A method for selecting one or more targets for non-invasively treating a cardiac arrhythmia in a patient includes receiving a mapping associated with the patient's heart and generating a segmented model of the mapping associated with the patient's heart. The segmented model divides the mapping into a plurality of segments. The method includes identifying one or more abnormality in the segmented model of the mapping associated with the patient's heart, determining which segment or segments of the plurality of segments include the identified one or more abnormality, and selecting a target for non-invasive treatment of the cardiac arrhythmia based on the determined segment or segments of the plurality of segments that include the identified one or more abnormality.

Disclosed is a general PET device (1) with a gradually narrowed head, the device comprising a body (2), a head (3) and a top (4) closely arranged in sequence, wherein the body (2) is composed of a plurality of body module rings (21); the head (3) is composed of N head module rings (31), with N being a natural number and being at least two; the top (4) is composed of a plurality of top PET detection modules (41); each of the body module rings (21) is composed of several body PET detection modules (22) evenly distributed in a circumferential direction thereof, and all the body module rings (21) are closely arranged in an axial direction to form the body (2); in the N head module rings (31), the rings sequentially decreases in size, and are closely arranged in the axial direction in a sequence from the first head module ring (31) to the Nth head module ring (31); and the detection surfaces of the plurality of top PET detection modules (41) are located in the same plane, and all the detection surfaces face the head (3) or the body (2).

A pressure control system for providing an interventional pressure to be applied to a defined area of a patient during a pre-interventional imaging process with an imaging system is provided. Therein, the interventional pressure corresponds to an interventional pressure applied to the defined area of the patient during an intervention via a medical technology device. The pressure control system includes a pressure plate, a force module, and a positioning apparatus. Therein, the force module is configured to apply a force on the pressure plate. Therein, the force on the pressure plate generates the interventional pressure. Therein, the positioning apparatus is configured to position the pressure plate and the force module relative to the patient.

Disclosed is apparatus and method for motion tracking of a subject in medical brain imaging. The method comprises providing a light projector and a first camera; projecting a first pattern sequence (S1) onto a surface region of the subject with the light projector, wherein the subject is positioned in a scanner borehole of a medical scanner, the first pattern sequence comprising a first primary pattern (P.sub.1,1) and/or a first secondary pattern (P.sub.1,2); detecting the projected first pattern sequence (S1′) with the first camera; determining a second pattern sequence (S2) comprising a second primary pattern (P.sub.2,1) based on the detected first pattern sequence (S1′); projecting the second pattern sequence (S2) onto a surface region of the subject with the light projector; detecting the projected second pattern sequence (S2′) with the first camera; and determining motion tracking parameters based on the detected second pattern sequence (S2′).

A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry obtains image data rendering a blood vessel of a patient. The processing circuitry performs a fluid analysis on the obtained image data and calculates an index value related to a blood flow in the blood vessel with respect to each of a plurality of positions in the blood vessel. With respect to the index values to be calculated, the processing circuitry selects a position in which a first value is to be obtained from among the plurality of positions or selects a value serving as the first value from among the index values exhibited in positions. The processing circuitry causes a display to display the first value in a predetermined display region thereof used for displaying the first value.

This present invention discloses a dynamic dual-tracer PET reconstruction method based on a hybrid-loss 3D CNN, which selects a corresponding 3D convolution kernel for a 3D format of dual-tracer PET data, and performs feature extraction in a stereoscopic receptive field (down-sampling) and the reconstruction (up-sampling) process, which accurately reconstructs the three-dimensional concentration distributions of two different tracers from the dynamic sinogram. The method of the invention can better reconstruct the simultaneous-injection single-acquisition dual-tracer sinogram without any model constraints. The scanning time required for dual-tracer PET can be minimized based on the method of the present invention. Using this method, the raw sinogram data of dual tracers can be reconstructed into two volumetric individual images in a short time.

Uptake of hypoxia-sensitive PET tracers is dependent on tissue transport properties, specifically, distribution volume. Variability in tissue transport properties reduces the sensitivity of static PET imaging to hypoxia. When tissue transport (v.sub.d) effects are substantial, correlations between the two methods of determining hypoxic fractions are greatly reduced—that is, trapping rates k.sub.3 are only modestly correlated with tumour-to-blood ratio (TBR). In other words, the usefulness of dynamic- and static-PET based hypoxia surrogates, trapping rate k.sub.3 and TBR, in determining hypoxic fractions is reduced in regions where diffusive equilibrium is achieved slowly. A process is provided for quantifying hypoxic fractions using a novel biomarker for hypoxia, hypoxia-sensitive tracer binding rate k.sub.b, based on PET imaging data. The same formalism can be applied to model the kinetics of non-binding CT and MT contrast agents, giving histopathological information about the imaged tissue.

The present disclosure is related to systems and methods for image processing. The method may include obtaining an image including at least one of a first type of artifact or a second type of artifact. The method may include determining, based on a trained machine learning model, at least one of first information associated with the first type of artifact or second information associated with the second type of artifact in the image. The trained machine learning model may include a first trained model and a second trained model. The first trained model may be configured to determine the first information. The second trained model may be configured to determine the second information. The method may include generating a target image based on at least part of the first information and the second information.

Systems and methods for reconstructing medical images are disclosed. Measurement data, such as magnetic resonance (MR) data and positron emission tomography (PET) data, is received from an image scanning system. Attenuation maps are generated based on the PET data and a determined background level of radiation of the image scanning system. The background level of radiation can be caused by the radioactive decay of crystal material of the image scanning system. MR images are reconstructed based on the MR data. Further, a neural network, such as a deep learning neural network, is trained with the attenuation maps and the reconstructed MR images to determine attenuation map based on a reconstructed MR image. The trained neural network can be applied to MR data received for a patient to determine a corresponding attenuation map. A final image is generated based on PET data received for the patient and the determined attenuation map.

A list mode image reconstruction method includes a step of dividing list mode data into a plurality of subsets and a step of acquiring a subset balance coefficient based on the number of events in the plurality of subsets.