TREATMENT PLANNING SYSTEMS AND METHODS FOR BODY CONTOURING APPLICATIONS

• US 20100081971A1
• Filed: 09/23/2009
• Published: 04/01/2010
• Est. Priority Date: 09/25/2008
• Status: Active Grant

• Current Assignee: ZELTIQ Aesthetics, Inc. (Abbvie Incorporated)
• Original Assignee: ZELTIQ Aesthetics, Inc. (Abbvie Incorporated)
• Inventors: Allison, John W.
• Granted Patent: US 8,275,442 B2
• Time in Patent Office: Days
• US Class Current: 601/2
• CPC Class Codes: A61B 18/203 applying laser energy to the outside of the body
• CPC Class Codes: A61B 2018/00452 Skin
• CPC Class Codes: A61B 2034/102 Modelling of surgical devices, implants or prosthesis
• CPC Class Codes: A61B 2034/256 having a database of accessory information, e.g. including context sensitive help or scientific articles
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61F 2007/0075 using a Peltier element, e.g. near the spot to be heated or cooled
• CPC Class Codes: A61F 7/00 Heating or cooling appliances for medical or therapeutic treatment of the human body heating or cooling means in connection with bedsteads or mattresses A47C21/00 ; massage, therapeutic baths A61H; hyperthermia using electric or magnetic fields or ultrasound A61N; electric heating elements H05B
• CPC Class Codes: A61F 7/10 Cooling bags, e.g. ice-bags
• CPC Class Codes: A61N 2005/1041 using a library of previously administered radiation treatment applied to other patients
• CPC Class Codes: A61N 2007/0008 Destruction of fat cells
• CPC Class Codes: A61N 5/00 Radiation therapy ultrasound therapy A61N7/00; devices or apparatus applicable to both therapy and diagnosis A61B6/00
• CPC Class Codes: A61N 7/00 Ultrasound therapy lithotripsy A61B17/22, A61B17/225; massage using supersonic vibration A61H23/00 ; using ultrasound for introducing media into the body A61M37/0092
• CPC Class Codes: A61N 7/02 Localised ultrasound hyperthermia hyperthermia in general A61F7/00
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Methods and system for treatment planning for non- and minimally-invasive alteration of body adipose tissue for reduction and contouring of body fat are described herein. Treatment plans can be generated by capturing current body part data (e.g., positioning, contour/shape, thickness of adipose tissue, etc.), determining desired outcome of treatment (e.g., percent reduction of adipose tissue thickness, degree of contour change, etc.), and determining treatment parameters to achieve desired results. Algorithms can be used to determine best-fit treatment parameters to use in treatment sessions. In some embodiments, the system can provide a predictive end-result image for communication to patient and/or for determining alteration of desired outcome. In various embodiments, real-time monitoring of feedback data can be used to determine treatment plan efficacy. Additional algorithms can provide real-time comparison of feedback data to anticipated feedback data, and can be used to change treatment parameters in real-time to achieve desired effects.

Claims

1. A system for generating a patient-specific treatment plan for non-invasive fat removal from a patient comprising:

a computer network for transmitting information relating to a patient'"'"'s target region, the target region comprising subcutaneous fat, wherein the information includes one or more of the treatment plan requests, data, images, and treatment plans;

a client computer associated with a treatment provider and in communication with the computer network;

a database connected to the computer network for storing a plurality of model data sets and a plurality of treatment parameters, wherein the plurality of model data sets include at least one of empirically-derived data and a priori information, and wherein the model data sets correspond to unique combinations of the treatment parameters; and

a treatment plan generator in communication with the computer network and configured to receive patient-specific data and treatment plan requests from the client computer, compare patient-specific data to the plurality of model data sets in the database, and calculate a best-fit combination of treatment parameters from the plurality of treatment parameters to generate the patient-specific treatment plan;

wherein the best-fit combination of treatment parameters includes at least one of transdermal application of energy or removal of energy from the patient'"'"'s target region.

2. The system of claim 1 wherein patient-specific data includes pre-treatment data derived from a patient'"'"'s target region and objective post-treatment data relating to one or more desired treatment results.

3. The system of claim 2 wherein the treatment plan generator comprises:

a data acquisition module configured to—

receive patient-specific pre-treatment data from the client computer to create a patient-specific pre-treatment data set, the pre-treatment data relating to at least the patient'"'"'s subcutaneous fat at the target region;

receive patient-specific objective post-treatment data from the client computer to create a patient-specific objective post-treatment data set, the patient-specific objective post-treatment data relating to a desired change in the patient'"'"'s subcutaneous fat at the target region; and

deposit the patient-specific pre-treatment and objective post-treatment data sets into one or more data set libraries stored in the database;

a treatment plan request module configured to—

receive a treatment plan request from the client computer, wherein the request indicates the patient-specific pre-treatment data set;

retrieve the patient-specific pre-treatment data set, patient-specific objective post-treatment data set and the plurality of model data sets from the database; and

initiate a treatment plan generation session, wherein the session corresponds to the indicated patient-specific pre-treatment and objective post-treatment data sets;

a predictive modeling module configured to rank the plurality of model data sets in accordance with a degree of affinity to the patient-specific pre-treatment data set and the patient-specific objective post-treatment data set; and

a treatment plan formulation module configured to calculate the best-fit combination of treatment parameters from the plurality of treatment parameters by determining the unique combination of treatment parameters corresponding to one or more model data sets having a highest affinity to the patient-specific data.

4. The system of claim 3 wherein the one or more model data sets having a highest affinity to the patient-specific data includes the model data sets having an affinity over a pre-determined threshold affinity.

5. The system of claim 3 wherein the treatment plan generator further comprises an optimization module configured to:

query the client computer to request additional patient-specific data;

receive additional patient-specific data from the client computer and update at least one of patient-specific pre-treatment data set and patient-specific objective post-treatment data set; and

transmit updated patient-specific data to the predictive modeling module for ranking model data sets.

6. The system of claim 3 wherein the treatment plan generator further comprises a real-time optimization module configured to:

receive real-time feedback data during treatment administration from the client computer;

compare the real-time feedback data to an anticipated feedback data based on the one or more model data sets having a highest affinity to the patient-specific data;

calculate a difference between the real-time feedback data and the anticipated feedback data to create a patient-specific actual data set;

modify the best-fit combination of treatment parameters to generate a modified treatment plan based on at least the patient-specific actual data set; and

transmit the modified treatment plan to the client computer for changing treatment administration in real-time.

7. The system of claim 2 wherein the treatment plan generator is further configured to:

generate a first graphical image representing the pre-treatment data; and

generate a second graphical image representing a desired post-treatment outcome, wherein the second graphical image is based on the pre-treatment data and the objective post-treatment data.

8. The system of claim 2, further comprising a predictive modeling module configured to:

rank the plurality of model data sets in accordance with a degree of affinity to the patient-specific pre-treatment data set and the patient-specific objective post-treatment data set

transmit to the client computer a first graphical image representing the model data set having a highest degree of affinity to the patient-specific pre-treatment data set; and

generate and transmit to the client computer a second graphical image representing a desired post-treatment outcome, wherein the second graphical image is based on the first graphical image and the objective post-treatment data set.

9. The system of claim 8 wherein:

the treatment plan generator further comprises an optimization module configured to—

receive an optimization command from the client computer following transmission of the first graphical image or the second graphical image;

receive additional patient-specific data from client computer and update at least one of patient-specific pre-treatment data set and patient-specific objective post-treatment data set; and

transmit updated patient-specific data to the predictive modeling module for re-ranking the model data sets; and

the predictive modeling module is configured to modify at least one of the first graphical image and the second graphical image.

10. The system of claim 1 wherein the treatment plan generator is further configured to receive actual patient-specific post-treatment data, and wherein the post-treatment data is generated at one or more time points following treatment.

11. The system of claim 10 wherein the patient-specific data includes pre-treatment data, and wherein the pre-treatment data, the actual patient-specific post-treatment data, and the best-fit combination of treatment parameters are deposited in the database as a new model data set.

12. The system of claim 11 wherein:

the patient-specific treatment plan is a first patient-specific treatment plan provided for a patient; and

the treatment plan generator is further configured to—

rank the plurality of model data sets in accordance with a degree of affinity to patient-specific pre-treatment data; and

positively weigh the new model data set when ranking the plurality of model data sets for generating a second patient-specific treatment plan for the patient.

13. The system of claim 1 wherein the patient-specific data includes one or more image files corresponding to the patient'"'"'s target region to be treated, and wherein the treatment plan generator is configured to extract target region data from the image files to compare to the plurality of model data sets in the database.

14. The system of claim 1 wherein the treatment plan generator is configured to query the client computer for additional patient-specific data.

15. The system of claim 1 wherein the treatment plan generator assigns a unique identification number to a patient-specific pre-treatment data set.

16. The system of claim 1 wherein the treatment plan generator resides on a server connected to the computer network.

17. The system of claim 1 wherein the treatment plan generator resides on the client computer.

18. A system for developing and administering a patient-specific treatment plan comprising:

a computing device;

a user interface in communication with the computing device for enabling a user to request a treatment plan and to specify patient-specific data, wherein patient-specific data includes at least one of a target region pre-treatment data element and an objective post-treatment data element;

a predictive modeling module configured to—

receive and compare the patient-specific data to a plurality model data sets, wherein the model data sets comprise at least one of empirically-derived data and a priori information, and wherein the model data sets correspond to unique combinations of treatment parameters; and

rank the plurality of model data sets in accordance with a degree of affinity to the patient-specific data;

a treatment plan formulation module configured to calculate a best-fit combination of treatment parameters from a plurality of treatment parameters to generate the patient-specific treatment plan, wherein calculating the best-fit combination includes determining the unique combination of treatment parameters corresponding to one or more model data sets having a highest affinity to the patient-specific data; and

a treatment system in communication with the computing device for non-invasive, transdermal alteration of a lipid-rich target region contour, the treatment system configured to receive the patient-specific treatment plan from the treatment formulation module, and administer treatment to the lipid-rich target region, wherein the treatment includes the best-fit combination of treatment parameters.

19. The system of claim 18 wherein the treatment system is configured to alter a lipid-rich target region contour through non-invasive, transdermal removal of heat from subcutaneous lipid-rich cells of a patient.

20. The system of claim 18 wherein the treatment system is configured to alter a lipid-rich target region contour through non-invasive, transdermal positive heat transfer to subcutaneous lipid-rich cells of a patient.

21. The system of claim 18 wherein the treatment system is configured to alter a lipid-rich target region contour by directing ultrasonic energy at the lipid-rich target region.

22. The system of claim 18 wherein the treatment system includes one or more high intensity focused ultrasound transducers.

23. The system of claim 18 wherein the treatment system includes one or more radio frequency electrodes.

24. The system of claim 18 wherein the predictive modeling module is further configured to:

generate and transmit to the user interface a first graphical image representing the model data set having a highest degree of affinity to the patient-specific data; and

determine an anticipated feedback data set based on the one or more model data sets having a highest affinity to the patient-specific data and the best-fit combination treatment parameters, wherein the anticipated feedback data includes one or more heat flux measurements.

25. The system of claim 24, further comprising a real-time optimization module configured to:

receive real-time feedback data during treatment administration from the computing device;

compare real-time feedback data to the anticipated feedback data set;

calculate a difference between the real-time feedback data and the anticipated feedback data;

modify the best-fit combination of treatment parameters to generate a modified treatment plan; and

transmit the modified treatment plan to the treatment system for changing treatment administration in real-time.

26. The system of claim 18 wherein the treatment system includes:

a treatment device having an applicator and one or more heat exchanging units; and

a controller for modifying operation of the treatment device upon receiving treatment plan instructions and modified treatment plan instructions, wherein the controller is in communication with the computing device.

27. The system of claim 26 wherein the treatment parameters include cooling temperature and duration profiles for the one or more heat exchanging units.

28. The system of claim 18 wherein:

the patient-specific data comprises one or more objective post-treatment data elements;

the system further comprises a real-time optimization module configured to—

receive real-time feedback data during preliminary treatment administration from the computing device to determine actual target region data; and

deliver the patient-specific treatment plan to the computing device in real-time;

the predictive modeling module is configured to receive and compare the actual target region data to the plurality model data sets, and rank the plurality of model data sets in accordance with a degree of affinity to the actual target region data and the objective post-treatment data elements;

the treatment plan formulation module is configured to calculate the best-fit combination of treatment parameters to generate the patient-specific treatment plan, wherein calculating the best-fit combination includes determining the unique combination of treatment parameters corresponding to one or more model data sets having a highest affinity to the actual target region data and the objective post-treatment data elements; and

the treatment system is configured to receive the patient-specific treatment plan from the computing device in real-time and modify treatment parameters during treatment based on the patient-specific treatment plan.

29. The system of claim 18 wherein the patient data comprises one or more target region pre-treatment data elements, and wherein the predictive modeling module is configured to generate a first graphical image representing the target region pre-treatment data elements and generate a second graphical image representing a recommended post-treatment outcome.

30. The system of claim 18, further comprising a position sensor in communication with the computing device, the position sensor configured to detect and transmit data relating to the position and size of the lipid-rich target region relative to a reference point.

31. The system of claim 18, further comprising a medical imaging device for generating one or more images defining one or more aspects of the lipid-rich target region.

32. The system of claim 18 wherein at least one of the predictive modeling module and the treatment plan formulation module resides on a server in communication with the computing device.

33. The system of claim 18 wherein at least one of the predictive modeling module and the treatment plan formulation module reside on the computing device.

34. A system for developing and administering a patient-specific treatment plan comprising:

a treatment system for non-invasive, transdermal removal of heat from subcutaneous lipid-rich cell of a patient;

a computing device in communication with the treatment system and configured to receive and transmit patient-specific data and patient-specific treatment plans, wherein the patient-specific data includes one or more objective post-treatment data elements and real-time feedback data;

a treatment plan generator in communication with the computing device and configured to receive and compare the patient-specific data to a plurality of model data sets, wherein the model data sets correspond to unique combinations of treatment parameters, and wherein the treatment plan generator includes—

a real-time optimization module configured to receive real-time feedback data from the computing device during preliminary treatment administration to determine actual target region data;

a predictive modeling module configured to receive and compare the actual target region data to the plurality model data sets, and rank the plurality of model data sets in accordance with a degree of affinity to the actual target region data and the one or more objective post-treatment data elements; and

a treatment plan formulation module configured to calculate a best-fit combination of treatment parameters to generate the patient-specific treatment plan, wherein calculating the best-fit combination includes determining the unique combination of treatment parameters corresponding to one or more model data sets having a highest affinity to the actual target region data and the one or more objective post-treatment data elements; and

wherein the treatment system is configured to receive the patient-specific treatment plan from the computing device in real-time and modify treatment parameters during treatment based on the patient-specific treatment plan.

35. The system of claim 34 wherein the real-time feedback data includes one or more heat flux measurements, and wherein the one or more heat flux measurements is used to determine a subcutaneous adipose tissue thickness.

36. The system of claim 34 wherein the treatment system includes a treatment device having an applicator and one or more heat exchanging units, and wherein the treatment parameters include cooling temperature and duration profiles for the one or more heat exchanging units.

37. A computer-implemented method for generating a patient-specific treatment plan, the method comprising:

receiving patient-specific data and a request for generating a patient-specific treatment plan, wherein the patient-specific data relates to a current body contour at a lipid-rich target region of a patient, and wherein the patient-specific treatment plan includes operational parameters for administering treatment to the patient for altering the current body contour;

creating a pre-treatment data set comprising lipid-rich target region data elements;

comparing the pre-treatment data set to a plurality of model data sets, wherein the model data sets correspond to unique combinations of treatment parameters;

ranking the plurality of model data sets in accordance with a degree of affinity to the pre-treatment data set;

calculating a best-fit combination of treatment parameters from the unique combination of treatment parameters corresponding to one or more model data sets having a highest affinity to the pre-treatment data set; and

generating the patient-specific treatment plan for implementation by a treatment system, wherein the patient-specific treatment plan includes the best-fit combination of treatment parameters.

38. The method of claim 37, further comprising:

receiving real-time feedback data during treatment administration, wherein the real-time feedback data includes a heat flux measurement;

comparing real-time feedback data to an anticipated feedback data set;

calculating a difference between the real-time feedback data and the anticipated feedback data set to create a patient-specific actual data set; and

modifying the best-fit combination of treatment parameters to generate a modified treatment plan based on at least the patient-specific actual data set.

39. The method of claim 37 wherein the treatment plan includes a treatment plan for non-invasive, transdermal removal of heat from subcutaneous lipid-rich cells of a patient, and wherein receiving patient-specific data includes receiving data relating to at least one of a target region body position and a subcutaneous adipose tissue thickness.

40. The method of claim 39 wherein:

receiving data relating to at least one of target region body position and a subcutaneous adipose tissue thickness includes receiving data relating to an estimated subcutaneous adipose tissue thickness;

the treatment plan is generated based at least in part on the estimated thickness; and

wherein the method further comprises—

receiving real-time feedback data during treatment administration, wherein the real-time feedback data includes an actual subcutaneous adipose tissue thickness measurement; and

modifying the best-fit combination of treatment parameters to generate a modified treatment plan based on the actual thickness measurement.

41. The method of claim 37, further comprising:

displaying a first graphical image, wherein the first graphical image represents the current body contour; and

displaying a second graphical image, wherein the second graphical image represents a predicted post-treatment body contour.

42. The method of claim 37 wherein receiving patient-specific data includes receiving target region pre-treatment data and objective post-treatment data, and wherein ranking the plurality of model data sets includes ranking in accordance with a degree of affinity to the target region pre-treatment data and the objective post-treatment data.

43. The method of claim 37 wherein the treatment plan includes a treatment plan for non-invasive, transdermal ablation of subcutaneous lipid-rich cells of a patient using high intensity focused ultrasound energy.

44. A computer-readable medium whose contents cause at least one computer to perform a method for providing a treatment plan for altering a patient body contour, the method comprising:

receiving patient-specific objective post-treatment data relating to a desired body contour;

receiving actual patient-specific data relating to a current body contour generated during preliminary treatment administration;

ranking a plurality of model data sets in accordance with a degree of affinity to the actual patient-specific data and the patient-specific objective post-treatment data, wherein the model data sets include at least one of empirically-derived data and a priori information, and wherein the model data sets correspond to unique combinations of treatment parameters;

calculating a best-fit combination of treatment parameters from the unique combination of treatment parameters corresponding to one or more model data sets having a highest affinity to the actual patient-specific data and the patient-specific objective post-treatment data; and

providing a treatment plan for administering treatment to alter the current body contour to a body contour at least approximate to the desired body contour, wherein the treatment plan includes the best-fit combination of treatment parameters.

45. The computer-readable medium of claim 44 wherein the treatment parameters include cooling temperature and duration profiles for one or more heat exchanging units applied to a skin of a patient.

46. The computer-readable medium of claim 44 wherein receiving actual patient-specific data relating to a current body contour includes receiving the actual patient-specific data in real-time, and wherein providing a treatment plan for administering treatment includes providing the treatment plan for administering treatment in real time.

47. The computer-readable medium of claim 44, further comprising:

displaying a first graphical image, wherein the first graphical image represents the actual patient-specific data; and

displaying a second graphical image, wherein the second graphical image represents an anticipated post-treatment result, and wherein the anticipated post-treatment result is based on the actual patient-specific data and the patient-specific objective post-treatment data.

48. The computer-readable medium of claim 44 wherein receiving actual patient-specific data relating to a current body contour includes receiving one or more heat flux measurements, and wherein the heat flux measurements are used to determine a subcutaneous adipose tissue thickness.

49. A method performed by a system for providing a user interface relating to generating a treatment plan for cooling a subcutaneous lipid-rich target region of a patient, comprising:

receiving patient-specific data relating to the subcutaneous lipid-rich target region;

displaying a first graphical image representing the patient-specific data;

receiving objective post-treatment data relating to a desired treatment result; and

displaying a second graphical image representing the desired treatment result, wherein the second graphical image is based upon the patient-specific data and the objective post-treatment data.

50. The method of claim 49, further comprising displaying a treatment plan, wherein the treatment plan includes a calculated best-fit combination of treatment parameters for achieving the desired treatment result.

51. The method of claim 49, further comprising receiving additional patient-specific data for modifying at least the second graphical image.