Koninklijke Philips Corporation N.v.

Sharon Xiaorong Wang

Thomas Leroy Laurence

Jerome John Griesmer

Jinghan Ye

Zhiqiang Hu

Xiyun Song

Steffen Renisch

Thomas Blaffert

David Sowards-Emmerd

Time of flight (TOF) corrections for radiation detector elements of a TOF positron emission tomography (TOF PET) scanner are generated by solving an over-determined set of equations defined by calibration data acquired by the TOF PET scanner from a point source located at an isocenter of the TOF PET scanner, suitably represented as matrix equation at=CS whererepresents TOF time differences, C is a relational matrix encoding the radiation detector elements, and S represents the TOF corrections. A pseudo-inverse Cof relational matrix C may be computed to solve S=C. TOF corrections can be generated for a particular type of detector unit by identifying the radiation detector elements in C by detector unit. Further, multi-photon triggering time stamps can be adjusted to first-photon triggering based on &#x221a;{square root over (P1/Pm)} where P1 is average photon count using first-photon triggering and Pm is a photon count using multi-photon triggering.

Time of flight calibration in digital positron emission tomography

The present application relates generally to positron emission tomography (PET). It finds particular application in conjunction with energy calibration of a digital PET (DPET) detector and will be described with particular reference thereto. In one aspect, a difference spectrum is produced by finding a difference between a background radiation spectrum with no radioactive source loaded and a calibration source radiation spectrum with a radioactive source loaded. The difference spectrum may then be used to identify an energy peak.

Energy calibration with Lu spectrum subtraction

Time of flight (TOF) corrections for radiation detector elements of a TOF positron emission tomography (TOF PET) scanner are generated by solving an over-determined set of equations defined by calibration data acquired by the TOF PET scanner from a point source located at an isocenter of the TOF PET scanner, suitably represented as matrix equation Formula I=CS where Formula I represents TOF time differences, C is a relational matrix encoding the radiation detector elements, and S represents the TOF corrections. A pseudo-inverse Cof relational matrix C may be computed to solve S=CFormula I. TOF corrections can be generated for a particular type of detector unit by identifying the radiation detector elements in C by detector unit. Further, multi-photon triggering time stamps can be adjusted to first-photon triggering based on Formula II where P1 is average photon count using first-photon triggering and Pm is a photon count using multi-photon triggering.

A system () 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 () 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.

Digital positron emission tomography (DPET) energy calibration method

A system () and a method () compensate for one or more dead pixels in positron emission tomography (PET) imaging. A pixel compensation processor receives PET data describing a target volume of a subject. The PET data is missing data for one or more dead pixels. The pixel compensation estimates PET data for the dead pixels from the received PET data.

Dead pixel compensation in positron emission tomography (PET)

A diagnostic imaging system includes a plurality of radiation detectors () configured to detect radiation events emanating from an imaging region. The system includes a calibration phantom () configured to be disposed in the imaging region spanning substantially an entire field of view and to generate radiation event pairs that define lines-of-response, wherein the calibration phantom is thin such that each LOR intersects the calibration phantom along its length, the thickness of the phantom being smaller than the length of the LORs. A calibration processor () receives input of the radiation detectors and calculates an incidence angle independent crystal delay Tfor each detector. The calibration processor () constructs a first look-up table for the timing correction of each LOR and a second look-up table for the angle depth of interaction correction for each crystal by combining Tand &#x3b7;.

Pet detector timing calibration

A medical nuclear imaging system () and corresponding method () are provided. A plurality of pixels () detect radiation events and estimate the energy of the detected radiation events. A memory () stores a plurality of energy windows (), the energy windows corresponding to the pixels. An event verification module () windows the radiation event with the energy windows corresponding to the detecting pixels. A reconstruction processor () reconstructs the windowed radiation events into an image representation.

Scatter reject method via energy calibration

A detector maintains thermal stability between two different operating modes. The detector includes at least one controller which sets the detection sensitivity of the detector to a level disabling the detection of gamma photons. The controller further controls a heat generator to maintain the temperature of the detector at a predetermined temperature. The predetermined temperature is the steady state temperature of the detector when the detection sensitivity of the detector is set to a level enabling the detection of gamma photons. A method for maintaining thermal stability of a detector between two different operating modes is also provided. Approaches are also disclosed for normalize acquired imaging data during image reconstruction using dark current-dependent normalization factors.

Temperature stability for a digital positron emission tomography (PET) detector

A method of aligning multiple volumetric sections of imaging data is provided. The method comprises aligning a primary volumetric section and a secondary volumetric section which is adjacent to the primary volumetric imaging section, for moving the secondary volumetric section into alignment with the primary volumetric section. A related apparatus for performing the method is also provided.

Systems and methods for aligning multiple sections of imaging data

A nuclear imaging system includes a crystal identification system which receives a flood image which includes a plurality of peaks, each peak responsive to radiation detected by a corresponding scintillator crystal. A crystal identification processor partitions the flood image into a plurality of candidate regions with a watershed segmentator implementing a watershed algorithm. The candidate regions are linked in an adjacency graph, and then classified as background or relevant, where relevant regions contain a peak within the watershed lines. The regions are then assigned to a crystal according to an objective function and an assignability score. A calibration processor maps the peaks to a rectangular grid.

Automated crystal identification achieved via watershed segmentation

A nuclear imaging system () includes a crystal identification system () which receives a flood image () which includes a plurality of peaks, each peak responsive to radiation detected by a corresponding scintillator crystal. A crystal identification processor () partitions the flood image () into a plurality of regions (), each region being masked to correspond to one of an array of nuclear detectors. A model image () is generated in which the at least one Gaussian models represents the identified peaks. Misidentified peaks in the model image () in which locations of the peaks in the flood image () differ from the corresponding scintillator crystal are determined and the locations of the misidentified peaks in the flood image () are corrected. A calibration processor () corrects geometric distortions in acquired projection data according to the corrected peaks.

Growing community of inventors

Highland Heights, OH, United States of America

Sharon Xiaorong Wang