22 results about "Arterial input function" patented technology

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 system and method for the measurement of radiation emitted from the body, for example, is presented. In one example, radiation sensors (e.g., gamma radiation sensors) may be used to measure activity proximate an injection site as a function of time. In some embodiments, one or more rangefinders may be employed to determine a size and / or position of a subject relative to the radiation sensors to better account for varying material densities within the system in estimating, for example, the amount of radioactive material in the tissue proximate the injection site. With an estimated function of radioactive material proximate the injection site as a function of time known, an estimated arterial input function may be determined, allowing for calculation of a correction factor that may be applied by a clinician during nuclear medical imaging. The magnitude, location, and volume of the radioactive source in the body may also be estimated.

This invention provides a method for assessing the risk of hemorrhagic transformation based on a two-compartment model, comprising: acquiring CT perfusion image sequences related to the risk of hemorrhagic transformation; extracting arterial input functions and tissue concentration-time curves from the CT perfusion image sequences; extracting spatiotemporal features from the CT perfusion image sequences using a Transformer network; inputting the spatiotemporal features into a physical information neural network and processing them through a two-compartment model to obtain preliminary parameter estimates and parameter constraint terms; determining a joint loss function based on the preliminary parameter estimates and parameter constraint terms, and using the arterial input function and tissue concentration-time curves, wherein the joint loss function includes a data fitting loss term for fitting the observed data and a physical constraint loss term for constraining the physical consistency of the two-compartment model; and assessing the risk of hemorrhagic transformation based on the joint loss function. The parameters estimated by this invention have clear physical meaning and strictly conform to the physical model, making them easy for clinicians to understand and apply, and improving the reliability of the results.

The invention relates to the technical field of image processing, and discloses a rapid dynamic PET quantitative parameter imaging method and system based on a linear regression model, a terminal and a storage medium, and the method comprises the steps: constructing a fitting data set and a test data set; constructing a standard Patlak analysis model, and selecting a descending aorta slice dynamic signal as an artery input function; establishing a linear regression model between the early integral value and the late mean value by using the fitting data set; extracting a late mean value of the artery input function from the test data set, and estimating an early integral value corresponding to the late mean value in the test data set by using the optimal linear fitting parameter of the linear regression model; and combining the estimated early integral value with a standard Patlak analysis model to calculate each voxel to obtain a voxel-level Patlak quantitative parameter image. According to the method, parameter images with higher quality are obtained by using fewer dynamic scanning sequences, and short-time dynamic PET quantitative parameter imaging is realized.

The application relates to a method and device for determining an arterial input function curve and a computer device, wherein the method for determining the arterial input function curve comprises the following steps: acquiring CT perfusion (CTP) scanning data and CT angiography (CTA) scanning data of an imaging device for a region of interest, and the CTP scanning region and the CTA scanning region have an overlapping region; determining an initial arterial input function curve based on the CTP scanning data; and supplementing the initial arterial input function curve based on the overlapping region data to obtain a target arterial input function curve, wherein the overlapping region is CTA scanning data of the overlapping region. Through the application, the problem that the arterial input function curve is inaccurate is solved, and the technical effect of improving the accuracy of the arterial input function curve is achieved.

An improved method and system are provided for determining a perfusion metric in a subject using magnetic resonance imaging and physiologically induced contrast. A respiratory device induces a stepwise increase in arterial partial pressure of oxygen by sequentially delivering hypoxic and oxygenated gas mixtures. Magnetic resonance signal data are acquired from a selected voxel, and a change in effective transverse relaxation rate (ΔR2*) is computed over time. A perfusion metric is determined based on the ΔR2* time course without requiring deconvolution of an arterial input function. In some examples, the ΔR2* response is characterized using a sigmoidal model such as a Gompertz function to extract physiologically relevant parameters including relative cerebral blood volume, relative cerebral blood flow, and mean transit time. A processor may compute perfusion metrics across multiple voxels and output a perfusion map or compare values to a reference population to assess tissue abnormality for diagnostic or treatment purposes.

A system and method for the measurement of radiation emitted from the body, for example, is presented. In one example, radiation sensors (e.g., gamma radiation sensors) may be used to measure activity proximate an injection site as a function of time. With that data, a function describing an amount of radioactive material in tissue proximate the injection site as a function of time may be estimated where an amount of radioactive material in the tissue at a time t is known. When an array of sensors is employed, the amount of radioactive material in the tissue proximate the injection site may be determined directly by the system. With an estimated function of radioactive material proximate the injection site as a function of time known, an estimated arterial input function may be determined, allowing for calculation of a correction factor that may be applied by a clinician during nuclear medical imaging.

Conventionally, the arterial input function is determined by administering a contrast agent and measuring the responsive magnetic signal in a reference voxel located in a large artery such as the middle cerebral artery. By instead measuring the signal in a voxel of the choroid plexus, a more accurate profile for the arterial input function may be obtained. The metabolic activity in the choroid plexus is negligible, which provides greater certainty for signal sampling.

The present invention relates to a method (100) for post-processing sampled time-dependent experimental perfusion signals (S, 10, 12) in order to generate (140) pharmacokinetic parameters (QI). Such a method is implemented by a processing unit (4) of a medical imaging analysis system, the unit being pre-trained in a process (101) that enables learning of the destructive influence of the acquisition of a perfusion sequence on the arterial signal and the redundancy of information related to the arterial input function (AIF, AIF´) shared by at least two sets of tissue signals. Such an arterial input function (AIF, AIF´) is thus directly generated (110) in step (120) by the processing unit (4) trained from the first arterial input function (AIF0) and the pre-selected tissue signal (S) as such.

A method for post-processing a sampled time-dependent experimental perfusion signal to generate a pharmacokinetic parameter is implemented by a processing unit of a medical-imaging analysis system, said unit having been trained beforehand in a process allowing the disrupting effect of acquisition of a perfusion sequence on arterial signals and redundancy of information related to an arterial input function shared by a set of at least two tissual signals to be learnt. Such an arterial input function is produced directly in a step by said processing unit thus trained from a first arterial input function and from tissual signals selected beforehand.

Hypoxia-induced deoxyhemoglobin concentration ([dOHb]) may be used as a susceptibility contrast agent in subjects. With sequential gas delivery methods, reoxygenation can be implemented abruptly, inducing an arterial input function with a square shape. Since the concentration of deoxyhemoglobin is known, the arterial input function can be pre-determined and perfusion metrics can be completed without error-prone measurements. Pre-determined arterial input functions provide a faster, more reliable method of analyzing BOLD-MRI images, and the resolution is not limited by the breath rate.

The application relates to a kind of brain CT perfusion image full-automatic post-processing methods, comprising: reading CT perfusion image;The CT perfusion image is preprocessed, and the skull perfusion image with skull is obtained, and the brain tissue image containing lateral ventricle after removing skull;Obtain lateral ventricle segmentation result binary graph, comprising: using the skull perfusion image with skull, obtain the optimal image layer of the largest connected domain area in skull;In the brain tissue image, from the optimal image layer, find the first distance towards the direction of skull top, find the second distance towards the direction of skull bottom, obtain the image layer containing lateral ventricle;The image layer containing lateral ventricle is converted into feature image, and lateral ventricle segmentation is carried out on each layer feature image, and lateral ventricle segmentation result binary graph is obtained;Using the brain tissue image, arterial input function and perfusion parameter map are sequentially obtained;The perfusion parameter map is binarized, and the lesion area is obtained in combination with the lateral ventricle segmentation result binary graph.

The present application relates to a perfusion image analysis method, apparatus, device, and storage medium. The method includes: acquiring a brain perfusion image and determining multiple arterial segments in the brain in the brain perfusion image; using a preset segmentation network to segment the blood supply area in the brain perfusion image to determine the multiple divided blood supply areas, wherein the segmentation network is trained based on multiple training perfusion images and reference blood supply areas corresponding to each training perfusion image; matching each blood supply area with each arterial segment to determine the matching relationship between each blood supply area and each arterial segment; constructing a brain perfusion model based on the matching relationship to analyze the brain perfusion image. The use of this method can reduce the error of the obtained arterial input function AIF, thereby reducing the error of the results of the perfusion image analysis.

The invention discloses an automatic artery input function selection method based on an attention mechanism in DCE-MRI, and the method comprises the steps: carrying out the fast Fourier transform of data, and obtaining the corresponding amplitude and phase; inputting double-domain data into an HDC Block residual convolutional network layer, capturing multi-scale features, and fusing shallow-layer details and deep-layer semantic features through residual connection; data feature representation output by the HDC Block layer enters the RWK layer to realize feature time sequence dependent modeling, and parameter learning of the RWKV layer is completed in advance through a sequence reconstruction task; classifying all data by utilizing a classification head; and according to a category priority principle, performing score calculation to obtain a final highest score AIF. According to the method, accurate and efficient AIF automatic selection with clinical practicability can be realized, the ability of capturing blood flow dynamic details is remarkably enhanced, the strong robustness of an algorithm to scanning parameter differences is kept, and the clinical uncertainty of perfusion parameter interpretation is effectively reduced.

The invention provides a rapid parameter imaging method in dynamic PET (positron emission tomography) based on grey correlation analysis. The rapid parameter imaging method comprises the following steps: acquiring a complete dynamic PET image and a short-time dynamic PET image; extracting an artery input function from the complete dynamic PET image; tissue time activity curves are extracted from different tissues in the short-time dynamic PET image; on the basis of the artery input function and the tissue time activity curve, a Loga analysis model is constructed, and a gold standard parameter image is generated; optimizing slope estimation of the Loga analysis model through a grey correlation analysis method; and generating a short-time parameter image based on the optimized slope estimation. According to the parameter imaging method provided by the invention, a parameter image with higher quality is obtained by utilizing shorter dynamic scanning time.

The application discloses a CT perfusion image parameter map correction method and a computer readable storage medium. The method comprises the following steps: obtaining a dynamic-venous candidate layer according to a raw CT perfusion image after pre-processing; performing baseline correction, integral screening and roughness screening on voxels in the dynamic-venous candidate layer in sequence to obtain a first type of voxels; screening a group of arterial candidate points from the first type of voxels, analyzing the group of arterial candidate points by using a maximum connected domain algorithm to obtain a group of arterial result points, and then obtaining an arterial input function; screening a group of venous candidate points from the first type of voxels, analyzing the group of venous candidate points by using the maximum connected domain algorithm to obtain a group of venous result points, and then obtaining a venous output function; obtaining a CT perfusion image parameter map; and performing partial volume effect correction on the CT perfusion image parameter map by using the arterial input function and the venous output function to obtain a corrected CT perfusion image parameter map.

Examples of the present disclosure include a method and system for deriving a computer implemented trained artificial intelligence (AI) model that is capable of predicting arterial input function (AIF) from blood and myocardial signal intensity curves of a subject obtained using a magnetic resonance (MR) scanner during injection of a single high dosage bolus of contrast agent that would otherwise cause signal saturation. The method and system requires an input data set of a large number of prior obtained dual bolus or dual sequence sets of scan measurements to be used as training data for the AI model. Once the computer implemented trained AI model has been obtained, it is deployed in a further method and system which receives MR data for a particular subject for which myocardial perfusion parameters such as the AIF is to found, the MR data being obtained from an MR scan taken during injection of a single bolus of contrast agent at a concentration sufficient to give MR signal saturation. The trained AI model is then able to predict, from the single bolus saturated MR data, myocardial perfusion parameters such as the AIF for the subject. The trained AI model is also able to convert between dual bolus datasets and duals sequence datasets.

The application relates to a method and device for determining an arterial input function for perfusion imaging, a computer device and a storage medium. The method comprises: acquiring three-dimensional image data corresponding to four-dimensional perfusion data scanned by a medical imaging device for a target organ; detecting whether a target arterial blood vessel exists in a slice image of the three-dimensional image data in a target direction, wherein the target direction is a transverse cross-sectional axial direction of a scanning channel of the medical imaging device; if the target arterial blood vessel exists in the slice image of the three-dimensional image data in the target direction, segmenting a target arterial blood vessel image from the slice image; and determining an arterial input function required by the target organ during perfusion parameter calculation according to a first pixel cluster obtained by clustering pixels in the target arterial blood vessel image. The method can improve the accuracy of the arterial input function for perfusion imaging.

Embodiments of the present application disclose a medical image processing method and device, an image processing apparatus and a medium. The method comprises: determining an arterial input function of an arterial blood vessel for supplying blood to a target part and attenuation coefficient enhancement information of a single voxel corresponding to the arterial blood vessel according to a plurality of groups of dynamic enhancement CT images collected at different time points; inputting the arterial input function and the attenuation coefficient enhancement information into a preset estimation model to obtain a preset blood flow parameter of the target part, the preset blood flow parameter comprising a blood flow velocity and an average transit time, and the preset estimation model being constructed based on a box-type attenuation function. The technical problem that the prior art cannot accurately determine the blood flow parameter of the dynamic enhancement CT is solved.

The present invention relates to a method for calculating in silico an arterial input function (AIF), wherein the method comprises selecting a class (CM, CMC) and an order (n) of the pharmacokinetic model. A pharmacokinetic model class is selected from a parallel distribution (CM) model from a central compartment to one or more peripheral compartments or a serial distribution (CMC) model from a central compartment in series through one or more intermediate compartments to one or more peripheral compartments. The order (n) of the pharmacokinetic model is the number of compartments in the selected pharmacokinetic model. In particular, the invention provides a computation of the arterial input function (AIF) at the inlet to the target circulatory area (VOI). The computed arterial input function (AIF) is then used to calculate perfusion and in particular perfusion metrics. In silico computed arterial input functions (AIF) were compared with results obtained in vivo. Consistency was observed between in silico and in vivo results at a level allowing for medical application of the invention. In particular, allowing the determination of perfusion metrics.

The present invention relates to a computed tomography perfusion (CTP)-based venous output function (VOF) detection method and system, and the CTP-based VOF detection method performed by the CTP-based VOF detection system, according to the present invention, comprises: removing bias noise from a contrast agent concentration graph in a brain region of a CTP image; excluding a graph that has a peak earlier than the peak time of an arterial input function (AIF) and aligning, to the same position as the peak of the AIF, peak values of remaining graphs; calculating a Pearson correlation coefficient with the AIF so as to select a graph having a Pearson correlation coefficient greater than or equal to a predefined threshold; generating a mask on the basis of a peak value range of a VOF candidate group selected from the selected graphs, so as to extract a final VOF graph; generating a maximum intensity projection (MIP) from the CTP image and extracting a blood vessel image; and checking a veinous positions through an operation between the mask and the VOF candidate group so as to select, as a final VOF, the VOF candidate closest to a center relative to the midline.