Abstract: Systems, methods, and apparatus are disclosed for cardiac structure tracking. An example method includes segmenting a diaphragm or respiratory surrogate, heart, and target. The method also includes performing a peak-exhale to peak-inhale registration and generating a respiratory motion model. The method further includes tracking the diaphragm using X-ray imaging and estimating a target position for an x-ray guided cardiac radioablation treatment. The example method provides directly, precisely controlled x-ray guided cardiac radioablation that accurately targets the substrates of cardiac ablation while minimizing doses to healthy tissue.

Abstract: Disclosed is a method and system for three-dimensional tracking of a target located within a body, the method performed using at least one processing system. A two-dimensional scanned image of the body including the target is processed to obtain a two-dimensional image of the target. A first present dataset of the target is predicted using a previous dataset of the target and a state transition model, the first present dataset includes a three-dimensional present position value of the target. A second present dataset of the target is measured by template-matching of the two-dimensional image of the target with a model of the target. A third present dataset of the target is estimated by statistical inference using the first present dataset and the second present dataset. The previous dataset of the target is updated to match the third present dataset.

Abstract: A method for tracking a target located within a body, whilst scanning the body using X-ray projections, the method including the steps of: (a) providing a first prior image of the body and target indicating their relative position; (b) iteratively performing, for a series of time intervals, the steps of: (i) acquiring a few spatially distributed x-ray projections of the target area; (ii) determining one of a series of motion phases for each x-ray projection; (iii) performing an iterative reconstruction of the image for each of the motion phases; and (iv) outputting a resultant image for a current time interval.

Abstract: Disclosed is a method and system for three-dimensional tracking of a target located within a body, the method performed using at least one processing system. A two-dimensional scanned image of the body including the target is processed to obtain a two-dimensional image of the target. A first present dataset of the target is predicted using a previous dataset of the target and a state transition model, the first present dataset includes a three-dimensional present position value of the target. A second present dataset of the target is measured by template-matching of the two-dimensional image of the target with a model of the target. A third present dataset of the target is estimated by statistical inference using the first present dataset and the second present dataset. The previous dataset of the target is updated to match the third present dataset.

Abstract: A method for tracking a target located within a body, whilst scanning the body using X-ray projections, the method including the steps of: (a) providing a first prior image of the body and target indicating their relative position; (b) iteratively performing, for a series of time intervals, the steps of: (i) acquiring a few spatially distributed x-ray projections of the target area; (ii) determining one of a series of motion phases for each x-ray projection; (iii) performing an iterative reconstruction of the image for each of the motion phases; and (iv) outputting a resultant image for a current time interval.

Abstract: A method of adaptive suppression of over-smoothing in noise/artefact reduction techniques such as total variation minimization or other compressed sensing strategies for Cone Beam Computed Tomography (CBCT) images, the method including the steps of: (a) inputting a CBCT image; (b) identifying the anatomical structures of interest in the CBCT images by exploiting their likely shapes, attenuation coefficients, sizes, positions, or any other similar features that can be used to identify them from an image; (c) extracting intensity, gradient, or other image-related information of the identified anatomical structures from the CBCT image; (d) adaptively suppressing over-smoothing in noise/artefact reduction techniques such as total-variation minimization or other compressed sensing strategies at the anatomical structures of interest using the information of the anatomical structures extracted previously.