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 discussion relates to the use of deep learning techniques to accelerate iterative reconstruction of images, such as CT, PET, and MR images. The present approach utilizes deep learning techniques so as to provide a better initialization to one or more steps of the numerical iterative reconstruction algorithm by learning a trajectory of convergence from estimates at different convergence status so that it can reach the maximum or minimum of a cost function faster.
Abstract:
The use of the channelized preconditioners in iterative reconstruction is disclosed. In certain embodiments, different channels correspond to different frequency sub-bands and the output of the different channels can be combined to update an image estimate used in the iterative reconstruction process. While individual channels may be relatively simple, the combined channels can represent complex spatial variant operations. The use of channelized preconditioners allows empirical adjustment of individual channels.
Abstract:
A method includes receiving, with at least one processor, a first projection dataset corresponding to X-rays at a first energy level projected towards a subject at a first set of view angles and receiving, with the at least one processor, a second projection dataset corresponding to X-rays at a second energy level projected towards the subject at a second set of view angles. The method further includes identifying, with the at least one processor, a metal trace from at least one of the first projection dataset and the second projection dataset. Moreover, the method includes converting, with the at least one processor, at least a portion of the first projection dataset to a pseudo dataset at the second energy level. The method also includes generating, with the at least one processor, a final image of the subject based on the second projection dataset, the pseudo dataset, and the metal trace.
Abstract:
An approach is disclosed for acquiring multi-sector computed tomography scan data. The approach includes activating an X-ray source during heartbeats of a patient to acquire projection data over a limited angular range for each heartbeat. The projection data acquired over the different is combined. An image having good temporal resolution is reconstructed using the combined projection data.
Abstract:
An X-ray tube and methods for imaging are disclosed. The X-ray tube includes an emitter configured to generate an electron beam. Further, the X-ray tube includes a target configured to generate X-rays in response to the electron beam, where a target surface includes at least a first region having a first elevation and a second region having a second elevation different from the first elevation. The X-ray tube also includes a detector configured to generate projection data based on the X-rays and a computing device coupled to the emitter, the detector and/or the target. The computing device is configured to deflect a focal spot on the target surface by controlling target rotation such that the electron beam impinges alternatively on the first and second regions. The computing device processes the projection data corresponding to the deflected focal spot positions and reconstructs images of a subject using the processed projection data.
Abstract:
Methods, systems, and non-transitory computer readable media for image reconstruction are presented. Measured data corresponding to a subject is received. A preliminary image update in a particular iteration is determined based on one or more image variables computed using at least a subset of the measured data in the particular iteration. Additionally, at least one momentum term is determined based on the one or more image variables computed in the particular iteration and/or one or more further image variables computed in one or more iterations preceding the particular iteration. Further, a subsequent image update is determined using the preliminary image update and the momentum term. The preliminary image update and/or the subsequent image update are iteratively computed for a plurality of iterations until one or more termination criteria are satisfied.
Abstract:
Systems are provided for a patient isolation unit for use with a medical imaging system includes an enclosure comprised of a pathogen impermeable material compatible with one or more imaging systems. The enclosure includes a base, a first end wall coupled to a first end of the base, a second end wall coupled to a second end of the base, and a cover coupled to a first side of the base, second side of the base, the first end wall and the second end wall for substantially enclosing a patient therein. In another exemplary embodiment, a patient isolation unit for use with a medical imaging system includes a head enclosure comprised of a pathogen impermeable material and a body enclosure coupled to the head enclosure and comprised of a pathogen impermeable material.