Method and system for automated positioning of a medical diagnostic device

A method and system are provided for automatically positioning a medical diagnostic device. The method and system use a database of images that includes reference images and user-preferred images to position the medical diagnostic device. The medical diagnostic device in an initial position is moved to a final position based on the images in the database.

CROSS REFERENCE TO RELATED APPLICATIONS

This present patent document is a § 371 nationalization of PCT Application Serial Number PCT/EP2016/051988, filed Jan. 29, 2016, designating the United States, which is hereby incorporated in its entirety by reference. This patent document also claims the benefit of 197/KOL/2015, filed on Feb. 23, 2015 which is also hereby incorporated in its entirety by reference.

FIELD

Embodiments relate to a method and system for automated positioning of a medical diagnostic device based on one or more images from a database.

BACKGROUND

Alignment of medical diagnostic device during intervention or surgery is a time consuming process. A physician requires experience to reduce radiation exposure time for a patient. In the current diagnostic devices allow a user to align the diagnostic device manually. Moving the medical diagnostic device manually to arrive at an optimal position is cumbersome and requires experience.

SUMMARY

The scope of the present invention is defined solely by the appended claims and is not affected to any degree by the statements within this summary. The present embodiments may obviate one or more of the drawbacks or limitations in the related art. Embodiments provide for automatically moving a medical diagnostic device to a desired position allowing a user to save time and effort in manually adjusting the position of the medical diagnostic device.

A method for positioning a medical diagnostic device is provided. The method includes determining, by a processor, a current position of a medical diagnostic device (e.g. medical imaging device) by comparing a current image with at least one reference image in a first database. The first database may be a database of reference images. The reference images may include images of a subject at various positions of the medical diagnostic device. Further, the method includes determining a target position coordinates by comparing the current image with at least one preferred image in a second database and computing a similarity score. The second database is a database of user preferred images to which the diagnostic device needs to be repositioned. The similarity score is computed based on the similarity in the features of the images as well as the position coordinates. The similarity score is a measure of the closeness of the medical diagnostic device to the target position. Further, motion parameters of a target position coordinates of the medical diagnostic device are computed iteratively, based on the similarity score. The motion parameters are calculated based on the position coordinates of the images from at least one of the first and the second database. The motion parameters are generated such that the medical diagnostic device eventually reaches the target position. Finally, a movement of the medical diagnostic device is actuated to arrive at the target position based on the motion parameters. The target position may be a view of the subject's anatomy as recorded in the user preferred image.

In an embodiment, the similarity score is based on the similarity between the features of the first image and the at least one reference image. Further, the similarity score may also depend upon position coordinates associated with the first image and the reference image.

In another embodiment, one or more intermediate position coordinates are computed, resulting in one or more intermediate positions, if the similarity score is below a threshold value. When the similarity score is above a threshold value, the medical diagnostic device will have reached the target position.

In another embodiment, the first image is updated when the imaging device is in the one or more intermediate positions. The medical diagnostic device captures a new image at every intermediate position and compares the new image with a reference image to track the movement of the medical diagnostic device towards the target position.

In another embodiment, the second database includes one or more user preferred images generated during a manual operation of the medical diagnostic device. The user preferred images may be obtained by the user during the manual operation of the medical diagnostic device. The user may desire the same view to be repeated by automatic positioning.

In another embodiment, machine learning models are used for receiving feedback position coordinates in case the target position is changed manually. When the user manually adjusts the position of the medical diagnostic device the position coordinates, the feedback is recorded and analyzed to make adjustment automatically next time a similar target position is desired.

In another embodiment, personalized position coordinates and motion parameters are generated for different users based on machine learning models. The user profile may be stored and the most common target positions may be automatically retrieved based on the user profile.

In an embodiment, the images in the first and second databases include position coordinates as meta information. The position coordinates translate the region of the anatomy in the image to a position of the medical diagnostic device.

In an embodiment, a system for performing positioning of the medical diagnostic device is provided to reduce the time and effort for positioning the medical diagnostic device. The system includes a processor and a memory coupled with the processor. The memory includes processor executable instructions in the form of position guidance module for determining a current position of the medical diagnostic device by comparing a current image with at least one reference image in a first database. The position guidance module is configured for determining a target position coordinates by comparing the current image with at least one preferred image in a second database and computing a similarity score. Thereafter, the position guidance module is configured to compute motion parameters of a target position coordinates of the medical diagnostic device iteratively, based on the similarity score. The position guidance module is configured to compute the position coordinates of the medical diagnostic device iteratively. At every iteration, the medical diagnostic device is positioned closer to the target position. Further, the position guidance module is configured to actuate movement of the medical diagnostic device to the target position based on the motion parameters. For example, the position guidance module the motion coordinates may be electrical signals that actuate one or more motors of the medical diagnostic device to move in a direction.

In an embodiment, the position guidance module is configured to compute the similarity score based on the similarity between at least one of the current image and the preferred image, and the motion coordinates associated with the current image and the preferred image.

In another embodiment, the position guidance module is configured to use machine learning models to incorporate feedback based on a change in target position. The change in the position from the target position, that the user performs manually, is analyzed and the target position coordinates may be updated to the new target position based on the manual fine tuning of the user.

In another embodiment, the position guidance module is configured to compute one or more intermediate position coordinates and intermediate motion parameters based on the similarity score. The position guidance is an iterative process where the motion coordinates are calculated based on the similarity between a current image and a preferred image. The position guidance module is configured to generate a similarity score based on the similarity between the current image and a preferred or target image. When the similarity score exceeds a threshold value then the target position of the medical diagnostic device is met.

In another embodiment, the position guidance module is configured to generate personalized position coordinates and motion parameters for different users based on machine learning models. The machine learning models may be employed to learn the positioning requirements of different users and automatically position the medical diagnostic device to the requirement when a particular user profile is invoked. In another embodiment, the medical diagnostic device is a medical imaging device or a patient care device.

DETAILED DESCRIPTION

Various embodiments are described with reference to the drawings, where like reference numerals are used to refer to like elements throughout. In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide thorough understanding of one or more embodiments. Such embodiments may be practiced without these specific details.

FIG. 1illustrates an exemplary device1for positioning a medical diagnostic device, in accordance with an embodiment. The device1includes a processor2, a memory4, a storage unit6, an Input/Output (I/O) module12, and a communication module14. The components of the device1are communicatively coupled using a communication bus13. Further, the storage unit6may include image databases8and10that include one or more images used in positioning the medical diagnostic device.

The processor2may be any type of computational circuit, such as, but not limited to, a microprocessor, microcontroller, complex instruction set computing microprocessor, reduced instruction set computing microprocessor, very long instruction word microprocessor, explicitly parallel instruction computing microprocessor, graphics processor, digital signal processor, or any other type of processing circuit. The processor2may also include embedded controllers, such as generic or programmable logic devices or arrays, application specific integrated circuits, single-chip computers, and the like.

The memory4may be volatile memory and non-volatile memory. The memory4may be coupled for communication with the processor4. The processor2may execute instructions and/or code stored in the memory4. A variety of computer-readable storage media may be stored in and accessed from the memory4. The memory4may include any suitable elements for storing data and machine-readable instructions, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, a hard drive, a removable media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like. In the present embodiment, the memory includes a position guidance module5that is stored in the form of machine-readable instructions on any of the above-mentioned storage media and may be in communication with and executed by the processor2. The position guidance module5further includes modules that, when executed by the processor2, result in the automatic positioning of the medical diagnostic device based on one or more images in the database. The functionalities of the modules are described in greater detail in conjunction withFIG. 2.

The storage unit8may be a non-transitory storage medium that stores the images. In an embodiment, the storage unit8may store image databases. The storage unit8may include a database of reference images8and a database of user preferred images10. The images from the databases8and10are used for positioning the medical diagnostic device. The communication interface14allows the device1to communicate with other connected devices via wired or wireless communication protocol known in the art. The input/output unit12may include inputs such as a keypad, a touch sensitive display configured to receive input signal, and outputs such as a speaker, a printer, or a display for outputting, for example, the model of the vessel stent. The communication bus14provides an interconnect between the processor4, the memory6, the storage unit8, the communication interface7, and the input/output unit10.

FIG. 2illustrates an exemplary block diagram of position guidance module5, in accordance with an embodiment. The position guidance module5is configured to position the medical diagnostic device to a target position based on reference images and user preferred images. The medical diagnostic device may include, for example, an imaging modality or a patient care device. The position guidance module5includes a current position determination module22, a target position determination module24, a motion parameter computation module26, and an actuation module28. In an embodiment, the current position determination module22is configured to determine a current position of the medical diagnostic device based on a current image. The current image may be an image capturing the current position of the medical diagnostic device. For example, the resting position of the medical diagnostic device may be facing a facial region of a patient. The current image may capture a facial region of the subject. The current image is compared with one or more reference images to determine the current position of the medical diagnostic device. The database of reference images8is created by storing images captured by the medical diagnostic device with possible positions with respect to a patient. The database of reference images8may be manually created. Further, the target position determination module24is configured to determine a target position of the medical diagnostic device as preferred by a user. In order to determine the target position, the database of user preferred images10is used. The database of user preferred images10may include one or more images of desired views of users using the medical diagnostic device. The images in the database8and database10may include position coordinates as meta information. The position coordinates may be used to determine the path of the medical diagnostic device. The target position is determined based on the user and the corresponding view the user is desired to have. The target position of the medical diagnostic device may be manually adjusting the position of the medical diagnostic device and generating a target image. For example, a user may require a target position of the medical diagnostic device facing the lung region of the patient. In such a case, the user may manually adjust the medical diagnostic device to the desired lung region and capture the image. The image is then stored in the database of user preferred images10, that may be accessed by the target position determination module26. The target position determination module26computes the target position coordinates based on the comparison between the current image and the target image. Further, the target position determination module26computes a similarity score between the current image and the user preferred image. The similarity score may be based on the features of the images or the motion coordinates associated with the images. For example, if the current position of the medical diagnostic device is facing the leg portion of the patient and the user preferred position is the head portion of the patient, then the similarity score will be smaller or lower and position coordinates are calculated to move the medical diagnostic device towards the head portion of the patient. Similar computation may be performed based on the position coordinates associated with the current image and the user preferred image. In another example, the target position coordinates are computed iteratively while traversing towards the target position. For example, the medical diagnostic device facing a leg portion of the patient may iteratively compare the images while traversing from the leg portion to the head portion in order to confirm that the medical diagnostic device is traversing in the right path.

Upon determining the target position coordinates, motion parameters associated with the target position coordinates are computed by the motion parameter computation module26. The motion control of the medical diagnostic device may be performed by a Programmable Logic Controller (PLC) (not shown). The motion parameters may be computed for other motion control mechanisms. The motion parameters may include signals to the motors of the medical diagnostic device to change the position of the medical diagnostic device closer to the target position. For example, the target position coordinates may indicate that the medical diagnostic device is to be moved towards the right from the current position. The motion parameter computation module26determines the values to be provided to the PLC so that the medical diagnostic device moves closer to the desired target position. The actuating module28is configured to move the medical diagnostic device towards the target position. The actuating module28may control the mechanical parts of the medical diagnostic device that provide the movement thereof.

During the traversal of the medical diagnostic device from the current position to the target position, there may be one or more intermediate positions. For example, when the medical diagnostic device traverses from the leg portion of the patient towards the head portion, there may be intermediate positions taken by the medical diagnostic device. The target position determination module26iteratively checks the current position and the target position to realign the movement of the medical diagnostic device. The deviation of the medical diagnostic device from the target position is determined by the similarity score. For example, the similarity score may range from 0 to 1, where 0 indicates that the position is heavily deviating from the target position and 1 indicates that the target position is achieved. Accordingly, a threshold value may be set in the target position determination module to indicate if the target position is to be recalculated. In an embodiment, the threshold value of the similarity score is set at 0.95. Further, one or more machine learning models may be implemented in the target position determination module26. The machine learning models implemented in the target position determination module26may include, for example, Support Vector Machine (SVM), Bayesian Models, deep neural networks, random forests and the like. The machine learning models enable the target position determination module26to learn from the deviations made by the medical diagnostic device and gradually reduce the deviations. The machine learning models may be trained using reference images and user preferred images to increase the accuracy and reliability.

In an embodiment, the machine learning models may be used to provide motion guidance to the medical diagnostic device based on user profiles. The position guidance module5may include a way (e.g., a device) to create user profiles and submit user preferred images separately. The machine learning models implemented in the position guidance module5learn the frequently used positions of the users and suggest the target positions based on the user profile without requiring new user preferred images. Further, the machine learning models may analyze the manual changes performed after reaching a target position and fine tune the target position of the medical diagnostic device.

FIG. 3illustrates an exemplary system30for positioning the diagnostic medical device, in accordance with an embodiment. The system30includes the device1as a central server. The device1has access to the database of reference images8and database of user preferred images. The device1is communicatively coupled with one or more medical diagnostic devices36.1-36.N through a network34. The network34may be any of known wired or wireless networks. The medical diagnostic devices may include imaging modalities such as MRI scanning devices, Ultrasound scanning devices, X-ray based devices and PET scanning devices. The motion of the medical diagnostic device may be performed by an electromechanical system that includes, for example, programmable logic controllers (PLCs), stepper motor drives, and other similar devices. The medical diagnostic devices may include patient care devices such as a ventilator. The device1accesses the database8and database10for providing motion guidance to the medical diagnostic devices36.1-36.N. The images in the database8and the database10may be segregated based on the type of the medical diagnostic devices36.1-36.N. Further, the images in the database8and the database10may be grouped based on the user profiles. The position guidance module5stored in the device1may position the medical diagnostic devices36.1-36.N, as described above with reference toFIG. 2.

FIG. 4illustrates an exemplary block diagram40of positioning the medical diagnostic device36based on reference images, in accordance with an embodiment. InFIG. 4, a current image42, with corresponding position coordinates as meta information, is received by the device1. The device1may receive the current image42from a medical diagnostic device. Further, the device1accesses the database of reference images8to determine the matching reference image44. The matching reference image includes the associated position coordinates45as meta information. Using the matching reference image, the current position coordinates of the medical diagnostic36is determined. In an example, the current image42capture by the medical diagnostic device36may include a leg portion of the patient. The database of reference images8is searched for a substantially matching image44. The similarity score is computed, and the position coordinates are determined based on the matching reference image44.

FIG. 5illustrates an exemplary block diagram of positioning the medical diagnostic device based on user-preferred images, in accordance with an embodiment. Upon determining the current position of the medical diagnostic device36, the database of user-preferred images10is accessed, by device1, to determine the target position of the medical diagnostic device. The device1retrieves a user preferred image48having associated position coordinates49. The device1compares the current image42with the user preferred image48and computes a similarity score. Based on the similarity score, the device1communicates motion parameters52to the medical diagnostic device30. The medical diagnostic device36is moved, based on the motion parameters52, to match with the user preferred image48. The movement of the medical diagnostic device36may be performed iteratively referring to the reference images and user preferred images to guide the motion of the medical diagnostic device36. If any manual adjustments in the target position are performed by the user, the adjustments are provided to the device1as feedback signal53. The feedback signal53is analyzed by the machine learning models to fine tune the target position during subsequent positioning of the medical diagnostic device36.

FIG. 6illustrates a schematic60of one or more exemplary paths (62,64) traversed by the medical diagnostic device, in accordance with an embodiment. The device1may cause the medical diagnostic device36to traverse a path while positioning the medical diagnostic device to a target position. InFIG. 6, an initial position is defined as I and the target position is defined as T. The device1may guide the medical diagnostic device from I to T through paths such as62and64. Path62is more laborious than path64. Initially, the device1may guide the medical diagnostic device along path62. The machine learning models of the position guidance module5, over many such traversals, learns that the path64is easier and hence adopts the path64for subsequent traversals. The path64may be set as a favorite for a particular user who requires the target position T more often.FIG. 6illustrates an exemplary two dimensional case, whereas the motion of the medical diagnostic device is in the three dimensional space and may be extended to six dimensions when orientation parameters are considered.

FIG. 7illustrates an exemplary block diagram70of method acts involved in positioning the medical diagnostic device, in accordance with an embodiment. At act72, a current position of the medical imaging device is determined by comparing a current image with at least one reference image in a first database. At act74, target position coordinates and a similarity score are determined, iteratively, by comparing the current image with at least one of the reference image and a user preferred image in a second database (e.g., the reference image and/or the user preferred image). The target position coordinates are determined iteratively by traversing along a path based on the similarity score. The similarity score is a measure of the closeness of the current position with the target position. There may be one or more intermediate positions of the medical diagnostic device before arriving at the target position. Further, one or more machine learning models may be employed to learn the target positioning and fine tuning. In an example, the machine learning algorithms are configured to learn motion parameters and cause the medical diagnostic device to move from one position to the next in optimal steps. Further, machine learning models may be used to provide personalized target positioning based on user profiles. At act76, motion parameters associated with the target position coordinates of the medical diagnostic device are computed. The motion parameters are the signals provided to the PLC units (not shown) of the medical diagnostic devices in order to actuate movement. Based on the target position coordinates, the motion parameters may vary. At act78, movement of the medical diagnostic device to the target position is actuated based on the motion parameters. The mechanical components responsible for the motion of the medical diagnostic device are actuated.

The method and system disclosed herein reduces exposure of patients to radiation dosage during alignment of the medical diagnostic device. The target position is reached by taking a minimum number of images. The method eliminates the time and energy expended by physicians in manually adjusting the view of the medical diagnostic device. The best view or position is attained in less time. Further, maintaining individual profiles of views coupled with machine learning models provides personalized target view positioning. The method and system minimizes the time required for interventions by positioning the medical device in a short time interval.