23 results about "Multi contrast" patented technology

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 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.

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.

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.

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.

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 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.

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.

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 construction deviation high-precision detection system and method based on three-dimensional laser scanning, and the system comprises a point cloud collection module, a data preprocessing module, a noisy point elimination module, a deviation analysis module, and a report generation module. The method comprises the following steps: carrying out noisy point removal on point cloud data according to regions and components, registering a processed lightweight point cloud model with a BIM model, calculating a deviation value from a point cloud to a design surface by adopting a distance in a normal direction, enabling a result to directly correspond to construction acceptance indexes such as perpendicularity and flatness, and automatically generating a visual comprehensive analysis report through a report generation module. And the coordinate of the maximum deviation point is output for structural safety recheck, so that high-precision, traceable and lightweight intelligent evaluation of the whole-field construction quality is realized, the problems of multiple interferents, contrast distortion, extensive analysis and the like in a complex construction field are effectively solved, and the method has a relatively high popularization value.

The present application relates to the technical field of image reconstruction, and provides a multi-contrast magnetic resonance image reconstruction method, device, equipment, medium and product, wherein the method comprises: acquiring a plurality of initial magnetic resonance images of different contrasts; updating each initial magnetic resonance image by iteratively solving a target function until a preset convergence condition is met, to obtain a plurality of target magnetic resonance images of different contrasts after reconstruction; and the target function is used to constrain each initial magnetic resonance image to satisfy an amplitude constraint relationship and a phase constraint relationship. On the basis of ensuring the reconstruction quality of multi-contrast magnetic resonance images, the present application significantly improves the imaging acceleration multiple, i.e. improves the reconstruction efficiency of multi-contrast magnetic resonance images.

Automated processing and radiomic analysis of magnetic resonance imaging (“MRI”), such as multi-contrast MR images, and magnetic resonance fingerprinting (“MRF”) data, such as quantitative parameter maps, are integrated into a single workflow.

A method of imaging a biological material having a component that exhibits an ultra-short transverse relaxation time after excitement by electromagnetic energy is disclosed. More specifically, balanced steady state free precession ultra-short echo time (bSSFP UTE) magnetic resonance imaging (MRI) is used in combination with 3D center-out trajectory data collection and image subtraction techniques, to yield accurate high spatial resolution images of a material component having an ultra-short transverse relaxation time. In an embodiment, the 3D center-out trajectory can be a 3D rosette k-space trajectory. The disclosed methods can be used to image, among other things, biological materials such as without limitation, cortical and trabecular bones, lung parenchyma, tendons, and ligaments. In a particular example, the disclosed methods are used to image the myelin bilayer in brain white matter, such as for example, to detect, treat, or monitor brain lesions in multiple sclerosis patients.

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.

The application belongs to the technical field of medical image processing and computer-aided diagnosis, and discloses a multi-contrast learning coronary artery high-risk plaque detection method, system and terminal. A two-dimensional image sequence is extracted as a sample along a coronary artery center line point marked by a doctor on three-dimensional medical scanning data, and a sample data set is divided. A multi-contrast learning coronary artery high-risk plaque detection network based on a Transformer is constructed by taking a coronary artery two-dimensional image sequence sample as input and whether an image contains a high-risk plaque as output, so that coronary artery high-risk plaque detection is realized. The coronary artery high-risk plaque detection method does not need to label the extracted data sample, and thus labeling errors are avoided. A multi-path twin network based on the Transformer is trained using a large amount of unlabeled data, which is helpful to improve the ability of the network to generate feature representation. Patient-level feature representation is generated through feature recoding for final prediction, so that the working time is shortened.

The application discloses a kind of based on double-domain feature and cold diffusion degeneration multi-contrast MRI arbitrary scale super-resolution reconstruction method.The method first obtains multi-contrast MRI slice and carries out spatial alignment processing;Second, construct 2D K space cold diffusion degeneration model, through Gaussian variable density mask simulates real MRI physical undersampling law;Subsequently, design based on time perception's double-domain multimodal feature extraction network, in spatial domain correction texture dislocation, in frequency domain enhances structural details, and generates multi-scale conditional feature pyramid;Finally, construct based on scale self-adaptive conditional mechanism's implicit diffusion reconstruction network, dynamically allocates feature fusion ratio, and breaks through pixel grid limit by continuous spatial coordinate decoding.Combining DDIM recursion and K space data consistency correction, the application effectively solves the scale fixed, generation exists illusion and other problems of existing method, realizes high-efficiency, high-fidelity arbitrary scale MRI super-resolution reconstruction.

The application discloses a multi-contrast MRI joint reconstruction method, system, device and medium, relates to the technical field of medical imaging and deep learning, and comprises the following steps: collecting under-sampling MRI image data of different contrasts, and generating an optimization objective function based on multiple contrasts; the optimization objective function is decomposed into a first sub-problem about auxiliary variables and a second sub-problem about target variables; the first sub-problem and the second sub-problem are alternately solved in sequence, and multiple optimal MRI reconstruction images are obtained. The application realizes efficient complementary and collaborative modeling of multi-contrast features by performing feature interaction of multi-contrast data in the spatial domain and the frequency domain, and overcomes the problem of insufficient utilization of information between contrasts in the traditional method. By perceiving the features of each contrast, targeted prompts can be generated to guide the reconstruction process. Thus, the quality of low-quality contrast is improved, and the overall reconstruction result is enhanced.

The invention discloses a multi-contrast MRI reconstruction method based on a contrast invariant hidden space diffusion model, and relates to the technical field of medical imaging. Acquiring an under-sampling image of at least one contrast in the nuclear magnetic resonance imaging process; encoding the undersampled image of each contrast to obtain an implicit feature of each contrast, and determining an implicit feature representation of each target contrast according to a dependency relationship between the implicit feature of each contrast and each target contrast; performing cross-contrast information fusion on the hidden feature representation of each target contrast to obtain a hidden space representation; performing iterative denoising on the hidden space representation through inverse diffusion operation of the multi-contrast invariant hidden space diffusion model to obtain a noiseless hidden space representation; and decoding the noiseless hidden space representation through the embedded representation of each target contrast to obtain a reconstructed image of each target contrast. According to the method, high-quality multi-contrast reconstruction can still be realized under the condition that partial contrast is missing.

The invention discloses an unsupervised multi-contrast MRI noise reduction framework based on an optimal transmission theory. The unsupervised multi-contrast MRI noise reduction framework is composed of a generator, a discriminator and a compression excitation module. Unsupervised learning is adopted, large-scale data pairing is not needed, and training can be carried out only through a small number of high-quality reference images and unpaired low-field low-SNR images. Noise and anatomical information are separated through cross-contrast feature decoupling, performance is optimized by combining double-contrast feature fusion, and a framework is optimized for MRI data features to achieve stable and efficient training. The frame can improve the noise reduction effect and generalization ability, gives consideration to the perception quality and signal fidelity, and assists the popularization and application of low-field MRI.