A position-signal processing method for flat panel gamma imaging probe includes a modeling phase and a use phase. In the modeling phase, a weight direction for an imaging detector is defined, position centers and weight ratios of the imaging detector in the weight direction are utilized to obtain a distribution graph of the weight ratios to the position centers, and curve fitting is performed upon the distribution graph to obtain a position estimation curve. In the use phase, the position estimation curve is utilized to derive a position estimation value of a probe trigger event in a 2D crystal diagram, a position value of the probe in the 2D crystal diagram with respect to the position estimation value of the probe trigger event is obtained, and a crystal code is located in a crystal code look-up table for the position value of the probe in the 2D crystal diagram.

Gamma cameras may be used to obtain two-dimensional images of an emitting object, of which the most common form is the “Anger-type” gamma camera. The primary components in a conventional Anger-type gamma camera include, but are not limited to: a plurality of photo-multiplier tubes, a scintillator material, and a collimator. The disclosed invention claims a novel use of a gamma camera which eliminates the collimator. The new method is a method of forming an initial image from the incident radiation, which does not depend on any mechanical or other means of restricting the incident radiation to be passed on to a position-sensitive radiation detector. This method then uses mathematical deconvolution to produce an image of the object without the need for a collimator and without reliance on a pre-existing image.

An image processing device includes: an image data acquisition unit for acquiring SPECT image data of a brain; a brain-region ROI definition unit for defining a brain-region ROI in the SPECT image; a striatum ROI definition unit for defining a striatum ROI in the SPECT image; and a threshold determination unit for, based on counts in the SPECT image's background which is the brain-region ROI except the striatum ROI, determining a threshold for distinguishing ventricles and sulci in the SPECT image; a region distinction unit for distinguishing between a region whose number of counts is smaller than or equal to the threshold and a region whose number of counts is larger than the threshold.

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.

In the scatter estimation method of the present invention, Step S1 (first TOF projection data generation) and Step S4 (non-TOF scatter estimation algorithm) are performed, and Step S2 (second TOF projection data generation) and Step S3 (calculation of TOF direction distribution ratio) are performed, and Step S5 (calculation of TOF scatter projection data) is performed. A distribution ratio is obtained from the second TOF projection data measured in a scattered radiation energy window (low energy window). Since the target of distribution is non-TOF scatter projection data in a reconstruction data energy window (standard energy window), post-distribution TOF scatter projection data is obtained as approximate TOF scatter projection data in the reconstruction data energy window (standard energy window), and scatter estimation can be accurately performed.

A method of imaging reconstruction includes providing a detector and a collimator, operating the detector to acquire a measured image of a target object from photons passing through the collimator, partitioning the collimator such that the collimator can be represented by a first matrix, providing an initial estimated image of the target object, and calculating an estimated image of the target object based on the measured image and the first matrix. The calculating of the estimated image includes an iteration using the initial estimated image as a starting point. The method also includes partitioning the collimator such that the collimator can be represented by a second matrix larger than the first matrix, and calculating a refined estimated image of the target object based on the measured image and the second matrix. The calculating of the refined estimated image includes an iteration using the estimated image as a starting point.

An instrument and software methodology to detect a radioactive source and incorporates the following:

1) two radiation detectors in a co-axial configuration, housed in a handheld probe, and

2) a gamma detection control unit executing software algorithms to limit the functional field of view to the front aspect of the probe, vary the depth and width of the field of view to provide collimation without the use of metallic shielding, and allowing the instrument to measure the distance to the radiation source.

The present invention relates to a calibration method for a gamma ray detector (100) including a pixelated scintillator array (110) for emitting scintillation photons at photo conversion positions (94) in response to incident gamma rays (90), and a pixelated photodetector array (120) for determining a spatial intensity distribution of the scintillation photons. The present invention bases on the idea that using the concept of optical light sharing of scintillation photons, which are emitted in one element, i.e., one scintillator pixel (112) of the scintillator array (110) and distributed over multiple photodetector pixels (122) of the pixelated photodetector array (120), allows obtaining an estimate for the time skew between adjacent photodetector pixels (122). The present invention further relates to a calibration module (200) for a gamma ray detector (100) including a recorder (210) and a processing module (220) for performing the function of the above-explained method. Still further, the present invention relates to a gamma ray detector (100) as well as to a medical imaging device (50) comprising this gamma ray detector (100).

For dosimetry, a miniaturized nuclear imaging system with a solid-state detector is used to determine the activity and/or injected dose for a radiopharmaceutical. By being sized to scan the syringe or vial, the injected dose may be determined using the solid-state detector with greater accuracy than current dose calibrators and with less frequent use of a calibrated or standardized source. This miniaturized nuclear imaging system reconstructs activity in a same way as the nuclear imaging system scanning a patient, so may be used to calibrate the dose model. A tissue mimicking object with a solid-state dosimeter measures dose from the radiopharmaceutical, which dose is used to calibrate the dose model.

A computer-implemented method (200) of radiation events localizations is indicated for a pixelated radiation detector (10) having a scintillator array (24) of scintillator array elements (26) arranged in an (m)×(n) array, and an optical sensor array (28) of optical sensors (30) arranged in a (q)×(z) array and coupled to the scintillator array (24) in light sharing mode. The method includes the steps of sampling (72) spatial intensity distributions of scintillation photons emitted by the scintillator array (24) in response to multiple incident radiation events; performing a clustering analysis (76) based on the sampled spatial intensity distributions, to obtain clusters (84) of radiation events attributed to scintillator array elements (26), wherein the dimension of the sampled spatial intensity distributions correspond to the (q)×(z) dimensions of the optical sensor array (28), and determining the localization of the radiation events based on the clustering analysis (76).