Patent Application
20250209690
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. Pat. No. 7,565,205. TTFields are induced non-invasively into a region of interest by transducers placed on the patient's body 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.
Various embodiments are described in detail below with reference to the accompanying drawings, where like reference numerals represent like elements.
This application describes exemplary techniques to computationally reconstruct a truncated medical image and further provide one or more recommended transducer layouts for applying TTFields to a subject based on the reconstructed medical image. As an example, a truncated medical image may result from generating a medical image of a torso of a subject, and the left and right sides of the subject may be truncated in the medical image.
Previously, a truncated medical image may have been acceptable when considering a radiation treatment plan to treat a tumor of a subject. However, a truncated medical image may be unacceptable for TTFields treatment planning, which may require knowing conductivities of certain areas of the subject to determine energy flow for TTFields treatment planning. If a portion of the subject is missing due to truncation in the medical image (i.e., a truncated medical image), conductivities of this missing portion of the subject will not be known, and, as such, energy flow in this missing portion cannot be determined, which results in the inability to provide accurate TTFields treatment planning. If the subject only has one medical image, which is a truncated medical image, the subject would need to re-schedule to obtain a medical image without truncation, thereby delaying TTFields treatment as well as increasing the costs of the TTFields treatment. If the subject has multiple medical images including a truncated medical image having particularly relevant information, the truncated medical image may not be used for TTFields treatment planning, thereby losing the particularly relevant information.
To alleviate these problems, the inventors discovered computational techniques to reconstruct a truncated medical image based on a trained machine learning model to obtain a reconstructed medical image, which may then be used for TTFields treatment planning. As such, medical images with truncated portions that may have previously not been usable for TTFields treatment planning may now be used for TTFields treatment planning by employing the inventive techniques. The inventive techniques are particularly integrated into a practical application of providing a reconstructed medical image, which may be used in further computational processing techniques, such as generating one or more recommended transducer layouts for applying TTFields to the subject based on the reconstructed medical image. With the inventive techniques, the process of generating a reconstructed medical image may result in quicker, less expensive, and/or more accurate TTFields treatment planning.
In some embodiments, the inventive techniques include training a machine learning model using a plurality of existing medical images of a plurality of the subjects to obtain a trained machine learning model that produces a reconstructed medical image from a truncated medical image. With the reconstructed medical image generated with the inventive techniques, a plurality of transducer layouts for application of TTFields to the subject may be generated, and one or more of the transducer layouts may be recommended for applying TTFields treatment to the subject.
With reference to
At step 104, the method 100 may include processing the medical images by truncating a portion of each medical image to obtain truncated medical images. As an example, the truncated portion of each medical image may correspond to a right side and a left side of each subject.
At step 106, the method 100 may include designating a set of the truncated medical images as training truncated medical images. In some embodiments, step 106 may be computer implemented. In some embodiments, step 106 may be implemented based on user input, such as identifying which and/or how many of the truncated medical images should be designated as the training truncated medical images.
At step 108, the method 100 may include training a machine learning model to obtain a trained machine learning model, where the machine learning model is trained with the training truncated medical images, and where the machine learning model is trained to generate a reconstructed medical image without truncation. In some embodiments, step 108 may be computer implemented. Prior to providing the training truncated medical images to the machine learning model, a quality test may be performed on the training truncated medical images by, for example, human eye or calculating a loss value of a network itself, to help provide better training quality.
The machine learning model may include one or more algorithms and/or structures. In some embodiments, the machine learning model may be an unsupervised machine learning model. In some embodiments, the trained machine learning model may be a deep learning neural network. In some embodiments, the machine learning model may include at least one of a generative adversarial network (GAN), a MedGAN, a super resolution GAN, a pix2pix GAN, a cycleGAN, a discoGAN, a fila-sGAN, a projective adversarial network (PAN), a variational autoencoder (VAE), or an unsupervised neural network. As one example, the machine learning model may be a GAN including a deconvolutional neural network as a generator and a convolutional neural network as a discriminator.
At step 110, the method 100 may include designating a set of the truncated medical images as testing truncated medical images. The testing truncated medical images may be different than the training truncated medical images. In some embodiments, each truncated medical images may be identified as either a training truncated medical image in step 106 or a testing truncated medical image in step 110. In some embodiments, step 110 may be computer implemented. In some embodiments, step 110 may be implemented based on user input, such as identifying which and/or how many of the truncated medical images should be designated as the testing truncated medical images. A quality test applied may also be applied to the testing truncated medical images.
At step 112, the method 100 may include generating reconstructed medical images using the trained machine learning model and the testing truncated medical images. In some embodiments, step 112 may be computer implemented.
At step 114, the method 100 may include comparing the reconstructed medical images to the medical images corresponding to the training truncated medical images to obtain comparison results. In some embodiments, step 114 may be computer implemented.
At step 116, the method 100 may include determining if the comparison results are satisfactory or unsatisfactory. In some embodiments, the comparison may be determined as satisfactory if the reconstructed medical images and the medical images corresponding to the training truncated medical images have a same dose of TTFields treatment and/or a same ranking of transducer layouts for administration of TTFields. Upon determining that that the comparison results are satisfactory, the process goes to step 118.
Upon determining that the comparison results are unsatisfactory, the process goes back to repeat steps 108-114 to further train the machine learning model and to obtain a re-trained machine learning model. In particular, this re-training may involve utilizing updated training truncated medical images. In some embodiments, step 116 may be computer implemented.
At step 118, the method 100 may include obtaining the trained machine learning model. The trained machine learning model may be obtained once the comparison results are determined to be satisfactory. In some embodiments, step 118 may be computer implemented.
With reference to
At step 204, the method 200 may include determining if the medical image has a truncated portion of the subject.
In some embodiments, step 204 may be computer implemented. In some embodiments, the medical image may be determined as having a truncated portion of the subject upon determining that the subject depicted in the medical image has at least two flat surfaces. In some embodiments, the medical image may be determined as having a truncated portion of the subject upon determining that the subject depicted in the medical image has at least one flat surface. In some embodiments, the determination may be based on a determined perimeter of the subject in the medical image. In some embodiments, step 204 may be manually performed by a user or based on input from a user. As an example, the determination may include presenting the medical image on a display and receiving a user input identifying the medical image as having a truncated portion of the subject. Additional details regard step 204 are illustrated in
At step 206, the method 200 may include generating, using a trained machine learning model and the medical image, a reconstructed medical image. The voxels of the reconstructed medical image in the truncated portion having tissue image values. The trained machine learning model is trained to generate a reconstructed medical image without truncation. The trained machine learning model may be obtained according to method 100 of
At step 208, the method 200 may include determining if there is another medical image of the subject to access. Upon determining yes, the process goes back to repeat steps 202-206. Upon determining no, the process continues to step 210.
At step 210, the method 200 may include defining a region of interest (ROI) in the medical images (in one or more reconstructed medical images and/or one or more medical images that did not need to be reconstructed) for application of TTFields to the subject. The ROI may define where the TTFields are to focus. In some embodiments, the ROI may be defined based on user input or may be defined partially or entirely by the computer.
At step 212, the method 200 may include creating a three-dimensional (3D) model of the subject based on the reconstructed medical image(s) and the medical images that did not need to be reconstructed, where the 3D model of the subject may include the ROI. In some embodiments, the 3D model may include a 3D conductivity map depicting electrical conductivity of tissues of the subject. In some embodiments, the 3D model may be generated by the computer and may be based on user input
In some embodiments, creating the 3D model may include performing calculations to determine conductivity of the tissues of the subject based on the reconstructed medical image(s) and any other medical images, and the tissue types therein. As one example, creating the 3D model may include assigning tissue types and associated conductivities to voxels of the 3D model of the subject. In some embodiments, creating the 3D model of the subject may include automatically segmenting normal tissue in the medical images. In some embodiments, the 3D model may be created based on user input, such as user approval on a 3D conductivity map associated with the 3D model.
At step 214, the method 200 may include generating a plurality of transducer layouts for application of TTFields to the subject based on the 3D model of the subject, which is created based on the reconstructed medical image at step 212. The transducer layouts may define one or more locations, relative to the subject, for placing one or more transducers. In some embodiments, a transducer layout may include four locations on the subject to place four respective transducers, such as on a head or torso of the subject. In some embodiments, the plurality of the transducer layouts may include two locations on the subject to place two respective transducers, such as on a head or torso of the subject. In some embodiments, the transducer may include one electrode element or a plurality of electrode elements. The electrode elements may be any suitable type or material. For example, at least one electrode element may include a ceramic dielectric layer, a polymer film, and/or the like including combinations and/or multiples thereof. In some embodiments, step 214 may be computer implemented.
At step 216, the method 200 may include selecting one or more of the transducer layouts as recommended transducer layouts. In some embodiments, one, two, three, four or more transducer layouts may be selected as recommended transducer layouts. In some embodiments, step 216 may be computer implemented. In some embodiments, the selection may be based on user input.
At step 218, the method 200 may include presenting the recommended transducer layouts on a display. In some embodiments, the recommended transducer layouts may be presented on a display through one or more output devices for a user's consideration.
At step 220, the method 200 may include receiving a user selection of at least one recommended transducer layout. The user selection may indicate a preferred transducer layout to apply the TTFields in the ROI of the subject.
At step 222, the method 200 may include providing a report for the at least one selected recommended transducer layout, where the report indicates the selection result from step 122.
With reference to
At step 304, the method 300 may include determining if the perimeter of the subject has a straight line with a length greater than or equal to a straight-line threshold. In some embodiments, the straight-line threshold may be a maximum straight-line length allowed in a medical image so as to be considered not truncated. In some embodiments, the straight-line threshold may be determined by a user. As one example, the straight-line threshold may be between 3 cm to 5 cm. Upon determining that the perimeter of the subject has a straight line with a length greater than or equal to the straight-line threshold in step 304, the process goes to step 306. Upon determining that the perimeter of the subject does not have a straight line with a length greater than or equal to the straight-line threshold in step 304, the process goes to step 308.
In step 306, the medical image is designated as having a truncated portion of the subject and, hence, is designated as a truncated medical image.
In step 308, the medical image is designated as not having a truncated portion of the subject.
As an example of
With reference to
At step 404, the method 400A may include comparing the perimeter of the subject to an expected perimeter of the subject. In some embodiments, the expected perimeter of the subject may be based on a view of the subject in the slice of the medical image and at least one of gender, age, body mass index, a perimeter measurement, a height measurement, or a measurement of a portion of the subject.
At step 406, the method 400A may include determining if the perimeter of the subject is outside a tolerance of the expected perimeter of the subject. The tolerance of the expected perimeter of the subject may be represented by a percentage. In some embodiments, the tolerance of the expect perimeter of the subject may be determined by the user. As one example, the tolerance of the expected perimeter of the subject may between 3% and 5%. Upon determining that the perimeter of the subject is outside a tolerance of the expected perimeter of the subject, the process goes to step 408. Upon determining that the perimeter of the subject is not outside a tolerance of the expected perimeter of the subject, the process goes to step 410,
In step 408, the medical image is designated as having a truncated portion of the subject and, hence, is designated as a truncated medical image.
In step 410, the medical image is designated as not having a truncated portion of the subject.
As an example of
Turning to
At step 424, the method 400B may include determining if the perimeter of the subject touches one or more edges of the medical image. In some embodiments, the perimeter of the subject touches the one or more edges of the medical image if one or more straight lines are formed at the subject's area, rather than the one or more edges of the medical image merely being tangent to the perimeter of the subject. Upon determining that the perimeter of the subject touches one or more edges of the medical image, the process goes to step 426. Upon determining that the perimeter of the subject does not touch an edge of the medical image, the process goes to step 428.
In step 426, the medical image is designated as having a truncated portion of the subject and, hence, is designated as a truncated medical image.
In step 428, the medical image is designated as not having a truncated portion of the subject.
In comparing
The example apparatus 700 depicts an example system having four transducers (or “transducer arrays”) 700A-D. Each transducer 700A-D may include substantially flat electrode elements 702A-D positioned on a substrate 704A-D and electrically and physically connected (e.g., through conductive wiring 706A-D). The substrates 704A-D may include, for example, cloth, foam, flexible plastic, and/or conductive medical gel. Two transducers (e.g., 700A and 700D) may be a first pair of transducers configured to apply an alternating electric field to a target region of the subject's body. The other two transducers (e.g., 700B and 700C) may be a second pair of transducers configured to similarly apply an alternating electric field to the target region.
The transducers 700A-D may be coupled to an AC voltage generator 720, and the system may further include a controller 710 communicatively coupled to the AC voltage generator 720. The controller 710 may include a computer having one or more processors 724 and memory 726 accessible by the one or more processors. The memory 726 may store instructions that when executed by the one or more processors control the AC voltage generator 720 to induce alternating electric fields between pairs of the transducers 700A-D according to one or more voltage waveforms and/or cause the computer to perform one or more methods disclosed herein. The controller 710 may monitor operations performed by the AC voltage generator 720 (e.g., via the processor(s) 724). One or more sensor(s) 728 may be coupled to the controller 710 for providing measurement values or other information to the controller 710.
In some embodiments, the voltage generation components may supply the transducers 700A-D with an electrical signal having an alternating current waveform at frequencies in a range from about 50 kHz to about 1 MHz and appropriate to deliver TTFields treatment to the subject's body
The electrode elements 702A-D may be capacitively coupled. As an example, the electrode elements 702A-D may be ceramic electrode elements coupled to each other via conductive wiring 706A-D. When viewed in a direction perpendicular to its face, the ceramic electrode elements may be circular shaped or non-circular shaped. In other embodiments, the array of electrode elements may not be capacitively coupled, and there is no dielectric material (such as ceramic, or high dielectric polymer layer) associated with the electrode elements.
The structure of the transducers 700A-D may take many forms. The transducers may be affixed to the subject's body or attached to or incorporated in clothing covering the subject's body. The transducer may include suitable materials for attaching the transducer to the subject's body. For example, the suitable materials may include cloth, foam, flexible plastic, and/or a conductive medical gel. The transducer may be conductive or non-conductive.
The transducer may include any desired number of electrode elements (e.g., one electrode element, or more than one electrode element). Various shapes, sizes, and materials may be used for the electrode elements. Any constructions for implementing the transducer (or electric field generating device) for use with embodiments of the invention may be used as long as they are capable of (a) delivering TTFields to the subject's body and (b) being positioned at the locations specified herein. In some embodiments, at least one electrode element of the first, the second, the third, or the fourth transducer may include at least one ceramic disk that is adapted to generate an alternating electric field. In some embodiments, at least one electrode element of the first, the second, the third, or the fourth transducer may include a polymer film that is adapted to generate an alternating electric field.
The apparatus 900 may include one or more processors 902, memory 903, one or more input devices 905, and one or more output devices 906.
Input to the apparatus 900 may be provided by one or more input devices 905, provided from one or more input devices in communication with the apparatus 900 via link 901 (e.g., a wired link or a wireless link; e.g., with a direct connection or over a network), and/or provided from another computer(s) in communication with the apparatus 900 via link 901. As an example, based on input 901, the one or more processor 902 may generate control signals to control the AC voltage generator 720. As an example, the input 701 may be user input. As an example, the input 701 may be from another computer in communication with the apparatus 700.
Output for the apparatus 900 may be provided by one or more output devices 906, provided to one or more output devices in communication with the apparatus 900 via link 901, and/or provided from another computer(s) in communication with the apparatus 900 via link 901. The one or more output devices 906 may provide the status of the operation of the invention, such as transducer layout selection, voltages being generated, and other operational information. The output device(s) 905 may provide visualization data according to certain embodiments of the invention.
In some embodiments, one or more input devices 905 and one or more output devices 906 may be combined into one or more unitary input/output devices (e.g., a touch screen).
In some embodiments, based on input from one or more input devices 905 or input from outside the apparatus 900 via the link 901, the one or more processors 902 may perform operations as described herein. As an example, user input may be received from the one or more input devices 905. As an example, input may be from another computer in communication with the apparatus 900 via link 901. As an example, input may be from one or more input devices in communication with the apparatus 900 via link 901.
In some embodiments, the one or more processors 902 may perform operations as described herein and provide results of the operations as output. As an example, output may be provided to the one or more output devices 906. As an example, output may be provided to another computer in communication with the apparatus 900 via link 901. As an example, output may be provided to one or more output devices in communication with the apparatus 900 via link 901.
The memory 903 may be accessible by the one or more processors 902 so that the one or more processors 902 may read information from and write information to the memory 903. The memory 903 may store instructions that, when executed by the one or more processors 902, implement one or more embodiments described herein. The memory 903 may be a non-transitory computer readable medium (or a non-transitory processor readable medium) containing a set of instructions thereon for reconstructing a truncated medical image and/or training a machine learning model to generate a reconstructed medical image, wherein when executed by a processor (such as one or more processors 902), the instructions cause the processor to perform one or more methods discussed herein.
The apparatus 900 may be an apparatus for reconstructing a truncated medical image and/or training a machine learning model to generate a reconstructed medical image, the apparatus including: one or more processors (such as one or more processors 902); and memory (such as memory 903) accessible by the one or more processors, the memory storing instructions that when executed by the one or more processors, cause the apparatus to perform one or more methods described herein.
The memory 903 may be a non-transitory processor readable medium containing a set of instructions thereon for reconstructing a truncated medical image and/or training a machine learning model to generate a reconstructed medical image, wherein when executed by one or more processors (such as one or more processors 902), the instructions cause the one or more processors to perform one or more methods described herein.
The invention includes other illustrative embodiments (“Embodiments”) as follows.
Embodiment 1. A computer-implemented method for reconstructing a truncated medical image, the method comprising: accessing from memory a medical image of a subject, the medical image comprising voxels; determining the medical image has a truncated portion of the subject, the voxels of the truncated portion having non-tissue image values; generating, using a trained machine learning model and the medical image, a reconstructed medical image, the trained machine learning model trained to generate a reconstructed medical image without truncation, the voxels of the reconstructed medical image in the truncated portion having tissue image values.
Embodiment 2: The method of Embodiment 1, wherein determining the medical image has a truncated portion of the subject comprises: determining the subject depicted in the medical image has at least two flat surfaces.
Embodiment 2A: The method of Embodiment 1, wherein determining the medical image has a truncated portion of the subject comprises: determining the subject depicted in the medical image has at least one flat surface.
Embodiment 3: The method of Embodiment 1, wherein determining the medical image has a truncated portion of the subject comprises: determining a perimeter of the subject in the medical image; determining if the perimeter of the subject has a straight line with a length greater than or equal to a straight-line threshold; if the perimeter of the subject has a straight line with a length greater than or equal to the straight-line threshold, designating the medical image as having a truncated portion of the subject; and if the perimeter of the subject does not have a straight line with a length greater than or equal to the straight-line threshold, designating the medical image as not having a truncated portion of the subject.
Embodiment 4: The method of Embodiment 1, wherein determining the medical image has a truncated portion of the subject comprises: determining a perimeter of the subject in a slice of the medical image; comparing the perimeter of the subject to an expected perimeter of the subject; if the perimeter of the subject is outside a tolerance of the expected perimeter of the subject, designating the medical image as having a truncated portion of the subject; and if the perimeter of the subject is not outside a tolerance of the expected perimeter of the subject, designating the medical image as not having a truncated portion of the subject.
Embodiment 5: The method of Embodiment 4, wherein the expected perimeter of the subject is based on a view of the subject in the slice of the medical image and at least one of gender, age, body mass index, a perimeter measurement, a height measurement, or a measurement of a portion of the subject.
Embodiment 6: The method of Embodiment 1, wherein determining the medical image has a truncated portion of the subject comprises: presenting the medical image on a display; and receiving a user input identifying the medical image as having a truncated portion of the subject.
Embodiment 7: The method of Embodiment 1, wherein the non-tissue image values of the truncated portion are identical image values.
Embodiment 8: The method of Embodiment 1, wherein the non-tissue image values of the truncated portion correspond to background image values.
Embodiment 9: The method of Embodiment 1, wherein the medical image comprises a torso of the subject.
Embodiment 10: The method of Embodiment 1, wherein the medical image comprises a lung of the subject.
Embodiment 11: The method of Embodiment 1, wherein the medical image comprises a computed tomography (CT) medical image, a magnetic resonance imaging (MRI) medical image, or a positron emission tomography (PET) medical image.
Embodiment 12: The method of Embodiment 1, further comprising: defining a region of interest (ROI) in at least one of the medical image or the reconstructed medical image for application of tumor treating fields to the subject; creating a three-dimensional model of the subject based on the reconstructed medical image, the three-dimensional model of the subject including the region of interest; generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the three-dimensional model of the subject; selecting at least one of the transducer layouts as recommended transducer layouts; presenting the recommended transducer layouts; receiving a user selection of at least one recommended transducer layout; and providing a report for the at least one selected recommended transducer layout.
Embodiment 12A: A non-transitory processor readable medium containing a set of instructions thereon for reconstructing a truncated medical image, wherein when executed by a processor, the instructions cause the processor to perform a method comprising: accessing from memory a medical image of a subject, the medical image comprising voxels; determining the medical image has a truncated portion of the subject, the voxels of the truncated portion having non-tissue image values; generating, using a trained machine learning model and the medical image, a reconstructed medical image, the trained machine learning model trained to generate a reconstructed medical image without truncation, the voxels of the reconstructed medical image in the truncated portion having tissue image values.
Embodiment 12B: An apparatus for reconstructing a truncated medical image, 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 perform a method comprising: accessing from memory a medical image of a subject, the medical image comprising voxels; determining the medical image has a truncated portion of the subject, the voxels of the truncated portion having non-tissue image values; generating, using a trained machine learning model and the medical image, a reconstructed medical image, the trained machine learning model trained to generate a reconstructed medical image without truncation, the voxels of the reconstructed medical image in the truncated portion having tissue image values.
Embodiment 13: A computer-implemented method for reconstructing a truncated medical image, the method comprising: accessing from memory a plurality of medical images of a plurality of subjects, the medical images being of a torso of each of the subjects, the medical images comprising voxels; processing the medical images by truncating a portion of each medical image to obtain truncated medical images, wherein the truncated portion of each medical image corresponds to a right side and a left side of each subject; designating a set of the truncated medical images as training truncated medical images; and training a machine learning model to obtain a trained machine learning model, wherein the machine learning model is trained with the training truncated medical images, wherein the machine learning model is trained to generate a reconstructed medical image without truncation.
Embodiment 14: The method of Embodiment 13, further comprising: designating a set of the truncated medical images as testing truncated medical images, wherein the testing truncated medical images are different than the training truncated medical images; generating reconstructed medical images using the trained machine learning model and the testing truncated medical images; comparing the reconstructed medical images to the medical images corresponding to the training truncated medical images to obtain comparison results; and if the comparison results are unsatisfactory, re-training the trained machine learning model to obtain a re-trained machine learning model.
Embodiment 15: The method of Embodiment 13, wherein the machine learning model is a generative adversarial network.
Embodiment 16: The method of Embodiment 13, wherein the machine learning model is a generative adversarial network comprising a deconvolutional neural network as a generator and a convolutional neural network as a discriminator.
Embodiment 17: The method of Embodiment 13, wherein the trained machine learning model is a deep learning neural network.
Embodiment 18: The method of Embodiment 13, wherein the machine learning model is an unsupervised machine learning model
Embodiment 19: The method of Embodiment 13, wherein the machine learning model comprises a generative adversarial network (GAN), a MedGAN, a super resolution GAN, a pix2pix GAN, a cycleGAN, a discoGAN, a fila-sGAN, a projective adversarial network (PAN), a variational autoencoder (VAE), or an unsupervised neural network.
Embodiment 19A: A computer-implemented method for reconstructing a truncated medical image, the method comprising: accessing from memory a medical image of a subject, the medical image being of a torso of the subject, the medical image comprising voxels; determining the medical image has a truncated portion of the subject, the truncated portion of the medical image corresponding to a right side and a left side of the subject, the voxels of the truncated portion having non-tissue image values; generating, using a trained machine learning model and the medical image, a reconstructed medical image, the trained machine learning model trained to generate a reconstructed medical image without truncation, the voxels of the reconstructed medical image in the truncated portion having tissue image values; and generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the reconstructed medical image.
Embodiment 19B: A non-transitory processor readable medium containing a set of instructions thereon for reconstructing a truncated medical image, wherein when executed by a processor, the instructions cause the processor to perform a method comprising: accessing from memory a medical image of a subject, the medical image being of a torso of the subject, the medical image comprising voxels; determining the medical image has a truncated portion of the subject, the truncated portion of the medical image corresponding to a right side and a left side of the subject, the voxels of the truncated portion having non-tissue image values; generating, using a trained machine learning model and the medical image, a reconstructed medical image, the trained machine learning model trained to generate a reconstructed medical image without truncation, the voxels of the reconstructed medical image in the truncated portion having tissue image values; and generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the reconstructed medical image.
Embodiment 20: An apparatus for reconstructing a truncated medical image, 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 perform a method comprising: accessing from memory a medical image of a subject, the medical image being of a torso of the subject, the medical image comprising voxels; determining the medical image has a truncated portion of the subject, the truncated portion of the medical image corresponding to a right side and a left side of the subject, the voxels of the truncated portion having non-tissue image values; generating, using a trained machine learning model and the medical image, a reconstructed medical image, the trained machine learning model trained to generate a reconstructed medical image without truncation, the voxels of the reconstructed medical image in the truncated portion having tissue image values; and generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the reconstructed medical image.
Embodiment 21: A method, machine, manufacture, and/or system substantially as shown and described.
Optionally, for each embodiment described herein, the voltage generation components supply the transducers with an electrical signal having an alternating current waveform at frequencies in a range from about 50 kHz to about 1 MHz and appropriate to deliver TTFields treatment to the subject's body.
Embodiments illustrated under any heading or in any portion of the disclosure may be combined with embodiments illustrated under the same or any other heading or other portion of the disclosure unless otherwise indicated herein or otherwise clearly contradicted by context. For example, and without limitation, embodiments described in dependent claim format for a given embodiment (e.g., the given embodiment described in independent claim format) may be combined with other embodiments (described in independent claim format or dependent claim format).
Numerous modifications, alterations, and changes to the described embodiments are possible without departing from the scope of the present invention defined in the claims. It is intended that the present invention need not be limited to the described embodiments, but that it has the full scope defined by the language of the following claims, and equivalents thereof.
This application claims priority to U.S. Provisional Patent Application No. 63/613,796, filed Dec. 22, 2023, which is incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63613796 | Dec 2023 | US |
