Abstract: The disclosure provides a method for breeding Carassius auratus strains without intermuscular bones, which comprises designing knockout target sites for the two copies of the bmp6, bmp6a and bmp6b, in the Carassius auratus genome, and then obtaining F2 generation individuals from a homozygous line with bmp6a and bmp6b double gene mutation through two rounds of gene knockout and screening, and then propagating by using the F2 generation individuals from the homozygous line with bmp6a and bmp6b double gene mutation to form a new Carassius auratus strain with intermuscular bone-deficient. Strains of Carassius auratus with less than 20 intermuscular bones and without intermuscular bones are obtained by the method of the present disclosure.

Abstract: In some embodiments, the present disclosure provides an algorithm for a data-driven intelligent adaptive model of an automobile cab based on a biometric identification technology. The algorithm includes the following steps: acquiring driving posture data, the driving posture data including driver information, automobile man-computer parameter information, and seat position information; building a feature extraction model of the driving posture data and screening feature vectors playing a significant role in predicting a seat position by the feature extraction model; building a prediction model of a comfortable seat position by a back propagation (BP) neural network and inputting the screened feature vectors to perform training on the prediction model; and inputting specific feature vectors to obtain the seat position information and accordingly adjust the seat position.

Abstract: Methods, devices and apparatus for reconstructing a PET image are provided. According to an example of the method, a PET initial image may be reconstructed from PET data obtained by scanning on a target object with a PET device, and an MRI image may be reconstructed from MRI data obtained by scanning on the target object with a MRI device, a fusion image retaining only a boundary of the target object is generated based on the PET initial image and the MRI image, and a PET reconstructed image is obtained by combining the PET initial image with the fusion image, where the definition of the boundary of the target object in the PET reconstructed image is higher than that in the PET initial image.

Abstract: A radioactive source and preparing as well as applying methods are disclosed. According to the method of preparing the radioactive source, when a preset dose of a radiopharmaceutical with a half-life shorter than a preset threshold is provided, a solid radioactive source having a predetermined shape may be obtained by mixing and moulding the radiopharmaceutical with a preset solution and a moulding material. The moulding material may comprise a curing agent and/or a water-absorbing material.

Abstract: Methods, devices and apparatus for reconstructing an image are provided. According to an example of the method, scanning data is obtained for a scanned subject, an initially-updated image is reconstructed from the scanning data, image boundary prior information is generated by performing at least two sparse transforms on the initially-updated image, and a reconstructed image is obtained by performing a weighted reconstruction with the image boundary prior information and the initially-updated image.

Abstract: Methods, devices, and apparatus, including computer programs encoded on a computer storage medium for reconstructing image are provided. In one aspect, a method of reconstructing image includes obtaining scanning data for a subject in a continuous incremental scanning of medical equipment including real crystals for detection, associating each of the real crystals with one or more virtual crystals in a virtual scanning system, determining delay random coincidence data of two virtual crystals connected by a response line in the virtual scanning system, obtaining random coincidence data by denoising the delay random coincidence data based on crystal receiving efficiency for each of the real crystals, and reconstructing an image with the scanning data by taking the random coincidence data into account.

Abstract: Methods, devices, and systems for reconstructing Positron Emission Computed Tomography (PET) images are provided. In one aspect, a method includes: for each of coincidence events including at least one true coincidence event and at least one scattering coincidence event, determining an emission path of the coincidence event according to photon information of the coincidence event, the photon information of the coincidence event including time data, position data, and angle data of each of two photons involved in the coincidence event, determining an annihilation position of the coincidence event according to the emission path of the coincidence event and the time data of each of the two photons involved in the coincidence event, and reconstructing a PET image according to the annihilation position, the emission path and the photon information of each of the coincidence events.

Abstract: Method, systems and machine-readable storage mediums for reconstructing images are provided. In one aspect, a method includes: acquiring data of a response line, the data including an actual energy parameter of each of photons in a photon pair corresponding to the response line, the actual energy parameter being detected by a detector and within a preset energy range, determining an energy factor of the response line according to the actual energy parameter of each of the photons in the photon pair and a theoretical energy parameter of the photon, obtaining a system parameter according to the energy factor, the system parameter including an element indicating a probability that a photon pair generated in a region of a subject corresponding to an image voxel is received by the detector, constructing a system response model with the system parameter, and reconstructing an image based on the system response model.

Abstract: A method of reconstructing an image that may include in at least one example: determining coincidence events based on detection by a detector during a continuous incremental scanning; determining an axial position for each of the coincidence events; storing data for each of the coincidence events including the axial position in a list mode; sorting the data for each of the coincidence events according to a spatial order; and obtaining an image by performing iterative reconstruction with the sorted data for each of the coincidence events.

Abstract: Methods, systems, and machine-readable storage mediums for reconstructing a PET image are provided. In one aspect, a method includes: determining a plurality of LORs associated with multiple-coincidence data in coincidence data detected by a PET device through scanning, obtaining a respective line integral value along each of the LORs according to a time difference between two single events corresponding to the LOR, allocating the multiple-coincidence data to the LORs according to the respective line integral values of the LORs to obtain respective multiple allocation data on the LORs, correcting respective double-coincidence data in the coincidence data corresponding to each of the LORs based on the respective multiple allocation data on the LOR to obtain data of the LOR, and reconstructing an image according to the data of each of the LORs.

Abstract: Methods, devices and apparatus for reconstructing an image are provided. According to an example of the method, scanning data is obtained for a scanned subject, an initially-updated image is reconstructed from the scanning data, image boundary prior information is generated by performing at least two sparse transforms on the initially-updated image, and a reconstructed image is obtained by performing a weighted reconstruction with the image boundary prior information and the initially-updated image.

Abstract: Methods, devices and apparatus for reconstructing a PET image are provided. According to an example of the method, a PET initial image may be reconstructed from PET data obtained by scanning on a target object with a PET device, and an MRI image may be reconstructed from MRI data obtained by scanning on the target object with a MRI device, a fusion image retaining only a boundary of the target object is generated based on the PET initial image and the MRI image, and a PET reconstructed image is obtained by combining the PET initial image with the fusion image, where the definition of the boundary of the target object in the PET reconstructed image is higher than that in the PET initial image.

Abstract: A radioactive source and preparing as well as applying methods are disclosed. According to the method of preparing the radioactive source, when a preset dose of a radiopharmaceutical with a half-life shorter than a preset threshold is provided, a solid radioactive source having a predetermined shape may be obtained by mixing and moulding the radiopharmaceutical with a preset solution and a moulding material. The moulding material may comprise a curing agent and/or a water-absorbing material.

Abstract: A PET image reconstruction method and device are provided. According to an example, a first diffusion expression representing a diffusion degree of projection data from a first incident point to a first crystal may be obtained with a first diffusion distribution, a second diffusion distribution, a point source position and position information of the first crystal in a projection direction. A second diffusion expression representing a diffusion degree of projection data from a second incident point to a second crystal may be obtained with the first diffusion distribution, the second diffusion distribution, the point source position and position information of the second crystal in the projection direction. A diffusion relationship representing a correspondence relationship between the point source position and projection data in the projection direction may be obtained with the first diffusion expression and the second diffusion expression.

Abstract: A method and apparatus for determining PET scanning time are provided. According to an example of the method, a CT image is divided into multiple single-bed CT images according to bed information of bed positions for a PET scan, wherein the CT image is obtained by performing a CT scan on a subject of the PET scan, and a one-to-one corresponding relation exists between the multiple single-bed CT images and all of the beds. A residual true coincidence count ratio is estimated for each of the beds based on corresponding single-bed CT image of the bed, and then a scanning time proportion for each of the beds may be determined based on each of the residual true coincidence count ratios for the beds.

Abstract: A method for reconstructing a PET multi-bed image is provided, which may be applied to a PET scan having an axial overlapping region existed between every two adjacent beds. According to an example of the method, a combined set of PET projection data for at least two beds may be obtained by combining a set of PET projection data for at least two successive beds together in such a manner that two sets of PET projection data corresponding to an axial overlapping region between every two adjacent beds are added up. And then, a PET image for at least two beds may be reconstructed based on an image reconstruction iteration model and the combined set of PET projection data for the plurality of beds.

Abstract: Methods, devices, and systems for reconstructing Positron Emission Computed Tomography (PET) images are provided. In one aspect, a method includes: for each of coincidence events including at least one true coincidence event and at least one scattering coincidence event, determining an emission path of the coincidence event according to photon information of the coincidence event, the photon information of the coincidence event including time data, position data, and angle data of each of two photons involved in the coincidence event, determining an annihilation position of the coincidence event according to the emission path of the coincidence event and the time data of each of the two photons involved in the coincidence event, and reconstructing a PET image according to the annihilation position, the emission path and the photon information of each of the coincidence events.

Abstract: Methods, systems, and machine-readable storage mediums for reconstructing a PET image are provided. In one aspect, a method includes: determining a plurality of LORs associated with multiple-coincidence data in coincidence data detected by a PET device through scanning, obtaining a respective line integral value along each of the LORs according to a time difference between two single events corresponding to the LOR, allocating the multiple-coincidence data to the LORs according to the respective line integral values of the LORs to obtain respective multiple allocation data on the LORs, correcting respective double-coincidence data in the coincidence data corresponding to each of the LORs based on the respective multiple allocation data on the LOR to obtain data of the LOR, and reconstructing an image according to the data of each of the LORs.

Abstract: Method, systems and machine-readable storage mediums for reconstructing images are provided. In one aspect, a method includes: acquiring data of a response line, the data including an actual energy parameter of each of photons in a photon pair corresponding to the response line, the actual energy parameter being detected by a detector and within a preset energy range, determining an energy factor of the response line according to the actual energy parameter of each of the photons in the photon pair and a theoretical energy parameter of the photon, obtaining a system parameter according to the energy factor, the system parameter including an element indicating a probability that a photon pair generated in a region of a subject corresponding to an image voxel is received by the detector, constructing a system response model with the system parameter, and reconstructing an image based on the system response model.

Abstract: A method of reconstructing an image that may include in at least one example: determining coincidence events based on detection by a detector during a continuous incremental scanning; determining an axial position for each of the coincidence events; storing data for each of the coincidence events including the axial position in a list mode; sorting the data for each of the coincidence events according to a spatial order; and obtaining an image by performing iterative reconstruction with the sorted data for each of the coincidence events.