137 results about "Positron emission tomography" patented technology

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.

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 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).

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.

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.

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.

A positron emission tomography (PET) scanner may include a plurality of gamma radiation detector modules arranged to form a detector ring. Each detector module may include an array of elongated scintillation crystals. With respect to the detector ring, each elongated scintillation crystal includes a proximal end-face, two axially oriented lateral faces, two transaxially oriented lateral faces, and a distal end-face radially oriented into the detector ring to receive a gamma photon. An array of photosensors is positioned along a first of the axially oriented lateral faces of each elongated scintillation crystal to detect scintillation photons. A reflective material is positioned on the proximal end-face, the distal end-face, the transaxially oriented lateral faces, and a second of the axially oriented lateral faces of each elongated scintillation crystal to internally reflect scintillation photons. In various embodiments, a dual-channel processing circuit provides distinct timing and energy signals from the photosensors.

The 18F-labeling of unprotected peptides and sugars via thioarylation using a Au(III)-[˜F]fluoroaryl complex is reported. The chemoselective method generates 18F-labeled S-aryl bioconjugates in an aqueous environment in 15 min with high radiochemical yields and displays excellent functional group tolerance. This approach utilizes an air and moisture stable, robust organometallic Au(III) complex and highlights the versatility of designer organometallic reagents as efficient agents for rapid radiolabeling.

The invention discloses a PET-CT (positron emission tomography-computed tomography) radiopharmaceutical closed automatic dispensing and dosing machine which comprises a machine base and an injection bin fixedly mounted on the machine base, an air cylinder mechanism is fixedly mounted at the top of the back of the injection bin, and a closed box is fixedly mounted on one side of the injection bin. And a transmission mechanism pushed by the air cylinder mechanism is installed in the closed box in a sliding mode, a mechanical clamping mechanism is installed at the bottom of the transmission mechanism in a transmission mode, and an arm support is fixedly installed at the position, located at the placing opening, in the injection bin. According to the invention, the mechanical clamping mechanism repeatedly acts to clamp the disposable syringe, the problem of more pipeline consumables is solved by directly adopting the mechanical clamping mechanism to extract liquid medicine at a fixed point and inject the liquid medicine at a fixed point, meanwhile, the flowing time of the stock solution in the pipeline is also reduced, the disposable syringe directly injects the stock solution into the heparin cap, and the operation is simple and convenient. And the accuracy of the nuclide value is ensured to the greatest extent.

A positron emission tomography (PET) scanner includes multiple gamma radiation detector modules arranged to form a detector ring. Each detector module includes an array of elongated scintillation crystals. With respect to the detector ring, each elongated scintillation crystal includes a proximal end face, two axially-oriented side faces, two cross-axially-oriented side faces, and a distal end face oriented radially into the detector ring to receive gamma photons. An array of photosensors is disposed along a first of the axially-oriented side faces of each elongated scintillation crystal to detect scintillation photons. Reflective material is disposed on a second of the proximal end face, the distal end face, the cross-axially-oriented side face, and the axially-oriented side face of each elongated scintillation crystal to internally reflect scintillation photons. In various embodiments, a dual-channel processing circuit provides separate timing and energy signals from the photosensors.

The present invention relates to novel, selective compounds with affinity to TDP-43, and to novel, selective radiolabelled TDP-43 ligands which are useful for imaging TDP-43 aggregates, using positron-emission tomography (PET). The invention is also directed to compositions comprising such compounds, to processes for preparing such compounds and compositions, to the use of such compounds and compositions for imaging TDP-43 aggregates in a tissue or a subject, in vitro or in vivo, and to precursors of said compounds.

Two major treatment strategies employed in fighting non-small cell lung cancer (NSCLC) are tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). The choice of strategy is based on heterogeneous biomarkers expressed by the lung tumor tissue. A major challenge for molecular testing of these biomarkers is the insufficiency of biopsy specimens from patients with advanced NSCLC. Disclosed herein is a method for predicting a response to immune-checkpoint blockade immunotherapy. The method generally involves imaging the subject with positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro-D-glucose integrated with computed tomography to produce 18F-FDG PET / CT images of the tumor, analyzing the images using PET, CT, and Kulbek Leibler Divergence statistical (KLD) features or, alternatively using deep leaning such as Neural Networks; generating a radiomic signature from the identified features or Network characteristics; and computing a radiomic score based on the radiomic signature that is predictive of responsiveness to ICIs or TKIs.

Combining positron emission tomography (PET) scan 111 and computed tomography (CT) scan 121 of portion of body (i.e. lung) into combined image scan 132 for segmentation. Training machine learning for

This invention relates to a convolution-based method for noise suppression in positron emission tomography (PET) flow field images, comprising the following steps: S1, obtaining consistent data through CFD flow field simulation and GATE detection system simulation, and reconstructing a low-resolution initial image of the PET flow field containing noise using the OSEM algorithm; S2, constructing a noise suppressor model, inputting the low-resolution initial image into the model, and outputting a noise-suppressed flow field feature image using a deep convolutional network and multi-scale feature fusion technology; S3, constructing a multi-loss fusion evaluation system, using mean squared error loss, minimum absolute value deviation loss, and perceptual loss to jointly constrain model training, and iteratively updating the weights using the Adam optimizer. This invention not only improves the enhancement model's ability to distinguish noise information, thereby enhancing the model's ability to remove noise information from PET images, but also significantly improves evaluation metrics such as PSNR and SSIM.

The invention belongs to the technical field of inorganic scintillator materials, and particularly relates to a multi-element co-doped yttrium lutetium silicate scintillator and a preparation method and application thereof, and the material has excellent comprehensive performance of high light output, short decay time, high radiation hardness, high matching degree of emission spectrum and novel photoelectric sensors and the like. The invention further relates to a preparation method of the scintillator material and application of the scintillator material in radiation detection fields such as positron emission tomography (PET), time flight PET (TOF-PET), interaction depth PET (DOI-PET), single photon emission computed tomography (SPECT), high-energy physical experiments, nuclear medicine imaging, industrial nondestructive inspection, safety detection (such as luggage and cargo container inspection) and the like.

The current disclosure provides systems and methods for increasing a quality of positron emission tomography (PET) images generated by a PET system with detectors including bismuth germinate (BGO) crystals. In one example, a method for the PET system comprises extracting a higher-quality 2-D image from a first image volume reconstructed using higher-quality time-of-flight (TOF) data acquired from a subject during a scan performed using the PET system, the TOF data based on Cherenkov radiation detected at the BGO crystals; extracting a lower-quality 2-D image from a second image volume reconstructed using lower-quality TOF data acquired from the subject by the PET system; generating an enhanced-quality 2-D image from the lower-quality 2-D image using a trained image quality enhancement model; merging the enhanced-quality 2-D image with the higher-quality 2-D image; and displaying the merged 2-D image on a display device of the PET system.

The application provides a metabolic abnormality detection method and device based on cross-modal generation, a terminal, a medium and a program product. The method comprises the following steps: based on the obtained head real 18F-FDG positron emission tomography image of a subject, a trained explicit cross-modal image conversion model is used to reconstruct a pseudo-normal 18F-FDG positron emission tomography image of the subject; according to the head real 18F-FDG positron emission tomography image and the reconstructed pseudo-normal 18F-FDG positron emission tomography image, a residual image is obtained by calculating the intensity difference between the two; and based on the residual image, a metabolic abnormality of the subject is detected by using a set abnormality evaluation index. The application uses the trained explicit cross-modal image conversion model to process the head 18F-FDG positron emission tomography image in three-dimensional space, obtains a residual image, and directly shows the metabolic abnormality mode of the brain of the subject, thereby assisting doctors to carry out efficient and accurate diagnosis work.

The present invention relates to novel, selective compounds with affinity to TDP-43, and to novel, selective radiolabelled TDP-43 ligands which are useful for imaging TDP-43 aggregates, using positron-emission tomography (PET). The invention is also directed to compositions comprising such compounds, to processes for preparing such compounds and compositions, to the use of such compounds and compositions for imaging TDP-43 aggregates in a tissue or a subject, in vitro or in vivo, and to precursors of said compounds.

This application discloses a three-dimensional dose reconstruction method and related equipment based on in-beam positron emission tomography (PET) data. The method includes: acquiring list-pattern data during proton therapy beam exit; performing conformation processing on the list-pattern data and reconstructing a three-dimensional activity distribution map; performing multi-nucleus dynamic deconvolution processing on the activity time series corresponding to the three-dimensional activity distribution map according to the physical half-life of the target nuclide and preset biological elution parameters to obtain the initial activity distribution corresponding to the target nuclide; mapping the initial activity distribution to a three-dimensional relative dose distribution using a preset dose mapping algorithm; converting the three-dimensional relative dose distribution into a three-dimensional absolute dose distribution according to a pre-established water phantom calibration curve; and generating a cumulative dose distribution report based on the three-dimensional absolute dose distribution. The embodiments of this application can reconstruct the three-dimensional absolute dose distribution with high accuracy and support inter-session dose accumulation analysis. This application can be widely applied in the field of dose reconstruction technology.

This invention discloses a radioactive molecular probe for targeting CSPG4 / NG2, its preparation method, and its application. The radioactive molecular probe comprises the structure shown in Formula I or Formula II. The technical solution of this invention uses a radioactive molecular probe to specifically recognize CSPG4 / NG2 in vivo through the target polypeptide portion, thereby achieving specific nuclear medicine positron emission tomography (PET) or single-photon emission computed tomography (SPECT) imaging diagnosis of CSPG4 / NG2 positive tumors or CSPG4 / NG2 positive lesions, such as autoimmune diseases and cardiovascular and cerebrovascular diseases, thereby realizing early diagnosis and efficacy evaluation of malignant tumors.

A method for performing dynamic positron emission tomography (PET) is disclosed. The method includes collecting volumetric radioactive measurement data associated with an administered radioactive tracer present in a target site of a subject over multiple scanning intervals, capturing a magnetic resonance image of the target site, and performing a motion correction process to the volumetric radioactive measurement data to produce motion corrected PET data. The method further includes co-registering the magnetic resonance image and motion corrected data to generate a co-registered dynamic PET volume, and applying a model corrected input function (MCIF) to the co-registered dynamic PET volume to calibrate an uptake amount of the radioactive tracer in the target site.

The present disclosure provides a modular radiation therapy apparatus comprising a plurality of modules configured to sequentially interlock within one another in an annular arrangement, wherein the plurality of modules are individually extractable from the annular arrangement and at least a subset of the plurality of modules comprise computing elements configured to communicate with one another, the plurality of modules comprising a collimator module comprising a multi-leaf collimator (MLC) and at least one sensor, a linear accelerator (LINAC) module comprising at least one of a magnetron or a klystron, at least one imaging module comprising at least one of an image detector, an imaging source, a magnetic imaging system, a single photon emission computed tomography (SPECT) system, or a positron emission tomography (PET) system, a patient tracking module, a cooling module, and a vacuum module, and performing at least one of: monitoring a respective status of the plurality of modules via onboard sensors and computing elements to determine whether any module is in an error state and provisioning status information and module identifiers to a remote computing environment; or distributing computational workloads among the plurality of modules and communicating with a remote computing environment to offload processing tasks using federated processing operations.

The invention belongs to the technical field of artificial intelligence assisted medical treatment, and particularly relates to a PET / CT tumor imaging examination result prediction system based on a large language model. The system comprises an input module used for inputting information of a patient; the prediction module is used for analyzing the information of the patient according to the large language model to obtain a prediction result that the patient is possibly positive in PET / CT tumor imaging examination; and the output module is used for outputting the prediction result. The method can assist a doctor in deciding whether a patient needs to be subjected to PET / CT examination or not, so that the PET / CT examination is more reasonable, and the positive rate of the PET / CT examination is effectively improved. Therefore, the method has a good application prospect.

Provided are a fluorine-18-labeled positron emission tomography (PET) radiotracer for diagnosing neuroinflammation, stroke or cerebral infarction and a method for diagnosing cancer in a subject including administering a fluorine-18-labeled PET radiotracer a subject, obtaining in vivo PET images of the uptake of the fluorine-18-labeled positron emission tomography (PET) radiotracer in a lesion in the subject, and evaluating the uptake of the fluorine-18-labeled radiotracer in the lesion.

The invention belongs to the technical field of PET (positron emission tomography), and relates to a PET probe extensive field image multi-model adaptive deformation and FPGA (field programmable gate array) curing method, which comprises the following steps of: acquiring single-lift event data of a PET probe; generating an original extensive field image based on the single-lift event data; performing coordinate traversal transformation on the original extensive field images by adopting an image deformation model in a preset multi-model transformation library; performing off-line analysis on each model and the parameters thereof in the multi-model transformation library by utilizing a preset evaluation index, and screening out an optimal model and an optimal parameter; and transforming the original pan-field image by using the optimal model and the optimal parameters to generate an enhanced image and a crystal position lookup table, and solidifying the enhanced image and the crystal position lookup table to the FPGA. The method has the beneficial effects that the local adhesion area can be stretched in a targeted manner through the nonlinear transformation model, the distinction degree of crystal event clusters is remarkably improved, and the LUT calibration precision is fundamentally improved.

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.

The invention discloses a double-target small-molecule probe, a radionuclide-labeled double-target small-molecule probe as well as a preparation method and application of the radionuclide-labeled double-target small-molecule probe. The medicine < 68 > Ga-DOTA-PRE / RGD is obtained by labeling < 68 > Ga radionuclide, and is applied to PET / MR (positron emission tomography / magnetic resonance imaging) imaging of pancreatic cancer tumors. A radioactive product with high purity and high stability (greater than 93%) is obtained through a simple marking method; the obtained double-target radioactive small molecular probe 68Ga-DOTA-PRE / RGD has a huge potential for the accurate diagnosis of pancreatic cancer.

The application relates to the field of positron emission tomography technology, in particular to a multi-channel high-precision PET circuit acquisition system and a use method thereof, and the system comprises the following: an FPGA control board, which is used for controlling bias voltage programming output voltage values, controlling current programming output voltage values and current values according to DAC bias voltage values received from an upper computer, and sending DUT board configuration information received from the upper computer to a DUT board; and the DUT board, which comprises multi-channel high-precision PET circuits, each circuit is configured through an SPI protocol according to DUT board configuration information received from the upper computer, 16 channels are adopted to collect time electric signal information output by a PET detector, and 64 channels are adopted to collect energy electric signal information output by the PET detector. In the application, the multi-channel high-precision PET circuit acquisition software and the hardware of the control system are portable, the upper computer software program is programmable, and the operation is simple.

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.