RECONSTRUCTION OF A TRUNCATED MEDICAL IMAGE

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.

2 . The method of claim 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.

3 . The method of claim 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.

4 . The method of claim 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.

5 . The method of claim 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.

6 . The method of claim 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.

7 . The method of claim 1 , wherein the non-tissue image values of the truncated portion are identical image values.

8 . The method of claim 1 , wherein the non-tissue image values of the truncated portion correspond to background image values.

9 . The method of claim 1 , wherein the medical image comprises a torso of the subject.

10 . The method of claim 1 , wherein the medical image comprises a lung of the subject.

11 . The method of claim 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.

12 . The method of claim 1 , further comprising:

defining a region of interest 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.

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.

14 . The method of claim 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.

15 . The method of claim 13 , wherein the machine learning model is a generative adversarial network.

16 . The method of claim 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.

17 . The method of claim 13 , wherein the trained machine learning model is a deep learning neural network.

18 . The method of claim 13 , wherein the machine learning model is an unsupervised machine learning model.

19 . The method of claim 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.

20 . 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.