Abstract: A mechanism for controlling the operation of a CT scanner during a scanning procedure. A radiation outputting system is configured to output radiation during an imaging phase of the scanning procedure. The subject support (patient table) of the CT scanner is controlled to move linearly, e.g. according to some predetermined linear pattern, during this imaging phase. The linear movement of the subject support includes at least one acceleration and/or deceleration phase. At least a beam width of radiation output by the CT scanner is controlled throughout the imaging phase responsive to the speed of the subject support.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform an imaging method (100). The method includes: receiving imaging data on a frame by frame basis for frames along an axial direction with neighboring frames overlapping along the axial direction wherein the frames include at least a volume (k) and a succeeding volume (k+1) at least partially overlapping the volume (k) along the axial direction; and generating an image of the volume (k) using an iterative image reconstruction process in which an iteration of the iterative image reconstruction process includes: computing a local penalty function for suppressing noise over the volume (k) including reducing the value of the local penalty function in an overlap region; generating an update image of the volume (k) using imaging data from the volume (k) and further using the local penalty function.

Abstract: An imaging system includes a computed tomography (CT) imaging device (10) (optionally a spectral CT), an electronic processor (16, 50), and a non-transitory storage medium (18, 52) storing a neural network (40) trained on simulated imaging data (74) generated by Monte Carlo simulation (60) including simulation of at least one scattering mechanism (66) to convert CT imaging data to a scatter estimate in projection space or to convert an uncorrected reconstructed CT image to a scatter estimate in image space. The storage medium further stores instructions readable and executable by the electronic processor to reconstruct CT imaging data (12, 14) acquired by the CT imaging device to generate a scatter-corrected reconstructed CT image (42). This includes generating a scatter estimate (92, 112, 132, 162, 182) by applying the neural network to the acquired CT imaging data or to an uncorrected CT image (178) reconstructed from the acquired CT imaging data.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform an imaging method (100). The method includes: receiving imaging data on a frame by frame basis for frames along an axial direction with neighboring frames overlapping along the axial direction wherein the frames include at least a volume (k) and a succeeding volume (k+1) at least partially overlapping the volume (k) along the axial direction; and generating an image of the volume (k) using an iterative image reconstruction process in which an iteration of the iterative image reconstruction process includes: computing a local penalty function for suppressing noise over the volume (k) including reducing the value of the local penalty function in an overlap region; generating an update image of the volume (k) using imaging data from the volume (k) and further using the local penalty function.

Abstract: An imaging system includes a computed tomography (CT) imaging device (10) (optionally a spectral CT), an electronic processor (16, 50), and a non-transitory storage medium (18, 52) storing a neural network (40) trained on simulated imaging data (74) generated by Monte Carlo simulation (60) including simulation of at least one scattering mechanism (66) to convert CT imaging data to a scatter estimate in projection space or to convert an uncorrected reconstructed CT image to a scatter estimate in image space. The storage medium further stores instructions readable and executable by the electronic processor to reconstruct CT imaging data (12, 14) acquired by the CT imaging device to generate a scatter-corrected reconstructed CT image (42). This includes generating a scatter estimate (92, 112, 132, 162, 182) by applying the neural network to the acquired CT imaging data or to an uncorrected CT image (178) reconstructed from the acquired CT imaging data.

Abstract: According to one embodiment, a method of image analysis is provided. The method includes binning image data into a plurality of sinogram frames, identifying a plurality of initial stationary frames by applying a first analysis technique on the plurality of binned sinogram frames, extracting a plurality of first statistical parameters applying a second analysis technique on the plurality of binned sinogram frames, combining the plurality of first statistical parameters with boundaries of plurality of initial stationary frames to generate a presentation of a joint analysis combining at least some of the plurality of the first statistical parameters and at least some of the plurality of the second statistical parameter, identifying a plurality of final stationary frames from the presentation of the joint analysis, independently reconstructing each of the plurality of final stationary frames, and registering each of the plurality of final stationary frames to a first state.

Abstract: A method includes receiving Emission Tomography (ET) data of a subject from an ET/Computed Tomography (CT) scanner. The method further includes generating a first motion signal corresponding to a first bed position of the ET/CT scanner from the received ET data and determining whether the first motion signal indicates a periodic motion. The method also includes calculating a first time period from the first motion signal in response to determining that the first motion signal indicates the periodic motion and sending a first acquire notification to the ET/CT scanner to acquire Cinematographic (CINE) Computed Tomography (CT) data from the first bed position based on the first time period.

Abstract: According to one embodiment, a method of image analysis is provided. The method includes binning image data into a plurality of sinogram frames, identifying a plurality of initial stationary frames by applying a first analysis technique on the plurality of binned sinogram frames, extracting a plurality of first statistical parameters applying a second analysis technique on the plurality of binned sinogram frames, combining the plurality of first statistical parameters with boundaries of plurality of initial stationary frames to generate a presentation of a joint analysis combining at least some of the plurality of the first statistical parameters and at least some of the plurality of the second statistical parameter, identifying a plurality of final stationary frames from the presentation of the joint analysis, independently reconstructing each of the plurality of final stationary frames, and registering each of the plurality of final stationary frames to a first state.

Abstract: Embodiments of a solid state photomultiplier are provided herein. In some embodiments, a solid state photomultiplier may include a plurality of pixels, wherein each pixel of the plurality of pixels comprises a plurality of subpixels; and a first set of buffer amplifiers, wherein each buffer amplifier of the first set of buffer amplifiers is respectively coupled to a subpixel of the plurality of subpixels.

Abstract: Systems and method for identifying bone marrow in medical images are provided. A method includes obtaining a three-dimensional (3D) computed tomography (CT) volume data set corresponding to an imaged volume and identifying voxels in the 3D CT volume data set having a Hounsfield Unit (HU) value below a bone threshold. The voxels are identified without using image continuity. The method further includes marking the identified voxels as non-bone voxels, determining definite tissue voxels based on the identified non-bone voxels and expanding a region defined by the definite tissue voxels. The method also includes segmenting the expanded region to identify bone voxels and bone marrow voxels and identifying bone marrow as voxels that are not the bone voxels.

Abstract: A method includes acquiring scan data for an object to be imaged using an imaging scanner. The method also includes reconstructing a display image using the scan data. Further, the method includes determining one or more aspects of a quantitation imaging algorithm for generating a quantitation image, wherein the one or more aspects of the quantitation imaging algorithm are selected to optimize a quantitation figure of merit for lesion quantitation. The method also includes reconstructing a quantitation image using the scan data and the quantitation imaging algorithm; displaying, on a display device, the display image; determining a region of interest in the display image; determining, for the region of interest, a lesion quantitation value using a corresponding region of interest of the quantitation image; and displaying, on the display device, the lesion quantitation value.

Abstract: A scintillator block is presented. The scintillator block includes at least one scintillator having an isotropic volume. Furthermore, the scintillator block includes a laser-generated three-dimensional pattern positioned within the isotropic volume of the at least one scintillator, where the laser-generated three-dimensional pattern is configured to modify one or more optical properties within the isotropic volume of the at least one scintillator, and where the three-dimensional pattern varies along one or more of a depth, a width, and an angular orientation of the at least one scintillator.

Abstract: A method includes acquiring scan data for an object to be imaged using an imaging scanner. The method also includes reconstructing a display image using the scan data. Further, the method includes determining one or more aspects of a quantitation imaging algorithm for generating a quantitation image, wherein the one or more aspects of the quantitation imaging algorithm are selected to optimize a quantitation figure of merit for lesion quantitation. The method also includes reconstructing a quantitation image using the scan data and the quantitation imaging algorithm; displaying, on a display device, the display image; determining a region of interest in the display image; determining, for the region of interest, a lesion quantitation value using a corresponding region of interest of the quantitation image; and displaying, on the display device, the lesion quantitation value.

Abstract: According to some embodiments, emission projection data and second source scan data are received. A prior map and a prior weight map are generated from second source scan data. A penalty function calculates voxel-wise differences between the prior map and a given image, transforms the voxel-wise differences and calculates a weighted sum of the transformed differences, using weights based on the prior weight map. Joint reconstruction of an emission image and an attenuation map proceeds iteratively and uses the penalty function.

Abstract: Imaging system and method are presented. Emission scan (ES) and anatomical scan (AS) data corresponding to a target volume in a subject are received. One or more at least partial AS images are reconstructed using AS data. An image-space certainty (IC) map representing a confidence level (CL) for attenuation coefficients of selected voxels in AS images and a preliminary attenuation (PA) map based on AS images are generated. One or more of selected attenuation factors (AF) in projection-space are initialized based on PA map. A projection-space certainty (PC) map representing CL for the selected AF is generated based on IC map. An emission image of the target volume is initialized. The selected AF and emission image are iteratively updated based on the ES data, PC map, initial AF, and/or initial emission image. A desired emission image and/or AF values are determined based on the iteratively updated AF and/or emission image.

Abstract: Embodiments of a solid state photomultiplier are provided herein. In some embodiments, a solid state photomultiplier may include a plurality of pixels, wherein each pixel of the plurality of pixels comprises a plurality of subpixels; and a first set of buffer amplifiers, wherein each buffer amplifier of the first set of buffer amplifiers is respectively coupled to a subpixel of the plurality of subpixels.

Abstract: A method implemented using at least one processor includes receiving time-varying image dataset generated by a medical imaging modality. The image dataset corresponds to a bed position and is affected by quasi-periodic motion data. The method also includes applying a signal decomposition technique to the time-varying image dataset to generate a plurality of dataset components and a plurality of motion signals. The method also includes determining reference data based on the time-varying image dataset, wherein the reference data is representative of a direction of the quasi-periodic motion. The method further includes deriving polarity of each of the plurality of motion signals based on the reference data to generate a plurality of sign corrected motion signals. The method also includes determining a gating signal corresponding to the bed position based on at least one of the plurality of sign corrected motion signals.

Abstract: A method implemented using at least one processor includes receiving time-varying image dataset generated by a medical imaging modality. The image dataset corresponds to a bed position and is affected by quasi-periodic motion data. The method also includes applying a signal decomposition technique to the time-varying image dataset to generate a plurality of dataset components and a plurality of motion signals. The method also includes determining reference data based on the time-varying image dataset, wherein the reference data is representative of a direction of the quasi-periodic motion. The method further includes deriving polarity of each of the plurality of motion signals based on the reference data to generate a plurality of sign corrected motion signals. The method also includes determining a gating signal corresponding to the bed position based on at least one of the plurality of sign corrected motion signals.

Abstract: Imaging system and method are presented. Emission scan (ES) and anatomical scan (AS) data corresponding to a target volume in a subject are received. One or more at least partial AS images are reconstructed using AS data. An image-space certainty (IC) map representing a confidence level (CL) for attenuation coefficients of selected voxels in AS images and a preliminary attenuation (PA) map based on AS images are generated. One or more of selected attenuation factors (AF) in projection-space are initialized based on PA map. A projection-space certainty (PC) map representing CL for the selected AF is generated based on IC map. An emission image of the target volume is initialized. The selected AF and emission image are iteratively updated based on the ES data, PC map, initial AF, and/or initial emission image. A desired emission image and/or AF values are determined based on the iteratively updated AF and/or emission image.

Abstract: A method includes receiving Emission Tomography (ET) data of a subject from an ET/Computed Tomography (CT) scanner. The method further includes generating a first motion signal corresponding to a first bed position of the ET/CT scanner from the received ET data and determining whether the first motion signal indicates a periodic motion. The method also includes calculating a first time period from the first motion signal in response to determining that the first motion signal indicates the periodic motion and sending a first acquire notification to the ET/CT scanner to acquire Cinematographic (CINE) Computed Tomography (CT) data from the first bed position based on the first time period.