272 results about "Positron emission tomography" patented technology

Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and / or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.

The invention relates to the field of image segmentation, in particular to a multi-modal nuclear medicine image segmentation method and system and a storage medium. Comprising the steps that image type classification is carried out on multi-modal medical examination images, focus area positioning is carried out, and CT (computed tomography) focus images and PET (positron emission tomography) focus images are obtained respectively; carrying out lesion geometric mutation form prediction on the CT fault lesion image, and carrying out multi-modal image fusion boundary correction on the CT fault lesion image and the PET lesion image to obtain multi-modal lesion image fusion boundary correction data; according to the multi-modal lesion image fusion boundary correction data, performing multi-modal lesion region segmentation combination optimization on the CT image and the PET image to obtain a lesion region segmentation combination optimization strategy; and carrying out image segmentation model construction on the lesion region segmentation combination optimization strategy based on a strategy gradient algorithm to obtain a lesion image segmentation model. According to the method, the image segmentation technology is optimized, so that the image segmentation technology is more perfect.

The invention discloses a three-dimensional positron emission tomography data image classification method based on multi-stage progressive feature extraction, and is applied to the technical field of Alzheimer's disease auxiliary diagnosis. The auxiliary diagnosis method comprises the following steps: acquiring and preprocessing PET image data of an Alzheimer's disease patient; improving the reliability of the data set by using data enhancement; performing long-range dynamic modeling on the three-dimensional voxel sequence through a stacked Lmamba block; global context semantic adaptive fusion is realized through a layer-by-layer cross-scale channel attention fusion module (CSACF), and a channel spatial perception module (CSPM) is constructed to optimize spatial feature fusion; an inverted bottleneck module is mixed with long-distance space and position information to enhance the capturing capability of the model on detail features; and finally, predicting the disease category probability through global average pooling, full connection and softmax functions. According to the method, the precision of AD early diagnosis and MCI conversion risk prediction can be greatly improved, the defects of a medical image diagnosis method of a convolutional neural network (CNN) and Transform in long-range dependence on modeling and calculation complexity are overcome, and the method has good application prospects and is suitable for AD early detection and MCI conversion risk assessment.

The invention discloses a nervous system image analysis method and a nervous system image analysis device, and relates to the technical field of nervous system disease diagnosis and treatment. Fusing and collecting multi-modal data: collecting nervous system image data of functional magnetic resonance imaging (fMRI), magnetic resonance spectrum imaging (MRS), diffusion tensor imaging (DTI) and positron emission tomography (PET) modals of a patient through an internal system of a hospital; according to the method, multi-modal data of functional magnetic resonance imaging, magnetic resonance spectrum imaging, diffusion tensor imaging and positron emission tomography are integrated, quality evaluation and integration processes are executed in the central data processing unit, the accuracy and reliability of the data are remarkably enhanced, meanwhile, a space-time joint feature analysis model is introduced, and the accuracy and reliability of the data are improved. The time and space dimension information in the image data is linked, the change rule and internal correlation of the data in the time and space dimensions are mined, and the diagnosis accuracy of nervous system diseases is improved.

According to various embodiments, machine learning techniques for determining a pharmacokinetic parameter of a radiotracer in a patient are presented. The techniques include: obtaining a time sequence of positron emission tomography (PET) scans of a target tissue of the patient, where the time sequence of PET scans includes images of a radiotracer in the target tissue; providing the time series of the PET scans to a first neural network system, which provides features extracted from the time series of the PET scans; providing the features extracted from the time series of the PET scans to a second neural network system, which provides an estimated arterial input function; modeling the pharmacokinetic parameter of the radiotracer based on the estimated arterial input function and the time sequence of PET scans; and outputting an estimate of the pharmacokinetic parameter of the radiotracer based on the modeling.

A positron emission tomography (PET) scanner includes a plurality of gamma radiation detector modules arranged to form a detector ring. Each detector module includes an array of scintillator detectors. Each scintillator detector comprises a monolithic scintillation crystal and a plurality of photodetector arrays, such as silicon photomultipliers (SiPMs). A photodetector array is positioned on at least two nonparallel faces of each scintillation crystal. In some examples, a photodetector array is positioned on each of three orthogonal faces of each scintillation crystal.

Embodiments of the present application relate to the technical field of medical image processing, and provide a medical image recognition method and apparatus, a device and a storage medium. The method comprises: acquiring a target image set, the target image set comprising a plurality of target whole-body tomographic images collected in a first preset time sequence; extracting organ tomographic images of a target organ from the target whole-body tomographic images; constructing an initial organ matrix for the target organ tomographic images of each target whole-body tomographic image; calculating a metabolic reference matrix and the initial organ matrix on the basis of a preset object metabolic difference sub-network, to obtain an organ metabolic difference matrix; and performing metabolic difference recognition on the basis of the organ metabolic difference matrix and the metabolic reference matrix, to obtain a target metabolic difference map that represents metabolic difference information between any two target organs in the target whole-body tomographic images. The embodiments of the present application can improve the recognition accuracy of images acquired by positron emission tomography, thereby improving the accuracy of determining metabolic conditions of individual objects.

The invention discloses a PET / MRI (positron emission tomography / magnetic resonance imaging) image data governance model training method and device and a data governance method. The PET / MRI image data governance model training method comprises the following steps: acquiring a tissue component label, a tissue component prediction result and a tissue component identification model through a medical image control system and an enhanced deep residual network model; obtaining a diagnostic book label, a diagnosis result and a diagnostic book identification model according to a preset language model and a preset questioning mode; performing cross task optimization on the tissue component identification model and the diagnostic book identification model according to the clearness of the MRI / PET image of the same body position of the same person in the sample medical image, the MRI / PET image of the same person in the preset time period, the text type digital medical record of the same person in the preset time period and the sample medical image; and associating the tissue component identification model with the diagnostic book identification model through a graph neural network model to obtain a data treatment model of the PET / MRI image. According to the invention, by means of the compensation function between the AI tool and the body, more modal evaluation results can be obtained, so that the human health can be evaluated more accurately.

Systems and methods for reconstructing medical images based on motion estimation are disclosed. Measurement data from positron emission tomography (PET) measurement data, and modality measurement data from an anatomy modality, such as computed tomography (CT) data, is received from an image scanning system. A trained deep learning process is applied to the PET measurement data and the modality measurement data to generate displacement vector field (DVF) data characterizing motion between the PET measurement data and the modality measurement data. A modality image is reconstructed from the modality measurement data, and the modality image is adjusted based on the DVF data. A PET image is then reconstructed from the PET measurement data and the adjusted modality image, and the PET image is adjusted based on a computed inverse of the DVF data. The adjusted PET image and the modality image spatially match, and are displayed.

Systems and methods for training end-to-end deep learning reconstruction processes, and for reconstructing medical images based on the trained deep learning processes, are disclosed. In some examples, input projection data is received. An untrained machine learning process is applied to the input projection data and, based on the application of the machine learning process to the projection data, an output image is generated. Further, a forward projection process is applied to the output image and, based on the application of the forward projection process to the output image, forward projected image data is generated. A loss value is then determined based on the forward projected image data and the input projection data. The loss value is then compared to a threshold value to determine whether the machine learning process is trained. The trained machine learning process may be employed to reconstruct images, such as positron emission tomography (PET) images.

The invention relates to the technical field of animal experiments, and particularly discloses an experimental animal PET-CT examination auxiliary fixing device which comprises a fixing base, the upper surface of the fixing base is fixedly connected with a first supporting column, the upper surface of the first supporting column is fixedly connected with a first limiting plate, and the side surface of the first limiting plate is fixedly connected with two supporting limiting blocks. The experimental animal PET-CT examination auxiliary fixing device is provided with a supporting limiting block and a positioning sliding rail, when the experimental animal PET-CT examination auxiliary fixing device works, by sliding a second supporting column, a transmission rolling wheel at the lower end of the second supporting column is used for supporting, friction force is dropped through rotation of the transmission rolling wheel, and therefore practicability is improved; and a second supporting column is conveniently guided, so that the movement stability of the second supporting column is improved, the distance between a first limiting plate and a second limiting plate is conveniently changed, and the convenience of fixing different rabbits is improved.

The invention relates to an antenna and an antenna arrangement for an imaging method, a method for adjusting the length of an antenna, and a tomography system, in particular for magnetic resonance imaging (MRI) or simultaneous positron emission tomography-MRI (PET-MRI). An antenna (1) for an imaging method comprises a radiation section (2) and a feed section (3). The feed section (3) comprises a capacitor (11) and an inductor (15).

Disclosed is a compound represented by the following structural formula (I): or a pharmaceutically acceptable salt thereof. The variables of structural formula (I) are described herein. The compound of the invention can be attached to chelating groups for radionuclide binding and are therefore suitable for radioimaging and / or radiotherapy applications, for example the disclosed compounds can be radiolabeled with a positron emitter such as 18F, 68Ga or 64Cu, and used for positron emission tomography (PET). Alternatively, the compounds can be radiolabeled with an alpha particle emitter such as 225 Ac, a beta particle emitter such as 67Cu or 177Lu, or an Auger electron emitter (e.g. 111In, 67Ga, 99mTc, 195mPt, 125I and 123I). The compounds can also be attached to a cytotoxic agent for targeted delivery of the cytotoxic agent to tumors, for example conjugated to gemcitabine or doxycycline, or a venom. Likewise, the compounds can be conjugated to compounds having physiological effects, such as TLR agonists to stimulate the immune response of a recipient.

The invention relates to an oxidase response type molecular probe as well as a preparation method and application thereof, and belongs to the technical field of medical imaging. Aiming at the problem that the inflammation activity is difficult to accurately evaluate in the prior art, the invention provides a molecular imaging probe capable of responding to the activity of in-vivo myeloperoxidase (MPO), and the molecular imaging probe can be used for real-time and non-invasive imaging monitoring of the activity of the in-vivo MPO, so that the activity of the inflammatory reaction of a focus is accurately evaluated. The invention provides a chelating agent with structural characteristics of a chelating unit and an enzyme response unit, and is characterized in that the enzyme response molecular probe is constructed by taking rigid framework cyclohexanediamine (CDTA) as a transition metal chelating unit and taking electron-rich functional groups (such as phenol, catechol or 5-hydroxytryptamine) as the enzyme response unit. The probe is chelated with paramagnetic metal (such as Mn < 2 + >) for magnetic resonance imaging (MRI) or radioactive metal (such as 68Ga) for positron emission tomography (PET), so that specific response to MPO activity is realized. The dynamic stability of the probe is remarkably improved; after the MPO is responded, the relaxation efficiency is improved by 2-3 times, and the sensitivity is optimized; the cytotoxicity is low; in an acute pancreatitis model, the MRI contrast noise ratio (delta CNR) reaches 77.39 + / -7.04 and is 4.2 times that of a control group, the PET uptake rate is remarkably improved, bimodal imaging is supported, and accurate positioning and quantitative evaluation of the inflammation activity are achieved.

The present invention relates to a method for non-invasive quantitative measurement of cerebrovascular reserve (CVR) and cerebral blood flow (CBF) with 15O -water positron emission tomography (PET) in a human, and to a vasodilatant for use in said method.

The present invention discloses methods for automatically computing an arterial input function from one or more regions of interest, the method comprising: a. obtaining a plurality of dynamic image data sets comprising volumetric image data from the regions of interest over multiple scanning intervals; b. utilizing an artificial neural network to segment the plurality of dynamic image data sets displaying one or more arterial input function(s) (AIF) in the region(s) of interest; c. automatically estimating, using artificial intelligence, an arterial input function based on plurality of dynamic image data sets combined with one or more time activity curves (TAC) in the region(s) of interest in target organ(s); and d. computing a pre-trained predictive pharmacokinetic AI model arterial input function using time activity curve input associated with region(s) of interest of target organ(s).

A back illuminated sensor is included as a collector component of a detector for use in intraoral and extraoral 2D and 3D dental radiography, digital tomosynthesis, photon-counting computed tomography, positron emission tomography (PET), and single-photon emission computed tomography (SPECT). The disclosed imaging method includes one or more intraoral or extraoral emitters for emitting a low-dose gamma ray or x-ray beam through an examination area; and one or more intraoral or extraoral detectors for receiving the beam, each detector including a back illuminated sensor. Within the detector, the beam is converted into light and then focused and collected at a photocathode layer without passing through the wiring layer of the back illuminated sensor.

A computer-implemented method is provided for segmentation of Positron emission tomography (PET) / computed tomography (CT). The method comprises: acquiring an original medical image including a PET image and CT image of a subject; transforming the original medical image into an input image with a predetermined resolution and a plurality of channels; processing the input image using an ensembled CNNs to output an intermediate segmentation mask; and taking the intermediate segmentation mask as input to a refiner model to output a final segmentation mask, where the final segmentation mask has a resolution same as the resolution of the original medical image.

Described herein are compounds, compositions, and methods for diagnosing and / or monitoring pathogenic disease using positron emission tomography.

The invention provides a PET-CT (positron emission tomography-computed tomography) lung cancer image fusion segmentation method and system, and relates to the technical field of image segmentation. Carrying out convolution processing on the data through a convolutional neural network to obtain PET metabolic parameters and CT density value distribution data; performing cross-modal feature association on the PET feature map and the CT feature map to obtain an associated PET feature map and an associated CT feature map; segmenting the PET feature segmentation result and the CT feature segmentation result to obtain a PET feature segmentation result and a CT feature segmentation result; according to the segmentation result, combining PET metabolic parameters and CT density value distribution data to obtain fusion feature parameters; and carrying out conjoint analysis on the PET feature segmentation result, the CT feature segmentation result and the fusion feature parameters to obtain a PET-CT image fusion segmentation result of the early-stage tiny lesion of the lung cancer, thereby realizing accurate PET-CT image fusion segmentation of the early-stage tiny lesion of the lung cancer.

One aspect relates to a method for providing information for predicting a risk group for developing Alzheimer's disease dementia or a risk group for early onset of Alzheimer's symptoms, or a risk group for developing amnestic mild cognitive impairment and / or a positron emission tomography (PET)-positive risk group for amyloid β deposition, based on a European population-East Asian data. According to one aspect, the method makes it possible to accurately predict a risk group for developing Alzheimer's disease dementia or a risk group for early onset of Alzheimer's symptoms, or a risk group for developing amnestic mild cognitive impairment and / or a positron emission tomography (PET)-positive risk group for amyloid β deposition by using only at least 12 single-nucleotide polymorphisms, and the ability to predict the risk groups is further enhanced when up to and at most 80 additional single-nucleotide polymorphisms are used. In addition, the method further includes identifying age, sex, years of education, and APOE genotype as indicators, and thus the ability to predict the risk groups is further enhanced, and the risk groups can be predicted at an early stage with high accuracy.

The invention discloses a day-ahead optimization method and system for a PET-containing AC / DC hybrid power distribution network based on a cloud model, and belongs to the field of day-ahead optimization scheduling of a power system. The method comprises the following steps: calculating three characteristic quantities of expectation, entropy and hyper-entropy of a cloud model to obtain the expectation, entropy and hyper-entropy of the cloud model; selecting a kernel function, and calculating probability density distribution of wind power and photovoltaic power prediction errors by adopting nonparametric kernel density estimation; setting confidence, finding the shortest interval to enable the probability that the prediction error value falls into the interval to be equal to the confidence, and obtaining upper and lower limits of wind power and photovoltaic power; constructing a day-ahead optimal scheduling model of the AC / DC hybrid power distribution network containing the PET considering the uncertainty of wind and light output prediction; and solving the model through a commercial solver to obtain a day-ahead optimal scheduling scheme of the AC / DC hybrid power distribution network containing the PET. According to the method, the uncertainty of wind and light output is considered, the power distribution network is allowed to accept more wind and light output, the total cost is reduced, and the power coordinating range of each port is expanded.

The invention provides a PET-MR joint reconstruction method and system based on an expert network modular regularizer. The method comprises the following steps: constructing a PET-MR joint reconstruction backbone network based on a decoupling iteration framework; s2, constructing and integrating a U-Net joint regularizer for expert network modularization, and integrating the U-Net joint regularizer into the iterative reconstruction framework in the step S1 to obtain a final joint reconstruction network model; applying a comprehensive loss function to supervise an end-to-end learning process of the joint reconstruction network model; constructing a training data set, and performing end-to-end joint training on the joint reconstruction network model by using a loss function; in the reasoning stage, PET and MR measurement data to be reconstructed are input into the trained joint reconstruction network model, and finally a reconstructed PET image and a reconstructed MR image are output. According to the method, the functional metabolism information accuracy of the reconstructed PET image and the anatomical structure detail definition of the MR image are enhanced, and the high feature fidelity of each modal image is ensured, so that the overall quality and clinical diagnosis value of the reconstructed image are improved.

The present disclosure relates to the field of nuclear imaging and therapy, and more particularly, to high purity copper radiotracer compositions that can be used in imaging (e.g., positron emission tomography (PET) and single photon emission computed tomography (SPECT)) and therapy. More specifically, the present disclosure relates to novel compositions useful for imaging and treating conditions (e.g., prostate cancer, somatostatin receptor expressing tumors, such as neuroendocrine tumors, epithelial tumors), and methods of making such compositions.

Systems and methods for reconstructing medical images based on the trained deep learning processes, and for training deep learning processes, are disclosed. In some examples, image measurement data is received. A histo-image is generated based on the image measurement data. Further, an attenuation map, such as a μ-map, is received. An attenuation histo-image is generated based on the attenuation map. Further, a trained machine learning process, such as a trained neural network, is applied to features generated from the histo-image and the attenuation histo-image. Based on the application of the machine learning process to the histo-image and the attenuation histo-image, output image data characterizing an image volume is generated. In some examples, a machine learning process is trained based on histo-images and corresponding attenuation histo-images. The trained machine learning process may be employed to reconstruct images, such as positron emission tomography (PET) images.

The invention discloses a non-pairing PET (positron emission tomography) image enhancement method based on a diffusion model, and provides a two-stage learning framework: in the first stage, an unconditional diffusion model is trained through a low-quality PET data set, and a low-frequency part of a generated image is replaced by controlling a noise adding step number during sampling so as to maintain structural consistency; generating a pseudo low-quality image paired with the high-quality image, and constructing a pseudo pairing data set; and in the second stage, supervised training is carried out based on the pseudo-pairing data set, and the image quality is improved by utilizing an end-to-end neural network. According to the method, pairing data dependence is broken through, training can be completed through non-pairing data, the efficiency of application of the method is improved through two-stage optimization, and the problem that time consumption is long when a diffusion model method is directly used for reasoning is solved through two objectives of decoupling data generation and quality enhancement. The method supports flexible selection of the supervised learning network, and provides an efficient and high-generalization enhancement solution for the low-quality PET image.

The present application relates to positron emission tomography (PET) imaging in the medical and industrial fields, and discloses a low-dose PET image enhancement method and system, used for solving the problem in the prior art of having poor effect when an MR image of anatomical structure information is used as prior information to generate a normal-dose PET image from low-dose PET image data. According to the solution, spatial features of an anatomical structure are obtained from an MR image and spatial anatomical alignment information, and the spatial features of the anatomical structure are used to spatially adjust low-dose PET image features to achieve adaptive enhancement of spatial regional information and fusion of different modalities, so as to improve the display quality of the anatomical structure of low-dose image data, improve the definition of the anatomical structure, provide effective reference for clinical diagnosis, reduce the use amount of a tracer, reduce economic costs, and lessen the radiation load on patients.

A multiplexing scheme for both energy and timing information is provided for a particle detection system having an optical sensor array with multiple optical sensors. Each optical sensor is associated with multiple scintillator modules. The system has a segmented prismatoid light guide comprising multiple prismatoid segments. Each segment is associated with multiple optical sensors, where the optical sensors are adjacent. One end each scintillator module is in contact with its associated optical sensor and the other is in contact with its associated segment. Multiple optical sensors may be connected to an energy readout channel, respectively, such that optical sensors associated with the same segments are not connected to the same energy readout channel. Each energy readout channel has at least two timestamps associated therewith.

A multiplexing scheme for both energy and timing information is provided for a particle detection system having a photosensor array with multiple photosensors. Each photosensor is associated with multiple scintillator modules. The system has a segmented pseudo-prismatic light guide with multiple pseudo-prismatic segments. Each segment is associated with multiple photosensors, and the photosensors are adjacent. Each scintillator module contacts its associated photosensor at one end and its associated segment at the other end. Each of the multiple photosensors may be connected to an energy readout channel, such that photosensors associated with the same segment are not connected to the same energy readout channel. Each energy readout channel has at least two timestamps associated therewith.

Systems and methods include acquisition of a computed tomography image of an object, determination of a linear attenuation coefficient map based on the computed tomography image, acquisition of positron emission tomography (PET) data of the object, determination of a histoimage of the object based on the PET data, determination of a scatter histoimage based on the histoimage and the linear attenuation coefficient map, determination of a scatter-corrected histoimage based on the histoimage and the scatter histoimage, and input of the computed tomography image and the scatter-corrected histoimage to a trained neural network to generate a PET image.