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.

Methods, devices and apparatus for reconstructing a PET image are provided. According to an example of the method, a PET initial image may be reconstructed from PET data obtained by scanning on a target object with a PET device, and an MRI image may be reconstructed from MRI data obtained by scanning on the target object with a MRI device, a fusion image retaining only a boundary of the target object is generated based on the PET initial image and the MRI image, and a PET reconstructed image is obtained by combining the PET initial image with the fusion image, where the definition of the boundary of the target object in the PET reconstructed image is higher than that in the PET initial image.

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.

Systems and methods are provided for a visualization of a multi-modal medical image for diagnostic medical imaging. The systems and methods receive first and second image data sets of an anatomical structure of interest, register the first and second image data sets to a geometrical model of the anatomical structure of interest to form a registered image. The geometrical model includes a location of an anatomical marker. The systems and methods further display the registered image.

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.

A device and a method for obtaining densitometric images which comprise at least one radiological device, at least one depth sensor, and image processing means, which combine the radiological absorption information from the set of recorded radiological images obtained with the radiological systems with the distances of the traversed material, provided by the three-dimensional reconstruction of the objects obtained by means of the depth sensors.

An exemplary system, method and computer-accessible medium for generating a magnetic resonance (MR) image(s) and a positron emission tomography (PET) image(s) of a tissue(s) can be provided, which can include, for example, receiving information related to a combination of MR data and PET data as a single data set, separating the information into at least two dimensions, at least one first of the dimensions corresponding to the MR data and at least one second of the dimensions corresponding to the PET data, and generating the MR image(s) and the PET image(s) based on the separated information.

A method and system for CT image correction are provided. Contour data of a scanned subject can be obtained, and a shape image of the scanned subject can be reconstructed according to the contour data, wherein the contour data of the scanned subject can be obtained by scanning the scanned subject through a piece of visual equipment. Original CT projection data of the scanned subject can be obtained, and a CT image can be reconstructed according to the original CT projection data. For determining a supervision field image, image matching can be performed between the shape image and the CT image of the scanned subject. Supervision field projection data can be obtained, and the supervision field projection data can be used to correct the CT image.

Reconstruction results of X-ray recordings are to be improved without any additional radiation burden. For this purpose, a method for creating an X-ray reconstruction by way of acquiring an X-ray recording of an object and creating a first reconstruction from the X-ray recording is provided. Within the first reconstruction, an uncertainty region is determined. In addition, an ultrasound recording restricted to a part of the object that corresponds to the uncertainty region is acquired. Further, the data of the first reconstruction is combined with data of the ultrasound recording to create a second reconstruction.

A method of operating an electronic apparatus configured to generate holotomographic image data according to one embodiment of the present disclosure includes: acquiring fusion data from a fusion imaging apparatus in which a holographic tomography apparatus and a super-resolution fluorescence microscope are combined, in which the fusion data includes input data corresponding to holotomographic image data and output data corresponding to image data of the super-resolution fluorescence microscope; generating a cross-modal inference-based deep learning engine configured to generate a super-resolution tomographic image based on the input data and the output data; and acquiring a molecule-specific tomographic image using the deep learning engine, wherein the molecule-specific tomographic image has nano-resolution.