objects; Including, said processing being performed in order from newest object to oldest object; The processing step comprises: copying each data element found into a composite object; and Discarding said remaining objects. Including, the system.

13. each of the plurality of objects includes a Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) object; 13. The system of claim 12, wherein the at least one processing circuit is configured to sort the remaining objects in the plurality of remaining objects from newest to oldest based on a content date field and a content time field based on a DICOM SR object.

14. each of the plurality of objects includes a Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) object; The at least one processing circuit, when evaluating the object, determining a DICOM-based leading unique identifier (UID) sequence for the object; and determining whether the object is a parent of another object based on a match of a sequence of preceding documents; The system of claim 12 configured to execute:

15. The system of claim 12 , wherein the at least one processing circuitry is configured to copy overlapping findings present in a single object when processing the plurality of remaining objects.

16. 13. The system of claim 12, wherein the at least one processing circuitry is configured, when processing the plurality of remaining objects, to discard findings that overlap with another remaining object that has already been processed.

17. The system of claim 12 , wherein the at least one processing circuit is configured to utilize artificial intelligence when applying the integration process.

18. 20. The system of claim 17, wherein the at least one processing circuitry is configured to utilize and / or apply artificial intelligence based learning to recognize common matching patterns in findings during processing of the plurality of remaining objects.

19. The system of claim 12 , wherein the at least one processing circuit is configured to set or adjust at least a portion of the integration process based on user input.

20. 13. The system of claim 12, wherein the at least one processing circuit is configured to maintain an audit log, the audit log including data tracking actions taken in connection with the consolidation of the plurality of objects.

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