Photon counting detectors are sparsely placed at predetermined positions in the fourth-generation geometry around an object to be scanned in spectral Computer Tomography (CT). An X-ray emitting source rotates radially outside the sparsely placed photon counting detectors. Furthermore, the integrating detectors are placed in the third-generation in combination to the sparsely placed photon counting detectors at predetermined positions in the fourth-generation geometry.

An apparatus is described herein. The apparatus comprises a first modality unit and a second modality unit. The first modality unit is located within a gantry. The second modality unit within the gantry is moveable along an examination axis to be concentric about with the first modality unit such that a field of view of the first modality unit and a field of view of the second modality unit are centered about a single point of interest.

A CBCT imaging system comprises a digital radiation detector and radiation source. A detector transport moves the detector along at least a portion of a first curved path and a radiation source transport moves the radiation source along at least a portion of a second curved source path. The detector is configured to travel at least a portion of the first curved path, and the radiation source is configured to travel at least a portion of the second curved path. The detector is configured to obtain a plurality of 2D projection images over a range of scan angles for reconstructing a 3D volume image using the plurality 2D projection images.

A method and apparatus is provided to reconstruct a collective image of a multiple method/geometry imaging system (e.g., a hybrid computed tomography system having energy-integrating detectors arranged in a third-generation geometry and photon-counting detectors arranged in a fourth generation geometry), wherein a splitting-based iterative algorithm using modified dual variables is used in the image reconstruction. Whereas a separate image for each method/geometry of the multiple method/geometry imaging system can be obtained by solving the distinct system-matrix equation corresponding to each respective method/geometry, the collective image is obtained by simultaneously solving a collective optimization problem including all respective system-matrix. The collective image is obtained more efficiently using variable splitting to subdivide the optimization into subproblems that are solved in an iterative fashion. For some applications, the collective image can be further improved by including a beam-hardening correction step.

Various methods and systems are provided for stationary CT imaging. In one embodiment, a method for an imaging system includes activating a plurality of emitters of a stationary distributed x-ray source unit to emit x-ray beams toward an object within an imaging volume, where the x-ray source unit does not rotate around the imaging volume, receiving attenuated x-ray beams with one or more detector arrays to form a sparse view projection dataset, where each attenuated x-ray beam generates a different view, and reconstructing an image from the sparse view projection dataset using a sparse view reconstruction method.

An X-ray detector unit is disclosed. In an embodiment, the X-ray detector unit includes: at least one analysis unit to process electrical signals delivered from a coupled converter unit and operatable by an operating voltage; an adjustable voltage supply, coupled to the at least one analysis unit, to provide an adjustable supply voltage; an identification unit, assigned to the at least one analysis unit, to provide identification information about the at least one analysis unit in a readable manner; and a communication unit, coupled to the adjustable voltage supply, to read the identification information provided from the identification unit, and based upon the identification information provided, to adjust the adjustable voltage supply to equate the provided supply voltage to the operating voltage of the at least one analysis unit.

This invention provides a close-range positron emission tomography (PET) system, where the detector modules are able to be moved or placed very close to the patient compared to conventional PET systems. As a result, the sensitivity and resolution of the PET system is greatly increased.

In some embodiments, a safety system for a close range scanning machine inhibits collision of moving detector heads with a patient or other object. For example a proximity sensor and/or a collision sensor may guide to bring the head close enough to a patient and/or warn to prevent a collisions and or reduce impact. Parts that are in that are in the FOV of the detector may be transparent to the detected signal and/or uniformly affect the signal, facilitating identification of the source of the signal from the detector. In some embodiments, a dynamic motion restrictor is set during scanning according to the desired scanning position and/or the position of the patient. In some embodiments, a motion restrictor acts to prevent unbalanced motions.

An apparatus and method are described using a forward model to correct pulse pileup in spectrally resolved X-ray projection data from photon-counting detectors (PCDs). To calibrate the forward model, which represents each order of pileup using a respective pileup response matrix (PRM), an optimization search determines the elements of the PRMs that optimize an objective function measuring agreement between the spectra of recorded counts affected by pulse pileup and the estimated counts generated using forward model of pulse pileup. The spectrum of the recorded counts in the projection data is corrected using the calibrated forward model, by determining an argument value that optimizes the objective function, the argument being either a corrected X-ray spectrum or the projection lengths of a material decomposition. Images for material components of the material decomposition are then reconstructed using the corrected projection data.

An image capturing apparatus for a breast examination in which a breakthrough mammary gland density can be adduced. A mammary gland density is calculated by recognizing a distribution of mammary gland tissues of a subject according to a distribution of radioactive pharmaceuticals distributed in a breast. An imaged image of the distribution of the radioactive pharmaceuticals is one type of function image, and represents activity in the subject. Therefore, it is possible to calculate the mammary gland density based on a mammary gland tissue of which the activity is active (an active mammary gland tissue) in the mammary gland tissues.