Ultrasound device capable of performing both ultrasound imaging and focused ultrasound procedure

Abstract

An ultrasound device according to one embodiment of the present disclosure is disclosed. The ultrasound device may comprise: a main body module including an ultrasound scanner for acquiring an ultrasound image; and a mounting module which is mounted on the main body module and which generates focused ultrasound (FU) waves in a target area, wherein the main body module and the mounting module are coupled such that at least some of the ultrasonic waves generated by the ultrasound scanner of the main body module pass through the inside of the mounting module and are emitted at the target area.

Description

Ultrasound device capable of performing both ultrasound imaging and focused ultrasound procedures

[0001] The present invention relates to an ultrasound device, and more specifically, to an ultrasound device capable of simultaneously performing ultrasound imaging and focused ultrasound procedures.

[0002]

[0003] Focused Ultrasound (FU) is a technology that utilizes High-Intensity Focused Ultrasound (HIFU) to non-invasively treat human tissues or apply them to cosmetic procedures, and is utilized in various medical and cosmetic fields such as improving skin elasticity, fat breakdown, and tumor treatment. However, conventional High-Intensity Focused Ultrasound (HIFU) procedures often rely on the operator's experience and skill, and there are difficulties in accurately identifying target tissues (e.g., fat layer, fascia layer, etc.) and optimizing energy delivery.

[0004] Until now, the practice of establishing a treatment plan using ultrasound images followed by HIFU treatment has been used only in the medical field for treating diseases such as tumors, and has not been introduced in the cosmetic field. Furthermore, existing ultrasound devices consist of an ultrasound scanner and a focused ultrasound generator configured independently, or there were limitations in image acquisition and procedure precision during the ultrasound beamforming process. In particular, there were problems with setting the distance between the target area and the contact surface of the device and accurately transmitting focused ultrasound.

[0005] Korean Patent Publication No. 10-2014-0113172 (September 24, 2014) discloses a method and apparatus for establishing an ultrasonic irradiation plan and an ultrasonic irradiation method.

[0006]

[0007] The present disclosure aims to provide an integrated ultrasound device capable of simultaneously performing ultrasound imaging and focused ultrasound procedures within a single device.

[0008] Meanwhile, the technical problem that the present disclosure aims to solve is not limited to the technical problem mentioned above, and various technical problems may be included within the scope obvious to a person skilled in the art from the contents described below.

[0009]

[0010] A computing device according to one embodiment of the present disclosure for realizing the aforementioned objectives is disclosed. The ultrasonic device comprises: a main body module including an ultrasonic scanner for acquiring an ultrasonic image; and a mounting module mounted on the main body module and configured to generate focused ultrasound (FU) in a target area, wherein the main body module and the mounting module may be coupled such that at least a portion of the ultrasound generated by the ultrasonic scanner of the main body module passes through the interior of the mounting module and is irradiated in the target area.

[0011] In one embodiment, the main body module may be a handpiece module, and the mounting module may be a cartridge module.

[0012] In one embodiment, the handpiece module and the cartridge module may be combined such that at least a portion of the beamforming geometry of the ultrasound generated by the ultrasonic scanner of the handpiece passes through the inside of the cartridge module.

[0013] In one embodiment, one edge of the cartridge module is configured to contact the skin, and the target area may include an area spaced apart from the one edge of the cartridge module to the outside of the ultrasonic device.

[0014] In one embodiment, the cartridge module is coupled to the lengthwise or L-shaped direction of the handpiece module, and the focused ultrasound generator of the cartridge module and the ultrasound generator of the ultrasound scanner included in the handpiece module may be arranged so as not to overlap each other in the lengthwise or L-shaped direction.

[0015] In one embodiment, the ultrasonic generator of the ultrasonic scanner included in the handpiece module is configured to irradiate ultrasound in the longitudinal direction, and the focused ultrasonic generator of the cartridge module may be configured to irradiate the focused ultrasound in a direction not parallel to the longitudinal direction.

[0016] In one embodiment, at least a portion of the beamforming geometry of the ultrasound generated by the ultrasound scanner of the handpiece and the focusing point generated by the focusing ultrasound generator of the cartridge module may simultaneously exist in the target area.

[0017] In one embodiment, the ultrasonic device further includes a motor module for changing the position of the focusing point of the focused ultrasound generated by the mounting module, and the motor module may be configured to move in multiple axial directions.

[0018]

[0019] The present disclosure integrates an ultrasound scanner of a main body module and a focused ultrasound generator of a mounting module, thereby enabling real-time imaging and precise focused ultrasound procedures to be performed simultaneously with a single device, which can significantly improve the accuracy and efficiency of medical diagnosis and treatment.

[0020] Meanwhile, the effects of the present disclosure are not limited to those mentioned above, and various effects may be included within the scope obvious to a person skilled in the art from the contents described below.

[0021]

[0022] FIG. 1 is a block diagram of a computing device for controlling a motor module included in an ultrasonic device according to one embodiment of the present disclosure.

[0023] FIG. 2 illustrates an exemplary structure of an artificial intelligence-based model according to one embodiment of the present disclosure.

[0024] FIG. 3 is a side view of an ultrasonic device according to one embodiment of the present disclosure.

[0025] FIG. 4 is a diagram showing the beamforming geometry of an ultrasound scanner according to one embodiment of the present disclosure and the focusing point generated by a focused ultrasound generator.

[0026] FIG. 5 is an enlarged view of the beamforming geometry of an ultrasound scanner and the focusing point generated by a focused ultrasound generator according to one embodiment of the present disclosure.

[0027] FIG. 6 is a perspective view showing the beamforming geometry of an ultrasound scanner and a focusing point generated by a focused ultrasound generator according to one embodiment of the present disclosure.

[0028] FIG. 7 is a flowchart illustrating a method for generating procedure planning information using focused ultrasound according to one embodiment of the present disclosure.

[0029] FIG. 8 is a brief and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0030]

[0031] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to provide an understanding of the present disclosure. However, it is evident that these embodiments can be practiced without such specific descriptions.

[0032] As used herein, terms such as “component,” “module,” “system,” etc. refer to computer-related entities, hardware, firmware, software, combinations of software and hardware, or executions of software. For example, a component may be, but is not limited to, a procedure executed on a processor, a processor, an object, an execution thread, a program, and / or a computer. For example, both an application executed on a computing device and the computing device itself may be a component. One or more components may reside within a processor and / or an execution thread. A component may be localized within a single computer. A component may be distributed among two or more computers. Additionally, these components may be executed from various computer-readable media having various data structures stored therein. Components may communicate through local and / or remote processes, for example, according to signals having one or more data packets (e.g., data from a component interacting with another component in a local system or distributed system, and / or data transmitted through signals to other systems and networks such as the Internet).

[0033] Furthermore, the term "or" is intended to mean an implicit "or" rather than an exclusive "or." That is, unless otherwise specified or evident from the context, "X uses A or B" is intended to mean one of the natural implicit substitutions. In other words, if X uses A; if X uses B; or if X uses both A and B, "X uses A or B" may apply to any of these cases. Additionally, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the enumerated related items.

[0034] Additionally, the terms “comprising” and / or “comprising” should be understood to mean that such features and / or components are present. However, the terms “comprising” and / or “comprising” should be understood not to exclude the presence or addition of one or more other features, components and / or groups thereof. Furthermore, unless otherwise specified or clearly evident from the context to indicate a singular form, the singular in this specification and claims should generally be interpreted to mean “one or more.”

[0035] And, the term "at least one of A or B" should be interpreted to mean "a case including only A," "a case including only B," or "a combination of A and B."

[0036] Those skilled in the art should recognize that the various exemplary logical blocks, configurations, modules, circuits, means, logics, and algorithmic steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly exemplify the interchangeability of hardware and software, various exemplary components, blocks, configurations, means, logics, modules, circuits, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented in hardware or software depends on the specific application and design constraints imposed on the overall system. Skilled technicians may implement the described functionality in various ways for each specific application. However, such decisions regarding implementation should not be construed as going beyond the scope of this disclosure.

[0037]

[0038] FIG. 1 is a block diagram of a computing device for controlling a motor module included in an ultrasonic device according to one embodiment of the present disclosure.

[0039] The configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. In one embodiment of the present disclosure, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0040] The computing device (100) may include a processor (110), memory (130), and a network unit (150).

[0041] The processor (110) may be composed of one or more cores and may include processors for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. The processor (110) may read a computer program stored in memory (130) and perform data processing for machine learning according to one embodiment of the present disclosure. According to one embodiment of the present disclosure, the processor (110) may perform operations for learning a neural network. The processor (110) may perform calculations for learning a neural network, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) may process the learning of a network function. For example, a CPU and a GPGPU can work together to process the learning of a network function and data classification using the network function. Additionally, in one embodiment of the present disclosure, processors of a plurality of computing devices can be used together to process the learning of a network function and data classification using the network function. Furthermore, a computer program executed on a computing device according to one embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

[0042] According to one embodiment of the present disclosure, the memory (130) can store any form of information generated or determined by the processor (110) and any form of information received by the network unit (150).

[0043] According to one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may operate in conjunction with web storage that performs the storage function of the memory (130) on the internet. The description of the memory described above is merely an example and the present disclosure is not limited thereto.

[0044] A network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0045] In addition, the network unit (150) presented in this specification may use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA), and other systems.

[0046] In the present disclosure, the network unit (150) can be configured regardless of the communication mode, such as wired and wireless, and can be configured as various communication networks such as a Local Area Network (LAN), a Personal Area Network (PAN), and a Wide Area Network (WAN). In addition, the network may be a known World Wide Web (WWW) and may utilize wireless transmission technology used for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth.

[0047] The technologies described in this specification can be used not only in the networks mentioned above but also in other networks.

[0048]

[0049] FIG. 2 illustrates an exemplary structure of an artificial intelligence-based model according to one embodiment of the present disclosure.

[0050] Throughout this specification, artificial intelligence model, artificial intelligence-based model, computational model, neural network, network function, and neural network may be used interchangeably.

[0051] A neural network can be composed of a set of interconnected computational units that may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is composed of at least one node. The nodes (or neurons) constituting neural networks may be interconnected by one or more links.

[0052] In a neural network, one or more nodes connected via links can form relative input and output node relationships. The concepts of input and output nodes are relative; any node in an output node relationship with respect to one node may be in an input node relationship with respect to another node, and vice versa. As described above, the input node versus output node relationship can be generated based on links. One or more output nodes may be connected to a single input node via links, and vice versa.

[0053] In a relationship between an input node and an output node connected through a single link, the value of the output node's data can be determined based on the data input to the input node. Here, the link interconnecting the input node and the output node may have a weight. The weight can be variable and can be varied by the user or an algorithm to enable the neural network to perform the desired function. For example, if one or more input nodes are interconnected to a single output node by respective links, the output node's value can be determined based on the values ​​input to the input nodes connected to the output node and the weights set on the links corresponding to each input node.

[0054] As described above, a neural network consists of one or more nodes interconnected through one or more links, forming input-output node relationships within the network. The characteristics of a neural network can be determined by the number of nodes and links within the network, the relationships between the nodes and links, and the weight values ​​assigned to each link. For example, if two neural networks exist with the same number of nodes and links but different weight values ​​for the links, the two neural networks may be recognized as different from each other.

[0055] A neural network can be composed of a set of one or more nodes. A subset of nodes constituting a neural network can form a layer. Some of the nodes constituting a neural network can form a layer based on their distances from an initial input node. For example, a set of nodes with a distance of n from an initial input node can form n layers. The distance from the initial input node can be defined by the minimum number of links that must be traversed to reach that node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the degree of a layer within a neural network can be defined in a way different from that described above. For example, a layer of nodes may be defined by its distance from a final output node.

[0056] In one embodiment of the present disclosure, a set of neurons or nodes may be defined by the expression a layer.

[0057] Initial input nodes may refer to one or more nodes within a neural network to which data is directly input without passing through links in their relationships with other nodes. Alternatively, in terms of link-based relationships between nodes within the neural network, they may refer to nodes that do not have other input nodes connected by links. Similarly, final output nodes may refer to one or more nodes within a neural network that do not have output nodes in their relationships with other nodes. Furthermore, hidden nodes may refer to nodes constituting the neural network that are neither initial input nodes nor final output nodes.

[0058] A neural network according to one embodiment of the present disclosure may have the number of nodes in the input layer equal to the number of nodes in the output layer, and may be a neural network in which the number of nodes decreases and then increases again as it progresses from the input layer to the hidden layer. Additionally, a neural network according to another embodiment of the present disclosure may have the number of nodes in the input layer less than the number of nodes in the output layer, and may be a neural network in which the number of nodes increases as it progresses from the input layer to the hidden layer. Additionally, a neural network according to yet another embodiment of the present disclosure may have the number of nodes in the input layer greater than the number of nodes in the output layer, and may be a neural network in which the number of nodes decreases as it progresses from the input layer to the hidden layer. A neural network according to yet another embodiment of the present disclosure may be a neural network in which the above-described neural networks are combined.

[0059] An artificial intelligence-based model according to one embodiment of the present disclosure may include a deep neural network (DNN). A deep neural network may refer to a neural network that includes a plurality of hidden layers in addition to an input layer and an output layer. By using a deep neural network, latent structures of data can be identified. That is, latent structures of photos, text, video, voice, protein sequence structures, gene sequence structures, peptide sequence structures, music (e.g., what objects are in the photo, what is the content and emotion of the text, what is the content and emotion of the voice, etc.), and / or binding affinity between peptides and MHCs can be identified. Deep neural networks may include convolutional neural networks (CNN), recurrent neural networks (RNN), autoencoders, restricted Boltzmann machines (RBM), deep belief networks (DBN), Q networks, U networks, Siamese networks, Generative Adversarial Networks (GAN), Transformers, etc. The description of deep neural networks described above is merely illustrative and the present disclosure is not limited thereto.

[0060] The artificial intelligence-based model of the present disclosure may be represented by a network structure of any structure described above, including an input layer, a hidden layer, and an output layer.

[0061] A neural network that can be used in an artificial intelligence-based model of the present disclosure may be trained in at least one of supervised learning, unsupervised learning, semi-supervised learning, transfer learning, active learning, or reinforcement learning. Training of a neural network may be a process of applying knowledge to the neural network to perform a specific operation.

[0062] Neural networks can be trained to minimize the error in their output. The training process involves repeatedly inputting training data into the network, calculating the error between the network's output and the target for the training data, and updating the weights of each node by backpropagating the error from the output layer to the input layer in a direction that reduces the error. In supervised learning, training data is used where the correct answer is labeled for each data point (i.e., labeled training data), whereas in unsupervised learning, the correct answer may not be labeled for each training data point. For instance, in the case of supervised learning for data classification, the training data may consist of data where each training point is labeled with a category. Labeled training data is input into the neural network, and the error can be calculated by comparing the network's output (category) with the labels of the training data. As another example, in the case of unsupervised learning for data classification, the error can be calculated by comparing the input training data with the neural network's output. The calculated error is backpropagated in the neural network (i.e., from the output layer to the input layer), and through backpropagation, the connection weights of each node in each layer of the neural network can be updated. The amount of change in the connection weights of each node being updated can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, a high learning rate can be used in the early stages of training to quickly achieve a certain level of performance and increase efficiency, while a low learning rate can be used in the later stages to improve accuracy.

[0063] In the training of neural networks, the training data is generally a subset of the real-world data (i.e., the data intended to be processed using the trained neural network). Consequently, a training cycle may exist where errors decrease on the training data but increase on the real-world data. Overfitting is a phenomenon where the network learns excessively on the training data, leading to increased errors on the real-world data. For example, a neural network trained on yellow cats might fail to recognize cats when seeing anything other than yellow, which can be considered a type of overfitting. Overfitting can act as a cause for increased errors in machine learning algorithms. Various optimization methods can be used to prevent this overfitting. To prevent overfitting, methods such as increasing the training data, regularization, dropout (which disables some nodes in the network during training), and the use of batch normalization layers can be applied.

[0064]

[0065] According to one embodiment of the present disclosure, the focused ultrasound (FU) mentioned below may be high-intensity focused ultrasound (HIFU). For reference, focused ultrasound (FU) is an ultrasound technology that operates by concentrating energy on a specific tissue. It induces a therapeutic or procedural effect by gathering multiple ultrasound beams at a single focus and delivering high energy to the corresponding area. In particular, high-intensity focused ultrasound (HIFU) is a technology that uses thermal energy to non-invasively treat a target area and can be utilized for skin aesthetics, tumor treatment, fat removal, etc. For example, focused ultrasound can be used to increase skin elasticity by promoting collagen regeneration (e.g., Ulthera, Shurink, Doublo, etc.). Additionally, focused ultrasound can be used for non-invasive lipolysis treatment that targets subcutaneous fat and destroys fat cells.

[0066] Meanwhile, the term "subject" mentioned below refers to a person receiving a procedure (medical procedure or treatment). That is, it may refer to a patient or customer receiving an ultrasound procedure or HIFU procedure. Additionally, "user" may refer to a person performing the procedure (doctor, medical staff).

[0067]

[0068] FIG. 3 is a side view of an ultrasonic device according to one embodiment of the present disclosure, FIG. 4 is a diagram showing a beamforming geometry of an ultrasonic scanner and a focal point generated by a focused ultrasound generator according to one embodiment of the present disclosure, FIG. 5 is an enlarged diagram of a beamforming geometry of an ultrasonic scanner and a focal point generated by a focused ultrasound generator according to one embodiment of the present disclosure, and FIG. 6 is a perspective view of a beamforming geometry of an ultrasonic scanner and a focal point generated by a focused ultrasound generator according to one embodiment of the present disclosure.

[0069]

[0070] Referring to FIG. 3 according to one embodiment of the present disclosure, the ultrasonic device (1) may include a main body module (10) and a mounting module (20). However, the configuration of the ultrasonic device (1) is not limited thereto. For example, the ultrasonic device (1) may further include a control module, a user interface module, a communication module, etc.

[0071] According to one embodiment, the main body module (10) may include an ultrasound scanner (11) for acquiring ultrasound images. For example, the main body module (10) may be a handpiece module. However, the main body module (10) is not limited thereto and may include a hand-held device form or various other types (robotic arm, standalone device, etc.). For example, the handpiece module (10) may be formed in a curved shape so that the user can operate it conveniently. Additionally, the handpiece module (10) may be formed so that the user can hold it in their hand to perform a procedure or diagnosis. Furthermore, an ultrasound scanner (11) may be included inside the handpiece module (10). The ultrasound scanner (11) may include an ultrasound transducer that converts an electrical signal into ultrasound using a piezoelectric element and converts the reflected ultrasound back into an electrical signal. For example, ultrasonic transducers can be classified into Linear Array, Convex Array (Curved Array), and Phased Array depending on the array type. A Phased Array transducer may be utilized for the ultrasonic scanner (11) to avoid hardware interference between focused ultrasound generators (21), but is not limited thereto. Additionally, the ultrasonic scanner (11) may include a Beamforming Module for controlling the direction, focus, and depth of the ultrasonic beam. For example, the Beamforming Module may use a hardware-based Analog Beamforming or a software-based Digital Beamforming method.

[0072] For example, the ultrasound generator (transducer) of the ultrasound scanner (11) included in the handpiece module (10) may be configured to irradiate ultrasound in the longitudinal direction. For instance, the ultrasound scanner (11) may utilize ultrasound distinct from focused ultrasound. For instance, the ultrasound scanner (11) may transmit an ultrasound signal of a specific frequency to the body part to be treated. The ultrasound scanner (11) may generate ultrasound using a range of frequencies from low to high to obtain an image of the body part to be treated. The ultrasound scanner (11) may irradiate ultrasound onto the body part to be treated using a probe (transducer) and receive the reflected signal to generate an image. Additionally, the ultrasound scanner (11) may obtain the image by utilizing ultrasound signals of different frequencies and intensities than the focused ultrasound generator (HIFU). The ultrasound scanner (11) may detect the structure and depth of the skin surface and obtain an image of the treatment area.

[0073] Alternatively, the ultrasound scanner (11) may be controlled by a control signal from the computing device (100). For example, the computing device (100) may adjust the penetration depth and resolution of the ultrasound by adjusting the frequency of the electrical signal applied to the transducer of the ultrasound scanner (11). For example, since high-frequency ultrasound is suitable for obtaining images near the skin surface and low-frequency ultrasound is suitable for obtaining images of deeper tissues, the computing device (100) may adjust the frequency of the ultrasound scanner (11). Additionally, the computing device (100) may adjust the amount of ultrasound energy by adjusting the output intensity of the transducer of the ultrasound scanner (11). Furthermore, the computing device (100) may acquire various types of images by changing the operating mode of the transducer of the ultrasound scanner (11). For example, the computing device (100) may change the operating mode of the transducer of the ultrasound scanner (11) to measure blood flow velocity using Doppler mode or to acquire three-dimensional images using 3D / 4D mode. For example, the computing device (100) can control the beamforming module to focus the ultrasonic beam to a specific depth. Additionally, the computing device (100) can control the beamforming module to adjust the direction of the ultrasonic beam.

[0074]

[0075] According to one embodiment of the present disclosure, a mounting module (20) is mounted on a main body module (10) and may be configured to generate focused ultrasound (FU) in a target area. Additionally, the mounting module (20) may be mounted or detached from the main body module (10) and may be configured to generate focused ultrasound in a target area. For example, the mounting module (20) may be a cartridge module. Alternatively, the cartridge module (20) may be configured to be reusable semi-permanently, or provided in a form that can be discarded after single-use as needed. For example, the cartridge module (20) may include a focused ultrasound generator (21). For example, the interior of the cartridge module (20) may contain an acoustic transmission medium such as water. The focused ultrasound generator (21) may include a transducer. For example, the focused ultrasound may be high-intensity focused ultrasound (HIFU). For example, ultrasound can be generated through a transducer when electrical energy is applied to a piezoelectric ceramic, which converts the energy into ultrasound energy. At this time, by using a geometric focus based on the curved shape of the ultrasound generator to concentrate energy at a specific depth, a TCP (thermal coagulation point) can be formed in a target area inside the skin. Additionally, the cross-sectional shape of the piezoelectric ceramic (22) included in the focused ultrasound generator (21) can be elliptical. High-intensity focused ultrasound (HIFU) irradiated from the focused ultrasound generator (21) is delivered to a target layer of the skin and can provide various treatment effects such as fat breakdown or fascia contraction.

[0076]

[0077] According to one embodiment, the cartridge module (20) may be coupled to the handpiece module (10) in the longitudinal direction or in an L-shape. At this time, the focused ultrasound generator (21) of the cartridge module (20) and the ultrasound generator of the ultrasound scanner (11) included in the handpiece module (10) may be arranged so as not to overlap each other in the longitudinal direction. Referring again to FIG. 3, the handpiece module (10) is formed in a rectangular shape and may include an ultrasound scanner (11) inside. Additionally, the cartridge module (20) may be coupled to the upper side (longitudinal direction) of the handpiece module (10) and may include a focused ultrasound generator (21) inside. For example, the longitudinal direction is the vertical direction (up and down), and the focused ultrasound generator (21) and the ultrasound generator of the ultrasound scanner (11) may be arranged so as not to overlap each other along the longitudinal direction. In other words, if the ultrasound scanner (11) included in the handpiece module (10) is positioned on the right side, the focused ultrasound generator (21) included in the cartridge module (20) can be positioned on the left side. Although FIG. 3 shows the focused ultrasound generator (21) positioned above the ultrasound scanner (11), it is not limited thereto and can be moved in the x-axis or y-axis direction by a motor module, so it may be positioned on the same line or lower than shown in FIG. 3. For reference, the x-axis may be the direction of the procedure and the y-axis may be the direction of skin depth. Meanwhile, by positioning the focused ultrasound generator (21) and the ultrasound generator of the ultrasound scanner (11) so that they do not overlap each other along the length direction, the beamforming geometry (111) of the ultrasound scanner (11) and the beamforming geometry (211) of the focused ultrasound generator (21) can be free from interference with all hardware. For example, the beamforming geometry (111) of the ultrasound scanner (11) may be cylindrical with a concave center, and the beamforming geometry (211) of the focused ultrasound generator (21) may be formed in a cone shape.The beamforming geometry (111) of the ultrasound scanner (11) is formed as a cylindrical shape with a concave center, allowing ultrasound waves to be focused to a specific depth. In contrast, the beamforming geometry (211) of the focused ultrasound generator (21) is in the shape of a cone, allowing high-intensity focused ultrasound energy to be efficiently delivered to a target area. Through these structural differences, the ultrasound scanner can optimize imaging, and the ultrasound generator can optimize treatment.

[0078] Alternatively, in an embodiment where the cartridge module (20) is coupled to the handpiece module (10) in an L-shape, the cartridge module (20) may be connected to one side of the handpiece module (10) in a vertical or oblique direction. This allows the placement of the ultrasound scanner (11) and the focused ultrasound generator (21) to be independently optimized and minimizes interference of ultrasound signals. Through this structure, the beamforming geometry generated by the ultrasound scanner can pass through the inside of the cartridge to reach the target area, and at the same time, it becomes easier for the focused ultrasound generator to irradiate focused ultrasound toward the target area. Additionally, by coupling the cartridge module in an L-shape, the operator's ease of operation is enhanced and the contact area with the skin surface can be optimized. Such a configuration may be particularly advantageous when performing procedures on narrow areas or areas with many curves (e.g., face, jawline, neck, etc.).

[0079]

[0080] According to one embodiment of the present disclosure, a user can treat the skin of a patient by positioning one side of the cartridge module (20) on the patient's skin and moving the handpiece module (10). One side of the cartridge module (20) that comes into contact with the patient's skin may be formed as a transparent window. At this time, the focusing point (2111) of the focused ultrasound generator (21) may be located in front of the transparent window of the cartridge module (20). Referring to FIG. 4 according to an embodiment, the main body module (10) and the mounting module (20) may be combined so that at least a portion of the ultrasound generated by the ultrasound scanner (11) of the main body module (10) passes through the interior of the mounting module (20) and is irradiated to a target area. In other words, at least a portion of the beamforming geometry (111) of the ultrasound generated by the ultrasound scanner (11) of the handpiece module (10) may be combined so that it passes through the interior of the cartridge module (20). For example, the focused ultrasound generator (21) of the cartridge module (20) may be configured to irradiate focused ultrasound in a direction not parallel to the longitudinal direction. For example, referring to FIG. 3, the top of the focused ultrasound generator (21) combined with the piezoelectric ceramic (22) may be formed at an angle. By forming the top of the focused ultrasound generator (21) at a predetermined angle, it may be configured to irradiate focused ultrasound (211) in a direction not parallel to the longitudinal direction. More specifically, referring to FIG. 4 and FIG. 5, by forming the top of the focused ultrasound generator (21) at a predetermined angle rather than parallel to the transmission window, at least a portion of the beamforming geometry (111) of the ultrasound generated by the ultrasound scanner (11) of the handpiece (10), and the focus point (2111) generated by the focused ultrasound generator of the cartridge module may simultaneously exist in the target area.

[0081] In other words, the ultrasound scanner (11) is located inside the handpiece module (10) and can irradiate ultrasound in the longitudinal direction (upward). Additionally, the focused ultrasound generator (21) is located inside the cartridge module (20) and can focus the focused ultrasound (211) in a diagonal direction. That is, the ultrasound scanner (11) irradiates the beamforming geometry (111) of the ultrasound in the vertical direction, and the focused ultrasound generator (21) can intersect it obliquely to form a focus point (2111) in the target area. Furthermore, ultrasound signal interference can be prevented by positioning the focused ultrasound generator (21) and the ultrasound scanner (11) so that they do not overlap in the same longitudinal direction. Additionally, the ultrasound scanner (11) allows a portion of the beamforming geometry to pass through the interior of the cartridge module (20), thereby enabling ultrasound imaging and the procedure to be performed simultaneously.

[0082]

[0083] Referring to FIG. 6 according to one embodiment, one edge of the cartridge module (20) may be configured to come into contact with the skin. The one edge of the cartridge module (20) may be arranged to surround a transmission window formed to transmit ultrasound to a target area. For example, the transmission window may be designed so that ultrasound energy can be transmitted under the skin without loss. Additionally, materials such as acrylic, polyurethane, and silicone may be used to minimize acoustic impedance differences, and a gel coupling layer may be required for matching with the skin. Furthermore, to reduce the phenomenon of reflection or scattering when ultrasound comes into contact with the skin, the surface of the transmission window may be processed to be smooth or an anti-reflective coating may be applied.

[0084] According to one embodiment, the target area may include an area spaced apart from one edge (23) of the cartridge module (20) to the outside of the ultrasound device (1). Here, the target area may refer to a specific area within the skin where ultrasound energy is intensively delivered during an ultrasound procedure. Additionally, the target area may be an area where ultrasound generated from the ultrasound generator of the ultrasound scanner (11) and high-intensity focused ultrasound (HIFU) generated from the focused ultrasound generator (21) work together to form a focal point (2111). More specifically, referring to FIGS. 5 and 6, one edge (23) of the cartridge module (20) may be configured to be in close contact with the surface of the skin of the subject. Additionally, the target area may be formed at a point spaced apart from one edge (23) to the inside of the skin by a preset range (e.g., 2 to 8 mm). At this time, the beamforming geometry (111) generated by the ultrasound scanner (11) passes through the interior of the cartridge module (20) and can be configured to exist simultaneously in the treatment target area along with the HIFU focal point (2111) generated by the focused ultrasound generator (21). This configuration allows for accurate delivery of HIFU energy to the same target area while performing real-time imaging with the ultrasound scanner (11), prevents interference with the housing (case) of the ultrasound device, and ensures accurate positioning of the treatment target area and efficiency of energy delivery. Therefore, the target area allows the imaging ultrasound and the focal point (2111) of the high-intensity focused ultrasound to be concentrated in the same internal skin area through the beamforming geometry (111) passing through the interior of the cartridge, thereby enabling precise and efficient HIFU treatment.

[0085]

[0086] Alternatively, one edge (23) of the cartridge module (20) may include a skin contact sensor. The one edge (23) of the cartridge module (20) may include a skin contact sensor for detecting whether skin contact is occurring. For example, a skin contact sensor (e.g., pressure sensor, capacitance sensor) may be placed on each of the four edges of a disposable or semi-permanent cartridge module (20) to detect whether all edges are in close contact with the skin. The skin contact sensor may be provided to prevent energy loss of ultrasound and to ensure accuracy in procedures and imaging. The computing device (100) may also control the ultrasound and high-intensity focused ultrasound (HIFU) of the ultrasound scanner to be activated only when all edges of the cartridge module (20) are in contact with the skin.

[0087]

[0088] FIG. 7 is a flowchart illustrating a method for controlling a motor module included in an ultrasonic device according to one embodiment of the present disclosure. For reference, the method for controlling a motor module included in an ultrasonic device illustrated in FIG. 7 may be performed by a computing device (100).

[0089] According to one embodiment of the present disclosure, the ultrasound device (1) may further include a motor module for changing the position of the focal point (211) of the focused ultrasound generated by the mounting module (10). In this case, the motor module may be configured to move in multiple axial directions. The motor module may be included in the main body module (10). By utilizing the motor module to change the position of the focal point (211) of the focused ultrasound, the focus can be changed with the same cartridge during multi-site procedures, thereby improving work efficiency based on the generated procedure plan information.

[0090] According to one embodiment, the computing device (100) can generate a control signal to move the position of a motor module included in the ultrasound device (1). First, the computing device (100) can acquire an image of the body part to be treated (S110). For example, the computing device (100) can acquire an image of the body part to be treated by utilizing an ultrasound scanner (11). The computing device (100) can receive the ultrasound image acquired from the ultrasound scanner in real time. Additionally, the computing device (100) can process the received ultrasound data in real time and convert it into an image format. Additionally, the computing device (100) can display the converted ultrasound image in real time on a display device. Additionally, the computing device (100) can transmit the ultrasound image in real time in a remote medical environment via a network. Additionally, the computing device (100) can support real-time viewing of the transmitted ultrasound image on a remote monitoring system. Additionally, the computing device (100) may apply an image compression and transmission technique that minimizes delay while maintaining the quality of the ultrasound image.

[0091] According to one embodiment, a computing device (100) can identify a plurality of different layers in a body part to be treated (S120). For example, the body part to be treated may include skin. In addition, the types of the plurality of different layers may include at least two of the dermis layer, fat layer, or fascia layer of the skin. However, this is not limited thereto, and the types of the plurality of different layers may be further subdivided according to the purpose of the treatment. For example, the computing device (100) can identify a plurality of different layers in a body part to be treated by performing preprocessing, semantic segmentation, binarization, post-processing, and boundary detection on an image (ultrasound image) of the body part to be treated. According to one embodiment, a computing device (100) can analyze medical image data using artificial intelligence (AI) models such as U-Net, V-Net, SegNet, DeepLab v3+, Mask R-CNN, and TransUNet, and can identify multiple different layers, including a fat layer and a fascia layer, in real time in a body part to be treated. For example, the computing device (100) can segment multiple different layers in a body part to be treated by utilizing at least one of models based on FCN (Fully Convolutional Networks), U-Net, DeepLab v3+, PSPNet (Pyramid Scene Parsing Network), SegNet, and Vision Transformers (ViT).

[0092] According to one embodiment, the computing device (100) can generate treatment plan information utilizing focused ultrasound for a plurality of different layers based on the types of a plurality of identified different layers (S130). For example, the computing device (100) can generate the treatment plan information using an artificial intelligence model or logic coding. For example, the treatment plan information may include information on the treatment location where focused ultrasound is utilized, information on the movement of the focused ultrasound, or information on the amount of energy of the focused ultrasound for a plurality of different layers. For reference, the focused ultrasound can focus ultrasound on the fat layer to deliver thermal energy to fat cells, thereby destroying the fat cell membrane and inducing the dissolution of fat cells. Additionally, by performing Shrinking, a procedure that uses focused ultrasound to concentrate thermal energy on the fascia layer to coagulate and contract the tissue, skin lifting and skin elasticity can be improved. For example, the computing device (100) can set a treatment exclusion location for the dermis layer. Since the dermis layer serves as a protective layer for the skin, the computing device (100) may exclude the dermis layer from the procedure. Additionally, the computing device (100) may set a first type of procedure location for the fat layer. For example, the computing device (100) may set a procedure location for the fat layer to utilize a first energy focused ultrasound. Additionally, the computing device (100) may set a procedure interval for utilizing a first energy focused ultrasound by considering the characteristics of the fat layer. Additionally, the computing device (100) may set a second type of procedure location for the fascia layer. For example, the computing device (100) may set a procedure location for utilizing a second energy focused ultrasound for the fascia layer. For instance, the computing device (100) may set a procedure location to apply a second energy focused ultrasound by analyzing the characteristics of the fascia layer (SMAS / Fascia) based on multiple different layers identified using an artificial intelligence model.Additionally, the computing device (100) can set a treatment interval that utilizes a second energy focused ultrasound higher than the first energy, taking into account the characteristics of the fascial layer.

[0093] According to one embodiment of the present disclosure, a computing device (100) can generate a control signal to move the position of a motor module included in an ultrasound device (1) (S140). According to the embodiment, the computing device (100) can generate an actuator control signal to automatically move a device generating focused ultrasound to a target treatment site based on generated treatment plan information. For example, the computing device (100) can generate a control signal for an actuator mounted on the focused ultrasound based on generated treatment plan information. For example, the actuator may be a motor module. For instance, the computing device (100) can calculate the position coordinates of a site to generate a focusing point (2111) of the focused ultrasound generated by the mounted module (10) based on generated treatment plan information. For example, the control signal of the motor module may include a control signal in the direction of two or more mutually orthogonal axes. For example, the x-axis may be the direction of the procedure and the y-axis may be the direction of skin depth. Alternatively, three-dimensional 3D movement including the z-axis may be enabled. Additionally, rotation and tilt adjustment functions may be added to maintain an optimal irradiation angle for a specific procedure area. The computing device (100) can control the motor module based on the procedure plan to automatically move the focused ultrasound generator (21) and perform ultrasound irradiation at a set target location.

[0094] According to one embodiment, the computing device (100) can generate an energy control signal to automatically adjust the energy of the focused ultrasound generator (21) in conjunction with movement by the motor module, based on the generated procedure plan information. For example, the energy control signal to automatically adjust the energy of the focused ultrasound generator (21) may include energy intensity (amount), ultrasound irradiation time, irradiation frequency, irradiation pattern (focusing mode, continuous mode, etc.), and irradiation depth control. Additionally, the computing device (100) can update the generated procedure plan information based on images acquired in real time and regenerate an energy control signal to automatically adjust the energy of the focused ultrasound generator (21) in conjunction with movement by the motor module.

[0095] Alternatively, the computing device (100) displays the procedure plan information generated by artificial intelligence on a display, and the user can view and modify the generated procedure plan information. Taking this into consideration, the computing device (100) may also regenerate an energy control signal to automatically adjust the energy of the focused ultrasound generator (21) in conjunction with movement by the motor module, based on the modified procedure plan information.

[0096] According to one embodiment of the present disclosure, a computing device (100) may display generated procedure plan information on an acquired image. Here, the computing device (100) may display the generated procedure plan information in different ways on identified different layers. For example, the computing device (100) may display each procedure location by applying different colors to each layer. Additionally, the computing device (100) may display procedure location and path information using points and lines to visually represent the procedure plan information. Furthermore, the computing device (100) may adjust the size of the points according to the procedure intensity. In summary, each point (●) represents a location where ultrasound energy is focused, and the spacing between the points serves as a factor for adjusting the procedure intensity and frequency. The lines connecting the points serve to visually highlight the path along which the procedure will proceed and to define the procedure pattern for each layer. All procedure points may be arranged to maintain a constant spacing along the path. Through this, the computing device (100) can more effectively display procedure planning information and provide real-time visual feedback to support medical staff in performing more precise procedures. For example, the computing device (100) can display procedure planning information overlaid with images acquired for the body part to be treated in real time. By displaying procedure planning information overlaid with images acquired in real time, the computing device (100) can support medical staff in immediately recognizing and adjusting the treatment area. For reference, the procedure planning information may be changed based on images acquired in real time.

[0097]

[0098] To summarize, the present invention relates to an ultrasound device, wherein the ultrasound device (1) may include a main body module (10) comprising an ultrasound scanner (11) for acquiring ultrasound images and a mounting module (20) for generating focused ultrasound (HIFU) in a target area. A structure is provided configured such that a portion of the beamforming geometry (111) generated by the ultrasound scanner (11) of the main body module (10) penetrates the interior of the mounting module (20) to reach the target area. Through this, ultrasound imaging and focused ultrasound can be simultaneously irradiated to a treatment target area (e.g., 2 to 8 mm below the skin) while the cartridge module (20) is in complete contact with the skin, and the effect is achieved that the beamforming geometry (111) and the focal point (2111) of the focused ultrasound can exist simultaneously within the same target area without hardware interference between the ultrasound scanner (11) and the focused ultrasound generator (21). In addition, by applying a configuration that moves the focusing point in multiple directions through a motor module included in the main body module (10), an ultrasound device capable of flexibly responding to various skin conditions and treatment requirements can be implemented.

[0099]

[0100] The steps mentioned in the foregoing description may be further subdivided into additional steps or combined into fewer steps, depending on the embodiment of the present disclosure. Additionally, some steps may be omitted as necessary, and the order of the steps may be changed.

[0101]

[0102] Meanwhile, a computer-readable medium storing a data structure is disclosed according to an embodiment of the present disclosure.

[0103] A computer-readable medium storing a data structure according to one embodiment of the present disclosure is disclosed. The aforementioned data structure may be stored in memory as described in the present disclosure, executed by a processor, and transmitted and received by a network unit.

[0104] A data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. A data structure can refer to the organization of data for solving specific problems (e.g., data analysis, data retrieval, data storage, data modification). A data structure may also be defined by physical or logical relationships between data elements designed to support specific data processing functions. Logical relationships between data elements may include connections between user-defined data elements. Physical relationships between data elements may include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., a permanent storage device). Specifically, a data structure may include sets of data, relationships between data, and functions or instructions applicable to the data. Through an effectively designed data structure, a computing device can perform operations while minimizing the use of the device's resources. Specifically, through an effectively designed data structure, a computing device can increase the efficiency of operations, reading, insertion, deletion, comparison, exchange, and retrieval.

[0105] Data structures can be classified into linear and non-linear data structures based on their form. A linear data structure is one where only one piece of data is connected to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a set of data that maintains an internal order. Lists can include linked lists. A linked list is a data structure where data is connected in a line, with each piece of data possessing a pointer. In a linked list, the pointer can contain information regarding the connection to the next or previous data. Depending on its form, a linked list can be represented as a singly linked list, a doubly linked list, or a circular linked list. A stack is a data arrangement structure that allows for restricted access to data. A stack can be a linear data structure where data can be processed (e.g., insertion or deletion) only at one end. Data stored in a stack can be a Last-In, First-Out (LIFO) data structure, meaning that the later an item is entered, the sooner it is retrieved. A queue is a data sequence structure that allows for limited access to data; unlike a stack, it can be a FIFO (First in First Out) data structure where data stored later is retrieved later. A deque is a data structure that can process data at both ends.

[0106] Non-linear data structures can be structures where multiple data are connected after a single piece of data. Non-linear data structures may include graph data structures. A graph data structure can be defined by vertices and edges, and an edge may include a line connecting two different vertices. Graph data structures may include tree data structures. A tree data structure may be a data structure where there is only one path connecting two different vertices among the multiple vertices included in the tree. In other words, it may be a data structure that does not form a loop in a graph data structure.

[0107] Throughout this specification, the terms artificial intelligence-based model, computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, they will be described uniformly as neural network. A data structure may include a neural network. Furthermore, a data structure including a neural network may be stored on a computer-readable medium. A data structure including a neural network may also include data preprocessed for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for learning the neural network, etc. A data structure including a neural network may include any of the components disclosed above. That is, a data structure including a neural network may be configured to include all or any combination thereof, such as data preprocessed for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, and loss functions for learning the neural network. In addition to the configurations described above, a data structure including a neural network may include any other information that determines the characteristics of the neural network. Furthermore, the data structure may include any form of data used or generated during the computational process of the neural network, and is not limited to the foregoing. A computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. A neural network may be composed of a set of interconnected computational units that may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is composed of at least one node.

[0108] A data structure may include data input to a neural network. A data structure including data input to a neural network may be stored on a computer-readable medium. Data input to a neural network may include training data input during the neural network learning process and / or input data input to a neural network after training is complete. Data input to a neural network may include pre-processed data and / or data subject to pre-processing. Pre-processing may include a data processing process for inputting data into a neural network. Accordingly, a data structure may include data subject to pre-processing and data generated by pre-processing. The aforementioned data structure is merely an example, and the present disclosure is not limited thereto.

[0109] The data structure may include weights of the neural network. (In this specification, weights and parameters may be used interchangeably.) The data structure including the weights of the neural network may be stored on a computer-readable medium. The neural network may include multiple weights. The weights may be variable and may be varied by a user or an algorithm to enable the neural network to perform a desired function. For example, if one or more input nodes are interconnected to a single output node by respective links, the output node may determine the data value output from the output node based on values ​​input to the input nodes connected to the output node and weights set on the links corresponding to each input node. The aforementioned data structure is merely an example and the present disclosure is not limited thereto.

[0110] As an example rather than a limitation, weights may include weights that vary during the neural network learning process and / or weights for which neural network learning is completed. Weights that vary during the neural network learning process may include weights at the start of the learning cycle and / or weights that vary during the learning cycle. Weights for which neural network learning is completed may include weights for which the learning cycle is completed. Accordingly, a data structure containing the weights of a neural network may include a data structure containing weights that vary during the neural network learning process and / or weights for which neural network learning is completed. Therefore, the weights and / or combinations of each weight described above are included in the data structure containing the weights of a neural network. The aforementioned data structure is merely an example and the present disclosure is not limited thereto.

[0111] Data structures containing the weights of a neural network may be stored on a computer-readable storage medium (e.g., memory, hard disk) after undergoing a serialization process. Serialization may be a process of converting a data structure into a form that can be stored on the same or different computing devices and later reconstructed for use. A computing device may serialize the data structure to transmit and receive data over a network. A serialized data structure containing the weights of a neural network may be reconstructed on the same or different computing devices through deserialization. Data structures containing the weights of a neural network are not limited to serialization. Furthermore, data structures containing the weights of a neural network may include data structures designed to increase computational efficiency while minimizing the use of computing device resources (e.g., B-Tree, R-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree in non-linear data structures). The foregoing is merely an example and the present disclosure is not limited thereto.

[0112] The data structure may include hyperparameters of the neural network. The data structure including the neural network hyperparameters may be stored on a computer-readable medium. The hyperparameters may be variables that are varied by the user. The hyperparameters may include, for example, a learning rate, a cost function, the number of learning cycle iterations, weight initialization (e.g., setting the range of weight values ​​subject to weight initialization), and the number of hidden units (e.g., the number of hidden layers, the number of nodes in the hidden layers). The aforementioned data structure is merely an example, and the present disclosure is not limited thereto.

[0113]

[0114] FIG. 8 is a brief and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0115] Although the present disclosure has been described as generally being implementable by a computing device, a person skilled in the art will be well aware that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that can be executed on one or more computers and / or as a combination of hardware and software.

[0116] Generally, a program module includes routines, programs, components, data structures, etc., that perform a specific task or implement a specific abstract data type. Furthermore, a person skilled in the art will be well aware that the method of the present disclosure can be implemented in other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. (each of which may be connected to and operated with one or more associated devices).

[0117] The embodiments described in this disclosure may also be implemented in a distributed computing environment in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0118] Computers typically include various computer-readable media. Any medium accessible by a computer may be a computer-readable medium, and such computer-readable media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media. By example, but not limiting, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD (digital video disk) or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other media that can be accessed by a computer and used to store desired information.

[0119] Computer-readable transmission media typically include all information transmission media that implement computer-readable instructions, data structures, program modules, or other data, etc., on a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal in which one or more of the characteristics of the signal are set or modified to encode information within the signal. By example, not limiting, computer-readable transmission media include wired media, such as wired networks or direct-wired connections, and wireless media, such as acoustic, RF, infrared, and other wireless media. Any combination of the media described above is also considered to be within the scope of computer-readable transmission media.

[0120] An exemplary environment for implementing various aspects of the present disclosure, including a computer (1102), is shown, wherein the computer (1102) includes a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including system memory (1106) (but not limited thereto), to the processing unit (1104). The processing unit (1104) may be any processor among various commercial processors. Dual processor and other multiprocessor architectures may also be used as the processing unit (1104).

[0121] The system bus (1108) may be any of several types of bus structures that can be additionally interconnected to a local bus using any of the memory bus, peripheral bus, and various commercial bus architectures. System memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). The basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, EEPROM, etc., and this BIOS includes basic routines that help transfer information between components within the computer (1102) at times such as during startup. The RAM (1112) may also include high-speed RAM, such as static RAM, for caching data.

[0122] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA)—this internal hard disk drive (1114) may also be configured for external use within a suitable chassis (not shown)—a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from a CD-ROM disk (1122) or reading from or writing to other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may each be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128). The interface (1124) for implementing an external drive includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

[0123] These drives and associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, etc. In the case of a computer (1102), the drives and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, a person skilled in the art will know that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc., may also be used in exemplary operating environments and that any of these media may contain computer-executable instructions for performing the methods of the present disclosure.

[0124] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or part of the operating system, application, module and / or data may also be cached in RAM (1112). It will be well known that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0125] The user can input commands and information into the computer (1102) through one or more wired / wireless input devices, such as a pointing device like a keyboard (1138) and a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, etc. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) connected to the system bus (1108), but may also be connected via other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc.

[0126] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface such as a video adapter (1146). In addition to the monitor (1144), the computer generally includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0127] The computer (1102) may operate in a networked environment using a logical connection to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communication. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), but for brevity, only the memory storage device (1150) is illustrated. The illustrated logical connection includes a wired / wireless connection to a local area network (LAN) (1152) and / or a larger network, e.g., a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a global computer network, e.g., the Internet.

[0128] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communication to the LAN (1152), and the LAN (1152) may also include a wireless access point installed therein to communicate with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communication computing device on the WAN (1154), or have other means to establish communication through the WAN (1154), such as through the Internet. The modem (1158), which may be an internal or external and a wired or wireless device, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, the program modules described for the computer (1102) or parts thereof may be stored in a remote memory / storage device (1150). It will be well known that the illustrated network connection is exemplary and that other means of establishing a communication link between computers may be used.

[0129] The computer (1102) operates to communicate with any wireless device or object that is deployed and operated via wireless communication, for example, a printer, scanner, desktop and / or portable computer, PDA (portable data assistant), communication satellite, any equipment or place associated with a wireless detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or simply ad hoc communication between at least two devices.

[0130] Wi-Fi (Wireless Fidelity) enables connectivity to the Internet and other sources without wires. Wi-Fi is a wireless technology, similar to a cell phone, that allows devices, such as computers, to transmit and receive data indoors and outdoors—that is, anywhere within the coverage area of ​​a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band).

[0131] Those skilled in the art of the present disclosure will understand that information and signals may be represented using any various different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0132] Those skilled in the art will understand that the various exemplary logical blocks, configurations, modules, logics, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented by electronic hardware, various forms of computer programs or design code (referred to herein as software for convenience), or a combination of all such. To clearly illustrate this interoperability between hardware and software, the various exemplary logical blocks, configurations, modules, logics, processors, means, circuits, and steps have been generally described above in relation to their functions. Whether such functions are implemented in hardware or software depends on the design constraints imposed on the specific application and the overall system. Those skilled in the art may implement the functions described in this disclosure in various ways for each specific application, but such implementation decisions should not be interpreted as being outside the scope of this disclosure.

[0133] The various embodiments presented herein may be implemented as methods, devices, or articles manufactured using standard programming and / or engineering techniques. The term "article manufactured" includes a computer program, a carrier, or a medium accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical discs (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Additionally, the various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0134] It should be understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but do not imply being limited to the specific order or hierarchy presented.

[0135] Description of the presented embodiments is provided so that a person skilled in the art may use or practice the present disclosure. Various modifications to these embodiments will be apparent to a person skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments presented herein, but should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein.

[0136]

[0137] As described above, the relevant details have been described in the best mode for carrying out the invention.

Claims

1. As an ultrasonic device, A main body module including an ultrasound scanner for acquiring ultrasound images; and A mounting module mounted on the above main body module and configured to generate focused ultrasound (FU) in a target area; Includes, The main body module and the mounting module are coupled such that at least a portion of the ultrasound generated by the ultrasound scanner of the main body module passes through the interior of the mounting module and is irradiated onto the target area. Ultrasonic device.

2. In Paragraph 1, The above main body module is a handpiece module, and The above-mentioned mounting module is a cartridge module, Ultrasonic device.

3. In Paragraph 2, The above handpiece module and the above cartridge module are, At least a portion of the beamforming geometry of the ultrasound generated by the ultrasonic scanner of the handpiece is coupled to pass through the inside of the cartridge module. Ultrasonic device.

4. In Paragraph 2, One edge of the above cartridge module is configured to come into contact with the skin, and The above target region includes a region spaced apart from the one edge of the cartridge module to the outside of the ultrasonic device. Ultrasonic device.

5. In Paragraph 2, The above cartridge module is coupled to the above handpiece module in the longitudinal direction or in an L-shape, and The focused ultrasound generator of the cartridge module and the ultrasound generator of the ultrasound scanner included in the handpiece module are arranged so as not to overlap each other in the longitudinal direction or L-shaped direction. Ultrasonic device.

6. In Paragraph 5, The ultrasonic generator of the ultrasonic scanner included in the handpiece module is configured to irradiate ultrasound in the longitudinal direction, and The focused ultrasound generator of the cartridge module is configured to irradiate the focused ultrasound in a direction not parallel to the longitudinal direction. Ultrasonic device.

7. In Paragraph 6, At least a portion of the beamforming geometry of the ultrasound generated by the ultrasound scanner of the handpiece, and the focusing point generated by the focused ultrasound generator of the cartridge module, are simultaneously present in the target area. Ultrasonic device.

8. In Paragraph 1, The above ultrasonic device is, Motor module for changing the position of the focusing point of the focused ultrasound generated by the above-mentioned mounting module Includes more, The above motor module is configured to move in multiple axes, Ultrasonic device.

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