Abstract: Methods and systems are provided for downsampling detector data in a computed tomography imaging system. In an example, a method for a photon counting computed tomography (PCCT) system includes, during a scan of an imaging subject, obtaining detector data from a photon counting detector of the PCCT system, the detector data comprising, for each pixel or detector element of the photon counting detector, photon counts partitioned into a plurality of energy bins based on an energy imparted by each photon on the photon counting detector, applying a bin factor to the plurality of energy bins for each pixel to downsample the plurality of energy bins into a reduced number of energy bins, and reconstructing one or more images from the reduced number of energy bins.

Abstract: Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Abstract: Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Abstract: Systems/techniques that facilitate generation of image denoising training data via photon-count splitting are provided. In various embodiments, a system can access a set of sinograms generated by a photon-counting computed tomography scanner. In various aspects, the system can split the set of sinograms into a first reduced-photon-count set of sinograms and a second reduced-photon-count set of sinograms. In various instances, the system can convert, via image reconstruction, the first reduced-photon-count set of sinograms into at least one training input image and the second reduced-photon-count set of sinograms into at least one training output image. In various cases, the system can train a deep learning neural network based on the at least one training input image and the at least one training output image.

Abstract: An x-ray apparatus includes an x-ray source capable of producing an x-ray beam with a focal spot having a spatial shape that is selected to pre-amplify predetermined spatial frequencies exceeding half a cutoff frequency as compared to spatial frequencies below half the cutoff frequency; an x-ray detector capable of detecting the x-ray beam; and a processor adapted to reconstruct an image from the detected x-ray beam using a filter that compensates the pre-amplified predetermined spatial frequencies. The spatial shape comprises two or more disconnected regions that preferably have widths less than that of a nominal focal spot, combined widths greater than or equal to that of a nominal focal spot, and are separated by less than three times a width of a nominal focal spot.

Abstract: Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Abstract: Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Abstract: An x-ray apparatus includes an x-ray source capable of producing an x-ray beam with a focal spot having a spatial shape that is selected to pre-amplify predetermined spatial frequencies exceeding half a cutoff frequency as compared to spatial frequencies below half the cutoff frequency; an x-ray detector capable of detecting the x-ray beam; and a processor adapted to reconstruct an image from the detected x-ray beam using a filter that compensates the pre-amplified predetermined spatial frequencies. The spatial shape comprises two or more disconnected regions that preferably have widths less than that of a nominal focal spot, combined widths greater than or equal to that of a nominal focal spot, and are separated by less than three times a width of a nominal focal spot.

Abstract: The present invention is directed to a method for enabling volumetric image reconstruction from unknown projection geometry of tomographic imaging systems, including CT, cone-beam CT (CBCT), and tomosynthesis systems. The invention enables image reconstruction in cases where it was not previously possible (e.g., custom-designed trajectories on robotic C-arms, or systems using uncalibrated geometries), and more broadly offers improved image quality (e.g., improved spatial resolution and reduced streak artifact) and robustness to patient motion (e.g., inherent compensation for rigid motion) in a manner that does not alter the patient setup or imaging workflow. The method provides a means for accurately estimating the complete geometric description of each projection acquired during a scan by simulating various poses of the x-ray source and detector to determine their unique, scan-specific positions relative to the patient, which is often unknown or inexactly known (e.g.

Abstract: The present invention is directed to a method for enabling volumetric image reconstruction from unknown projection geometry of tomographic imaging systems, including CT, cone-beam CT (CBCT), and tomosynthesis systems. The invention enables image reconstruction in cases where it was not previously possible (e.g., custom-designed trajectories on robotic C-arms, or systems using uncalibrated geometries), and more broadly offers improved image quality (e.g., improved spatial resolution and reduced streak artifact) and robustness to patient motion (e.g., inherent compensation for rigid motion) in a manner that does not alter the patient setup or imaging workflow. The method provides a means for accurately estimating the complete geometric description of each projection acquired during a scan by simulating various poses of the x-ray source and detector to determine their unique, scan-specific positions relative to the patient, which is often unknown or inexactly known (e.g.

Abstract: A method for determining a composition of an object using a spectral x-ray system is provided. X-ray photons of at least two different energies are transmitted through the object. The energy of each detected x-ray photon using a detector in the x-ray system is estimated. A first weighted sum of the number of detected photons of each energy is found using a first weighting function, wherein the first weighting function is dependent on the attenuation coefficient function of a first material. In another embodiment, the photons are binned into two energy bins wherein there is a gap between the energy bins.

Abstract: A method for determining a composition of an object using a spectral x-ray system is provided. X-ray photons of at least two different energies are transmitted through the object. The energy of each detected x-ray photon using a detector in the x-ray system is estimated. A first weighted sum of the number of detected photons of each energy is found using a first weighting function, wherein the first weighting function is dependent on the attenuation coefficient function of a first material. In another embodiment, the photons are binned into two energy bins wherein there is a gap between the energy bins.