A method for adaptive coincidence data processing is provided. The method includes detecting positron annihilation events with a detector array of a positron emission tomography (PET) scanner, wherein the PET scanner includes multiple detector rings disposed along a longitudinal axis of the PET scanner, and each detector ring includes multiple detectors. The method also includes, within a given time period, dynamically adjusting a number of positron annihilation events accepted and transmitted to acquisition circuitry for processing utilizing a numerical difference in detector rings along the longitudinal axis between a first detector and a second detector detecting respective annihilation photons from a positron annihilation event.

A method for adaptive coincidence data processing is provided. The method includes detecting positron annihilation events with a detector array of a positron emission tomography (PET) scanner, wherein the PET scanner includes multiple detector rings disposed along a longitudinal axis of the PET scanner, and each detector ring includes multiple detectors. The method also includes, within a given time period, dynamically adjusting a number of positron annihilation events accepted and transmitted to acquisition circuitry for processing utilizing a numerical difference in detector rings along the longitudinal axis between a first detector and a second detector detecting respective annihilation photons from a positron annihilation event.

A method for image reconstruction from plural copies, the method including receiving a series of measured projections p.sub.i of a target object h and associated background; iteratively reconstructing images h.sub.i(k) of the target object and images g.sub.i(k) of the background of the target object for each member i of the series of the measured projections p.sub.i over plural iterations k; and generating a final image of the target object h, based on the reconstructed images h.sub.i, when a set condition is met. The index i describes how many elements are in the series of projections p.sub.i, and iteration k indicates how many times the reconstruction of the image target is performed.

The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

A downloadable navigator for a mobile ultrasound unit having an ultrasound probe, implemented on a portable computing device. The navigator includes a trained orientation neural network to receive a non-canonical image of a body part from the mobile ultrasound unit and to generate a transformation associated with the non-canonical image, the transformation transforming from a position and rotation associated with a canonical image to a position and rotation associated with the non-canonical image; and a result converter to convert the transformation into orientation instructions for a user of the probe and to provide and display the orientation instructions to the user to change the position and rotation of the probe.

A alteration caused in a nerve fiber layer could not accurately be displayed. There is provided a tomographic imaging apparatus including a generation means for generating a nerve fiber bundle map, a designation means for designating an arbitrary nerve fiber bundle in the nerve fiber bundle map, and a display control means for causing display means to display a parameter of the designated nerve fiber bundle.

A magnetic resonance (MR) image is created by executing an imaging sequence with an MR apparatus, wherein data in k-space are acquired using multiple receiving antennae, and reconstruction of all image points that correspond to all k-space points belonging to the imaging sequence takes place using a sensitivity profile of the receiving antennae in order to also take account of data at k-space points at positions at which no data were acquired. Data acquired at a number of positions of particular k-space points, the number of the particular k-space points being smaller than the number of all k-space points belonging to the imaging sequence. The aperture of each of the receiving antennae is configured such that, for acquisition of data at a respective k-space point, the spectral main lobe of the respective receiving antenna also extends over k-space points adjacent to the respective k-space point.

An object information obtaining device includes a light source which emits light, an acoustic wave detecting unit which detects a photoacoustic wave generated by irradiation of an object with the light, and outputs an electric signal in response to detection of the photoacoustic wave, and a processing unit configured to perform two or more types of processing to photoacoustic signal data based on the electric signal to obtain object information corresponding to each of the two or more types of processing, and to display on a display unit the object information corresponding to at least one processing selected by a user out of the two or more types of processing.

A method displays spectral image data reconstructed from spectral projection data with a first reconstruction algorithm and segmented image data reconstructed from the same spectral projection data with a different reconstruction algorithm, which is different from the first reconstruction algorithm. The method includes reconstructing spectral projection data with the first reconstruction algorithm, which generates the spectral image data and displaying the spectral image data. The method further includes reconstructing the spectral projection data with the different reconstruction algorithm, which generates segmentation image data, segmenting the segmentation image data, which produces the segmented image data, and displaying the segmented image data.

Methods and apparatus distinguish invasive adenocarcinoma (IA) from in situ adenocarcinoma (AIS). One example apparatus includes a set of circuits, and a data store that stores three dimensional (3D) radiological images of tissue demonstrating IA or AIS. The set of circuits includes a classification circuit that generates an invasiveness classification for a diagnostic 3D radiological image, a training circuit that trains the classification circuit to identify a texture feature associated with IA, an image acquisition circuit that acquires a diagnostic 3D radiological image of a region of tissue demonstrating cancerous pathology and that provides the diagnostic 3D radiological image to the classification circuit, and a prediction circuit that generates an invasiveness score based on the diagnostic 3D radiological image and the invasiveness classification. The training circuit trains the classification circuit using a set of 3D histological reconstructions combined with the set of 3D radiological images.