11 results about "Inversion recovery" patented technology

The application relates to a magnetic resonance imaging method, device, computer equipment, storage medium and computer program product. The method comprises the following steps: in a first cardiac cycle, a characteristic electrocardio signal of a detection object is identified, a first sequence pulse is applied to the detection object after a preset delay time of identifying the characteristic electrocardio signal, and the first sequence pulse is used for reversing a longitudinal magnetization vector of the detection object; after the first sequence pulse is applied, a second sequence pulse of a target phase mode is applied to the detection object after a recovery time, and an initial cardiac imaging set is acquired, wherein the target phase mode is one of at least two different phase modes, and different target phase modes correspond to different positions of artifacts in the initial cardiac imaging sets; and the initial cardiac imaging sets obtained in each first cardiac cycle are subjected to first registration processing to obtain a cardiac imaging. The method can effectively weaken the artifacts in cardiac delayed enhancement imaging, and does not increase additional operations and additional scanning time.

The invention discloses intelligent diagnosis equipment for brain small vasculopathy MRI image enhancement, which comprises a multi-mode MRI image acquisition module, an examination bed is arranged on one side of the multi-mode MRI image acquisition module, the multi-mode MRI image acquisition module is used for acquiring multi-sequence MRI original images of a patient with brain small vasculopathy, and the multi-sequence MRI original images at least comprise T1 weighted imaging T1WI, T2 weighted imaging T2WI and liquid attenuation inversion recovery sequence FLAIR images; the invention belongs to the technical field of medical equipment, and aims at solving the problems that in the prior art, a traditional MRI image is limited in resolution and is interfered by noise and artifacts, the signal difference between a focus and normal brain tissue is not obvious, and missed diagnosis is prone to occurring. The method has the advantages that the space-channel attention mechanism of the intelligent image enhancement module is fused with the multi-modal features, the signal difference between the tiny focus and the normal brain tissue is remarkably enhanced, noise and artifact interference are weakened, the detection rate of the tiny focus is greatly increased, and the missed diagnosis risk is effectively reduced.

The invention relates to a magnetic resonance image reconstruction method and device, computer equipment and a storage medium, and the method comprises the steps: applying a phase-sensitive inversion recovery pulse sequence to a to-be-imaged object, collecting a magnetic resonance signal of the to-be-imaged object, and obtaining a to-be-processed image according to the magnetic resonance signal; removing a background first-order phase in the to-be-processed image to obtain a first dephasing image; according to the first dephasing image and a preset recognition model, removing a target background zero-order phase in the first dephasing image to obtain a second dephasing image; and extracting a real part component of the second dephasing image to obtain a target magnetic resonance image. By using the magnetic resonance image reconstruction method provided by the invention, the quality of the obtained target magnetic resonance image can be improved.

The application provides a kind of magnetic resonance image feature extraction method, system, equipment and feature application method, it is related to image feature extraction technical field, the application includes obtaining the magnetic resonance imaging data of sample to be measured;According to magnetic resonance T1 weighted image, magnetic resonance imaging liquid attenuation inversion recovery sequence image and diffusion magnetic resonance image, construct the structure broken link network of sample to be measured;According to resting state functional magnetic resonance imaging, construct the functional broken link network of sample to be measured;According to structure broken link network and functional broken link network, construct individual injury degree network;The one-dimensional vector obtained by converting the upper triangular value on the matrix of individual injury degree network is used as the feature extraction result of the magnetic resonance image of sample to be measured.The application can accurately and reasonably extract the characteristics of magnetic resonance image by constructing structure broken link network and functional broken link network, to improve the precision of model classification.

The application discloses an optimization method, device, equipment and medium for magnetic resonance imaging, and relates to the field of magnetic resonance imaging. The method comprises the following steps: acquiring a reference longitudinal relaxation time of an imaging magnetic resonance device with a preset main magnetic field strength; constructing a linkage fitting function between TR and TI parameters based on the reference longitudinal relaxation time, taking the maximum difference value between an image signal of a hemorrhagic focus and an image signal of healthy brain parenchyma as a target; determining a target TI according to a target TR and the linkage fitting function; optimizing scanning parameters of the magnetic resonance device by using a minimum effective echo time, the target TR and the target TI; acquiring an initial magnetic resonance image, which is obtained by scanning a stroke patient by using the magnetic resonance device with the optimized parameters; and enhancing the initial magnetic resonance image by using a nonlinear stretching function to obtain a stroke triage enhanced inversion recovery image. The application can improve the image contrast of the magnetic resonance imaging.

This invention relates to the field of intelligent medical image analysis technology, and in particular to a deep learning-based method and system for stroke image segmentation. The method includes: acquiring diffusion-weighted, apparent diffusion coefficient, and fluid attenuation inversion recovery images to form a dataset; obtaining standardized images and quality grades through resampling and intensity normalization; training a prior model with normal samples to generate a potential distribution centroid library and quality calibration curve; alternately updating the deformation field and anomaly editing field of the images to be segmented to generate candidate probability maps; zeroing the anomaly editing field to generate counterfactual healthy images; and combining differential evidence and contralateral mirror evidence to output lesion masks and suspicious region masks, thereby improving cross-quality stability and verifiability.

A method of analysing MRI images is described. The method comprising acquiring at least three medical MR images of a subject; analysing the at least three medical MR images to determine a water T1 map; applying a field strength correction and an iron correction to the determined water T1 map to generate a corrected water T1 map; generating one or more simulated MRI images based on the water T1 map; fitting the one or more simulated MR images to determine a standard cT1 image for the subject.

The invention relates to a magnetic resonance imaging method and a magnetic resonance imaging system, and the method comprises the steps: obtaining an initial inversion recovery scanning sequence which comprises an inversion module and an imaging collection module; obtaining first gradient coding position information of a set tissue of the target scanning part according to an imaging acquisition module; according to the first gradient coding position information, gradient coding parameters of an inversion module are determined, and a target inversion recovery scanning sequence is obtained; and scanning the target scanning part according to the target inversion recovery scanning sequence to obtain a magnetic resonance image. By adopting the method, the problem that a fat inhibition method cannot have a relatively good inhibition effect in various occasions can be solved.

The invention discloses a senile dementia related multi-contrast brain structure magnetic resonance imaging analysis method which comprises the following steps: acquiring multi-contrast brain structure magnetic resonance imaging data including T1 weighted imaging, T2 weighted imaging, proton density weighted imaging PD, liquid attenuation inversion recovery FLAIR and magnetic sensitivity weighted imaging SWI; constructing a model for multi-contrast brain structure magnetic resonance imaging analysis, wherein the model comprises an image denoising module, an image registration module and an image segmentation module; inputting the acquired magnetic resonance imaging data into the trained model; in the first step, noise suppression images corresponding to all contrast ratios are output, in the second step, multi-modal image registration is carried out, contrast ratio alignment images of unified voxel grids are generated, and in the third step, a final segmentation probability graph and a corresponding tissue label are output. By using the system and the method, high-quality, automatic and clinically deployable nuclear magnetic resonance imaging (MRI) brain image analysis is realized. The method can be widely applied to the field of medical image processing.

This application provides a magnetic resonance T1 mapping system and method, the system comprising: at least one storage device storing a set of instructions; and at least one processor communicating with the at least one storage device, characterized in that, when the instructions are executed, the at least one processor is configured to cause the system to perform the following operations: acquiring at least three images of a target during an inversion recovery process, each of the at least three images being acquired during breath-holding within a cardiac cycle of the target; and determining a T1 mapping image of the target based on the at least three images acquired during the inversion recovery process and a trained model.

The invention relates to a longitudinal relaxation time constant acquisition method and device and a magnetic resonance imaging system. The method comprises the following steps: acquiring a plurality of amplitude images corresponding to a plurality of inversion recovery moments; adjusting the plurality of amplitude images according to a first deviation value of the amplitude in each amplitude image; and obtaining a longitudinal relaxation time constant of a target position point in the amplitude image according to the multiple adjusted amplitude images and the corresponding inversion recovery moments. By adopting the method, the problem of low accuracy of a longitudinal relaxation time constant fitting result can be solved.