A system and method for determining accumulated radiation dose is presented. In some embodiments, the system and method include use of one or more RADFETs to measure and accumulated radiation dose over a desired period of time from an area of interest in a patient. In some embodiments, the one or more RADFETs may be arranged on a test strip, and electrical circuitry provided to selectively couple certain terminals of the RADFETS together to facilitate improved measurement of accumulated dose. A reader may also be utilized wherein the reader may receive a test strip, decouple the electrical connections between select terminals, inject a current into the RADFET and/or measure a voltage from the RADFET corresponding to an accumulated radiation dose.

Disclosed is a novel method of obtaining transmission scan data in a PET scanner by incorporating one or more stationary gamma-ray sources that provide forward scattered gamma-photons that can be used as transmission imaging radiation.

The invention relates to a combined detector (660) comprising a gamma radiation detector (100) and an X-ray radiation detector (661). The gamma radiation detector (100) comprises a gamma scintillator array (101.sub.x, y), an optical modulator (102) and a first photodetector array (103.sub.a, b) for detecting the first scintillation light generated by the gamma scintillator array (101.sub.x, y). The optical modulator (102) is disposed between the gamma scintillator array (101.sub.x, y) and the first photodetector array (103.sub.a, b) for modulating a transmission of the first scintillation light between the gamma scintillator array (101.sub.x, y) and the first photodetector array (103.sub.a, b). The optical modulator (102) comprises at least one optical modulator pixel having a cross sectional area (102′) in a plane that is perpendicular to the gamma radiation receiving direction (104). The cross sectional area of each optical modulator pixel (102′) is greater than or equal to the cross sectional area of each photodetector pixel (103′.sub.a, b).

The invention relates to a combined detector (660) comprising a gamma radiation detector (100) and an X-ray radiation detector (661). The gamma radiation detector (100) comprises a gamma scintillator array (101.sub.x, y), an optical modulator (102) and a first photodetector array (103.sub.a, b) for detecting the first scintillation light generated by the gamma scintillator array (101.sub.x, y). The optical modulator (102) is disposed between the gamma scintillator array (101.sub.x, y) and the first photodetector array (103.sub.a, b) for modulating a transmission of the first scintillation light between the gamma scintillator array (101.sub.x, y) and the first photodetector array (103.sub.a, b). The optical modulator (102) comprises at least one optical modulator pixel having a cross sectional area (102′) in a plane that is perpendicular to the gamma radiation receiving direction (104). The cross sectional area of each optical modulator pixel (102′) is greater than or equal to the cross sectional area of each photodetector pixel (103′.sub.a, b).

A separation unit that generates a separated image in which a plurality of structures are separated, from an image including the plurality of structures receives an input of an image pair that includes a target image relating to at least a part of the plurality of structures and a non-separation image not including the structure, to output a separation image in which one of the structures is extracted from the target image. The separation unit receives an input of a new image pair including the target image and the separation image, to output a new separation image in which another one of the structures is extracted from the target image. The separation unit repeats the reception of the input of the new image pair including the target image and the new separation image and the output of a new separation image in which another one of the structures is extracted from the target image.

For dead time determination for a gamma camera or other detector, a long-lived point source of emissions is positioned so that the gamma camera detects the emissions from the source while also being used to detect emissions from the patient. The long-lived point source, in the scan time, acts as a fixed frequency source of emissions, allowing for dead time correction measurements that include the crystal detector effects.

For dead time determination for a gamma camera or other detector, a long-lived point source of emissions is positioned so that the gamma camera detects the emissions from the source while also being used to detect emissions from the patient. The long-lived point source, in the scan time, acts as a fixed frequency source of emissions, allowing for dead time correction measurements that include the crystal detector effects.

An imaging system comprises a plurality of imaging detectors for acquiring imaging data. The plurality of imaging detectors is configurable to be arranged proximate to an anatomy of interest within a patient. Each of the plurality of imaging detectors has a field of view (FOV) and at least a portion of the plurality of imaging detectors image the anatomy of interest within the respective FOV. A processor receives the imaging data and processes the imaging data to form a multi-dimensional dataset having at least three dimensions.

A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.

A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.