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EP-4736176-A1 - DETERMINING CONDUCTIVITIES OF MEDICAL IMAGES BASED ON MEASURED RESISTANCES

EP4736176A1EP 4736176 A1EP4736176 A1EP 4736176A1EP-4736176-A1

Abstract

A method for generating at least one transducer location for delivering tumor treating fields to a subject is provided. The method includes obtaining a medical image of a subject, the medical image having a plurality of voxels, the medical image representing a plurality of tissue types of the subject, wherein at least one voxel is associated with each tissue type. The method further includes determining, using a trained machine learning model and the medical image of the subject, conductivities for the tissue types of the subject in the medical image, the trained machine learning model trained with medical images of a plurality of other subjects and resistances obtained from the application of tumor treating fields to the other subjects. The method further includes identifying a location of a tumor in the medical image and generating at least one transducer location for delivering tumor treating fields to the subject.

Inventors

  • BERGER, BRIAN
  • SHAPIRA, Nadav

Assignees

  • Novocure GmbH

Dates

Publication Date
20260506
Application Date
20240618

Claims (15)

  1. 1. A computer-implemented method for generating at least one transducer location for delivering tumor treating fields to a subject, the method comprising: obtaining a medical image of the subject, the medical image having a plurality of voxels, the medical image representing a plurality of tissue types of the subject, wherein at least one voxel is associated with each tissue type; determining, using a trained machine learning model and the medical image of the subject, conductivities for the tissue types of the subject in the medical image, the trained machine learning model trained with medical images of a plurality of other subjects and resistances obtained from an application of tumor treating fields to the other subjects; identifying a location of a tumor in the medical image of the subject; and generating the at least one transducer location for delivering the tumor treating fields to the subject based on the conductivities for the tissue types of the subject in the medical image and the location of the tumor in the medical image.
  2. 2. The method of claim 1, wherein determining conductivities for the tissue types of the subject produces a conductivity mapping of the medical image of the subject.
  3. 3. The method of claim 1, wherein identifying the location of the tumor in the medical image of the subject is based on user input.
  4. 4. The method of claim 1, wherein the resistances obtained from the application of tumor treating fields are for a plurality of voltages at one or more frequencies of the tumor treating fields.
  5. 5. The method of claim 1, wherein the trained machine learning model was trained over a range of voltages and over a range of frequencies for the application of tumor treating fields to the other subjects.
  6. 6. The method of claim 1, wherein the trained machine learning model is for determining conductivities for a designated tumor treating fields frequency and a designated cancer.
  7. 7. The method of claim 1, further comprising training a machine learning model to obtain the trained machine learning model, wherein the machine learning model is trained with the medical images of the other subjects and the resistances obtained from the application of tumor treating fields to the other subjects, wherein at least one medical image of the other subjects includes a tumor.
  8. 8. The method of claim 7, wherein training the machine learning model to obtain the trained machine learning model comprises: calculating the resistances based on currents measured from applying the tumor treating fields to the other subjects at associated voltages, wherein the currents are obtained from memory.
  9. 9. A computer-implemented method for obtaining a trained machine learning model to identify conductivities in a medical image, the method comprising: obtaining a plurality of medical images for a plurality of subjects, each medical image having a plurality of voxels, each medical image comprising a plurality of tissue types of the subject, wherein at least one voxel in each medical image is associated with each tissue type of the subject; obtaining measured resistances of each subject from application of tumor treating fields to each subject; and training a machine learning model to determine the conductivities in a medical image, the machine learning model being trained using the plurality of medical images for the plurality of subjects and the measured resistances of each subject from the application of tumor treating fields to each subject.
  10. 10. The method of claim 9, wherein the measured resistances are associated with a range of voltages and a range of frequencies from the application of tumor treating fields to the plurality of subjects.
  11. 11. The method of claim 9, further comprising calculating the measured resistances using currents measured from applying tumor treating fields to the plurality of subjects at associated voltages.
  12. 12. The method of claim 9, further comprising: applying tumor treating fields to the plurality of subjects at associated voltages to obtain measured currents; and calculating the measured resistances using the measured currents and the associated voltages.
  13. 13. The method of claim 12, wherein the measured currents are obtained from locations on the subject receiving the tumor treating fields.
  14. 14. The method of claim 9, wherein the trained machine learning model is able to determine conductivities for voxels in a medical image associated with tissue of a subject.
  15. 15. An apparatus for selecting transducer locations for delivering tumor treating fields to a subject, the apparatus comprising: one or more processors; and memory accessible by the one or more processors, the memory storing instructions that when executed by the one or more processors, cause the apparatus to: determine, using a trained machine learning model and medical image of the subject, conductivities for the tissue types of the subject in the medical image, the trained machine learning model trained with medical images of a plurality of other subjects and resistances obtained from an application of tumor treating fields to the other subjects; identify a location of a tumor in the medical image of the subject; and generate at least one transducer location for delivering the tumor treating fields to the subject based on the conductivities for the tissue types of the subject in the medical image and the location of the tumor in the medical image.

Description

DETERMINING CONDUCTIVITIES OF MEDICAL IMAGES BASED ON MEASURED RESISTANCES CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This Application claims priority to U.S. Patent Application No. 18/745,184 filed June 17, 2024, and U.S. Provisional Application No. 63/524,511 filed June 30, 2023, the contents of which are incorporations by reference herein in their entirety. BACKGROUND [0002] Tumor treating fields (TTFields) are low intensity alternating electric fields within the intermediate frequency range (for example, 50 kHz to 1 MHz), which may be used to treat tumors as described in U.S. Patent No. 7,565,205. TTFields are induced non- invasively into the region of interest by transducers placed on a subject’s body (i.e., the patient), and applying alternating current (AC) voltages between the transducers. Conventionally, a first pair of transducers and a second pair of transducers are placed on the subject’s body. AC voltage is applied between the first pair of transducers for a first interval of time to generate an electric field with field lines generally running in the front-back direction. Then, AC voltage is applied at the same frequency between the second pair of transducers for a second interval of time to generate an electric field with field lines generally running in the right-left direction. The system then repeats this two-step sequence throughout the treatment. BRIEF DESCRIPTION OF THE DRAWINGS [0003] FIG. 1 depicts an example method for training a machine learning model to determine conductivities in a medical image according to one or more embodiments described herein. [0004] FIG. 2 depicts an example method for determining conductivities of medical images based on measured resistances according to one or more embodiments described herein. [0005] FIG. 3 depicts an example apparatus to apply alternating electric fields to the subject’s body according to one or more embodiments described herein. [0006] FIG. 4A and 4B depict schematic views of exemplary designs of a transducer for applying alternating electric fields according to one or more embodiments described herein. [0007] FIG. 5 depicts an example placement of transducers on a subject’s head according to one or more embodiments described herein. [0008] FIG. 6 depicts an example computer apparatus according to one or more embodiments described herein. DESCRIPTION OF EMBODIMENTS [0009] This application describes exemplary techniques for training a machine learning model to predict conductivity measurements for different tissue types in a medical image and using the trained machine learning model to identify locations on a subject’s body to place transducers for applying TTFields. [0010] In general, one or more pairs of transducers are positioned on the subject’s body and used to alternately apply AC voltage (e.g., TTFields) to the subject’s body. Generally, it is preferred that there are at least two pairs of transducers that are arranged to target a specific location or structure (e.g., a tumor) within the subject. Accordingly, proper placement of transducers is important for treating the subject. To provide a subject with an effective TTFields treatment, precise locations at which to place the transducers on the subject’s body must be generated, and these precise locations are based on, for example, the type of the cancer, the size of the cancer, and the location of the cancer in the subject’s body. However, determining these precise locations is challenging, and this determination is typically accomplished using computer simulations of numerous possible locations to place the transducers on a three-dimensional model of a subject. Deriving the precise locations are time consuming and intensive. [0011] Conventional treatment planning uses a series of measurements taken from a subject’s magnetic resonance imaging (MRI) scans to measure aspects of the subject (e.g., the subject’s head size, a location of the tumor, a size of the tumor, and/or the like including combinations and/or multiples thereof). Using these measurements, a customized layout for the transducers is generated. Generating transducer layouts requires considering electrical properties, such as conductivity, for the tissue types of the subject. Health care providers need to identify the various tissue types by hand, which is very tedious and can take a significant amount of time due to the large number of voxels in medical images. Once the tissue types of the subject are determined for the medical images of the subject, each voxel in each medical image has a tissue type assigned to the voxel, and conductivities can then be assigned to each of the tissue types for each of the voxels. [0012] The inventors have now recognized that a need exists for determining conductivities of tissue types in medical images of a subject. The inventors have further discovered that measured resistances from other subjects can be used to predict conductivities of tissue types for another subject. [0