A lung segmentation processor (40) is configured to classify magnetic resonance (MR) images based on noise characteristics. The MR segmenatation processor generates a lung region of interest (ROI) and detailed structure segmentation of the lung from the ROI. The MR segmentation processor performs an iterative normalization and region definition approach that captures the entire lung and the soft tissues within the lung accurately. Accuracy of the segmentation relies on artifact classification coming inherently from MR images. The MR segmentation processor (40) correlates segmented lung internal tissue pixels with the lung density to determine the attenuation coefficients based on the correlation. Lung densities are computed using MR data obtained from imaging sequences that minimize echo and acquisition times. The densities differentiate healthy tissues and lesions, which an attenuation map processor (36) uses to create localized attenuation maps for the lung.

The invention relates to a combined imaging detector for detection of gamma and x-ray quanta comprising an x-ray detector (31) for generating x-ray detection signals in response to detected x-ray quanta and a gamma detector (32) for generating gamma detection signals in response to detected gamma quanta. The x-ray detector (31) and the gamma detector (32) are arranged in a stacked configuration along a radiation-receiving direction (33). The gamma detector (32) comprises a gamma collimator plate (320) comprising a plurality of pinholes (321), and a gamma conversion layer (322, 324) for converting detected gamma quanta into gamma detection signals.

A system (10) and method for energy correction of positron emission tomography (PET) event data by at least one processor. Event data for a plurality of strike events corresponding to gamma events is received. Each strike event is detected by a pixel of a detector module (50) and includes an energy and a time. The energy of the strike events is linearized using an energy linearity correction model including one or more parameters. Clusters of the strike events are identified based on the times of the strike events, and sub-clusters of the clusters are identified based on the pixels corresponding to the strike events of the clusters. Energies of the sub-clusters are corrected using a first set of correction factors, and energies of clusters including a plurality of sub-clusters are corrected using a second set of correction factors.

[Problem to be Solved] To improve stability of automatic normalization of a bone scintigraphy image. [Solution] A preferred embodiment includes: creating a pixel value histogram of image data representing a bone scintigraphy image; setting a plurality of thresholds related to pixel values based on the pixel value histogram; calculating respective average pixel values for the set thresholds; arranging the calculated average pixel values in order from the largest value; and determining a reference value for normalizing the image data based on at least part of a set of the average pixel values arranged in the order. The determining the reference value includes: determining one straight line that approximates a region of small average pixel values out of the set of the average pixel values arranged in the order; and calculating the reference value based on the straight line.

A nuclear medicine diagnostic apparatus according to an embodiment includes a scintillator configured to be formed of a single crystal and convert a gamma ray into light; a plurality of photodetectors configured to be arranged on different faces or tangents of the scintillator and each of which is configured to output an electric signal in response to incidence of the light resulting from the converting by the scintillator; storage circuitry configured to store, in advance, correspondence information in which each position in the scintillator is associated with a first intensity distribution indicating intensities of the electric signals that are output by the respective photodetectors; and specifying circuitry configured to specify a conversion position in which the gamma ray that is emitted from the subject is converted into the light in the scintillator by using the correspondence information and a second intensity distribution indicating the intensities of the electric signals.

A tomographic imaging apparatus includes an X-ray detector comprising a plurality of dual mode pixels and configured to detect radiation that has passed through an object, and at least one processor configured to obtain scan data from the X-ray detector, and control each pixel of the plurality of dual mode pixels to operate in one of a first mode and a second mode, wherein each pixel of the plurality of dual mode pixels includes a sensor configured to generate a scan signal by converting incident radiation into an electric signal, a first signal path circuit configured to transmit the scan signal in the first mode, a second signal path circuit configured to transmit the scan signal in the second mode, and a photon counter configured to count photons from the scan signal transmitted through one of the first and second signal path circuits.

An SiPM sensor chip includes pixels consisting of microcells Z, each pixel being associated with an xy position x1, x2, x3, . . . , xN or y1, y2, y3, . . . yM. A plurality of pixels form a block, and the microcells are connected to output channels for a linear coding.

A medical diagnostic-imaging apparatus of an embodiment includes plural converters and processing circuitry. The converters output an electrical signal based on an incident radioactive ray. The processing circuitry identifies a first signal intensity that is a signal intensity corresponding to a peak of the number of the radioactive rays based on a relationship between a signal intensity of an electrical signal output from the convertor and the number of incident radioactive rays, for each of the converters. The processing circuitry identifies a second signal intensity that is a signal intensity corresponding to energy of a radioactive ray that has entered therein without scattering, based on a relationship between the signal intensity and the number of radioactive rays in a higher intensity than the first signal intensity. The processing circuitry corrects a signal intensity of an electrical signal that is output from the respective converters such that the second signal intensity identified for each of the converters matches with a target signal intensity.

Described is a scintigraphic measurement device with extended area, including a measurement structure having a matrix of scintillation crystals and an optoelectronic network for converting photons into electrical signals; a collimator with collimation channels; an electronic processing unit applied to the measurement structure processing the electrical signals generated by the measurement structure. The optoelectronic network has a matrix of optoelectronic conversion modules interconnected according to a two-dimensional distribution to cover the entire measurement area, each optoelectronic conversion module including a two-dimensional matrix of individual elements Multi Pixel Photon Counter or individual Silicon PhotoMultiplier elements electrically interconnected, and wherein the optoelectronic conversion modules are electrically connected to each other along rows and columns by channels for each row or column and the electronic processing unit is connected to the optoelectronic network for measuring a total electric current of each channel delivered by the optoelectronic conversion modules positioned on the channel.

The present invention provides a method of calibrating gamma-ray and photon counting detectors, including, but not limited to, monolithic crystal detectors. The method of the present invention is based on the observation that measurement of fan beam datasets allows the synthesis of collimated beam data to derive MDRFs by use of an algorithm that finds the common or intersecting data subsets of two or more orthogonal calibration datasets. This makes the calibration process very efficient while still allowing the full benefits of maximum-likelihood event-parameter estimation that incorporates the statistical nature of the light sensor measurements.