Abstract: Disclosed herein are methods and systems for training artificial intelligence (AI) models. A central server may train an AI model by outputting, onto an electronic device operated by a user, results of execution of the AI model; monitoring the electronic device to identify a set of interactions between the user and the electronic device while the electronic device is outputting the results; generating a first training dataset corresponding to the user's interactions with the electronic device, the first training dataset corresponding to a frequency of correction of results; generating a second training dataset corresponding to the user's input to a prompt requesting the user to input a numerical value associated with an accuracy of the results; and training the AI model using the first and second training datasets.

Abstract: A method includes training, using a processing device, an Artificial Intelligence (AI) model using training data associated with a plurality of patients to predict biologic therapy treatment responses indicative of a patient survival rate based on a change in volume of a lesion of a patient. The training data is indicative of at least one of unique diagnostic imaging scans at baselines, follow-up intervals, or temporary changes in lesion volume. The method includes providing a pre-treatment image of one or more target lesions of a target patient to the AI model to generate a biologic therapy treatment response. The method includes generating, based on the biologic therapy treatment response, a recommended treatment plan indicating a pharmaceutical product to treat the one or more target lesions of the target patient.

Abstract: A system and method of using pre-treatment and intra-treatment serial imaging in at least one of predictive modeling or multi-modal predictive modeling of therapeutic agent response. The method includes acquiring pre-treatment features of one or more target lesions associated with a pre-treatment scan of a target subject prior to treating the target subject according to a treatment plan. The method includes determining a set of features indicative of a change in the one or more target lesions using the pre-treatment features. The method includes providing the set of features to one or more predictive models trained to predict therapeutic agent responses based on features of target lesions. The method includes generating a predicted treatment response score to for the treatment plan based on the set of features and the one or more predictive models prior to treating the target subject according to the treatment plan.

Abstract: A method of the present disclosure includes identifying, based on a reference image, a full motion range of a target, wherein the full motion range of the target defines a full internal target volume (ITV). The method further includes identifying a non-target object in a motion image or the reference image. The method further includes performing a volumetric alignment of the ITV and the non-target object. The method further includes modifying a non-target to target displacement vector based on the volumetric alignment. The method further includes tracking the target based on the modified non-target to target displacement vector.

Abstract: A method includes uniquely training, using a processing device, one or more deep learning models using one or more sets of training data associated with a plurality of patients to predict immunotherapy treatment responses indicative of a patient survival rate based on a change in volume of a lesion of a patient. The method includes providing a single pre-treatment image of a target lesion of a target patient to the one or more deep learning models that are uniquely trained using the sets of training data to generate the immunotherapy treatment responses. The method includes combining the immunotherapy treatment responses of the one or more deep learning models that are uniquely trained using the sets of training data to generate a predicted treatment response score.

Abstract: A method of calibration in a radiation delivery system. The method including acquiring, using a camera, an image of a radiation beam incident on a phantom, wherein the radiation beam being emitted by a radiation source of the radiation delivery system and the phantom comprising an X-ray luminescent material. The method further including determining a beam pointing offset based on the image. The method further including calibrating a position of the radiation source of the radiation delivery system based on the beam pointing offset and a relative position of the camera with respect to a beam axis of the radiation beam.

Abstract: A method of operating a radiation apparatus is described. The method includes selecting at least a first angle and a second angle from a set of angles for a first rotation of a gantry of a radiation apparatus. The method also includes generating, using an imaging device mounted to the gantry, a first tracking image of a target from the first angle during the first rotation of the gantry. The method further includes generating, using the imaging device, a second tracking image of the target from the second angle during the first rotation of the gantry.

Abstract: Example methods and systems for quality-aware continuous learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining an artificial intelligence (AI) engine that is trained to perform a radiotherapy treatment planning task. The method may also comprise: based on input data associated with a patient, performing the radiotherapy treatment planning task using the AI engine to generate output data associated with the patient; and obtaining modified output data that includes one or more modifications made by a treatment planner to the output data. The method may further comprise: performing quality evaluation based on (a) first quality indicator data associated with the modified output data, and/or (b) second quality indicator data associated with the treatment planner. In response to a decision to accept, a modified AI engine may be generated by re-training the AI engine based on the modified output data.

Abstract: A method of calibration in a radiation delivery system. The method including acquiring, using a camera, an image of a radiation beam incident on a phantom, wherein the radiation beam being emitted by a radiation source of the radiation delivery system and the phantom comprising an X-ray luminescent material. The method further including determining a beam pointing offset based on the image. The method further including calibrating a position of the radiation source of the radiation delivery system based on the beam pointing offset and a relative position of the camera with respect to a beam axis of the radiation beam.

Abstract: A method of operating imaging and tracking. The method includes determining, for each angle of a plurality of angles from which tracking images can be generated by an imaging device, a value of a tracking quality metric for tracking a target based on an analysis of a projection generated at that angle. The method also includes selecting, by a processing device, a subset of the plurality of angles that have a tracking quality metric value that satisfies a tracking quality metric criterion, one or more angles of the subset to be used to generate a tracking image of the target during a treatment stage, wherein the subset comprises at least a first angle and a second angle that is at least separated by a minimum threshold from the first angle.

Abstract: A method includes uniquely training, using a processing device, one or more deep learning models using one or more sets of training data associated with a plurality of patients to predict immunotherapy treatment responses indicative of a patient survival rate based on a change in volume of a lesion of a patient. The method includes providing a single pre-treatment image of a target lesion of a target patient to the one or more deep learning models that are uniquely trained using the sets of training data to generate the immunotherapy treatment responses. The method includes combining the immunotherapy treatment responses of the one or more deep learning models that are uniquely trained using the sets of training data to generate a predicted treatment response score.

Abstract: A phantom, calibration system and calibration method are described. The phantom having a phantom body and an X-ray luminescent material, wherein at least a portion of the X-ray luminescent material is on a surface of the phantom.

Abstract: A method of sequential monoscopic tracking is described. The method includes generating a plurality of projections of an internal target region within a body of a patient, the plurality of projections comprising projection data about a position of an internal target region of the patient. The method further includes generating external positional data about external motion of the body of the patient using one or more external sensors. The method further includes generating, by a processing device, a correlation model between the projection data and the external positional data by fitting the plurality of projections of the internal target region to the external positional data. The method further includes estimating the position of the internal target region at a later time using the correlation model.

Abstract: A method comprises providing a pre-treatment image of a target subject to at least one deep learning model uniquely trained to predict immunotherapy treatment responses. The method further comprises generating, by a processing device, a predicted treatment response score to a treatment based on the single pre-treatment image and the at least one deep learning model. The method further comprises providing, based on the predicted treatment response score, a recommended treatment plan.

Abstract: Systems and methods for tracking a target volume, e.g., tumor, in real-time during radiation treatment are provided. The system includes a memory to store a pre-acquired 3D image of the anatomy of interest in a first reference frame and a processor, operative coupled with the memory, to receive, from an ultrasound probe, a set-up ultrasound image of the anatomy of interest in a second reference frame. The processor further to establish a transformation between the first and second reference frames by registering the set-up ultrasound image with the pre-acquired 3D image and receive, from the ultrasound probe, an intrafraction ultrasound image of the anatomy of interest. The processor further to register the intrafraction ultrasound image with the set-up ultrasound image and track motion of the anatomy of interest based on the registered intrafraction ultrasound image.

Abstract: A method of sequential monoscopic tracking is described. The method includes generating a plurality of projections of an internal target region within a body of a patient, the plurality of projections comprising projection data about a position of an internal target region of the patient. The method further includes generating external positional data about external motion of the body of the patient using one or more external sensors. The method further includes generating, by a processing device, a correlation model between the projection data and the external positional data by fitting the plurality of projections of the internal target region to the external positional data. The method further includes estimating the position of the internal target region at a later time using the correlation model.

Abstract: A method of the present disclosure includes performing, by a processing device, a first image registration between a reference image of a patient and a motion image of the patient to perform alignment between the reference image and the motion image, wherein the reference image and the motion image include a target position of the patient. The method further includes performing, by the processing device, a second image registration between the reference image and a motion x-ray image of the patient, via a first digitally reconstructed radiograph (DRR) for the reference image of the patient. The method further includes tracking at least a translational change in the target position based on the first registration and the second registration.

Abstract: A method of operating imaging and tracking. The method includes determining, for each angle of a plurality of angles from which tracking images can be generated by an imaging device, a value of a tracking quality metric for tracking a target based on an analysis of a projection generated at that angle. The method also includes selecting, by a processing device, a subset of the plurality of angles that have a tracking quality metric value that satisfies a tracking quality metric criterion, one or more angles of the subset to be used to generate a tracking image of the target during a treatment stage, wherein the subset comprises at least a first angle and a second angle that is at least separated by a minimum threshold from the first angle.

Abstract: A method of operating a radiation apparatus is described. The method includes selecting at least a first angle and a second angle from a set of angles for a first rotation of a gantry of a radiation apparatus. The method also includes generating, using an imaging device mounted to the gantry, a first tracking image of a target from the first angle during the first rotation of the gantry. The method further includes generating, using the imaging device, a second tracking image of the target from the second angle during the first rotation of the gantry.

Abstract: A method of and apparatus for operating a radiation treatment delivery system. The method includes generating, by a processing device, a set of instructions for a volumetric imager based on a set of directionalities for a radiation beam of a linear accelerator (LINAC) to avoid a collision between the volumetric imager and the LINAC, wherein the set of instructions comprises physical locations of the volumetric imager and timing values corresponding to the physical locations. The method further includes operating the volumetric imager during the radiation treatment delivery according to the set of instructions.