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: Systems and methods are provided for computed tomography (CT) imaging. In one embodiment, a method comprises adaptively blending at least two input image volumes with different spatially-variant noise characteristics to generate an output image volume with uniform noise throughout the output image volume. In this way, images may be reconstructed from projection data with data redundancy without introducing image artifacts from stitching images or variance in image noise due to the data redundancy.

Abstract: Systems and methods are provided for computed tomography (CT) imaging. In one embodiment, a method comprises adaptively blending at least two input image volumes with different spatially-variant noise characteristics to generate an output image volume with uniform noise throughout the output image volume. In this way, images may be reconstructed from projection data with data redundancy without introducing image artifacts from stitching images or variance in image noise due to the data redundancy.

Abstract: Computer processing techniques are described for reducing streaks in computed tomography (CT) images. According to an embodiment, computer-implemented method comprises obtaining, by a system comprising a processor, a pair of CT images reconstructed from a same set of projection data, the pair comprising a first image reconstructed from the projection data using a standard reconstruction process and a second image reconstructed from the projection data using a filtering reconstruction process that results in the second image comprising a first reduced level of streaks relative to the first image. The method further comprises generating, by the system, a third image by fusing a first subset of pixels extracted from one or more non-uniform areas in the first image and a second subset of pixels extracted from one or more uniform areas in the second image, wherein the third image comprises a second reduced level of streaks relative to the first image.

Abstract: Systems and methods are provided for computed tomography (CT) imaging. In one embodiment, a method comprises adaptively blending at least two input image volumes with different spatially-variant noise characteristics to generate an output image volume with uniform noise throughout the output image volume. In this way, images may be reconstructed from projection data with data redundancy without introducing image artifacts from stitching images or variance in image noise due to the data redundancy.

Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

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: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.

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: Approaches for acquiring CT image data corresponding to a full scan, but at a reduced dose are disclosed. In one implementation, X-ray tube current modulation is employed to reduce the effective dose. In other implementations, acquisition of sparse views, z-collimation, and two-rotation acquisition protocols may be employed to achieve a reduced dose relative to a full-scan acquisition protocol.

Abstract: A system and method for CT projection extrapolation are provided. The method comprises receiving a CT projection for extrapolation. The method also comprises selecting a target patch comprising at least one pixel of a row to be extrapolated. The method further comprises generating a correlation profile between the target patch and one or more source patches, wherein the source patches comprise measured pixels in the CT projection in one or more rows adjacent to the target patch. The projection data is generated for at least one pixel of the target patch based on the correlation profile and the measured pixels of at least one of the source patches.

Abstract: Embodiments of the disclosure relate to projection-based volumetric dose estimation for X-ray systems, such as X-ray imaging systems. For example, in one embodiment, an X-ray system is capable of estimating an X-ray dose based on an energy interaction of the X-rays with respective portions of an object. In another embodiment, the X-ray dose estimate may be provided on a per voxel, per region, and/or per organ basis.

Abstract: Embodiments of the disclosure relate to projection-based dose estimation for X-ray systems, such as X-ray imaging systems. For example, in one embodiment, an X-ray system is capable of estimating an X-ray dose based on an intensity profile of the detected X-rays that have passed through a scanned object and an estimated mass of the object. In one embodiment, the intensity profile may be compared to a baseline scan to acquire an estimate of energy interaction with the object.

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.