DETERMINING CONDUCTIVITIES OF MEDICAL IMAGES BASED ON MEASURED RESISTANCES
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.
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 . 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 . 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 . The method of claim 1 , wherein identifying the location of the tumor in the medical image of the subject is based on segmenting tumor tissue from other tissue in the medical image.
5 . The method of claim 1 , wherein the plurality of tissue types includes one or more of skin, bone, skull, organ, or brain.
6 . 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.
7 . The method of claim 1 , wherein the resistances obtained from the application of tumor treating fields are based on currents measured from applying tumor treating fields to the other subjects at associated voltages.
8 . 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.
9 . 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.
10 . 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.
11 . The method of claim 10 , 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.
12 . 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.
13 . The method of claim 12 , wherein two or more of the medical images are associated with a similar region of a subject of the plurality of subjects.
14 . The method of claim 12 , 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.
15 . The method of claim 12 , further comprising calculating the measured resistances using currents measured from applying tumor treating fields to the plurality of subjects at associated voltages.
16 . The method of claim 12 , 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.
17 . The method of claim 16 , wherein the measured currents are obtained from locations on the subject receiving the tumor treating fields.
18 . The method of claim 16 , wherein the associated voltages are generated by a voltage generator used to generate the tumor treating fields.
19 . The method of claim 12 , wherein the trained machine learning model is able to determine conductivities for voxels in a medical image associated with tissue of a subject.
20 . 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.