A system is provided for augmenting a three-dimensional (3D) model of a heart to indicate the tissue state. The system accesses a 3D model of a heart, accesses two-dimensional (2D) images of tissue state slices of the heart, and accesses source location information of an arrhythmia. The system augments the 3D model with an indication of a source location based on the source location information. For each of a plurality of the tissue state slices of the heart, the system augments a 3D model slice of the 3D model that corresponds to that tissue state slice with an indication of the tissue state of the heart represented by the tissue state information of that tissue state slice. The system then displays a representation of the 3D model that indicates the source location of the arrhythmia and the tissue state of the heart.

A computer specific system for receiving a plurality of medical imaging specific 3D patient specific data sets from different 3D data sources, for a patient receiving reconstructive surgery, locating and applying a plurality of landmarks to each data set, and performing an overlay analysis procedure that aligns the 3D data sets from the different 3D data sources to create a 3D medical image representation of the patient's tissue. The 3D representation can be used to create 3D models for use by surgeons to perform reconstructive surgical procedures.

The present invention relates to a method and system that automatically finds, segments and measures lesions in medical images following the Response Evaluation Criteria In Solid Tumours (RECIST) protocol. More particularly, the present invention produces an augmented version of an input computed tomography (CT) scan with an added image mask for the segmentations, 3D volumetric masks and models, measurements in 2D and 3D and statistical change analyses across scans taken at different time points. According to a first aspect, there is provided a method for determining volumetric properties of one or more lesions in medical images comprising the following steps: receiving image data; determining one or more locations of one or more lesions in the image data; creating an image segmentation (i.e. mask or contour) comprising the determined one or more locations of the one or more lesions in the image data and using the image segmentation to determine a volumetric property of the lesion.

A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes generating, by a projection completion circuitry, an intermediate CT image data based, at least in part, on input CT projection data. The intermediate CT image data is configured to include relatively fewer artifacts than an uncorrected CT image reconstructed from the input CT projection data. The method further includes generating, by an artificial neural network (ANN), CT output image data based, at least in part, on the intermediate CT image data. The CT output image data is configured to include relatively fewer artifacts compared to the intermediate CT image data. The method may further include generating, by detail image circuitry, detail CT image data based, at least in part, on input CT image data. The CT output image data is generated based, at least in part, on the detail CT image data.

In a multi-session imaging study, information from a previous imaging session is stored in a Binary Large Object (BLOB). Current emission imaging data are reconstructed into a non-attenuation corrected (NAC) current emission image. A spatial transform is generated aligning a previous NAC emission image from the BLOB to the current NAC emission image. A previous computed tomography (CT) image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed with attenuation correction using the warped CT image. Alternatively, low dose current emission imaging data and a current CT image are acquired, a spatial transform is generated aligning the previous CT image to the current CT image, a previous attenuation corrected (AC) emission image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed using the current CT image with the warped AC emission image as prior.

Systems and methods for improving soft tissue contrast, characterizing tissue, classifying phenotype, stratifying risk, and performing multi-scale modeling aided by multiple energy or contrast excitation and evaluation are provided. The systems and methods can include single and multi-phase acquisitions and broad and local spectrum imaging to assess atherosclerotic plaque tissues in the vessel wall and perivascular space.

In a radiation therapy system, treatment X-rays are delivered to a target volume at the same time that imaging X-rays are also delivered to the target volume for generating image data of the target volume. That is, during an imaging interval in which imaging X-rays are delivered to the target volume, one or more pulses of treatment X-rays are also delivered to the target volume. In each pixel of an X-ray imaging device of the radiation therapy system, image signal is accumulated during portions of the imaging interval in which only imaging X-rays are delivered to the target volume and is prevented from accumulating in each pixel during the pulses of treatment X-rays.

A method and system is disclosed for acquiring image data of a subject. The image data can be collected with an imaging system with at least two different power characteristics. The image data can be reconstructed using dynamic or enhanced reconstruction techniques.

Described herein are systems and methods for positioning a radiation source with respect to one or more regions of interest in a coordinate system. Such systems and methods may be used in emission guided radiation therapy (EGRT) for the localized delivery of radiation to one or more patient tumor regions. These systems comprise a gantry movable about a patient area, where a plurality of positron emission detectors, a radiation source are arranged movably on the gantry, and a controller. The controller is configured to identify a coincident positron annihilation emission path and to position the radiation source to apply a radiation beam along the identified emission path. The systems and methods described herein can be used alone or in conjunction with surgery, chemotherapy, and/or brachytherapy for the treatment of tumors.

A method for customizing an interactive control boundary includes positioning a virtual implant model relative to a virtual bone model based on a user input, and extracting reference feature information associated with the virtual implant model, wherein the reference feature information describes one of a point, a line, a plane, and a surface associated with the virtual implant model. The method further includes mapping the extracted reference feature information to the virtual model of the bone, and receiving information indicative of a positional landmark associated with the bone, then estimating an intersection between the positional landmark and the mapped reference feature and generating a virtual boundary based, at least in part, on the estimated intersection between the positional landmark and the mapped reference feature.