77 results about "Cardiac magnetic resonance" patented technology

The invention discloses a method for fully-automatically segmenting and quantifying the left ventricle of a cardiac magnetic resonance image, comprising the following specific steps of: carrying out automatic denoising and edge enhancement processing on a 4D (four-dimensional) cardiac magnetic resonance image first; automatically and preprimarily determining the center of the left ventricle by utilizing Hough transform, and implementing a region growing technology by taking the center of the left ventricle as a starting seed point to provide a left ventricle full-voxel blood region, and taking the mass center of the left ventricle full-voxel blood region as a center of the left ventricle in the current layer; finding out the center of each layer by using a seed propagation technology; implementing a region growing technology based on an iterative falling threshold by taking the center of the left ventricle of each layer as a starting seed point to automatically provide a left ventricle blood region of each layer, and calculating the area of the left ventricle blood region of each layer; automatically segmenting the top of the left ventricle according to the time-space continuity of the area of the left ventricle and calculating the area of the top of the left ventricle; positioning the bottom of the left ventricle according to the area and shape time-space continuity of the left ventricle, and automatically segmenting the bottom of the left ventricle of the heart by adopting a region growing technology which is constricted by the left ventricle shape with time-space continuity, and calculating the area of the bottom of the left ventricle; and finally, realizing whole segmentation of the left ventricle image. The method disclosed by the invention is of a fully-automaticprocess without any manual intervene.

A system and method for providing a dark-blood technique for contrast-enhanced cardiac magnetic resonance, improving visualization of subendocardial infarcts or perfusion abnormalities that may otherwise be difficult to distinguish from the bright blood pool. In one technique the dark-blood preparation is performed using a driven-equilibrium fourier transform (DEFT) preparation with motion sensitizing gradients which attenuate the signal in the ventricular cavities related to incoherent phase losses resulting from non-steady flow within the heart. This dark-blood preparation preserves the underlying contrast characteristics of the pulse sequence causing a myocardial infarction to be bright while rendering the blood pool dark. When applied to perfusion imaging, this dark-blood preparation will help eliminate artifacts resulting from the juxtaposition of a bright ventricular cavity and relatively dark myocardium.

The disclosure relates to a method of automatically producing a three-dimensional (3D) segmentation of a heart chamber, the method comprising: obtaining data sets from cardiac magnetic resonance imaging (MRI) or ultrasound, generating a 3D segmentation of the heart chamber from the data sets using an active contour method, modifying the 3D segmentation by adding a plurality of intra-chamber structures; and identifying an enclosing myocardium using the 3D segmentation generated by the method.

A method is proposed for identifying an anatomical structure within a spatial-temporal image (i.e. a series of frames captured as respective times). A current frame of spatial-temporal medical image is processed using information from one or more previous and/or subsequent temporal frames, to aid in the segmentation of an object or a region of interest (ROI) in a current frame. The invention is applicable to both two- and three-dimensional spatial-temporal images (i.e., 2D+time or 3D+time), and in particular to cardiac magnetic resonance (CMR images). An initialisation process for this method segments the left ventricle (LV) in a CMR image by a fuzzy c-means (FCM) clustering algorithm which employs a circular shape function as part of the definition of the dissimilarity measure.

A hybrid filtering method called Karhunen Loeve Transform-Wavelet (KW) filtering is presented to de-noise dynamic cardiac magnetic resonance images that simultaneously takes advantage of the intrinsic spatial and temporal redundancies of real-time cardiac cine. This filtering technique combines a temporal Karhunen-Loeve transform (KLT) and spatial adaptive wavelet filtering. KW filtering has four steps. The first is applying the KLT along the temporal direction, generating a series of “eigenimages”. The second is applying Marcenko-Pastur (MP) law to identify and discard the noise-only eigenimages. The third applying a 2-D spatial wavelet filter with adaptive threshold to each eigenimage to define the wavelet filter strength for each of the eigenimages based on the noise variance and standard deviation of the signal. Lastly, the inverse KLT is applied to the filtered eigenimages to generate a new series of cine images with reduced image noise.

The invention provides a right ventricle multi-map partitioning method based on a cardiac magnetic resonance movie minor-axis image. A magnetic resonance imaging system is used for collecting a certain number of heart original magnetic resonance images of a tested person, and a region of interest is extracted. A fixed number of map images of right ventricle are selected from the original magneticresonance images to be added into a map set, and an expert manual partitioning result is obtained by an expert manually partitioning the map image. The map images and target images obtain a right ventricle coarse partitioning result by adopting B sample conversion based on normalized mutual information, COLLATE fusion is adopted for the coarse partitioning result, firstly log likelihood estimationis carried out on complete data, and then iterative solution is carried out by using a maximum expectation algorithm until convergence, so that the right ventricle final partitioning result is obtained through amending treatment. The method has higher robustness, the accuracy and precision of fusion can be improved, and the method is used for accurately partitioning the heart right ventricle minor-axis image.

The invention relates to a heart magnetic resonance image analysis and cardiomyopathy prediction method and device, and the method carries out the processing through a trained neural network, and theprocessing at least comprises the steps: obtaining a first image at least comprising a heart region according to a heart magnetic resonance image; obtaining a heart index according to the first image;and performing classification processing according to the heart indexes to obtain a classification result. In the embodiment of the invention, the artificial intelligence analysis technology of the heart magnetic resonance image is adopted, the full automation of image analysis can be realized through the trained neural network, the analysis of the heart magnetic resonance image can be quickly completed, and higher accuracy can be achieved.

The embodiment of the invention discloses a radial golden angle magnetic resonance heart film imaging method and device and equipment. The method comprises the steps of obtaining magnetic resonance K-space data sampled by a radial golden angle with a preset channel number of a target object; performing re-arranging on the K-space data to obtain preset frame number undersampled K-space data; inputting the preset frame number of undersampled K-space data to a cross region image rebuilding network to obtain a target image. According to the embodiment, the problem that long time is consumed in theimage rebuilding process on the basis of the radial golden angle sampled data is solved; the purpose of shortening the image rebuilding time of heart magnetic resonance data sampled by the radial golden angle can be achieved, and the quality of a rebuilt image is improved.

An exemplary system, method and computer-accessible medium can be provided for generating an image(s) of a tissue(s) that can include, for example, receiving magnetic resonance imaging information regarding the tissue(s) based on a golden-angle radial sampling procedure, sorting and synchronizing the MRI information into at least two dimensions, and generating the image(s) of the tissue(s) based on the sorted and synchronized information. The tissue(s) include cardiac tissue and respiratory-affected tissue. The MRI information can include a motion of the cardiac tissue and a motion of the respiratory tissue.

The invention belongs to the technical field of medical imaging, and particularly relates to a cardiac magnetic resonance image segmentation method. A cardiac anatomic structure-based left and right ventricle level set segmentation method specifically comprises the following steps: performing geometry decomposition to a cardiac structure; resolving a convex hull of an initialized contour; producing a signaled distance function; and generating intima and extima through a dual-level set algorithm, and the like. By using the method, the simultaneous segmentation of the ventricular intima and extima and cardiac muscle can be realized, and the segmentation results can meet the physiological structure characteristics of a heart as far as possible.

The invention relates to a cardiac nuclear magnetic resonance image segmentation method which comprises the following steps of: 1, carrying out Gaussian filtering pretreatment on an image; 2, calculating an external force field of an edge for keeping a normal gradient vector flow; 3, defining an initialization outline position of an inner membrane of the cardiac ventriculus sinister; 4, adding a circular energy constraint in a curve evolution process, and segmenting to obtain the inner membrane; 5, defining a final segmentation outline result of the inner membrane as an initialization outlineposition of an outer membrane; 6, setting the edge strength of a zone enclosed by the inner membrane outline in an original edge graph as 0, and re-calculating the external force field; and 7, addinga circular energy constraint in a curve evolution process, and segmenting to obtain the outer membrane. The invention has the advantages of large capturing range, strong noise proof capacity, better robustness to weak edge leakage, and capability of accurately segmenting the inner membrane and the outer membrane of the ventriculus sinister wall.

A system and method for regression-based segmentation of the mitral valve in 2D+t cardiac magnetic resonance (CMR) slices is disclosed. The 2D+t CMR slices are acquired according to a mitral valve-specific acquisition protocol introduced herein. A set of mitral valve landmarks is detected in each 2D CMR slice and mitral valve contours are estimated in each 2D CMR slice based on the detected landmarks. A full mitral valve model is reconstructed from the mitral valve contours estimated in the 2D CMR slices using a trained regression model. Each 2D CMR slice may be a cine image acquired over a full cardiac cycle. In this case, the segmentation method reconstructs a patient-specific 4D dynamic mitral valve model from the 2D+t CMR image data.

The invention provides a data processing method and a data processing system for cardiac magnetic resonance real-time film imaging. The method comprises the following steps that a selected imaging layer is obtained, and an image of the imaging layer is read; a reference line set on the image is obtained; a dynamic variation chart of a signal contour of the reference line along with acquisition time is obtained; a time interval from the beginning to the end of the end exhalation period is obtained from the dynamic variation chart; the diameter of the left ventricle within the time interval is calculated through the dynamic variation chart, and the heart phases of the end diastolic and the end systolic are determined in accordance with the diameter of the left ventricle; and images corresponding to the heart phases of the end diastolic and the end systolic are extracted and saved. By adopting the data processing method and the data processing system, the data processing speed can be increased.

The invention discloses a heart magnetic resonance imaging method based on a multi-scale low-rank model, and provides a new method for heart magnetic resonance imaging researching. A radial sampling track is adopted to realize undersampling of heart K space data, and full heart data radial undersampling is realized, and thereafter magnetic resonance data acquisition speed is accelerated, and then scanning time of a magnetic resonance device is reduced. The minimization expression of the heart magnetic resonance data based on multi-scale low-rank decomposition is used to improve the precision of the magnetic resonance imaging. A convex optimization problem of a reconstructed image is solved by an alternating direction multiplier method, and a speed of magnetic resonance imaging reconstruction is improved, and therefore the texture of the result of the reconstructed image is clear, and the edge is smooth.

The invention relates to a left ventricular segment recognition method and device, electronic equipment and a storage medium, and the method comprises the steps of obtaining a target image which comprises a cardiac magnetic resonance film short-axis image; segmenting the target image to obtain a plurality of masks of a plurality of target areas in the target image; obtaining a plurality of target contours according to the plurality of masks; and performing four-dimensional reconstruction on the target image comprising the plurality of target contours and then determining the left ventricular segment in the target image. According to the embodiment of the invention, the left ventricular segment can be quickly, automatically and accurately identified, so that a clinician can accurately judge the function and movement condition of each myocardial segment of the left ventricle, the efficiency of the clinician is greatly improved, and the invention has extremely high value in clinical application of heart magnetic resonance images.

The invention relates to a heart magnetic resonance image data enhancement method based on evolutionary GAN. According to the method, when a generator is trained, the generator is mutated to generatea plurality of sub-generation generators; the adaptability scores of the generators are judged through an adaptability score function; an optimal filial generation generator is selected as a parent generation generator of the next iteration according to the score; meanwhile, in the discriminator training stage, a new training sample is synthesized in combination with linear interpolation of the feature vector, and a related linear interpolation label is generated, so that the distribution of the whole training set is expanded, the discrete sample space is continued, the inter-domain smoothnessis improved, and the model can be better trained. According to the image enhancement method, high-quality and diverse samples can be generated to expand the training set, and finally various indexesof the classification result are improved.