Machine learning optimization of fluence maps for radiotherapy treatment

A system for generating fluence maps used in a radiotherapy treatment plan, the system comprising:

• one or more memory devices to store a three-dimensional set of image data corresponding to a subject of radiotherapy treatment, the image data indicating one or more target dose areas and one or more organs-at-risk areas in an anatomy of the subject;
• and
• one or more processors configured to perform operations comprising:
• obtaining anatomy projection images from the image data, each image of the anatomy projection images providing a view of the subject from a respective beam angle of the radiotherapy treatment;
• and
• executing a trained neural network model to generate computer-estimated fluence maps based on input of the anatomy projection images, each of the estimated fluence maps indicating a fluence distribution of the radiotherapy treatment at a respective beam angle,
• wherein the neural network model is a generative model produced from training in a generative adversarial network (GAN) that includes at least one generative model and at least one discriminative model,
• wherein the neural network model is trained in the GAN with corresponding pairs of training anatomy projection images and training fluence maps, and
• wherein training of the at least one generative model of the GAN is based on generating synthetic fluence maps from the training anatomy projection images,
• wherein training of the at least one discriminative model of the GAN is based on discriminating the synthetic fluence maps from the training fluence maps, and
• wherein neural network parameter values learned by the at least one generative model of the GAN and the at least one discriminative model of the GAN are established from adversarial training.

A non-transitory computer-readable storage medium comprising computer-readable instructions for generating fluence maps used in a radiotherapy treatment plan, the instructions performing operations comprising:

• identifying a three-dimensional set of image data corresponding to a subject of radiotherapy treatment, the image data indicating one or more target dose areas and one or more organs-at-risk areas in an anatomy of the subject;
• generating anatomy projection images from the image data, each image of the anatomy projection images providing a view of the subject from a respective beam angle of the radiotherapy treatment;
• and
• using a trained neural network model to generate computer-estimated fluence maps based on input of the anatomy projection images, each of the estimated fluence maps indicating a fluence distribution of the radiotherapy treatment at a respective beam angle,
• wherein the neural network model is a generative model produced from training in a generative adversarial network (GAN) that includes at least one generative model and at least one discriminative model,
• wherein the neural network model is trained in the GAN with corresponding pairs of training anatomy projection training images and training fluence maps, and
• wherein training of the at least one generative model of the GAN is based on generating synthetic fluence maps from the training anatomy projection images,
• wherein training of the at least one discriminative model of the GAN is based on discriminating the synthetic fluence maps from the training fluence maps, and
• wherein neural network parameter values learned by the at least one generative model of the GAN and the at least one discriminative model of the GAN are established from adversarial training.