Abstract:
The present invention provides a system and method for generating a CT slice image. The system comprises an MIP image generation module, a region of interest determination module, an angle setting module, a curve determination module, a match module and a slice generation module. The MIP image generation module generates MIP images of a reconstructed image; the region of interest determination module determines an image range in an original slice, and determines the parts of the MIP images within the image range as regions of interest; the angle setting module rotates the regions of interest to a plurality of specific angles for a plurality of times; the curve determination module generates a plurality of two-dimensional projected curves of the regions of interest for the plurality of specific angles; the match module selects a two-dimensional projected curve matching with a part to be diagnosed based on features of the plurality of two-dimensional projected curves; the slice generation module determines a slice position range and a slice angle based on the features of the matched curve and the corresponding specific angle.
Abstract:
Methods and apparatus for monitoring and improving imaging system operation are provided. An example apparatus includes a first deployed deep learning network (DLN) which operates with an acquisition engine to generate an imaging device configuration. The example apparatus includes a second deployed DLN which operates with a reconstruction engine based on acquired image data. The example apparatus includes a first assessment engine with a third deployed DLN. The assessment engine receives output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine. The first deployed DLN and the second deployed DLN are generated and deployed from first and second training DLNS, respectively.
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:
The present invention provides an apparatus and method for beam hardening artifact correction of CT image, comprising a bone tissue image obtain module, a first correction module, an orthographic projection module, and a correction image obtaining module. The bone tissue image obtain module is used to extract a bone tissue image from a reconstructed original image; the first correction module is used to increase a current CT value of the bone tissue image; the orthographic projection module is used to perform an orthographic projection on the bone tissue image with the CT value being increased to obtain an orthographic projection data of the bone tissue image; the correction image obtaining module is used to perform image reconstruction according to the orthographic projection data of the bone tissue image described above and obtain a correction image.
Abstract:
The present invention provides a system and method for generating a CT slice image. The system comprises an MIP image generation module, a region of interest determination module, an angle setting module, a curve determination module, a match module and a slice generation module. The MIP image generation module generates MIP images of a reconstructed image; the region of interest determination module determines an image range in an original slice, and determines the parts of the MIP images within the image range as regions of interest; the angle setting module rotates the regions of interest to a plurality of specific angles for a plurality of times; the curve determination module generates a plurality of two-dimensional projected curves of the regions of interest for the plurality of specific angles; the match module selects a two-dimensional projected curve matching with a part to be diagnosed based on features of the plurality of two-dimensional projected curves; the slice generation module determines a slice position range and a slice angle based on the features of the matched curve and the corresponding specific angle.
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:
Various methods and systems are provided for estimating and compensating for table deflection in reconstructed images. In one embodiment, a method for computed tomography (CT) imaging comprises reconstructing images from data acquired during a helical CT scan where table deflection parameters are estimated and the reconstruction is adjusted based on the table deflection parameters. In this way, images may be reconstructed without artifacts caused by table deflection.
Abstract:
An imaging system includes a computed tomography (CT) acquisition unit and a processing unit. The CT acquisition unit includes an X-ray source and a CT detector configured to collect CT imaging data of an object to be imaged. The processing unit includes at least one processor operably coupled to the CT acquisition unit. The processing unit is configured to control the CT acquisition unit to collect at least one sample projection during rotation of the CT acquisition unit about the object to be imaged, compare an intensity of the at least one sample projection to an intensity of a reference projection, select a time to perform an imaging scan based on the comparison of the intensity of the at least one sample projection to the intensity of the reference projection, and control the CT acquisition unit to perform the imaging scan.
Abstract:
An imaging system includes a computer programmed to estimate noise in computed tomography (CT) imaging data, correlate the noise estimation with neighboring CT imaging data to generate a weighting estimation based on the correlation, de-noise the CT imaging data based on the noise estimation and on the weighting, and reconstruct an image using the de-noised CT imaging data.
Abstract:
Methods and systems are provided for reconstructing and automatically segmenting an image. In one embodiment, a method comprises generating a first image from acquired projection data based on an iterative reconstruction algorithm, generating a second image from the acquired projection data based on a modified iterative reconstruction algorithm, segmenting the second image to obtain segments, segmenting the first image based on the segments of the second image, and outputting the segmented first image to a display. In this way, an image which may otherwise prove challenging for an automatic segmentation process may be accurately segmented without sacrificing textural details of the image.