Device and method for modeling and controlling ice formation during cryoablation therapy

Abstract

A method (800) of performing a cryoablation therapy includes obtaining (808) an ice formation model and preparing (814) a cryoablation therapy plan based on one or more characteristics of a target tissue and the ice formation model. The method also includes initiating (816) a cryoablation freeze cycle and obtaining (818) ice formation data describing one or more characteristics of ice being formed during the cryoablation freeze cycle. The method further includes determining a location of one or more isotherms of ice being formed based on the ice formation data and an ice formation model.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of U.S. patent application Ser. No. 17 / 664,228, filed May 20, 2022, which is incorporated herein by reference for all purposes as if fully set forth herein. Technical Field

[0003] The present disclosure relates to apparatus and methods for modeling and controlling ice formation during cryoablation treatment. Background Art

[0004] This section provides background information related to the present disclosure which is not necessarily prior art.

[0005] Systems and methods for providing cryoablation therapy can include a cryoablation probe introduced at or near target tissue in a patient's body. The cryoablation system can include an extremely cold cryogen (liquid, vapor, or mixed phase) that can pass through a probe that is in thermal contact with the target tissue. Heat from the tissue is transferred from the tissue, through the probe, and into the cryogen, which removes heat from the target tissue. This removal of heat causes the tissue to freeze, thereby destroying the target tissue. When the tissue freezes, ice typically forms in an ice ball. The ice ball can be a sphere, an ellipsoid, or other shape. It is desirable to perform cryoablation therapy so that the target tissue is completely frozen and freezing of surrounding tissue and / or body structures is minimized.

[0006] Conventional or existing systems and methods typically include predetermined testing procedures that are determined experimentally in a laboratory setting. Conventional or existing systems and methods do not include the ability to accurately predict and / or monitor ice formation during a cryoablation treatment. As a result, ice formed using existing and conventional methods and systems may not perform as efficiently and effectively as desired. Furthermore, treatment using existing and conventional methods may unnecessarily form ice that freezes healthy tissue or body structures near or surrounding the target tissue. Furthermore, conventional and existing systems and methods are poor at determining the location of isotherms or other characteristics of the freezing process. Therefore, there is a need for improved cryoablation systems and methods that can monitor, control, and adapt to the specific circumstances of a cryoablation treatment and efficiently and effectively predict and form ice of a desired size, shape, and location over a period of time. Summary of the Invention

[0007] According to a first aspect of the present invention, a system for performing a cryoablation treatment according to claim 1 is provided.

[0008] According to a second aspect of the present invention, a method of performing a cryoablation treatment according to claim 11 is provided.

[0009] Optional features are defined by the dependent claims.

[0010] This section provides a general summary of the disclosure, and is not a comprehensive disclosure of its full scope or all of its features.

[0011] In various embodiments of the present disclosure, apparatus and methods for performing cryoablation therapy are provided. These apparatus and methods may include improvements for determining ice formation models that predict the growth of ice during cryoablation therapy. The ice formation model may be implemented as an affine model that predicts the growth of ice in various directions from a location on the cryoprobe. The ice formation model may predict the location of one or more isotherms in an ice ball formed during a freezing cycle of a cryoablation therapy. Using this information, the operating conditions of the cryoablation apparatus may be adjusted, if necessary, to grow an ice ball having a predetermined size, shape, and / or temperature profile. The location of the isotherms may be positioned as desired to improve the effectiveness of the cryoablation therapy and / or minimize adverse effects on healthy tissue surrounding the target tissue.

[0012] In some embodiments of the present disclosure, a cryoablation system may include at least one computing device. The at least one computing device may be configured to obtain an ice formation model and prepare a cryoablation treatment plan based on one or more properties of the target tissue and the ice formation model. The computing device may be further configured to initiate a cryoablation freeze cycle and obtain ice formation data describing one or more properties of ice being formed during the cryoablation freeze cycle. The computing device may also determine the location of one or more isotherms of the forming ice based on the ice formation data and the ice formation model.

[0013] In one aspect, the ice formation model can describe the growth of ice in multiple directions from predetermined locations on the cryoablation probe during a cryoablation freezing cycle.

[0014] In another aspect, the ice formation model can include an affine model configured to predict ice growth in multiple directions from a predetermined location on the cryoablation probe.

[0015] In another aspect, the one or more characteristics of the target tissue may include the type of tissue, the size of the tissue, and / or the shape of the tissue.

[0016] In another aspect, the ice formation data may include imaging data obtained from an imaging device.

[0017] In another aspect, the imaging data can indicate the outer edge of ice that is forming during a cryoablation cycle.

[0018] In another aspect, each of the one or more isotherms can indicate a boundary where a predetermined temperature threshold has been reached.

[0019] In another aspect, at least one of the one or more isotherms corresponds to a boundary where the ice has reached -20°C.

[0020] In another aspect, the step of determining the location of the one or more isotherms can include determining a location relative to the cryoablation probe where the ice has reached a boundary of -20°C based on the location of an edge surface of the ice and an ice formation model.

[0021] In another aspect, the computing device can be further configured to compare the ice formation data to the ice formation model to determine whether ice being formed during cryoablation treatment is growing in a predicted manner using one or more affine ratios, and to adjust the growth of the ice being formed when the ice formation data indicates that the one or more affine ratios are greater than a predetermined upper threshold or less than a predetermined lower threshold.

[0022] In some embodiments of the present disclosure, a method of performing cryoablation therapy may include obtaining an ice formation model and preparing a cryoablation therapy plan based on one or more properties of target tissue and the ice formation model. The method may also include initiating a cryoablation freeze cycle and obtaining ice formation data describing one or more properties of ice being formed during the cryoablation freeze cycle. The method may also include determining the location of one or more isotherms of the forming ice based on the ice formation data and the ice formation model.

[0023] In one aspect, the ice formation model can describe the growth of ice in multiple directions from predetermined locations on the cryoablation probe during a cryoablation freezing cycle.

[0024] In another aspect, the ice formation model can include an affine model configured to predict ice growth in multiple directions from a predetermined location on the cryoablation probe.

[0025] In another aspect, the one or more characteristics of the target tissue may include the type of tissue, the size of the tissue, and / or the shape of the tissue.

[0026] In another aspect, the ice formation data may include imaging data obtained from an imaging device.

[0027] In another aspect, the imaging data can indicate the outer edge of ice formed during the cryoablation cycle.

[0028] In another aspect, each of the one or more isotherms may indicate a boundary where a predetermined temperature threshold has been reached.

[0029] In another aspect, at least one of the one or more isotherms corresponds to a boundary where the ice has reached -20°C.

[0030] In another aspect, the step of determining the location of the one or more isotherms can include determining a location relative to the cryoablation probe where the ice has reached a boundary of -20°C based on the location of an edge surface of the ice and an ice formation model.

[0031] In another aspect, the computing device can be further configured to compare the ice formation data to the ice formation model to determine whether ice being formed during cryoablation treatment is growing in a predicted manner using one or more affine ratios, and to adjust the growth of the ice being formed when the ice formation data indicates that the one or more affine ratios are greater than a predetermined upper threshold or less than a predetermined lower threshold.

[0032] In some embodiments of the present disclosure, a method for creating a model for predicting ice formation during cryoablation therapy is provided. A method for creating a model for predicting ice formation during cryoablation therapy may include: measuring ice growth at multiple time intervals, wherein the ice growth is measured in multiple directions from a predetermined location on the cryoablation probe; and determining a relationship between an operating condition of the cryoablation probe and ice growth in each of the multiple directions from the predetermined location on the cryoablation probe.

[0033] In one aspect, the plurality of directions are linear directions from a common location on the cryoablation probe.

[0034] In another aspect, determining the relationship may include determining, for each of the plurality of directions, one or more affine ratios at each of the plurality of time intervals.

[0035] In some embodiments of the present disclosure, a method for creating a treatment plan for use during cryoablation therapy is provided. The method may include obtaining patient data including information regarding the location of target tissue within the patient's body, and obtaining an ice formation model describing ice growth in multiple directions from a predetermined location on a cryoablation probe. The method may also include determining operating parameters of a cryoablation system based on the patient data and the ice formation model, wherein the cryoablation system is configured to provide a cryogen to the cryoablation probe.

[0036] In another aspect, the method may further include determining the duration and / or location of the final puck size.

[0037] In another aspect, the method may further include determining a target profile for one or more affine ratios based on the ice formation model.

[0038] In another aspect, a target profile can describe target values ​​of one or more affine ratios during multiple time intervals in a cryoablation treatment.

[0039] In another aspect, the method may further include predicting the location of one or more isotherms in the eventual ice formation.

[0040] In another aspect, each of the one or more isotherms may indicate a boundary where a predetermined temperature threshold has been reached.

[0041] In another aspect, at least one of the one or more isotherms corresponds to a boundary where final ice formation has reached -20°C.

[0042] In another aspect, the step of predicting the location of the one or more isotherms can include determining, based on an ice formation model, the location relative to the cryoablation probe of a boundary where ice has reached -20°C.

[0043] Further areas of applicability will become apparent from the description provided herein.The description and specific examples in this summary are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are for illustrative purposes only of selected embodiments and not all possible implementations, and are not intended to limit the scope of the present disclosure.

[0045] Figure 1 is a diagram illustrating an example cryoablation system according to some embodiments of the present disclosure.

[0046] Figure 2 is a diagram showing an example cryoprobe and an example ice ball formed during a cryoablation treatment, illustrating aspects of an example method for modeling ice ball growth.

[0047] Figure 3 is a graph showing the change in growth rate over time for an affine puck compared to a linear growth model.

[0048] Figure 4 is a graph showing example output of the ice ball growth model during a first time period.

[0049] Figure 5 It shows Figure 4 Illustration of example output of the puck growth model over the second time period.

[0050] Figure 6 is a graph showing the growth rate of an ice ball over time that can be used with one or more methods of the present disclosure.

[0051] Figure 7 is a flow chart illustrating an example method of modeling ice ball growth according to some embodiments of the present disclosure.

[0052] Figure 8is a flow chart illustrating an example method of performing cryoablation therapy using an affine ice ball growth model according to some embodiments of the present disclosure.

[0053] Figure 9 is a diagram illustrating an example computing device that may be used with one or more cryoablation systems of the present disclosure.

[0054] Corresponding reference numerals indicate corresponding parts throughout the several views of the drawings. DETAILED DESCRIPTION

[0055] Example embodiments will now be described more fully with reference to the accompanying drawings.

[0056] Example embodiments are provided to make this disclosure comprehensive and to fully convey the scope to those skilled in the art. Many specific details, such as examples of specific components, devices, and methods, are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. Those skilled in the art will understand that specific details need not be employed, and that example embodiments may be embodied in many different forms, and none should be construed as limiting the scope of the present disclosure. In some example embodiments, well-known processes, well-known device structures, and well-known technologies are not described in detail.

[0057] The terms used herein are intended only to describe specific example embodiments and are not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms "a," "an," and "the" may also be intended to include the plural forms. The terms "comprises," "comprising," "including," and "having" are inclusive and therefore specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Unless specifically identified as an order of execution, the method steps, processes, and operations described herein should not be construed as necessarily requiring them to be performed in the particular order discussed or described. It should be understood that additional or alternative steps may be employed.

[0058] When an element or layer is referred to as being "on," "engaged to," "connected to," or "coupled to" another element or layer, it may be directly on, engaged to, connected to, or coupled to the other element or layer, or there may be intervening elements or layers. In contrast, when an element is referred to as being "directly on," "directly engaged to," "directly connected to," or "directly coupled to" another element or layer, there may be no intervening elements or layers. Other words used to describe the relationship between elements should be interpreted in a similar manner (e.g., "between" versus "directly between," "adjacent" versus "directly adjacent," etc.). As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0059] Although the terms first, second, third etc. can be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms may only be used to distinguish an element, component, region, layer or section from another region, layer or section. Unless the context clearly indicates, terms such as "first", "second" and other numerical terms do not mean sequence or order when used in this article. Therefore, without departing from the teaching of example embodiments, the first element, component, region, layer or section discussed below may be referred to as the second element, component, region, layer or section.

[0060] For ease of description, spatial relative terms such as "inside," "outside," "below," "below," "lower," "above," "upper," etc. may be used herein to describe the relationship of one element or feature to another element or feature as shown in the figures. In addition to the orientation depicted in the figures, spatial relative terms may be intended to cover different orientations of the device in use or operation. For example, if the device in the figure is flipped, an element described as being "below" or "below" other elements or features will be oriented as being "above" the other elements or features. Thus, the example term "below" can cover both the orientations above and below. The device can be oriented in other ways (rotated 90 degrees or in other orientations), and the spatial relative descriptors used herein are interpreted accordingly.

[0061] In some embodiments of the present disclosure, methods are provided that allow cryoablation treatments to be performed using ice formation models. The ice formation models of the present disclosure can be used to develop a cryoablation treatment plan that can describe the operating conditions used during the cryoablation treatment. For example, the ice formation models of the present disclosure can be used to predict the growth, size, and / or shape of ice formed during cryoablation treatment. The methods of the present disclosure can utilize one or more ice formation models so that target tissue (e.g., a tumor or other abnormal tissue) can be destroyed during a freezing cycle of a cryoablation treatment.

[0062] The method of the present disclosure can be used in conjunction with a cryoablation device, wherein a cryoablation probe can be inserted into a patient's body at or near the target tissue. When a suitable cryogen is delivered to the probe, heat can be removed from the area near the cryoablation probe. When enough heat is removed to lower the temperature of the target tissue and the area surrounding the cryoablation probe, ice typically begins to form. It is desirable to lower the temperature to a sufficient threshold or freezing temperature range so that ice forms rapidly, thereby limiting the freezing cycles of the cryoablation treatment and / or treatment. In some cases, it is desirable to limit the freezing cycles to less than 10 minutes. In other examples, the duration of the freezing cycle can be based on a specific temperature. In many clinical settings, it may be desirable to lower the temperature and freeze the target tissue to at least -20°C for at least three minutes. When this temperature and duration are reached, the target tissue is likely to have been destroyed. In some example cryoablation treatments, the treatment may include multiple freezing cycles performed between thawing cycles.

[0063] As can be appreciated, it is desirable to ensure that the entire target tissue (e.g., the entire tumor) is destroyed during cryoablation therapy. In order to increase the likelihood of this occurring, it is desirable to understand the temperature and size of the ice that is being formed during cryoablation therapy. However, this information can be difficult to obtain in a clinical setting because existing equipment and cryoablation devices are limited in the amount and type of information that can be supplied to medical professionals in real time. For example, imaging systems can be used in conjunction with cryoablation therapy. Such imaging systems can include, for example, ultrasound equipment, CT scanning equipment, x-ray equipment, and / or MRI equipment. However, such imaging processes are poor at delivering real-time and / or accurate information about ice formation during treatment. Conventional and existing imaging equipment can generally only provide information or an indication of the overall size of the ice or ice ball formed during cryoablation therapy. Imaging systems do not provide temperature information.

[0064] Furthermore, it is desirable to minimize the impact of cryoablation treatment on the patient and to minimize damage and / or destruction of healthy tissue.Therefore, it is desirable to minimize the size of the cryoablation probe and to limit the number of additional measurement devices used during cryoablation treatment.

[0065] In view of the foregoing, conventional and existing cryoablation methods and systems are poor at indicating the location of a -20°C temperature boundary (or isotherm) that may be located within ice or a puck during cryoablation therapy. However, the location of such a boundary is very important in determining the effectiveness of cryoablation therapy. In existing or conventional methods, imaging systems can be used to determine the size, shape, or location of the puck, but such images may only indicate the outer contour of the puck (or overall size and shape), but the temperature of the ice and the temperature profile of the interior region of the puck are unknown.

[0066] The systems and methods of the present disclosure are improvements over conventional and existing systems by incorporating the use of an ice formation model that can predict the growth rate and other characteristics of ice formed during cryoablation treatment. Use of the ice formation model of the present disclosure can allow for the location of a -20°C temperature boundary or other isotherm at which ice forms to be determined. Use of the methods and systems of the present disclosure improves the likelihood that target tissue will be damaged during cryoablation treatment. Additionally, the duration and impact of cryoablation treatment on the patient's healthy tissue can be minimized. Furthermore, the size, shape, and / or growth rate of ice formed during cryoablation treatment can be adjusted and / or achieved to more accurately correspond to the size, shape, and / or location of target tissue than when using conventional or existing methods.

[0067] In some examples of the present disclosure, the ice formation model can be an affine model. In such examples, the model can predict ice growth in multiple directions from a predetermined location on the cryoablation probe. One or more affine ratios can be used to measure, predict, or otherwise characterize ice growth in the affine ice formation model.

[0068] Now refer to Figure 1 , an example cryoablation system 100 is shown. The cryoablation system 100 may include a cryoablation computing device 102, an intelligent control 104, a pump 108, a cryogen source 110, an inlet valve 112, a cryogen supply 114, and a cryoprobe 128. The pump 108, the cryogen source 110, the inlet valve 112, the cryogen supply 114, and the cryoprobe 128 may be operated to deliver cryogen from the cryogen source 100 to the cryoprobe 128 to perform a cryoablation treatment. The cryogen (e.g., liquid nitrogen) may be stored in the cryogen source 110, such as a Dewar flask or other suitable container, and then delivered to the cryoprobe 128 via the cryogen supply 114. The cryogen may expand at the tip 122 of the cryoprobe 128 and cool the tip 122 in the cryoprobe 128 to a temperature at which target tissue of the patient 120 begins to freeze to form an ice ball 118.

[0069] In this disclosure, the term ice ball may be used to describe various types of ice formation that may occur as a result of a cryoablation freezing cycle. Although the term "ice ball" may be used, it should be understood that the ice formed during a cryoablation treatment may have shapes other than a ball or sphere. The ice ball may be elliptical or have other shapes that may be formed due to ice growing from the cryoprobe 128 at different rates and to different extents in various directions.

[0070] The cryoprobe 128 can be positioned at or near a target tissue (e.g., a tumor) within the patient 120. In this manner, the target tissue can be frozen, thereby destroying the target tissue. One or more freezing cycles can be performed to destroy the target tissue. During the freezing cycle, an ice ball 118 can be formed at the target tissue within the patient 120. It is desirable to control and form the ice ball 118 in a predetermined manner so that the ice ball 118 forms a desired size, shape, and / or rate so that the target tissue freezes within the ice ball 118 within a desired period of time. It is also desirable to form the ice ball 118 with a desired size, shape, and / or rate so that healthy tissue or body structures near the target tissue are not harmed by the freezing cycle. For example, it may be desirable to limit the size of the ice ball 118 so that it does not form and freeze healthy tissue.

[0071] Before performing a cryoablation treatment, a treatment plan can be determined. The treatment plan can detail and / or describe the various steps of the process and various aspects of the treatment, such as one or more of the following: the type of equipment to be used, the positioning of the cryoprobe, the temperature of the cryoprobe, the duration of the freeze (and thaw cycles), and the number of cycles. The treatment plan can also include the size, location, shape, growth rate, and / or duration of the ice ball. The treatment plan can be determined by a medical professional and / or other person. As will be further described, one or more of the ice formation models created using the methods of the present disclosure can be used to determine the treatment plan.

[0072] In some examples, the cryoablation computing device 102 may determine or recommend a treatment plan after health, patient, and other information is input into the cryoablation computing device 102, or such information is retrieved or otherwise obtained by the cryoablation computing device 102. The cryoablation computing device 102 may be any suitable computing device, such as a workstation, computer, laptop, tablet computer, server, etc.

[0073] As further shown, the cryoablation system 100 includes an intelligent control 104, which can be coupled to the cryoablation computing device 102, the cryoprobe 128, and the pump 108. The intelligent control 104 can be any suitable controller, PLC, data acquisition unit, control unit, etc. that can perform the operations described herein. In some examples, the cryoablation computing device 102 and the intelligent control 104 can be combined in a single device. In other cases, they can be separate devices, as shown. The intelligent control 104 can be operated to obtain ice formation measurement information from measurement points. The measurement points can include suitable sensors, measurement locations, etc., which can obtain ice formation measurement information from locations on or near the tip 122 of the cryoprobe 128. The ice formation measurement information can provide temperature information at predetermined locations to provide information about ice growth in the target tissue. The measurement points can include temperature sensors, thermocouples, thermistors, and / or impedance sensors, etc. Such measurement points can be incorporated into the cryoprobe 128 and / or separate measurement leads.

[0074] The intelligent control 104 can be operated to obtain ice formation measurement information and provide this information to the cryoablation computing device 102. Although not shown, the intelligent control 104 and / or the cryoablation computing device 102 can also be coupled to an imaging device, such as an x-ray device, an ultrasound device, a CT scan device, an MRI device, etc. Imaging information from such a device can be used during, before, and / or after treatment to provide information about ice formation and / or information about the target tissue and other tissues of the patient 120. The cryoablation computing device 102 can then perform various operations to determine the characteristics of the ice being formed during the cryoablation treatment. The cryoablation computing device 102 can, for example, use the imaging information and one or more ice formation models to determine and / or predict characteristics of the ice ball, such as the location of isotherms or other temperature information.

[0075] After determining the characteristics of the ice, the cryoablation computing device 102 can take action to adjust, change, modify, or otherwise control one or more operating parameters of the cryoablation system 100. In some examples, the cryoablation computing device 102 can adjust the cryogen flow provided from the cryogen source 110 to the cryoprobe 128. The cryoablation computing device 102 can be coupled to a pump 108. The pump 108 can be an adjustable, programmable, or otherwise controllable pump. The pump 108 can allow the flow rate, flow rate, flow velocity, pressure, or other characteristics of the cryogen flow to be modified, controlled, or customized. In some examples, the pump 108 can be operated to deliver the cryogen to the cryoprobe 128 in a pulsed manner using pulse width modulation (PWM). In such examples, the pump 108 can be controlled to deliver the cryogen flow at a desired frequency, pulse width, pulse amplitude, or other desired flow characteristics.

[0076] Although not shown, the intelligent control 104 and / or the cryoablation computing device 102 can also be coupled to other information sources. The intelligent control 104 and / or the cryoablation computing device 102 can be coupled to a medical information database or other health status information source, which can include information about the patient 120 and / or other clinical treatment procedures. In some examples, the cryoablation computing device 102 can be coupled to a suitable database or information source, such as a server, which can store one or more ice formation models.

[0077] Figure 2 An example ice ball 118 and cryoprobe 128 are shown. Ice ball 118 can be formed during a cryoablation procedure. Ice ball 118 can be characterized by one or more isotherms. In this example, ice ball 118 is shown as having a first isotherm 202, a second isotherm 204, and a third isotherm 206. Isotherms 202, 204, and 206 define boundaries where predetermined temperatures have been reached. In the example shown, first isotherm 202 can define a 0°C isotherm. First isotherm 202 defines the outer edge of ice ball 118 and defines a boundary of the ice ball where all locations within first isotherm 202 have reached a temperature of 0°C or lower. It will be appreciated that the temperature of the interior area of ​​ice ball 118 may be below 0°C.

[0078] Second isotherm 204 may define a lower temperature than first isotherm 202. In the example shown, second isotherm 204 may define the boundary of a -20°C isotherm. Areas of ice ball 118 within the second boundary may have reached a temperature of -20°C or lower. Third isotherm 206 may define a lower temperature than both first isotherm 202 and second isotherm 204. Third isotherm 206 may define the boundary of a -40°C isotherm. Areas of ice ball 118 within third boundary 206 may have reached a temperature of -40°C or lower.

[0079] During the performance of a cryoablation treatment, it may be important to understand the location and size of the first isotherm 202, the second isotherm 204, and / or the third isotherm 206. It is understood that subjecting some tissues to a temperature of -20°C will reliably destroy the tissue. Other tissues will not be destroyed unless subjected to a lower temperature of -40°C. In many cryoablation treatments, it is desirable to subject abnormal tissue, such as tumors, cancers, etc., to a temperature of at least -40°C in order to reliably destroy the tissue. It is important to understand the location of the -40°C isotherm so that the target tissue (e.g., a tumor) is completely enclosed within this boundary, thereby destroying the target tissue. It is also desirable to minimize the tissue located within the -20°C isotherm so that damage to healthy tissue is minimized.

[0080] In the example shown, the first isotherm 202, the second isotherm 204, and the third isotherm 206 correspond to the 0° C. isotherm, the -20° C. isotherm, and the -40° C. isotherm, respectively. However, it should be understood that the apparatus and method of the present disclosure can be applied to determine a model for determining the locations of isotherms having different characteristics or other temperature values.

[0081] In one example, the ice formation model can be constructed as an affine model. In such a model, ice growth is predicted along one or more directions from a predetermined location on the cryoprobe 128. As shown, the model can include multiple directions from a predetermined location 230 on the cryoprobe. Predetermined location 230 can be any suitable location on the cryoprobe and can be approximately the center of the ice ball 118. Multiple directions can extend from predetermined location 230. In the example shown, the directions are expanded into linear paths along which ice growth can be measured and modeled.

[0082] Although any number of directions may be used, the ice formation model may include eight directions in two orthogonal planes. Figure 2 The illustration shows eight directions in the first plane (to be explained further). As can be appreciated, a similar arrangement of directions or vectors from the predetermined point 230 can also be used. Such additional directions or vectors can be located in the same plane as Figure 2 The ice formation model can model the growth of ice in three dimensions by at least two orthogonal planes. For simplicity, the ice formation model is relative to Figure 2 In other examples, other planes and other numbers of directions or vectors may be used.

[0083] The ice formation model may include eight directions. The eight directions may include a first direction 210, a second direction 212, a third direction 214, a fourth direction 216, a fifth direction 218, a sixth direction 220, a seventh direction 222, and an eighth direction 224. Each direction points from a predetermined point 230 and extends linearly at a predetermined angle. The directions may be evenly spaced from one another. In the example shown, the directions are positioned approximately 45 degrees apart.

[0084] An affine ice formation model can be constructed to predict the growth of ice along each of the eight directions as a function of time. The temperature at any location along each direction can also be constructed as part of the model. When the model is constructed, it can be used to determine the locations of the first isotherm 202, the second isotherm 204, and the third isotherm 206 at a specific time. For example, the model can predict the locations of the first isotherm 202, the second isotherm 204, and the third isotherm 206 based on time and the operating conditions of the cryoablation system. The operating conditions of the cryoablation system may include operating pressure, flow rate, flow pulse, and / or cryogen temperature, among others. Through the model, the operating conditions of the cryoablation system can be used to adjust and / or change the growth rate, size, isotherm position, or other characteristics of the ice ball 118.

[0085] In one example, an affine ice formation model can be developed using experimental data. A test bench can be configured in which a grid or mesh of temperature sensors can be positioned in an array around the cryoprobe and test tissue. A freezing cycle can be initiated, and the temperature at predetermined locations around the cryoprobe can be measured over time. The growth rate of the ice ball can be measured in various directions along the affine model. This process can be performed multiple times under various operating conditions and / or on various tissues and environments. This information can be used to establish correlations between operating conditions, tissue, and ice formation, including locations of isotherms. Regression models can also be used to construct ice formation models.

[0086] In other examples, machine learning or artificial intelligence can be used. Experimental data can be used to train a machine-learned ice formation model using a suitable neural network. Such a trained model can learn the complex relationships between the operating conditions of the cryogen, the patient's tissue and anatomy, and ice formation and isotherm location. In addition to experimental data, the machine learning model can also be adapted for retraining using clinical data from cryoablation treatments.

[0087] One or more affine parameters of the affine ratio can be used to model ice formation and / or monitor ice growth during cryoablation therapy. An example ratio is the ice ball size growth affine ratio. The ice ball size growth affine ratio is characterized by the following formula (1).

[0088]

[0089] Where Li is the length or distance from the predetermined point 230 along one of the i directions to the outer edge 202 of the puck 118 at time Tj measured from the start of the freeze cycle. The puck size growth affine ratio provides a characteristic of the puck growth rate.

[0090] Another affine ratio that can be used is the puck-size symmetric growth affine ratio, which is characterized by the following formula (2).

[0091]

[0092] Where Li or Lj is the length or distance from the predetermined point 230 to the edge of the puck measured along direction i or direction j. At the same time Tn, the lengths are compared in this ratio.

[0093] Another affine ratio that can be used is the ice ball thermal freezing energy affine ratio, which is represented by the following formula (3).

[0094]

[0095] Where Li or Lj is the length or distance from the predetermined point 230 to the edge of the puck along direction i or direction j. At the same time Tn, the lengths are compared using this ratio. However, this ratio adds up the lengths for each measurement time 1 to N.

[0096] Another ratio that can be used is the ice hockey affine symmetry morphology ratio, which is characterized by the following formula (4).

[0097]

[0098] The variables used are the same as those described above. This ratio provides an indication of the overall symmetry during the time period from 1 to N.

[0099] Another ratio that can be used is the puck affine shape morphology ratio, which is characterized by the following formula (5).

[0100]

[0101] The variables used are the same as above, but the ratio includes the sum of each direction i, j to M.

[0102] Each of the above ratios can be used as input to a model to train or construct an ice formation model. In some examples, the model can be constructed to include algorithms, rules, or other features that can be derived by observing or determining one or more of the ratios. Furthermore, these ratios can be calculated during cryoablation treatment and compared to a predicted or predetermined ratio target profile.

[0103] Figure 3 An example is shown in FIG. During the performance of a cryoablation treatment, each of the ratios (Formulas 1 to 5) may be calculated at time intervals and then compared to a predetermined target profile for each ratio. If the calculated ratio differs from the target profile by more than a predetermined threshold, an attempt may be made to adjust or modify the operating parameters of the cryoablation system to restore the ratio to a level closer to the target profile. Figure 3As shown, graph 300 shows the affine ratio of ice ball size growth (Equation 1) plotted against time during a freezing cycle of a cryoablation treatment. The target profile of the ratio is shown as target line 302, and the calculated ratio is shown as line 304. At time 306, the calculated ratio is greater than target line 302. In this case, the ice ball may be growing too fast, and the pressure, flow rate, or other operating characteristics of the cryogen can be adjusted to reduce ice growth. At time 308, the calculated ratio is less than profile line 302. The deviation is relatively small, and no action may be taken unless the deviation increases to exceed a predetermined threshold. At time 310, the calculated ratio is again greater than profile line 302. As previously described, action can be taken to reduce ice growth.

[0104] It will be appreciated that such analysis can be performed during cryoablation therapy by the cryoablation computing device 102. While the illustrated example shows the use of an affine ratio of ice ball size growth, any of the previously described ratios (i.e., Equations 1 through 5) can be used to determine whether adjustments or other actions are needed during cryoablation therapy.

[0105] Now refer to Figure 4 and Figure 5 , shows an example method for building an ice formation model. Experimental or clinical data on ice formation can be collected. Figure 4 The ice ball is represented by a unit or finite element shown as element 402 in FIG. Element 402 can be used to represent a portion of the ice ball that is being formed during the freezing cycle. For example, the element 400( Figure 4 ) may represent an ice ball formed at a first time T1 of a freeze cycle. Representation 500 may represent the same ice ball after continued formation during the same freeze cycle, but showing the ice ball at a second time T2 later than T1. Figure 4 and Figure 5 A two-dimensional puck is shown, but elements 402 could also be made into three-dimensional cubes or rectangular prisms to create a three-dimensional representation of the puck.

[0106] Representations 400 and 500 represent the size of the ice ball during corresponding times (e.g., T1 or T2). Element 402 may include an indication of the temperature of the corresponding portion of the ice ball. The temperature of element 402 is represented in text form and via the color or shading of the element. Using this approach, a representation can be created for the ice ball during the time period when data was collected or available. Algorithms, correlations, regressions, or mathematical models created along the various directions in the previously described affine diagrams can be used in conjunction with elements 402 and / or representations 400, 500 to determine the size, temperature, shape, and / or location of various isotherms, such as first isotherm 202, second isotherm 204, and third isotherm 206.

[0107] In constructing an ice formation model, it is desirable to understand the nonlinearities and / or other complexities that contribute to or influence ice growth during cryoablation therapy. In some cases or during some treatments, the target tissue may be located adjacent to other tissues and / or other body structures. Adjacent or nearby structures may act as heat sinks or otherwise influence ice formation during the freezing cycle. The environment of the tissue during freezing may reach an equilibrium point or other nonlinear events that may influence ice formation. It is desirable to understand these circumstances and events so that the ice formation model accurately and reliably predicts ice formation during cryoablation therapy.

[0108] Now refer to Figure 6 , shows a graph 600 that plots the growth rate 602 (e.g., mm / min) of an ice ball during a freezing cycle versus the time the ice ball has grown. As shown in this example, the growth rate exhibits at least two singular points where the growth rate increases rapidly rather than following a substantially linear path. As shown, the growth rate 602 includes a first singular point 604 and a second singular point 606. The first singular point 604 occurs at approximately 1 minute. The second singular point 606 occurs at approximately 2.5 minutes. These singular points can be incorporated into an ice formation model so that an accurate ice formation model can be constructed. Figure 6 A graph showing the growth rate of the ice ball is shown. In other examples, singularities can be determined for other variables, such as the affine ratios described above.

[0109] Now refer to Figure 7 , a method 700 for performing a cryoablation treatment is shown. For example, the method 700 can be performed by the previously described cryoablation system 100, wherein the steps can be performed by the cryoablation computing device 102. The method 700 can be performed by other cryoablation devices and systems and / or variations of the cryoablation system 100. For the sake of brevity, the method 700 is described with reference to the cryoablation system 100, but it should be understood that the method 700 is not limited to the described system and components.

[0110] Method 700 may begin at step 702. At step 702, a freezing cycle for a cryoablation treatment may be initiated. The cryoablation computing device 102 may initiate a flow of refrigerant to the tip 122 of the cryoprobe 128. Additionally, the cryoablation computing device 102 may begin monitoring ice formation data. Ice formation data may be received from imaging devices, temperature sensors, impedance sensors, and the like.

[0111] The method can then move to step 704. At step 704, the cryoablation computing device 102 can monitor ice forming in the target tissue at the cryoprobe 128. Ice formation data can be obtained by the cryoablation computing device 102 from an imaging device. As previously described, imaging data can provide information about ice growth, but typically only provides an indication of the outer edge of the ice ball growing at the target tissue. The imaging data can include two-dimensional image data or three-dimensional image data of the edge of the ice ball forming at the target tissue. The imaging data can be used to determine ice growth in each of the previously described affine directions. Growth can be determined along eight directions or another number of directions from a predetermined location on the cryoprobe 128.

[0112] Method 700 may proceed to step 708. At step 708, the cryoablation computing device 102 may continue to track, capture, and / or calculate various aspects characterizing ice growth. These aspects may include the size, shape, growth rate, or other characteristics of the ice puck. As previously described, characteristics may be obtained and / or determined from predetermined points on the cryoprobe 128 for multiple directions.

[0113] In step 710, the ice formation model can be modified, refined, or altered as needed for the clinical application and specific treatment. For example, the target tissue may be located in a specific location, which may require the model to be modified to account for healthy tissue or body structures that may be located near the target tissue. In other examples, the target tissue may have a specific size or shape, making the baseline ice formation model less suitable. In other examples, other differences in patients, tissue types, etc. may require the ice formation model to be refined, modified, or altered. In step 710, the ice formation model can be altered as needed and / or as requested by a medical professional. In some examples, the medical professional can input such changes via a user interface in the cryoablation computing device 102. In other examples, the cryoablation computing device can obtain information about the specifications of the cryoablation treatment and automatically make such modifications or changes to the ice formation model. In some examples, the number, location, and / or orientation of the directions of the affine model can be modified.

[0114] In some cases, the method may return to step 706 and perform the operations of step 706. When the ice formation model cannot be modified or changed based on the needs of the medical professional or clinical application, method 700 may return to step 706. At step 706, the ice formation model may be modified, changed, or adjusted based on previously received and monitored data. For example, one or more singularities may be observed that require refinement, adjustment, or change of the ice formation model.

[0115] Method 700 may alternatively proceed from step 710 to step 712. At step 712, the cryoablation computing device may determine and / or calculate various affine ratios as previously described. The cryoablation computing device 102 may continue to obtain ice formation data and calculate affine ratios using equations 1 to 5.

[0116] At step 714, the cryoablation computing device 102 may establish an ice formation model for the current treatment application. The ice formation model established at step 714 uses relationships (or baseline models) obtained from previous clinical treatments and / or experimental test data. The cryoablation computing device 102 may prepare a model to provide a predicted growth of the ice ball during a freezing cycle and predict the locations of the first isotherm, the second isotherm, and the third isotherm. Using this model, the location of the 0°C boundary, the location of the -20°C boundary, and the location of the -40°C boundary may be predicted.

[0117] In step 716, the cryoablation computing device 102 can determine whether the established affine ice formation model meets the requirements of cryoablation treatment. The cryoablation computing device 102 can determine, for example, whether the position of the -20°C isotherm covers the target tissue so that the target tissue is fully subjected to the -20°C freezing condition. In other examples, the cryoablation computing device 102 can determine whether the position of the -40°C isotherm corresponds to the target tissue. In other examples, the cryoablation computing device 102 can determine what healthy tissue (if any) is affected by the -40°C and / or -20°C isotherms. This information can be displayed or otherwise indicated to a medical professional.

[0118] If the established ice formation model meets the requirements for cryoablation therapy, the method can proceed to step 718. If the established ice formation model does not meet the requirements for cryoablation therapy, the method 700 can proceed to step 722. In step 722, the cryoablation computing device 102 can adjust, adjust, and / or optimize the operating conditions of the cryoablation system 100 to change the predicted growth of ice. In some examples, if the cryoablation computing device 102 determines that the predicted ice formation will grow too slowly or will not reach the desired size, shape, or position during the freezing cycle duration, the pressure, flow rate, flow pulse, or volume of the cryogen can be increased. In other examples, if the model indicates that the position of the ice and / or isotherms will not reach the desired position or will grow too slowly, the cryoablation computing device 102 can reduce the pressure, flow rate, flow pulse, or volume of the cryogen. In other examples, the cryoprobe 128 can be provided with one or more heating portions that can be actuated by the cryoablation computing device 102 to cause the ice to form the desired shape or size.

[0119] In step 718, the cryoablation computing device 102 may maintain the ice ball at a predetermined size and for a predetermined duration. The size and duration of the ice ball may be determined depending on the clinical needs of the treatment. For example, based on an established ice formation model, the location of the edge of the ice ball, the -20°C isotherm, and / or the -40°C isotherm are known. The cryoablation computing device 102 may operate the cryoablation system 100 to grow the ice ball to a predicted size, shape, and position so that the target tissue is destroyed during treatment. In many cases, the ice ball grows and remains at a specific size, shape, and position for a predetermined time period (e.g., 3 minutes, 5 minutes, 10 minutes, etc.). In other treatments, other predetermined durations may be used.

[0120] After step 718, the method may optionally proceed to step 720. At step 720, the medical professional may provide further input and / or adjust, change, or modify the ice formation model or treatment protocol. It will be appreciated that circumstances may arise during the course of treatment that may require adjustments or modifications. At step 720, the medical professional may make such adjustments or changes. In other examples, the cryoablation computing device 102 may automatically make changes based on data obtained and monitored during the course of treatment.

[0121] From step 718, method 700 may proceed to step 724. At step 718, the cryoablation procedure may continue according to the treatment plan. For example, the treatment plan may include a thaw cycle followed by a freeze cycle. At step 718, such further cycles or procedures may be performed to complete the cryoablation treatment.

[0122] Now refer to Figure 8 , another example method 800 for performing a cryoablation treatment is shown. Method 800, or portions thereof, can also be used to establish a baseline ice formation model and / or determine the relationship between operating parameters of a cryoablation system and ice growth during a freezing cycle. Method 800 can be performed using various cryoablation systems. In one example, method 800 can be performed using cryoablation system 100. For the sake of brevity, method 800 is described with reference to cryoablation system 100. However, it should be understood that other cryoablation systems can also be used.

[0123] Method 800 begins at step 802. At step 802, the cryoablation computing device 102 may determine whether an ice formation model is available for the current cryoablation treatment. The cryoablation computing device 102 may search for or reference a clinical identifier, or compare the treatment details to an existing ice formation model that may be stored in a suitable database or other repository. If the cryoablation computing device 102 determines that an ice formation model is available, the method proceeds to step 814. If the cryoablation computing device 102 determines that an ice formation model is not available, the method 800 proceeds to step 804.

[0124] At step 804, the cryoablation computing device 102 may obtain ice ball growth data or ice formation data. The ice formation data and / or ice ball growth data may include temperature data, impedance data, and / or imaging data characterizing ice growth during a freezing cycle. If such data is available from historical or previous clinical treatments, such data may be used. In other examples, such data may be obtained from a test procedure or an ongoing clinical treatment.

[0125] At step 806, the cryoablation computing device 102 may determine and / or calculate affine growth characteristics. The affine growth characteristics may include the position of the puck edge, the growth rate, and / or the affine ratio. These growth characteristics may be analyzed in relation to the operating conditions of the cryoablation system (pressure, flow, flow pulse, temperature, volume, etc.) to determine relationships. Regression, correlation, machine learning models, and / or other suitable mathematical tools may be used to determine such relationships.

[0126] At step 808, the relationship between the growth characteristics of the ice ball and the operating conditions of the cryoablation system can be used to build an ice formation model. The ice formation model can be an affine model that predicts the growth of the ice ball from a predetermined location on the cryoprobe 128 along one or more predetermined directions. The number of such directions can vary, and the complexity and granularity of the model can be increased by increasing the number of affine directions used to construct the model, or reduced by reducing the number of affine directions used to construct the model. In one example, the model can be constructed as an affine model, as described above, with eight directions oriented in each of two orthogonal planes. In other examples, the model can be constructed using more or fewer than eight directions in each plane. Additionally, more than two orthogonal planes can be used in other ice formation models.

[0127] At step 810, the cryoablation computing device 102 may determine whether a treatment plan is required. If a treatment plan is required, the method proceeds to step 814. If a treatment plan is not required, the method proceeds to step 812. At step 812, the cryoablation computing device 102 may store the ice formation model in a suitable database or repository for later use. It will be appreciated that steps 804 to 812 may be used to establish an ice formation model. Such a process may be used in a laboratory setting, for example, to prepare a baseline model that can be used during clinical treatment. Such a baseline model may be used in method 800 to initiate clinical treatment, or may be used in a process such as method 700, where the baseline model is adjusted, changed, or modified as needed for a particular treatment.

[0128] At step 814, the established ice formation model can be used to determine a treatment plan. The cryoablation computing device 102 can obtain details or other information regarding the patient, target tissue, clinical application, standard clinical procedures, etc. The cryoablation computing device 102 can also obtain input from a medical professional via a user interface to create a treatment plan. In addition to including operating parameters of the cryoablation system 100, the treatment plan can also include the number of freeze or thaw cycles, etc. Once the treatment plan is finalized, the method 800 can proceed to step 816.

[0129] At step 816, the cryoablation computing device 102 may initiate cryoablation treatment. At step 816, the cryoablation computing device may initiate refrigerant flow to the cryoprobe 128 to initiate ice formation. Although not shown, the cryoprobe 128 may be positioned at or within the target tissue. In this position, an ice ball may begin to form within the target tissue. The cryoablation computing device 102 may control and / or set operating parameters to initial conditions, as indicated by the ice formation model and / or treatment plan. Method 800 may then proceed to step 818.

[0130] At step 818, the cryoablation computing device 102 may obtain ice ball growth data or ice formation data. The ice formation data may characterize the ice forming during the freezing cycle. In some examples, the ice formation data may include temperature data, impedance data, and / or imaging data. The imaging data may include an indication of the edge of the ice forming at the target tissue. The size, position, and / or growth rate of the ice ball may be determined from the imaging data.

[0131] At step 820, the cryoablation computing device 102 can determine whether the ice ball is growing as predicted. The cryoablation computing device 102 can compare the size, shape, position, and / or growth rate of the ice ball to the predicted ice ball size, shape, and / or growth rate of the ice formation model. The ice formation model can be constructed as an affine model. The comparison can be performed along various affine directions from a predetermined position on the cryoprobe 128. Various affine ratios (Formulas 1 to 5) between the actual ice ball being formed at the target tissue and the ice ball predicted by the ice formation model can also be calculated and compared.

[0132] If the cryoablation computing device 102 determines that the ice ball is not growing or forming as expected, the method 800 proceeds to step 822. At step 822, the cryoablation computing device 102 may change, adjust, or modify the operating parameters of the cryoablation system 100. For example, the cryoablation computing device may adjust the pressure, flow rate, flow pulse, volume, temperature, or other characteristics of the cryogen. After step 822, the method 800 may return to step 818 and re-execute operations as previously described.

[0133] If the cryoablation computing device 102 determines that the ice ball is growing as desired, the method 800 proceeds to step 824. At step 824, the cryoablation computing device 102 determines whether the ice ball has reached the desired size and duration. The cryoablation computing device 102 may make this determination by comparing the ice formation data to an ice formation model and / or a treatment plan. The treatment plan may include a desired duration for the freezing cycle and may include a predetermined size and position of the ice ball. This may be determined so that the positions of the various isotherms of the ice ball are positioned relative to the target tissue so that the target tissue is destroyed during the freezing cycle. If the ice ball has not reached the desired size and duration, the method 800 returns to step 818 to re-perform the operations as previously described until the ice ball has reached the desired size and duration.

[0134] If the ice ball has reached the desired size and duration, the method 800 can end. As can be appreciated, the method 800 can be re-performed for cryoablation treatments that include more than one freezing cycle.

[0135] Now refer to Figure 9 , showing an example computing device 900. The cryoablation system 100 may include one or more computing devices 900. For example, the cryoablation computing device 102 may have Figure 9 The methods of the present disclosure, such as methods 700 and 800, may be performed by computing device 900, or steps of such methods may be performed thereby.

[0136] As shown, computing device 900 may include one or more processors 902, a working memory 904, one or more input / output devices 906, an instruction memory 908, a transceiver 912, one or more communication ports 914, and a display 916, all of which are operatively coupled to one or more data buses 910. Data buses 910 allow communication between various devices. Data buses 910 may include wired or wireless communication channels.

[0137] The processor 902 may include one or more different processors, each having one or more cores. Each of the different processors may have the same or different architectures. The processor 902 may include one or more central processing units (CPUs), one or more graphics processing units (GPUs), application-specific integrated circuits (ASICs), digital signal processors (DSPs), and the like.

[0138] The processor 902 may be configured to perform a specific function or operation by executing code embodying the function or operation stored in the instruction memory 908. For example, the processor 902 may be configured to perform one or more of any functions, steps, methods, or operations disclosed herein.

[0139] The instruction memory 908 may store instructions that may be accessed (e.g., read) and executed by the processor 902. For example, the instruction memory 908 may be a non-transitory computer-readable storage medium such as a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory, a removable disk, a CD-ROM, any non-volatile memory, or any other suitable memory.

[0140] The processor 902 can store data in and read data from the working memory 904. For example, the processor 902 can store a set of working instructions in the working memory, such as instructions loaded from the instruction memory 908. The processor 902 can also use the working memory 904 to store dynamic data created during operation of the cryoablation computing device 102. The working memory 904 can be a random access memory (RAM), such as static random access memory or dynamic random access memory, or any other suitable memory.

[0141] The input-output device 906 may include any suitable device that allows data to be input or output. For example, the input-output device 906 may include one or more devices such as a keyboard, a touchpad, a mouse, a stylus, a touch screen, physical buttons, a speaker, a microphone, or any other suitable input or output device.

[0142] Communication port 914 can include, for example, a serial port, such as a universal asynchronous receiver / transmitter (UART) connection, a universal serial bus (USB) connection, or any other suitable communication port or connection. In some examples, communication port 914 allows for programming of executable instructions in instruction memory 908. In some examples, communication port 914 allows for the transmission (e.g., uploading or downloading) of data, such as ice formation measurement data.

[0143] The display 916 can display a user interface 918. The user interface 918 can enable a user to interact with the cryoablation computing device 102. For example, the user interface 918 can be a user interface that allows an operator to interact, communicate, control, and / or modify different messages, settings, or features that may be presented or otherwise displayed to the user. The user interface 918 may include sliders, dialog boxes, or other input fields that allow the user to control, communicate, or modify settings, limitations, or inputs used in cryoablation treatments. In addition, the user interface 918 may include one or more input fields or controls that allow the user to modify or control optional features or customizable aspects of the cryoablation computing device 102 and / or operating parameters of the cryoablation system 100. In some examples, the user can interact with the user interface 918 by using the input-output device 906. In some examples, the display 916 can be a touch screen, wherein the user interface 918 is displayed on the touch screen. In other examples, the display 916 can be a computer display that can be interacted with using a mouse or keyboard.

[0144] Transceiver 912 allows for communication with a network. In some examples, the selection of transceiver 912 is based on the type of communication network in which cryoablation computing device 102 will operate. Processor 902 is operable to receive data from or send data to a network, such as a wired or wireless network coupling elements of cryoablation system 100.

[0145] The description of the foregoing embodiments is provided for the purpose of illustration and description. It is not intended to be exhaustive or to limit the present disclosure. The individual elements or features of a particular embodiment are generally not limited to that particular embodiment, but, where applicable, are interchangeable and may be used in selected embodiments even if not specifically shown or described. The same may vary in many aspects. Such variations should not be regarded as a departure from the present disclosure, and all such modifications are intended to be included within the scope of the present disclosure.

Claims

1. A system for performing cryoablation therapy, comprising at least one computing device, wherein the at least one computing device is configured to: Obtain ice formation models; preparing a cryoablation treatment plan based on the one or more characteristics of the target tissue and the ice formation model; Initiate cryoablation freezing cycle; obtaining ice formation data describing one or more characteristics of ice being formed during the cryoablation-freezing cycle; as well as The location of one or more isotherms of the ice being formed is determined based on the ice formation data and the ice formation model.

2. The system of claim 1, wherein the ice formation model describes the growth of ice in multiple directions from a predetermined location on the cryoablation probe during a cryoablation freezing cycle.

3. The system of claim 1 or claim 2, wherein the ice formation model comprises an affine model configured to predict the growth of ice in multiple directions from a predetermined location on the cryoablation probe.

4. The system of any one of the preceding claims, wherein the one or more characteristics of the target tissue include: The type of tissue, the size of the tissue, and the shape of the tissue.

5. The system of any preceding claim, wherein the ice formation data comprises imaging data obtained from an imaging device.

6. The system of claim 5, wherein the imaging data indicates an outer edge of the ice being formed during the cryoablation cycle.

7. The system of any one of the preceding claims, wherein each of the one or more isotherms indicates a boundary at which a predetermined temperature threshold has been reached.

8. The system of claim 7, wherein at least one of the one or more isotherms corresponds to a boundary where the ice has reached -20°C.

9. The system according to any one of the preceding claims, wherein the step of determining the position of one or more isotherms comprises: Based on the location of the edge surface of the ice and the ice formation model, the location relative to the cryoablation probe at which the ice has reached a boundary of -20°C is determined.

10. The system according to any one of the preceding claims, wherein the computing device is further configured to: comparing the ice formation data to the ice formation model to determine whether the ice being formed during the cryoablation treatment is growing in a predicted manner using one or more affine ratios; and When the ice formation data indicates that the one or more affine ratios are greater than a predetermined upper threshold or less than a predetermined lower threshold, growth of the ice being formed is adjusted.

11. A method of performing cryoablation therapy, comprising: Obtain ice formation models; preparing a cryoablation treatment plan based on the one or more characteristics of the target tissue and the ice formation model; Initiate cryoablation freezing cycle; obtaining ice formation data describing one or more characteristics of ice being formed during the cryoablation-freezing cycle; as well as The location of one or more isotherms of the ice being formed is determined based on the ice formation data and the ice formation model.

12. The method of claim 11, wherein the ice formation model describes the growth of ice in multiple directions from a predetermined location on the cryoablation probe during a cryoablation freezing cycle.

13. A method according to claim 11 or claim 12, wherein the ice formation model comprises an affine model configured to predict the growth of ice in multiple directions from a predetermined location on the cryoablation probe.

14. The method of any one of claims 11 to 13, wherein the one or more characteristics of the target tissue include the type of tissue, the size of the tissue, and the shape of the tissue.

15. The method of any one of claims 11 to 14, wherein the ice formation data comprises: Imaging data obtained from an imaging device.

16. The method of claim 15, wherein the imaging data indicates an outer edge of the ice being formed during the cryoablation cycle.

17. The method of any one of claims 11 to 16, wherein each of the one or more isotherms indicates a boundary at which a predetermined temperature threshold has been reached.

18. The method of any one of claims 11 to 17, wherein at least one of the one or more isotherms corresponds to a boundary where the ice has reached -20°C.

19. The method according to any one of claims 11 to 18, wherein the step of determining the position of one or more isotherms comprises: Based on the location of the edge surface of the ice and the ice formation model, the location relative to the cryoablation probe at which the ice has reached a boundary of -20°C is determined.

20. The method according to any one of claims 11 to 19, further comprising: comparing the ice formation data to the ice formation model to determine whether the ice being formed during the cryoablation treatment is growing in a predicted manner using one or more affine ratios; as well as When the ice formation data indicates that the one or more affine ratios are greater than a predetermined upper threshold or less than a predetermined lower threshold, growth of the ice being formed is adjusted.

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