Abstract: Provided in embodiments of the present invention are a method and a system for generating a magnetic resonance image and a computer-readable storage medium. The method comprises: acquiring a plurality of quantitative maps; synthesizing a first weighted image on the basis of the plurality of quantitative maps; and converting the first weighted image into a corresponding second weighted image on the basis of a trained deep learning network.

Abstract: Provided in the present application are a method and a device for extracting a blood vessel wall, a medical imaging system, and a non-transitory computer-readable storage medium. The method for extracting a blood vessel wall comprises acquiring a medical image, determining at least one first-order feature in the medical image, and extracting, on the basis of the at least one first-order feature, a blood vessel wall image from the medical image.

Abstract: The present disclosure relates to molecular probes for nucleic acid detection, and preparation and uses thereof. In particular, described herein is a molecular probe for detection of a nucleic acid, comprising, (1) a molecular probe carrier which comprises a cell penetrating peptide and a detectable label coupled to the cell penetrating peptide, and (2) a targeting oligonucleotide, wherein the targeting oligonucleotide is attached to the molecular probe carrier. Also described herein are a method for preparing the molecular probe, a method for detection of a nucleic acid, and a kit for detection of a nucleic acid.

Abstract: The present disclosure relates to an image registration method and a model training method thereof. The image registration method comprises obtaining a reference image and a floating image to be registered, performing image preprocessing on the reference image and the floating image, performing non-rigid registration on the preprocessed reference image and floating image to obtain a registration result image, and outputting the registration result image. The image preprocessing comprises performing, on the reference image and the floating image, coarse-to-fine rigid registration based on iterative closest point registration and mutual information registration. The non-rigid registration uses a combination of a correlation coefficient and a mean squared error between the reference image and the registration result image as a loss function.

Abstract: The present disclosure relates to a method, a system, and a storage medium for segmenting a lung image. The method for segmenting a lung image comprises: obtaining medical image data containing a lung region; performing lung lobe segmentation on the medical image data to generate a plurality of lung lobe data subsets; generating updated lung image data based on one or a plurality of lung lobe data subsets in the plurality of lung lobe data subsets; and performing nidus segmentation on the updated lung image data to generate a segmentation image that identifies a pneumonia nidus.

Abstract: Provided in the present application are an image noise reduction method and device, an imaging system, and a non-transitory computer-readable storage medium. The image noise reduction method includes: processing, based on a first deep learning network, an original scanned object image to acquire a noise image corresponding to the original scanned object image; and acquiring a denoised image based on the original scanned object image and the noise image; wherein the first deep learning network is obtained by training based on low signal-to-noise ratio images and high signal-to-noise ratio images.

Abstract: The present disclosure relates to an image registration method and a model training method thereof. The image registration method comprises obtaining a reference image and a floating image to be registered, performing image preprocessing on the reference image and the floating image, performing non-rigid registration on the preprocessed reference image and floating image to obtain a registration result image, and outputting the registration result image. The image preprocessing comprises performing, on the reference image and the floating image, coarse-to-fine rigid registration based on iterative closest point registration and mutual information registration. The non-rigid registration uses a combination of a correlation coefficient and a mean squared error between the reference image and the registration result image as a loss function.

Abstract: The present approach relates to determining a reference value based on image data that includes a non-occluded vascular region (such as the ascending aorta in a cardiovascular context). This reference value is compared on a pixel-by pixel basis with the CT values observed in the other vasculature regions. With this in mind, and in a cardiovascular context, the determined FFR value for each pixel is the ratio of CT value in the vascular region of interest to the reference CT value.

Abstract: Provided in embodiments of the present invention are a method for generating a magnetic resonance image, a magnetic resonance imaging system, and a computer-readable storage medium. The method comprises: generating a plurality of quantitative maps on the basis of a raw image, the raw image being obtained by executing a magnetic resonance scan sequence, and the magnetic resonance scan sequence having a plurality of scan parameters; performing image conversion on the plurality of quantitative maps on the basis of the plurality of scan parameters to generate a first converted image and a second converted image; generating a fused image of the first converted image and the second converted image; and generating a plurality of quantitative weighted images on the basis of the fused image.

Abstract: Embodiments of the present application provide a medical image synthesis device and method. According to an embodiment, a method includes acquiring a first medical image and a second medical image and registering the first medical image with the second medical image. The method includes determining a first parameter value at each pixel location on the registered first medical image and a second parameter value at each pixel location on the second medical image. The method includes multiplying the first parameter value with the second parameter value at the same pixel location on the registered first medical image and the second medical image and generating synthetic image data based on the multiplication result.

Abstract: Embodiments of the present application provide a medical image acquisition apparatus and method. The apparatus comprises: a preprocessing unit, for preprocessing an original image signal to obtain a first number of input images; and a determination unit, for using a training model to determine an analytic relationship between respective pixels at the same position in the first number of input images, and determining, according to the analytic relationship, a second number of medical functional parameter diagrams corresponding to the original image signal. The embodiments achieve fast calculation of SyMRI function parameter diagrams and high scalability.

Abstract: Provided in the present application are a method and a device for extracting a blood vessel wall, a medical imaging system, and a non-transitory computer-readable storage medium. The method for extracting a blood vessel wall comprises acquiring a medical image, determining at least one first-order feature in the medical image, and extracting, on the basis of the at least one first-order feature, a blood vessel wall image from the medical image.

Abstract: A cerebral stroke early assessment system for cerebral stroke early assessment, comprising a preprocessing module, configured to preprocess an acquired brain medical image set; a brain partitioning module, configured to perform brain region segmentation on the preprocessed brain medical image set, the brain partitioning module comprising an image segmentation neural network and the image segmentation neural network being trained with the aid of an auto-encoder; and a scoring module, configured to perform scoring on the basis of a brain partition image obtained by the brain partitioning module. The present disclosure can improve the segmentation accuracy of brain partition images and the accuracy of cerebral stroke early assessment.

Abstract: Provided in embodiments of the present invention are a method and a system for generating a magnetic resonance image and a computer-readable storage medium. The method comprises: acquiring a plurality of quantitative maps; synthesizing a first weighted image on the basis of the plurality of quantitative maps; and converting the first weighted image into a corresponding second weighted image on the basis of a trained deep learning network.

Abstract: The present disclosure relates to a method, a system, and a storage medium for segmenting a lung image. The method for segmenting a lung image comprises: obtaining medical image data containing a lung region; performing lung lobe segmentation on the medical image data to generate a plurality of lung lobe data subsets; generating updated lung image data based on one or a plurality of lung lobe data subsets in the plurality of lung lobe data subsets; and performing nidus segmentation on the updated lung image data to generate a segmentation image that identifies a pneumonia nidus.

Abstract: Disclosed in the present disclosure are an image registration method and a model training method thereof. The image registration method comprises obtaining a reference image and a floating image to be registered, performing image preprocessing on the reference image and the floating image, performing non-rigid registration on the preprocessed reference image and floating image to obtain a registration result image, and outputting the registration result image. The image preprocessing comprises performing, on the reference image and the floating image, coarse-to-fine rigid registration based on iterative closest point registration and mutual information registration. The non-rigid registration uses a combination of a correlation coefficient and a mean squared error between the reference image and the registration result image as a loss function. Further disclosed in the present disclosure are an apparatus and a system for image registration and a computer-readable medium corresponding to the method.

Abstract: Provided in the present application are an image noise reduction method and device, an imaging system, and a non-transitory computer-readable storage medium. The image noise reduction method includes: processing, based on a first deep learning network, an original scanned object image to acquire a noise image corresponding to the original scanned object image; and acquiring a denoised image based on the original scanned object image and the noise image; wherein the first deep learning network is obtained by training based on low signal-to-noise ratio images and high signal-to-noise ratio images.

Abstract: The present approach relates to determining a reference value based on image data that includes a non-occluded vascular region (such as the ascending aorta in a cardiovascular context). This reference value is compared on a pixel-by pixel basis with the CT values observed in the other vasculature regions. With this in mind, and in a cardiovascular context, the determined FFR value for each pixel is the ratio of CT value in the vascular region of interest to the reference CT value.