4results about How to "Avoid Motion Artifacts" patented technology

The application discloses a FRET sensitized channel super-resolution SIM image reconstruction parameter estimation method and system. The method comprises the following steps: obtaining a FRET three-channel original image data set and an optical transfer function OTF after structure light modulation; obtaining a FRET three-channel spread spectrum image by using Fourier transform and spectrum shift, and obtaining reconstruction parameters of DD, DA and AA channel image sets from the FRET three-channel spread spectrum image; replacing the structure light illumination frequency vector of the DA channel with the structure light illumination frequency vector in the reconstruction parameters of the DD channel image set; obtaining the initial phase and modulation depth of the DA channel image set by using a complex linear regression algorithm containing empirical mode decomposition; performing FRET three-channel structure light super-resolution linear Wiener reconstruction, and performing data processing based on a channel sensitization intensity measurement method to measure the FRET efficiency and the donor-acceptor concentration ratio of the FRET super-resolution image. The application enhances the robustness of the SIM-FRET method for low signal-to-noise ratio image reconstruction, and expands the usability of the SIM-FRET under extremely low FRET efficiency.

The invention discloses an oral cavity three-dimensional scanning system and method based on dual-band heterogeneous structure light coding, and belongs to the technical field of optical three-dimensional scanning. The system comprises a light source, a coding light modulation device, at least two image acquisition units and a data processing unit, the light source can respectively emit first wave band light and second wave band light with different wave bands; the coding light modulator modulates the first wave band light into a main coding pattern containing discontinuous stripes and modulates the second wave band light into an auxiliary coding pattern containing continuous stripes, and the stripes of the two coding patterns are alternately arranged at intervals; the modulated first wavelength light and the modulated second wavelength light irradiate the surface of a measured area, and each image acquisition unit acquires a coded image reflected by the measured area; and the data processing unit decodes the main coding image to obtain a reference matching point pair and parallax data, decodes the auxiliary coding image based on the result to obtain a high-density extended matching point pair and extended parallax data, and completes integrated three-dimensional reconstruction through triangulation after integrating the two types of data.

The utility model discloses a device of cooperation children anaesthetized under magnetic resonance examination relates to medical auxiliary technical field. Including split type mattress, this split type mattress is divided into the headrest area of supporting head portion and the main body support area of bearing body, and all are by the upper pad layer and lower pad layer of interconnection composition, head adjustment subassembly is set up above headrest area, is used for adjusting the head of patient is the head -up and holds the state of the chin. The utility model can promote the safety, accuracy and comfort of children nuclear magnetic resonance scanning examination.

The application discloses a multi-scale feature fusion neural network phase unwrapping method and system based on De Bruijn stripe coding, first constructs a wavelength-De Bruijn element mapping table; then, the wrapped phase and the background intensity are obtained by using the collected deformation stripes and the background reference map, and the nonlinear cosine component and the phase gradient feature are calculated according to the wrapped phase and the background intensity, so that a multi-channel physical feature input tensor is constructed. Then, the tensor is input into a multi-scale feature fusion neural network with multiple parallel branches. In the training stage, a loss function based on symbol physical wavelength weighting is introduced for constraint. The application directly realizes high-precision prediction of phase order by a deep learning network, does not need additional auxiliary decoding sequence, effectively avoids the complex mathematical principle process and the tedious logic retrieval in the traditional phase unwrapping algorithm, and significantly improves the efficiency of three-dimensional measurement and the robustness to complex environment.