Abstract: Disclosed herein are methods and systems for predicting how internal organs change and using the prediction in image-guided radiation therapy. A method comprises receiving an attribute of a radiation therapy treatment plan for a patient and at least one medical image of the patient; executing, by the processor, an artificial intelligence model to predict deformation data for at least one internal structure of the patient using the attribute and the at least one medical image, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants and their corresponding deformation data; and generating, using the artificial intelligence model, a synthetic medical image representing the at least one medical image deformed in accordance with the predicted deformation data.

Abstract: Disclosed herein are methods and systems for predicting how internal organs change and using the prediction in image-guided radiation therapy. A method comprises receiving an attribute of a radiation therapy treatment plan for a patient and at least one medical image of the patient; executing an artificial intelligence model to predict deformation data for at least one internal structure of the patient using the attribute and the at least one medical image, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants and their corresponding deformation data; and outputting the predicted deformation data.

Abstract: A method for simultaneously registering a plurality of computed tomography (CT) scans of a region of interest of a subject including a subject's lungs for quantitative lung analysis includes receiving a plurality of CT scans of the region of interest acquired with a CT imaging system, receiving a breathing surrogate data for the subject, the breathing surrogate data comprising an amplitude for each of the plurality of CT scans and determining a deformation based on the plurality of CT scans and the breathing surrogate data using an iterative optimization process of an objective function. The objective function can include a first term based on an approximation of local changes in Hounsfield Units (HU) adjusted for breathing of the subject and a second term based on error in the conservation of mass. At least one quantitative lung parameter may be determined based on the deformation.

Abstract: Provided is a method and system for performance evaluation using an AI model comprising automatic extraction of accurate call disposition details from the interactions for any domain, so that interactions can be tagged consistently and accurately, and actionable insights can be derived, by training AI models. It provides a method and system that can be used in real time for automating the call disposition detailing for any conversation over a call. In an example the call conversation may be a customer care call, or any other work-related call. It may allow users to extract issues, cause and resolution from a call transcript with utmost accuracy, also train it further on client specific data. The extracted text is then used to for performance evaluation using multiple KPIs and parameters.

Abstract: A method and apparatus for removing breathing motion artifacts in imaging CT scans is disclosed. The method acquires raw imaging data from a CT scanner, and processes the raw CT imaging data by removing motion-induced artifacts via a motion model. Processing the imaging data may be achieved by initially estimating a 3D image to provide an estimate of raw sinogram image data, comparing the estimate to an actual CT sinogram, determining a difference between the sinograms, and iteratively reconstructing the 3D image by using the difference to alter the 3D image until the sinograms agree, wherein the 3D image moves according to the motion model.

Abstract: A method and apparatus for removing breathing motion artifacts in imaging CT scans is disclosed. The method acquires raw imaging data from a CT scanner, and processes the raw CT imaging data by removing motion-induced artifacts via a motion model. Processing the imaging data may be achieved by initially estimating a 3D image to provide an estimate of raw sinogram image data, comparing the estimate to an actual CT sinogram, determining a difference between the sinograms, and iteratively reconstructing the 3D image by using the difference to alter the 3D image until the sinograms agree, wherein the 3D image moves according to the motion model.

Abstract: In one embodiment, a method for simulating organ dynamics includes generating a sequence of three-dimensional models of an organ during different stages of observed dynamic motion, generating a deformation transfer function from the sequence of three-dimensional models, generating a pressure-volume curve from the sequence of three-dimensional models, and generating an organ deformation model that simulates dynamic motion of the organ.