Abstract: The present disclosure relates to a genetic variation identification method, genetic variation identification apparatuses, and a storage medium. The method includes: obtaining at least one gene sequencing read corresponding to a genetic variation candidate site; obtaining base arrangement features of the genetic variation candidate site; determining non-base arrangement features of the genetic variation candidate site based on non-base arrangement information of the at least one gene sequencing read within a preset site interval, where the non-base arrangement features remain unchanged after a base arrangement order changes; and identifying genetic variation of the genetic variation candidate site based on the base arrangement features and the non-base arrangement features of the genetic variation candidate site.

Abstract: A time of flight (TOF) positron emission tomography (PET) image (38) is generated from TOF PET imaging data (10) acquired of a subject using a TOF PET imaging data acquisition device (6). Iterative image reconstruction (30) of the TOF PET imaging data is performed with TOF localization of counts along respective lines of response (LORs) to iteratively update a reconstructed image (32). Values for at least one regularization or filtering parameter are assigned to the TOF PET imaging data or to voxels of the reconstructed image based on an estimated TOF localization resolution for the TOF PET imaging data or voxels. Regularization (34) or filtering (36) of the reconstructed image is performed using the assigned values for the at least one regularization or filtering parameter. In some embodiments, the varying TOF localization resolution for the TOF PET imaging data or voxels is estimated based on related acquisition characteristics such as count rates or operating temperature of the detectors.

Abstract: In positron emission tomography (PET) imaging, PET imaging data (22) having TOF localization is reconstructed. TOF image reconstruction (30) is performed on the PET imaging data to produce a TOF reconstructed image (32). The TOF image reconstruction utilizes the TOF localization of the PET imaging data. Non-TOF image reconstruction (40) is also performed on the PET imaging data to produce a non-TOF reconstructed image (42). The non-TOF image reconstruction does not utilize the TOF localization of the PET imaging data. A comparison image (50) is computed which is indicative of differences between the TOF reconstructed image and the non TOF reconstructed image. An adjustment (54) is determined for the TOF image reconstruction based on the comparison image, such as alignment correction of an attenuation map (18), and the TOF image reconstruction is repeated on the PET imaging data with the determined adjustment to produce an adjusted TOF reconstructed image.

Abstract: Embodiments of the present disclosure disclose image processing methods, electronic devices, and a storage medium. According to one example of the method, an electronic device may: process a first image to obtain prediction results of a plurality of pixels in the first image, the prediction results including semantic prediction results and center relative position prediction results, wherein the semantic prediction results indicate that the pixels are located in an instance region or a background region, and the center relative position prediction results indicate relative positions between the pixels and an instance center; and determine an instance segmentation result of the first image based on the semantic prediction result and the center relative position prediction result of each of the plurality of pixels.

Abstract: Embodiments of the present disclosure disclose an image processing method, an electronic device, and a storage medium. The method includes: converting an original image into a target image conforming to a target parameter; obtaining a target numerical index according to the target image; and performing, according to the target numerical index, timing prediction processing on the target image to obtain a timing state prediction result. Left ventricular function quantization can be implemented, the image processing efficiency is improved, and the prediction accuracy of a cardiac function index is improved.

Abstract: The present disclosure relates to a gene mutation identification method and apparatus, and a storage medium. The method includes: obtaining at least one gene sequencing read segment corresponding to a gene mutation candidate site; determining a sequence feature and a non-sequence feature of the gene mutation candidate site according to attribute information of the at least one gene sequencing read segment, where the sequence feature is a feature related to the position of the site; and identifying gene mutation of the gene mutation candidate site based on the sequence feature and the non-sequence feature. According to embodiments of the present disclosure, the sequence feature and the non-sequence feature of the gene can be combined, thereby more comprehensively analyzing the features of a gene mutation site and improving accuracy of gene mutation identification.

Abstract: A radioemission scanner (12) is operated to acquire tomographic radioemission data of a radiopharmaceutical in a subject in an imaging field of view (FOV). An imaging system is operated to acquire extension imaging data of the subject in an extended FOV disposed outside of and adjacent the imaging FOV along an axial direction (18). A distribution of the radiopharmaceutical in the subject in the extended FOV is estimated based on the extension imaging data, and further based on a database (32) of reference subjects. The tomographic radioemission data are reconstructed to generate a reconstructed image (26) of the subject in the imaging FOV. The reconstruction includes correcting the reconstructed image for scatter from the extended FOV into the imaging FOV based on the estimated distribution of the radiopharmaceutical in the subject in the extended FOV.

Abstract: A non-transitory storage medium storing instructions readable and executable by an imaging workstation (18) including at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: receiving emission imaging data (22) from an image acquisition device (12) wherein the emission imaging data has been filtered using an acquisition energy passband (18); generating filtered imaging data by filtering the emission imaging data with a second energy passband (90) that is narrower than an acquisition energy passband; reconstructing the filtered imaging data to generate an intermediate image; estimating one or more scatter correction factors (SCFs) from the intermediate image; and reconstructing the emission imaging data corrected with the estimated SCFs to generate a reconstructed image.

Abstract: A device for processing gluten products includes an extruder. A press forming mold that includes a fixed mold and a movable mold is provided at a discharge end of the extruder. A bowl is provided at an end surface of the fixed mold, and a bowl matched with the bowl of the fixed mold is provided at an end surface of the movable mold. A feeding channel is provided inside the fixed mold. A through hole is provided in the movable mold. An feed tube which is movable relative to the movable mold is arranged in the through hole. A die holder is arranged at the other end surface of the movable mold for holding the feed tube, and is movable with respect to the movable mold. A push-and-pull mechanism is provided at an end of the die holder.

Abstract: A production system for improving yield of bayberry juice includes a steam generator, a main body, a feed hopper, an extraction device, a cooling liquid tank and a collection container. A method for improving yield of bayberry juice includes the following steps. Bayberries are selected, soaked, cleaned, squeezed, distilled and condensed. The bayberries are distilled and condensed after ordinary squeezing, which improves the yield of bayberry juice and avoiding the waste of nutrients in the bayberry.

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: estimating one or more figures of merit for a reconstructed image by applying a trained deep learning transform (30) to input data including at least imaging parameters and not including a reconstructed image; selecting values for the imaging parameters based on the estimated one or more figures of merit; generating a reconstructed image using the selected values for the imaging parameters; and displaying the reconstructed image.

Abstract: A PET detector array (8) comprising detector pixels acquires PET detection counts along lines of response (LORs). The counts are reconstructed to generate a reconstructed PET image (36, 46). The reconstructing is corrected for missing LORs which are missing due to dead detector pixels of the PET detector array. The correction may be by estimating counts along the missing LORs (60) by interpolating counts along LORs (66) neighboring the missing LORs. The interpolation may be iterative to handle contiguous groups of missing detector pixels. The correction may be by computing a sensitivity matrix having matrix elements corresponding to image elements (80, 82) of the reconstructed PET image. In this case, each matrix element is computed as a summation over all LORs intersecting the corresponding image element excepting the missing LORs. The computed sensitivity matrix is used in the reconstructing.

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 image reconstruction method (100) to reconstruct list mode data acquired over a frame acquisition time using a plurality of radiation detectors (17) in which the events of the list mode data is time stamped.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: performing iterative image reconstruction of imaging data acquired using an image acquisition device (12); selecting an update image from a plurality of update images produced by the iterative image reconstruction; processing the selected update image to generate a hot spot artifact map; and suppressing hot spots identified by the generated hot spot artifact map in a reconstructed image output by the iterative image reconstruction.

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 image reconstruction method (100). The method includes: determining a weighting parameter (13) of an edge-preserving regularization or penalty of a regularized image reconstruction of an image acquisition device (12) for an imaging data set obtained by the image acquisition device; determining an edge sensitivity parameter (?) of the edge-preserving algorithm for the imaging data set obtained by the image acquisition device; and reconstructing the imaging data set obtained by the image acquisition device to generate a reconstructed image by applying the regularized image reconstruction including the edge-preserving regularization or penalty with the determined weighting and edge sensitivity parameters to the imaging data set obtained by the image acquisition device.

Abstract: A non-transitory computer-readable medium storing instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform a quality control (QC) method (100). The method includes: receiving a current QC data set acquired by a pixelated detector (14) and one or more prior QC data sets acquired by the pixelated detector; determining stability levels of detector pixels (16) of the pixelated detector over time from the current QC data set and the one or more prior QC data sets; labeling a detector pixel of the pixelated detector as dead when the stability level determined for the detector pixel is outside of a stability threshold range; and displaying, on a display device (24) operatively connected with the workstation, an identification (28) of the detector pixels labelled as dead.

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 image reconstruction method (100). The method includes: operating a positron emission tomography (PET) imaging device (12) to acquire 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 a frame (k), a preceding frame (k?1) overlapping the frame (k), and a succeeding frame (k+1) overlapping the frame (k); reconstructing an image of the frame (k) using imaging data from the frame (k), the preceding frame (k?1), and the succeeding frame (k+1).

Abstract: A non-transitory storage medium stores instructions readable and executable by an electronic processor (20) to perform a method (100) for estimating singles rates for detectors (16) of a detector array (14) of a positron emission tomography (PET) imaging device (12).

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (14) operatively connected to a display device (20) and including at least one electronic processor (16) to perform an image acquisition and reconstruction method (101). The method includes: retrieving a non-voxel-based reconstructed image comprising non-voxel image elements from a picture and archiving communication system (PACS) database (24) to the workstation; at the workstation, generating at least one voxel-based resampled image from the non-voxel-based reconstructed image; and displaying the at least one voxel-based reconstructed image on the display device.

Abstract: An apparatus and method for measuring a structural angular acceleration based on dynamic centrifugal force measurement belong to the technical field of angular acceleration measurement. The apparatus has a solid ball. The solid ball can move freely along the radial direction of the outer wall packaging hood. The elastic block is used as a stress base. A rod for lateral limit and connection is used for connecting the rigid block and a pulley and limiting the displacement of solid ball so that the solid ball can only move longitudinally along the apparatus. The rigid block can move freely due to the pulley. Measurement of a transient angular acceleration is converted into dynamic measurement of the centrifugal force of the solid ball. Through the above design, the dynamic angular acceleration of the structure caused by dynamic load can be relatively accurately calculated.