Abstract: Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

Abstract: Example methods and systems for tomographic image reconstruction are provided. One example method may comprise: obtaining two-dimensional (2D) projection data and processing the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating 2D feature data by processing the 2D projection data using the multiple first processing layers, reconstructing first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and generating second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers.

Abstract: Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.

Abstract: A radiation therapy system is configured to enable imaging and treatment of a target volume during a single patient breath hold. The radiation system includes a rotating gantry on which are mounted a treatment-delivering X-ray source and multiple imaging X-ray sources and corresponding X-ray imaging devices. The multiple imaging X-ray sources and X-ray imaging devices enable the acquisition of volumetric image data for the target volume over a relatively short rotational arc, for example 30 degrees or less. Therefore, intra-fraction motion can be detected in near-real time, for example within about one second or less. The radiation therapy system can perform image guided radiation therapy (IGRT) that monitors intra-fraction motion using X-ray imaging. Detected anatomical variations can then either be compensated for, via patient repositioning and/or treatment modification, or the current treatment can be aborted.

Abstract: A radiation therapy system is configured to enable imaging and treatment of a target volume during a single patient breath hold. The radiation system includes a rotating gantry on which are mounted a treatment-delivering X-ray source and multiple imaging X-ray sources and corresponding X-ray imaging devices. The multiple imaging X-ray sources and X-ray imaging devices enable the acquisition of volumetric image data for the target volume over a relatively short rotational arc, for example 30 degrees or less. Therefore, intra-fraction motion can be detected in near-real time, for example within about one second or less. The radiation therapy system can perform image guided radiation therapy (IGRT) that monitors intra-fraction motion using X-ray imaging. Detected anatomical variations can then either be compensated for, via patient repositioning and/or treatment modification, or the current treatment can be aborted.

Abstract: One example method to reduce image artifacts, which may include obtaining measured projection data acquired using an imaging system. The measured projection data is associated with a target object and an artifact source within a radiation field of the imaging system. The method may also include generating virtual projection data associated with the artifact source by forward projecting a model representing one or more physical properties of the artifact source. The method may further include generating corrected projection data based on the measured projection data and the virtual projection data; and reconstructing the corrected projection data into reconstructed volume image data to reduce image artifacts caused by the artifact source.

Abstract: An apparatus includes: an input configured to receive a plurality of images, wherein the images include respective sub-images of a bodily part of a subject, and wherein a position of the bodily part relates to a breathing movement and a cardiac movement of the subject; a first registration engine configured to determine a first registration of at least two breathing correlated images, wherein the at least two breathing correlated images comprise two of the plurality of images or are derived from at least some of the plurality of images; a second registration engine configured to determine a second registration of at least two cardiac correlated images; and a volumetric image generator configured to generate a volumetric image using the first registration and the second registration.

Abstract: Reconstruction of projection images of a CBCT scan is performed by generating simulated projection data, comparing the simulated projection data to the projection images of the CBCT scan, determining a residual volume based on the comparison, and using the residual volume to determine an accurate reconstructed volume. The reconstructed volume can be used to segment a tumor (and potentially one or more organs) and align the tumor to a planning volume (e.g., from a CT scan) to identify changes, such as shape of the tumor and proximity of the tumor to an organ. These changes can be used to update a radiation therapy procedure, such as by altering a radiation treatment plan and fine-tuning a patient position.

Abstract: An apparatus includes: an input configured to receive a plurality of images, wherein the images include respective sub-images of a bodily part of a subject, and wherein a position of the bodily part relates to a breathing movement and a cardiac movement of the subject; a first registration engine configured to determine a first registration of at least two breathing correlated images, wherein the at least two breathing correlated images comprise two of the plurality of images or are derived from at least some of the plurality of images; a second registration engine configured to determine a second registration of at least two cardiac correlated images; and a volumetric image generator configured to generate a volumetric image using the first registration and the second registration.

Abstract: One example method to reduce image artifacts, which may include obtaining measured projection data acquired using an imaging system. The measured projection data is associated with a target object and an artifact source within a radiation field of the imaging system. The method may also include generating virtual projection data associated with the artifact source by forward projecting a model representing one or more physical properties of the artifact source. The method may further include generating corrected projection data based on the measured projection data and the virtual projection data; and reconstructing the corrected projection data into reconstructed volume image data to reduce image artifacts caused by the artifact source.

Abstract: System and method for automatic radiography imaging parameter generation by use of extant patient information. The patient information is categorized, and one or more radiography imaging parameters of a radiographic device are selected and processed according to a knowledge-based model applied to a selection of at least one patient information category. A value for the selected imaging parameter(s) is output. Values for several imaging parameters may be automatically generated according to the processing and optimization. Alternatively, a value, or a range of values, may be suggested for presentation to a clinician. The processing and optimization of the radiography imaging parameters may be based on a subset of the patient information.