50 results about "Multi contrast" patented technology

The invention discloses a medical image segmentation method based on high-resolution modal guidance and cross-modal boundary perception, and the method comprises the steps: carrying out the data preprocessing and enhancement of multi-contrast magnetic resonance imaging data, obtaining a boundary mask through a Canny operator and a Dilatation operation, constructing a multi-modal low-resolution data set, and carrying out the recognition of the multi-modal low-resolution data set; meanwhile, a high-resolution T2f modal data set is reserved, and the data set is divided into a training set, a verification set and a test set; a segmentation model is constructed, and the segmentation model comprises a high-resolution mode-guided double-encoder architecture module, a cross-level attention collaboration mechanism module, and a segmentation branch and boundary prediction branch decoder module; designing a training strategy of joint optimization of boundary contour detection and region segmentation, training the segmentation model by using a training set, and storing optimal model parameters on a verification set; and carrying out model performance verification in the test set, and segmenting a to-be-tested medical image by using the verified segmentation model.

The invention relates to a multi-contrast magnetic resonance image super-resolution reconstruction method and system. The method comprises the following steps: collecting an image data set, preprocessing the image data set, and dividing the image data set into a training set and a test data set; combining a Hilbert curve, a state space model and a frequency domain enhancement mechanism to construct a multi-contrast magnetic resonance super-resolution reconstruction model based on Hilbert double-domain fusion Mamba; and training a multi-contrast magnetic resonance super-resolution reconstruction model based on Hilbert double-domain fusion Mamba by using the training data set, and then completing the test of the test data set to obtain a super-resolution reconstruction image of the target contrast magnetic resonance image. Frequency domain features are scanned through a Hilbert curve, a cross-modal global frequency dependency relationship is captured, and high-frequency texture details are effectively recovered. And multi-modal local texture information is dynamically fused through a channel attention mechanism, so that neglect of local details by linear scanning is avoided, and the texture reconstruction precision is improved.

The invention relates to an MRI (Magnetic Resonance Imaging) image reconstruction method and device, an electronic device and a storage medium, and the method comprises the steps: obtaining multi-contrast MRI sample data which comprises a target modal high-resolution image and an auxiliary modal high-resolution image; performing frequency domain conversion on the target modal high-resolution image and cutting off high-frequency components to generate low-resolution frequency domain data; carrying out spatial domain conversion on the image to generate a spatial domain low-resolution image; inputting the low-resolution frequency domain data and the spatial domain low-resolution image into a diffusion model, and performing feature extraction on the two types of low-resolution data in a frequency domain and a spatial domain by using the diffusion model to generate a current time step feature; and carrying out feature fusion on the current time step feature and the feature of the high-resolution image of the auxiliary mode by using a structure attention mechanism to obtain a reconstructed image of the target mode. According to the invention, the balance problem of spectrum fidelity and detail enhancement in a traditional MRI super-division method is solved, and the reconstruction efficiency and the image quality are remarkably improved.

The invention discloses a multi-contrast magnetic resonance image reconstruction method and device based on detail contour feature perception. The method comprises the following steps: acquiring a target modal initial image and an auxiliary modal initial image; constructing an iterative network formed by alternately cascading image domain reconstruction units and data consistency layers, wherein each image domain reconstruction unit comprises an encoder and a decoder; in the first iteration, target modal initial images and auxiliary modal initial images are spliced and then input, an encoder extracts shared features firstly, then global contour features and high-frequency detail features are separated in parallel, and potential features are obtained through collaborative fusion; the decoder takes the potential features as input and outputs an image domain preliminary reconstruction result; the data consistency layer transforms the preliminary result into a k space, performs consistency correction on the preliminary result and a target modal sampling point, and then inversely transforms the preliminary result back to an image domain to complete one iteration; and splicing the current output and the auxiliary modal initial image again, inputting the spliced image into a next round of iteration, and repeating the process until a preset number of times to obtain a final target modal magnetic resonance image.

The invention provides a multi-anatomical-structure multi-contrast magnetic resonance imaging method and system. The method comprises the following steps: constructing an image reconstruction model combining sampling trajectory optimization and embedded decoupling representation; and defining a loss function based on decoupling representation learning as an optimization target. And constructing a multi-contrast multi-anatomical structure magnetic resonance data set, and jointly training a sampling trajectory optimization and image reconstruction model. And carrying out data sampling by utilizing the trained model and applying a sampling track generated by the sampling track generation network, and carrying out image reconstruction on undersampled data to obtain a reconstructed image without artifacts. By means of decoupling representation learning and sampling trajectory modeling of anatomical structure specificity, accelerated acquisition and reconstruction can be performed in a magnetic resonance data scene containing various anatomical structures and contrast ratios, and joint optimization of magnetic resonance sampling and reconstruction processes is realized.

The invention discloses a multi-contrast MRI joint reconstruction method, system and device and a medium, and relates to the technical field of medical imaging and deep learning, and the method comprises the following steps: collecting under-sampling MRI image data of different contrasts, and generating an optimization objective function based on a plurality of contrasts; decomposing the optimization objective function into a first sub-problem related to an auxiliary variable and a second sub-problem related to an objective variable; and alternately solving the first sub-problem and the second sub-problem in sequence to obtain a plurality of optimal MRI reconstructed images. According to the method, feature interaction of multi-contrast data is carried out in a spatial domain and a frequency domain, so that efficient complementation and collaborative modeling of multi-contrast features are realized, and the problem of insufficient utilization of information between contrasts in a traditional method is solved. By sensing the characteristics of each contrast, targeted prompts can be generated to guide the reconstruction process. Therefore, the quality of low-quality contrast is improved, and the overall reconstruction result is enhanced.

The invention relates to a multi-contrast MRI (Magnetic Resonance Imaging) super-resolution method based on Mama. The method comprises the following steps of selecting a public multi-contrast MRI image data set Q; performing data preprocessing on all the MRI images in the Q to form a training set Dall; constructing a super-resolution model M, and inputting data into a corresponding feature extraction module to extract features according to a data format requirement of the model; performing context feature matching on the extracted features to obtain a matching feature set FM of different scales; and performing feature fusion operation on all matching features in the Ftarlr and the FM in sequence, performing each merging operation in a corresponding reconstruction module, and finally obtaining a reconstructed high-resolution target image TargetSR. By using the method, features can be deeply extracted, and detail features can be transferred from a high-resolution short-time MRI image to a target low-resolution long-time MRI image.

Methods and systems are provided for synthesizing a contrast-weighted image in Magnetic resonance imaging (MRI). The method comprises: receiving a multi-contrast image of a subject, where the multi-contrast image comprises one or more images of one or more different contrasts; and generating, by a deep learning model, a synthesized image having a target contrast that is different from the one or more different contrasts of the one or more images. The deep learning model is trained by a framework comprising a segmentation network for generating a segmentation map, a classification network for generating a pathology aware map and a reconstruction network for generating a plurality of synthesized images with different brightness levels in a tissue area.

The embodiment of the invention provides a magnetic resonance imaging method and related equipment. The magnetic resonance imaging method comprises the following steps: applying a multi-spin echo sequence in each magnetization reset period, and collecting a group of multi-spin echo data sets until M groups of multi-spin echo data sets are obtained; after each magnetization reset period, a T1rho preparation pulse with spin locking duration is applied, then a multi-gradient echo sequence is applied, a group of multi-gradient echo data sets are collected until M groups of multi-gradient echo data sets are obtained, and the M groups of multi-spin echo data sets and the M groups of multi-gradient echo data sets correspond to M different spin locking durations respectively; and performing fitting operation on the M groups of multi-spin echo data sets and the M groups of multi-gradient echo data sets to generate a quantitative atlas. According to the technical scheme, the problems that a traditional T1rho sequence is long in scanning time, low in signal-to-noise ratio and single in contrast ratio are effectively solved, and the technical effects of time multiplexing and multi-contrast imaging are achieved.

The present disclosure relates to systems, methods, and apparatuses associated with reconstructing MR images based on undersampled magnetic resonance (MR) data. The MR data includes 2D or 3D information and may include multiple contrasts and multiple coils. The MR images are reconstructed using deep learning (DL) methods, which can accelerate the scanning and / or image generation process. By reconstructing the MR images based on respective subsets of contrasts, coils, and / or readout segments and then combining the reconstructed MR images to obtain a desired multi-contrast result, the challenges imposed by large amounts of MR data and hardware limitations are overcome.

Systems and methods are described for segmenting medical images, such as magnetic resonance images, using a deep learning model that has been trained using random dropped inputs, standardized inputs, or both. Medical images can be segmented based on anatomy, physiology, pathology, other properties or characteristics represented in the medical images, or combinations thereof. As one example, multi-contrast magnetic resonance images are input to the trained deep learning model in order to generate multiple segmented medical images, each representing a different segmentation class.

The invention discloses a multi-contrast magnetic resonance image reconstruction method based on frequency domain error priori guidance, and the method comprises the steps: carrying out the modeling of a characteristic decomposition strategy, space alignment and k-space data in the design of a deep expansion network through frequency domain error priori guidance, and giving a definite physical constraint to the network; and irrelevant background or noise in the reference image, data acquisition efficiency and reconstruction quality under a significant sub-sampling condition, and a signal-to-noise ratio and marginal definition of the target image are filtered out.

According to one embodiment, an ultrasound imaging system is configured to receive images from an ultrasound imaging device, and output, to a display in communication with the processor circuit, a screen display including an image and a user control comprising a plurality of control positions. The system is further configured to receive a control input associated with a single selected position of the user control, and based on the single selected position, determine at least two altered image setting values associated with at least two image settings of the plurality of image settings. The system then generates an updated image using the at least two altered image setting values. Accordingly, a single, intuitive control can change multiple visual aspects of an ultrasound image, such as changing the image from grayscale to color, and many contrast and / or brightness levels in between, in a single user action.

Disclosed in the present invention are an accelerated multi-contrast imaging method and system based on a diffusion model, and a terminal. The method comprises: acquiring an under-sampled measurement signal in magnetic resonance, inputting the under-sampled measurement signal into a trained convolutional neural network, and outputting joint distribution data by means of the trained convolutional neural network; determining a generation problem of an accelerated multi-contrast image on the basis of the under-sampled measurement signal, and using the generation problem of an accelerated multi-contrast image to construct a joint distribution diffusion model by means of a Bayesian method and a Langevin dynamics Markov chain Monte Carlo sampling method; and on the basis of the joint distribution data, iteratively processing the joint distribution diffusion model to obtain a target multi-contrast weighted graph, and outputting the target multi-contrast weighted graph. The present invention aims at solving the problem of it not being possible for existing accelerated multi-contrast imaging methods to meet both generalization and acceleration performance, and by means of the method, a multi-contrast weighted graph with good generalization and acceleration performance can be obtained.

This application belongs to the field of medical image processing technology, specifically disclosing a hybrid domain multi-contrast MRI super-resolution method and system based on variational networks. The method includes: acquiring and preprocessing high-resolution auxiliary images, high-resolution target images, and masks to obtain multi-channel high-resolution auxiliary images, low-resolution target images, and masks; iteratively updating the low-resolution target image of each channel using a hybrid domain multi-contrast variational network, and weightedly fusing the super-resolution target images of all channels to obtain the final super-resolution target image; measuring a loss function to train an MRI super-resolution model; and inputting the actual low-resolution MRI image into the MRI super-resolution model to obtain the actual super-resolution image. This application maintains the good interpretability of model-based methods, increasing the credibility in clinical practice, while leveraging the powerful feature representation capabilities of deep neural networks to effectively improve image reconstruction results, providing more accurate pathological information for precision medicine.

There is provided a computer-implemented method for synthesising magnetic resonance angiography (MRA) images from other types of inputted magnetic resonance (MR) images, in a subject-specific manner, the method comprising providing a conditional generative adversarial network (cGAN) that learns a combined latent representation of the inputted magnetic resonance images for each subject and learns to transform this combined latent representation to a magnetic resonance angiography image corresponding to that subject, providing a plurality of magnetic resonance (MR) images as input into the cGAN, and outputting a plurality of MRA images from the cGAN based on the plurality of inputted MR images.

The invention relates to a medical information analysis method and device suitable for a medical image. The method comprises the following steps: acquiring a multi-contrast image of a target part; the multi-contrast images comprise a water distribution image, a fat distribution image, an in-phase image and an anti-phase image; inputting the water distribution image, the fat distribution image, the in-phase image and the anti-phase image into a trained target recognition model to obtain a recognition result of a set tissue of the target part; and displaying the quantitative analysis result of the set tissue of the target part in a visual form according to the recognition result of the set tissue. By adopting the method, the accuracy of medical information analysis can be improved.

The invention belongs to the technical field of medical image processing, and particularly discloses a hybrid domain multi-contrast MRI super-division method and system based on a variational network, and the method comprises the steps: obtaining and preprocessing a high-resolution auxiliary image, a high-resolution target image and a mask, and obtaining a multi-channel high-resolution auxiliary image, a low-resolution target image and a mask; the mixed domain multi-contrast variational network iteratively updates the low-resolution target image of each channel, and weights and fuses the super-resolution target images of all channels to obtain a final super-resolution target image; measuring a loss function to train an MRI super-resolution model; and inputting the actual low-resolution MRI image into the MRI super-resolution model to obtain an actual super-resolution image. According to the method, good interpretability based on a model method can be kept, the credibility of clinical practice is improved, the image reconstruction effect is effectively improved by means of the powerful feature expression ability of the deep neural network, and more accurate pathological information is provided for precise medical treatment.

Methods and systems are provided for synthesizing a contrast-weighted image in Magnetic resonance imaging (MRI). The method comprises: receiving a multi-contrast image of a subject, where the multi-contrast image comprises one or more images of one or more different contrasts; generating an input to a transformer model based at least in part on the multi-contrast image; and generating, by the transformer model, a synthesized image having a target contrast that is different from the one or more different contrasts of the one or more images, where the target contrast is specified in a query received by the transformer model.

The present disclosure provides a method, device and medium for deep clustering of time series based on multi-contrast learning, the method comprising: firstly obtaining a time series data set and performing data enhancement processing to generate an enhanced data set; then extracting the position information of each sequence data in the time series data set, encoding each position information, and generating a corresponding spatial information vector set; then training a multi-contrast clustering model using the time series data set, the enhanced data set and the spatial information vector set; evaluating the target loss of the model through a preset loss function, adjusting the model parameters according to the evaluation results and repeating the training until the preset clustering requirements are met, and obtaining a trained multi-contrast clustering model; finally, using the trained model, clustering the time series data set according to the spatial information vector set to obtain a final clustering result. The present disclosure improves the accuracy and robustness of clustering by fusing time and space information and introducing a multi-contrast learning mechanism.

The present invention provides a fast multi-contrast magnetic resonance diffusion imaging and reconstruction method and system, including: using a keyhole echo plane sequence to undersample k-space data of multiple diffusion-weighted images through multiple receiving coils; constructing an objective function including a multi-coil image fidelity term and an image regularization term based on the coil-space-contrast domain tensor low-rank (Low-RankTensorConstraint); minimizing the objective function through an optimization algorithm, reconstructing the diffusion images of all receiving coils and obtaining relevant quantitative parameter images according to the specific way of contrast change. The present invention implements undersampling of k-space through a keyhole-EPI sequence, improves the resolution of multi-contrast diffusion imaging, shortens the echo time, thereby improving the deformation artifacts of the image and reducing the imaging time; avoids the step of solving the coil sensitivity map, reduces the image artifacts caused by variable-density EPI imaging, and improves the robustness and accuracy of the reconstruction result.

The application discloses a kind of based on single-balanced detector's multiple contrast Jones matrix OCT imaging system, belong to optical imaging technical field.Sweeping laser light is divided into two beams by 1*2 broadband fiber coupler, one enters sample arm polarization delay module, generates two orthogonal sample lights of equal power, optical path difference is ξ, detects biological tissue, and sample returns the backscattering light transmission to 50 / 50 broadband fiber coupler by three-port fiber ring;With another into reference arm polarization delay module, generated two orthogonal reference lights of equal power, optical path difference is 2ξ, after interference, it is collected by a balanced detector.High-speed data acquisition card directly processes interference signal to realize real-time imaging;Another channel acquires k-clock signal, improves the detection accuracy of system by calculating correlation to system phase compensation.The application is suitable for medical fields and the like, to simplify system structure, reduce cost, save data processing time.

According to one embodiment, an ultrasound imaging system is configured to receive images from an ultrasound imaging device, and output, to a display in communication with the processor circuit, a screen display including an image and a user control comprising a plurality of control positions. The system is further configured to receive a control input associated with a single selected position of the user control, and based on the single selected position, determine at least two altered image setting values associated with at least two image settings of the plurality of image settings. The system then generates an updated image using the at least two altered image setting values. Accordingly, a single, intuitive control can change multiple visual aspects of an ultrasound image, such changing the image from grayscale to color, and many contrast and / or brightness levels in between, in a single user action.

The application discloses an anatomic structure perception low-field multi-contrast fast MRI joint reconstruction method, and is specifically implemented according to the following steps: step 1, constructing an anatomic structure perception low-field multi-contrast magnetic resonance image joint reconstruction model; step 2, constructing an anatomic structure perception low-field multi-contrast magnetic resonance image joint reconstruction network according to the reconstruction model constructed in step 1; step 3, training the anatomic structure perception low-field multi-contrast magnetic resonance image joint reconstruction network; and step 4, applying the trained anatomic structure perception low-field multi-contrast magnetic resonance image joint reconstruction network to magnetic resonance imaging. The method solves the problem of insufficient imaging optimization of a lesion part in the existing reconstruction method.

This application provides a method and apparatus for rapid multi-contrast magnetic resonance imaging based on helical trajectory acquisition. The method includes: constructing a modular multi-contrast imaging pulse sequence composed of multiple preparation modules, each preparation module being used to generate different magnetic resonance signals; generating magnetic resonance signals according to the modular multi-contrast imaging pulse sequence, and acquiring the magnetic resonance signals using a helical trajectory to obtain acquired magnetic resonance signals; and performing imaging reconstruction based on the acquired magnetic resonance signals to obtain magnetic resonance images with different contrasts. This method improves the generation efficiency of magnetic resonance signals by generating magnetic resonance signals with different contrasts through a modular multi-contrast imaging pulse sequence. Since the helical acquisition trajectory has high encoding efficiency, using a helical trajectory acquisition can effectively improve the acquisition efficiency of magnetic resonance signals, thus increasing the imaging speed of multi-contrast magnetic resonance images.

The present application relates to an MRI image reconstruction method, device, electronic device, and storage medium, wherein the MRI image reconstruction method includes: obtaining multi-contrast MRI sample data, including target modality and auxiliary modality high-resolution images; performing frequency domain conversion on the target modality high-resolution image and truncating high-frequency components to generate low-resolution frequency domain data; then performing spatial domain conversion on the target modality high-resolution image to generate a spatial domain low-resolution image; inputting the low-resolution frequency domain data and the spatial domain low-resolution image into a diffusion model, using the diffusion model to extract features of the two types of low-resolution data in the frequency domain and spatial domain respectively to generate current time step features; and using a structural attention mechanism to perform feature fusion on the current time step features and the features of the auxiliary modality high-resolution image to obtain a reconstructed image of the target modality. Through this application, the difficult problem of balancing spectral fidelity and detail enhancement in traditional MRI super-resolution methods is solved, significantly improving reconstruction efficiency and image quality.

The invention provides a segmented frequency domain self-supervised multi-contrast MRI (Magnetic Resonance Imaging) super-resolution reconstruction method. The method comprises the following steps: constructing and training a super-resolution reconstruction model, inputting to-be-reconstructed MRI image information of a tested object into the trained super-resolution reconstruction model, and outputting a continuous-scale high-resolution reconstruction image. According to the super-resolution reconstruction model provided by the invention, high-quality reconstruction can be completed by utilizing low-resolution data of a patient, and dependence on an external big data set is reduced; multiple MRI contrast ratios are processed at the same time, so that complementary information of all modes is fused to the maximum extent, and the overall reconstruction effect is improved; the MRI data information is effectively supervised, so that the constraint of low-frequency signals can be ensured, and a better high-frequency detail and edge reconstruction effect can be achieved.

The invention provides a blood vessel analysis method and system based on a multi-contrast magnetic resonance image, and relates to the technical field of medical image processing. In the embodiment of the invention, the multi-contrast brain magnetic resonance scanning sequence (including T1W and TOF), T1W and TOF can provide various contrast information, the problem that manual preprocessing needs to be carried out on the skull part when blood vessel parameter extraction is carried out only by using a single TOF image in the prior art is solved, and a transformation result in a standard brain space can be additionally provided. In addition, compared with a manual and semi-automatic measurement method adopted at present, the embodiment of the invention can realize a full-automatic parameter extraction process, more manpower and time are saved, artificial factor intervention is avoided, and the result is more stable. In addition, multiple blood vessel parameters of multiple blood vessel groups can be provided, and the blood vessel variation degree can be quantified according to the transformation result in the standard brain space.

Magnetic resonance imaging (“MRI”) using a spin- and gradient-echo (“SAGE”) pulse sequence with blip up-down acquisition (“BUDA”) encoding enables distortion-free, high-resolution diffusion-weighted imaging and / or quantitative parameter mapping. Phase-encoding polarities are alternated across shots during a multi-shot acquisition. In each shot, multi-contrast data are acquired at echo times associated with a gradient echo, a mixed gradient-and-spin echo, and a spin echo. High in-plane resolution and distortion-free quantitative parameter maps can be generated, such as T2 maps. T2* maps, paramagnetic susceptibility maps, and diamagnetic susceptibility maps. Diffusion-weighted data can be acquired using diffusion encoding gradients and BUDA encoding, where multi-contrast data are acquired in the b=0 acquisition. Diffusion parameter maps can be generated from the b=0 and diffusion-weighted data.

The present invention provides a method and system for improving the contrast of low-field magnetic resonance imaging, which belongs to the field of image processing technology and includes: step S1, collecting multi-contrast low-field magnetic resonance data, the low-field magnetic resonance data including T1-weighted images and T2-weighted images. Step S2, predicting T1 maps, T2 maps and proton density maps based on T1-weighted images and T2-weighted images; step S3, performing contrast enhancement processing on the predicted T1 maps and T2 maps, and the contrast of the proton density map remains unchanged; step S4, synthesizing the proton density map with the contrast-enhanced T1 map and T2 map to obtain a corresponding contrast image. Beneficial effect: The present invention can improve the contrast of different tissues in magnetic resonance images under low field strength, thereby improving the visual perception and diagnostic difficulty of the image, and improving the diagnostic accuracy.