A mechanical linkage system of PET and CT/MRI and a linkage scanning method thereof are disclosed. According to an example of the disclosure, the mechanical linkage system includes a PET host, a CT/MRI host, a scanning bed, and a moving system. The PET host includes a PET detector installed in a PET detector fixing plate and configured to form a positron emission computed tomography image. The CT/MRI host has a larger aperture than the PET detector. A front end of the PET host and a front end of the CT/MRI host are oppositely disposed. The scanning bed is located to an end of the CT/MRI host. The moving system is connected with at least one of the PET host or the CT/MRI host and configured to move the PET detector into or out of a scanning field of view of the CT/MRI host.

A handheld radiation image detecting system and an operation method thereof are provided. The handheld radiation image detecting system includes a handheld device including a radiation emitter and a first transceiver and a sensing device including a radiation image sensor and a second transceiver. The first transceiver is coupled to the radiation emitter and used for generating a first wave with directionality. The second transceiver is used for receiving the first wave and for generating a second wave with directionality, and the first transceiver is used for receiving the second wave.

Detector systems for enhanced radiographic imaging incorporate Compton and PET imaging capabilities. The detector designs employ one or more layers of detector modules comprised of edge-on or face-on detectors, or a combination of edge-on and face-on detectors, which may employ structured detectors. The detectors implement tracking capabilities and operate in a non-coincidence or coincidence detection mode.

Embodiments access an image of a region of interest (ROI) demonstrating cancerous pathology; extract radiomic features from the ROI; define a radiomic feature expression scene based on the ROI and radiomic features; generate a cluster map by superpixel clustering the expression scene; generate an expression map by repartitioning the cluster map into expression levels; compute a textural and spatial phenotypes for the expression map based on the expression levels; construct a radiomic spatial textural (RADISTAT) descriptor by concatenating the textural and spatial phenotypes; provide the RADISTAT descriptor to a machine learning classifier; receive, from the machine learning classifier, a first probability that the ROI is a responder or non-responder, or a second probability that the ROI will experience long-term survival or short-term survival, based, at least in part, on the RADISTAT descriptor; and generate a classification of the ROI as a responder or non-responder, or long-term survivor or short-term survivor.

Detector systems for enhanced radiographic imaging incorporate x-ray CT imaging capabilities. The detector designs employ a layer of detector modules comprised of edge-on or face-on detectors, or a combination of edge-on and face-on detectors, and may employ structured detectors. The detectors can operate in a non-coincidence or coincidence detection mode.

A dual mode radiation detector includes an x-ray detector layer configured to convert incident x-ray radiation into x-ray electrical data, where the x-ray detector forms an incident face of the dual mode radiation detector. The dual mode radiation detector further includes a collimator disposed below the x-ray detector layer, and a gamma photon detector layer disposed below the collimator to convert incident gamma photons into gamma photon electrical data.

Detector systems for enhanced radiographic imaging incorporate one or more Compton and nuclear medicine imaging, PET imaging, and x-ray CT imaging capabilities. The detector designs employ one or more layers of detector modules comprising edge-on or face-on detectors, or a combination of edge-on and face-on detectors, which can employ gas, scintillator, semiconductor, low temperature (such as Ge and superconductor) or structured detectors. The detectors implement tracking capabilities, and operate in non-coincidence or coincidence detection modes.

An imaging apparatus for imaging ex-vivo tissue specimens includes a positron emission tomography imaging module having at least one pair of PET detectors; a computed tomography imaging module including an X-ray source and an X-ray detector; a tissue specimen receiving element configured to receive the tissue specimen to be imaged.

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 present disclosure relates to a new positron emission tomography (PET) scanning method that generates images with improved spatial resolution. The method includes placing a plurality of radiation-attenuating rods in a parallel arrangement near the target region of a patient, where the rods are in a first orientation with respect to the patient and conducting one or more PET scans of the target region generating a projection data that includes the radiation-attenuating rods, and reconstructing an image of the target region from the projection data.