Abstract:
The present disclosure relates to training one or more neural networks for vascular vessel assessment using synthetic image data for which ground-truth data is known. In certain implementations, the synthetic image data may be based in part, or derived from, clinical image data for which ground-truth data is not known or available. Neural networks trained in this manner may be used to perform one or more of vessel segmentation, decalcification, Hounsfield unit scoring, and/or estimation of a hemodynamic parameter.
Abstract:
An X-ray filter assembly is disclosed having a stack of X-ray attenuating sheets that are angled so as to have a focus point. When implemented in an imaging system, the focus point of the filter assembly is spatially offset (e.g., behind) the X-ray emission location. The filter assembly may be used (e.g., translated, rotated, and so forth) to adjust the intensity profile of the X-rays seen in an imaging volume.
Abstract:
Approaches related to performing calibration of a CT scanner or of processes (e.g., correction and/or reconstruction) performed on acquired CT scan data are described. In certain described approaches, calibration is attained without performing a calibration scan using a dedicated calibration phantom. In certain embodiments, calibration is performed using a feature intrinsic to the imaged object, such as a jacket disposed about a drilled core sample.
Abstract:
An X-ray filter assembly is disclosed having a stack of X-ray attenuating sheets that are angled so as to have a focus point. When implemented in an imaging system, the focus point of the filter assembly is spatially offset (e.g., behind) the X-ray emission location. The filter assembly may be used (e.g., translated, rotated, and so forth) to adjust the intensity profile of the X-rays seen in an imaging volume.
Abstract:
The present approaches relate to frequency-split iterative reconstruction approaches. In some embodiment, such approaches provide for the combination of the low frequency components of an analytical reconstruction (e.g., a filtered back projection) and the high frequency components of an iterative reconstruction. In certain embodiments, frequency-split iterative reconstruction is used for generating region of interest images.
Abstract:
An imaging system includes a computed tomography (CT) acquisition unit and a processing unit. The CT acquisition unit includes an X-ray source and a CT detector configured to collect CT imaging data of an object to be imaged. The X-ray source and CT detector are configured to be rotated about the object to be imaged and to collect a series of views of the object as the X-ray source and CT detector rotate about the object to be imaged. The processing unit is operably coupled to the CT acquisition unit and configured to control the CT acquisition unit to vary a view duration for the views of the series. The view duration for a particular view defines an imaging information acquisition period for the particular view, wherein the series of views includes a first group of views having a first view duration and a second group of views having a second view duration that is different than the first view duration.
Abstract:
In accordance with the present disclosure, the present technique finds a diagnostic scan timing for a non-static object (e.g., a heart or other dynamic object undergoing motion) from raw scan data, as opposed to reconstructed image data. To find the scan timing, a monitoring scan of a patient's heart is performed. In the monitoring scan, the patient dose may be limited or minimized. As the projection data is acquired during such a monitoring scan, the projection data may be subjected to sinogram analysis in a concurrent or real-time manner to determine when to start (or trigger) the diagnostic scan.
Abstract:
The present disclosure relates to image reconstruction with favorable properties in terms of noise reduction, spatial resolution, detail preservation and computational complexity. The disclosed techniques may include some or all of: a first-pass reconstruction, a simplified datafit term, and/or a deep learning denoiser. In various implementations, the disclosed technique is portable to different CT platforms, such as by incorporating a first-pass reconstruction step.
Abstract:
A method for characterizing anatomical features includes receiving scanned data and image data corresponding to a subject. The scanned data comprises sinogram data. The method further includes identifying a first region in an image of the image data corresponding to a region of interest. The method also includes determining a second region in the scanned data. The second region corresponds to the first region. The method further includes identifying a sinogram trace corresponding to the region of interest. The sinogram trace comprises sinogram data present within the second region. The method includes determining a data feature of the subject based on the sinogram trace and a deep learning network. The method also includes determining a diagnostic condition corresponding to a medical condition of the subject based on the data feature.
Abstract:
The present approaches relate to frequency-split iterative reconstruction approaches. In some embodiment, such approaches provide for the combination of the low frequency components of an analytical reconstruction (e.g., a filtered back projection) and the high frequency components of an iterative reconstruction. In certain embodiments, frequency-split iterative reconstruction is used for generating region of interest images.