An apparatus (IP) and a method to generate temporally-aligned image frames for image-streams (LS, RS) in a multi-channel (CR, CL) imaging system (100). The apparatus (IP) allows reducing or removing temporal distance artifacts that occur when processing the frames into combined image material. The apparatus can also be used to improve signal-to-noise ratio of the frames. The multi-channel imaging system (100) may be a stereoscopic imager.

The present disclosure relates to a method and system for registering multi-modality images. The method may include: acquiring a first image relating to one or more reference objects; acquiring a second image relating to the one or more reference objects; determining a set of reference points based on the one or more reference objects; determining a set of mapping data corresponding to the set of reference points in the first image and the second image; and determining one or more registration parameters by comparing a plurality of back-projection errors generated in a plurality of iterations that are performed based on the set of mapping data.

A method of data presentation in which NM data and x-ray data (or other structural and/or functional data sets) are combined for display. In some exemplary embodiments of the invention, the combination uses rules, for example Boolean rules to generate a display. In some exemplary embodiments of the invention, the combination uses a marker visible in the NM and x-ray modalities. Optionally, the marker includes a removable radioactive section.

A method of NM image reconstruction, including: (a) acquiring a first set of NM data of a part of the body; (b) collecting a probe position and/or probe NM data from an intrabody probe; (c) reconstructing an NM image from said NM data using said collected probe data.

Also described is a method of navigating to a target in a body, including: (a) acquiring a NM image of a part of the body; (b) collecting NM data from an intrabody probe; (c) correlating said image and said data; and (d) extracting location information of said probe relative to said target based on said correlated data.

The generation of a 3D reconstruction of an object is disclosed, which includes illuminating the object, capturing image data in relation to the object, and calculating the 3D reconstruction of the object from the image data. The image data contains first image data and second image data, wherein the first image data are captured when the object is illuminated with illumination light, at least some of which, in relation to an object imaging beam path, is reflected light which illuminates the object, wherein the second image data are captured from different recording directions when the object is illuminated with illumination light, at least some of which is guided in the object imaging beam path, and wherein the 3D reconstruction of the object is calculated from the first image data and the second image data.

The present disclosure relates to a method and system for registering multi-modality images. The method may include: acquiring a first image relating to a registration model, wherein the registration model includes a plurality of reference objects; acquiring a second image relating to the registration model; determining a set of reference points based on the plurality of reference objects; determining a set of mapping data corresponding to the set of reference points in the first image and the second image; and determining one or more registration parameters based on the set of mapping data.

System for image correction in PET is provided. The system may acquire a PET image and a CT image of a subject. The system may generate, based on the PET image and the CT image, an attenuation-corrected PET image of the subject by application of an attenuation correction model. The attenuation correction model may be a trained cascaded neural network including a trained first model and at least one trained second model downstream to the trained first model. During the application of the attenuation correction model, an input of each of the at least one trained second model may include the PET image, the CT image, and an output image of a previous trained model that is upstream and connected to the trained second model.

A method of NM image reconstruction, including: (a) acquiring a first set of NM data of a part of the body; (b) collecting a probe position and/or probe NM data from an intrabody probe; (c) reconstructing an NM image from said NM data using said collected probe data. Also described is a method of navigating to a target in a body, including: (a) acquiring a NM image of a part of the body; (b) collecting NM data from an intrabody probe; (c) correlating said image and said data; and (d) extracting location information of said probe relative to said target based on said correlated data.

A method include obtaining at least one first PET image of a subject acquired by a PET scanner and at least one first MR image of the subject acquired by an MR scanner. The method may also include obtaining a target neural network model. The target neural network model may provide a mapping relationship between PET images, MR images, and corresponding attenuation correction data, and output attenuation correction data associated with a specific PET image of the PET images. The method may further include generating first attenuation correction data corresponding to the subject using the target neural network model based on the at least one first PET image and the at least one first MR image of the subject, and determining a target PET image of the subject based on the first attenuation correction data corresponding to the subject.

A method for determining a surrogate respiratory signal for four-dimensional computed tomography using ultrasound data includes acquiring computed tomography data with a computed tomography imaging system (402), acquiring ultrasound data with an ultrasound probe of an ultrasound imaging system (404) concurrently with acquiring the computed tomography data during one or more respiratory cycles, wherein the ultrasound probe is aligned to acquire an image of a diaphragm of a subject, synchronizing the acquired computed tomography data and the acquired ultrasound data, and determining a surrogate respiratory signal from the acquired ultrasound data.