Abstract: The present disclosure provides a lifting device comprising: a telescopic boom, comprising a basic section, at least one primary telescopic section and at least one secondary telescopic section; a telescopic control mechanism, comprising a primary telescopic drive mechanism that controls synchronous telescoping of the at least one primary telescopic section and a secondary telescopic drive mechanism that controls synchronous telescoping of the at least one secondary telescopic section; a torque limiter, comprising a first length sensor and a second length sensor, wherein a first sensor body and a telescopic end of a first length measuring cable of the first length sensor are respectively connected to tails of adjacent two section in the basic section and the at least one primary telescopic section; and a second sensor body and a telescopic end of a second length measuring cable of the second length sensor are respectively connected to tails of adjacent two section in the primary telescopic section adjacent to

Abstract: The present disclosure provides a lifting device comprising: a telescopic boom, comprising a basic section, at least one primary telescopic section and at least one secondary telescopic section; a telescopic control mechanism, comprising a primary telescopic drive mechanism that controls synchronous telescoping of the at least one primary telescopic section and a secondary telescopic drive mechanism that controls synchronous telescoping of the at least one secondary telescopic section; a torque limiter, comprising a first length sensor and a second length sensor, wherein a first sensor body and a telescopic end of a first length measuring cable of the first length sensor are respectively connected to tails of adjacent two section in the basic section and the at least one primary telescopic section; and a second sensor body and a telescopic end of a second length measuring cable of the second length sensor are respectively connected to tails of adjacent two section in the primary telescopic section adjacent to

Abstract: Camera calibration includes capturing a first image of an object by a first camera, determining spatial parameters between the first camera and the object using the first image, obtaining a first estimate for an optical center, iteratively calculating a best set of optical characteristics and test setup parameters based on the first estimate for the optical center until the difference in a most recent calculated set of optical characteristics and previously calculated set of optical characteristics satisfies a predetermined threshold, and calibrating the first camera based on the best set of optical characteristics. Multi-camera system calibration may include calibrating, based on a detected misalignment of features in multiple images, the multi-camera system using a context of the multi-camera system and one or more prior stored contexts.

Abstract: Various methods and systems for dual energy spectral computed tomography imaging are provided. In one embodiment, a method for dual energy imaging comprises generating an image from a higher energy dataset and an updated lower energy dataset, wherein the updated lower energy dataset comprises a combination of a lower energy dataset and a pseudo projection dataset generated from the higher energy dataset. In this way, a weak low energy signal may be recovered, thereby enabling image reconstruction in spite of photon starvation and sparse views.

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 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 X-ray source and CT detector are configured to be rotated about the object to be imaged and to collect a series of views of the object as the X-ray source and CT detector rotate about the object to be imaged. The processing unit is operably coupled to the CT acquisition unit and configured to control the CT acquisition unit to vary a view duration for the views of the series. The view duration for a particular view defines an imaging information acquisition period for the particular view, wherein the series of views includes a first group of views having a first view duration and a second group of views having a second view duration that is different than the first view duration.

Abstract: Various methods and systems for dual energy spectral computed tomography imaging are provided. In one embodiment, a method for dual energy imaging comprises generating an image from a higher energy dataset and an updated lower energy dataset, wherein the updated lower energy dataset comprises a combination of a lower energy dataset and a pseudo projection dataset generated from the higher energy dataset. In this way, a weak low energy signal may be recovered, thereby enabling image reconstruction in spite of photon starvation and sparse views.

Abstract: An imaging system includes a computer programmed to reconstruct original CT projection data, estimate noise in image space, forward project the image noise estimate to generate an initial projection noise estimate, modify the initial projection noise estimate using a statistical property of noise in projection space, remove noise in the original CT projection data by subtracting the modified noise estimate therefrom to generate noise-removed projection data, and reconstruct a final image based on the noise-removed projection data.

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 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 X-ray source and CT detector are configured to be rotated about the object to be imaged and to collect a series of views of the object as the X-ray source and CT detector rotate about the object to be imaged. The processing unit is operably coupled to the CT acquisition unit and configured to control the CT acquisition unit to vary a view duration for the views of the series. The view duration for a particular view defines an imaging information acquisition period for the particular view, wherein the series of views includes a first group of views having a first view duration and a second group of views having a second view duration that is different than the first view duration.

Abstract: A method for reducing noise in a medical diagnostic image includes acquiring an initial three-dimensional (3D) volume of projection data, generating a projection space noise estimate using the 3D volume of projection data, generating an initial 3D volume of image data using the 3D volume of projection data, generating an image space noise estimate using the 3D volume of image data, generating a noise projection estimate using the projection space noise estimate and the image space noise estimate, and reconstructing an image using the generated noise estimate. A system and non-transitory computer readable medium are also described.

Abstract: An imaging system includes a computer programmed to reconstruct original CT projection data, estimate noise in image space, forward project the image noise estimate to generate an initial projection noise estimate, modify the initial projection noise estimate using a statistical property of noise in projection space, remove noise in the original CT projection data by subtracting the modified noise estimate therefrom to generate noise-removed projection data, and reconstruct a final image based on the noise-removed projection data.

Abstract: A method for reconstructing an image of an object that includes a plurality of image elements. The method includes accessing image data associated with a plurality of image elements, and reconstructing an image of the object by optimizing an objective function, where the objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. The algorithm is composed of nested iterative loops, in which an inner loop iteratively optimizes an objective function approximating the outer loop objective function, and an outer loop that utilizes the solution of the inner loop to optimize the original objective function.

Abstract: A method for reconstructing an image of an object includes acquiring a set of measured projection data, reconstructing the measured projection data using a first algorithm to generate a first reconstructed image dataset, reconstructing the measured projection data using a second algorithm to generate a second reconstructed image dataset, the second algorithm being utilized to improve the temporal resolution of the second reconstructed image dataset, and generating a final image dataset using both the first and second image datasets.

Abstract: A tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer programmed to acquire a plurality of helical projection datasets of the object, reconstruct a first image using the acquired plurality of helical projection datasets and using a first reconstruction algorithm, reconstruct a second image using the acquired plurality of helical projection datasets and using a second reconstruction algorithm that is different from the first reconstruction algorithm, extract frequency components from each of the first and second images, sum the frequency components from each of the first and second images, and inverse transform the sum of the frequency components to generate a final image.

Abstract: A method for reducing noise in a medical diagnostic image includes acquiring an initial three-dimensional (3D) volume of projection data, generating a projection space noise estimate using the 3D volume of projection data, generating an initial 3D volume of image data using the 3D volume of projection data, generating an image space noise estimate using the 3D volume of image data, generating a noise projection estimate using the projection space noise estimate and the image space noise estimate, and reconstructing an image using the generated noise estimate. A system and non-transitory computer readable medium are also described.

Abstract: A tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer programmed to acquire a plurality of helical projection datasets of the object, reconstruct a first image using the acquired plurality of helical projection datasets and using a first reconstruction algorithm, reconstruct a second image using the acquired plurality of helical projection datasets and using a second reconstruction algorithm that is different from the first reconstruction algorithm, extract frequency components from each of the first and second images, sum the frequency components from each of the first and second images, and inverse transform the sum of the frequency components to generate a final image.

Abstract: A method for reconstructing an image of an object includes acquiring a set of measured projection data, reconstructing the measured projection data using a first algorithm to generate a first reconstructed image dataset, reconstructing the measured projection data using a second algorithm to generate a second reconstructed image dataset, the second algorithm being utilized to improve the temporal resolution of the second reconstructed image dataset, and generating a final image dataset using both the first and second image datasets.