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 approach relates to avoiding azimuthal blur in a computed tomography context, such as in dual energy imaging with fast kV switching. In accordance with certain aspects, the focal spot position is held stationary in the patient coordinate system within each respective view and the detector signals within the view are summed. In one embodiment, this results in the low and high energy views within the signal being collected from the same position within the patient coordinate system.

Abstract: An X-ray tube is provided. The X-ray tube includes an electron beam source including a cathode configured to emit an electron beam. The X-ray tube also includes an anode assembly including an anode configured to receive the electron beam and to emit X-rays when impacted by the electron beam. The X-ray tube further includes a gridding electrode disposed about a path of the electron beam between the electron beam source and the anode assembly. The gridding electrode, when powered at a specific level, is configured to grid the electron beam in synchronization with planned transitions during a dynamic focal spot mode.

Abstract: An imaging method includes executing a low-dose preparatory scan to an object by applying tube voltages and tube currents in an x-ray source, and generating a first image of the object corresponding to the low-dose preparatory scan. The method further includes generating image quality estimates and dose estimates view by view at least based on the first image. The method includes optimizing the tube voltages and the tube currents to generate optimal profiles for the tube voltage and the tube current. At least one of the optimal profiles for the tube voltage and the tube current is generated based on the image quality estimates and the dose estimates. The method includes executing an acquisition scan by applying the tube voltages and the tube currents based on the optimal profiles and generating a second image of the object corresponding to the acquisition scan. An imaging system is also provided.

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 approach relates to the use of machine-learning in convolution kernel design for scatter correction. In one aspect, a neural network is trained to replace or improve the convolution kernel used for scatter correction. The training data set may be generated probabilistically so that actual measurements are not employed.

Abstract: The present approach relates to a detector design that allows detector-based wobble using an electronic control scheme. In one implementation, each detector pixel is divided into sub-pixels. The readout of the sub-pixels can be binned with minimal noise penalty to enable the detector wobble without physically shifting the detector or alternating the physical focal spot location, though, as discussed herein alternation of the focal spot location may be used in conjunction with the present approach to further improve radial and longitudinal imaging resolution as well as suppressing artifacts resulted by limited spatial sampling.

Abstract: The present approach relates to the use of machine learning and deep learning systems suitable for solving large-scale, space-variant tomographic reconstruction and/or correction problems. In certain embodiments, a tomographic transform of measured data obtained from a tomography scanner is used as an input to a neural network. In accordance with certain aspects of the present approach, the tomographic transform operation(s) is performed separate from or outside the neural network such that the result of the tomographic transform operation is instead provided as an input to the neural network. In addition, in certain embodiments, one or more layers of the neural network may be provided as wavelet filter banks.

Abstract: A system and method of characterizing a blood vessel. An imaging modality is utilized to take an image of the blood vessel that is under a first stimulated condition and an image of the blood vessel is under a second stimulated condition. The first and second images of the blood vessel are then compared to one another to determine stress and strain data of the blood vessel in order to characterize the blood vessel.

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 present disclosure relates to the use of a hierarchical tomographic reconstruction approach that employs data representations in intermediate steps are between a full line integral and a voxel (e.g., an intermediate line integral). Each of the steps is progressively more local in nature and therefore has computational advantages and is also amenable to a deep learning solution using trained neural networks. The proposed hierarchical structure provides a mechanism to divide a large-scale inverse problem into a series of smaller-scale problems.

Abstract: An X-ray tube is provided. The X-ray tube includes an electron beam source including a cathode configured to emit an electron beam. The X-ray tube also includes an anode assembly including an anode configured to receive the electron beam and to emit X-rays when impacted by the electron beam. The X-ray tube further includes a gridding electrode disposed about a path of the electron beam between the electron beam source and the anode assembly. The gridding electrode, when powered at a specific level, is configured to grid the electron beam in synchronization with planned transitions during a dynamic focal spot mode.

Abstract: Acquisition of X-ray transmission data at three or more energy levels is described. Various implementations utilize generator waveforms that utilize fast-switching, slow-switching, or a combination of fast- and slow-switching to transition between X-ray energy levels. In addition, various sampling arrangements for sampling and/or binning three or more energy levels of X-ray transmission data are discussed. The use of these data in subsequent processing steps, such for material decomposition and/or improvement of dual-energy material decomposition processing, are also described.

Abstract: A computationally efficient dictionary learning-based term is employed in an iterative reconstruction framework to keep more spatial information than two-dimensional dictionary learning and require less computational cost than three-dimensional dictionary learning. In one such implementation, a non-local regularization algorithm is employed in an MBIR context (such as in a low dose CT image reconstruction context) based on dictionary learning in which dictionaries from different directions (e.g., x,y-plane, y,z-plane, x,z-plane) are employed and the sparse coefficients calculated accordingly. In this manner, spatial information from all three directions is retained and computational cost is constrained.

Abstract: The present approach provides a non-invasive methodology for estimation of coronary flow and/or fractional flow reserve. In certain implementations, various approaches for personalizing blood flow models of the coronary vasculature are described. The described personalization approaches involve patient-specific measurements and do not assume or rely on the resting coronary flow being proportional to myocardial mass. Consequently, there are fewer limitations in using these approaches to obtain coronary flow and/or fractional flow reserve estimates non-invasively.

Abstract: The present approach relates to a detector design that allows detector-based wobble using an electronic control scheme. In one implementation, each detector pixel is divided into sub-pixels. The readout of the sub-pixels can be binned with minimal noise penalty to enable the detector wobble without physically shifting the detector or alternating the physical focal spot location, though, as discussed herein alternation of the focal spot location may be used in conjunction with the present approach to further improve radial and longitudinal imaging resolution as well as suppressing artifacts resulted by limited spatial sampling.

Abstract: A signal processing method is disclosed, which includes detecting a total intensity of X-rays passing through an object comprising multiple materials; obtaining at least one set of basis information of basis material information of the multiple materials and basis component information of photon-electric absorption basis component and Compton scattering basis component of the object; estimating a scatter intensity component of the detected X-rays based on the at least one set of basis information and the detected total intensity; and obtaining an intensity estimate of primary X-rays incident on a detector based on the detected total intensity and the estimated scatter intensity component. An imaging system adopting the above signal processing method is also disclosed.

Abstract: A high-resolution imaging approach is described. The described approach includes use of a small focal spot size and positioning of the patient offset from the center of the imaging volume. The off-center displacement is combined with a small focal spot size and with modified image reconstruction methods to provide high intrinsic spatial resolution without hardware changes to the imaging system.

Abstract: The present approach relates to the use of a database (i.e., a dictionary) of image patterns to be avoided or de-emphasized during an image reconstruction process, such as an iterative image reconstruction process. Such a dictionary may be characterized as a negative or “bad” dictionary. The negative dictionary may be used to constrain an image reconstruction process to avoid or minimize the presence of the patterns present in the negative dictionary.

Abstract: A detector panel is described having readout circuitry integrated with the photodetectors, such as in the light imager panel. The detector is useful in high spatial resolution and low-dose or low-signal imaging contexts and may be used in adaptive 2D binning configurations. Adaptive binning of detector elements may be accomplished using control logic and X-ray intensity detector circuitry capable of assessing an incident X-ray intensity and controlling binning of an associated group of detector elements.