Koninklijke Philips Corporation N.v.

Yu-Lung Hsieh

Jinghan Ye

Zhiqiang Hu

Andriy Andreyev

Xiyun Song

Bin Zhang

Chuanyong Bai

Shekhar Dwivedi

Manoj Narayanan

Michael Allen Miller

Yang-Ming Zhu

Andreas Goedicke

Douglas B McKnight

Thomas Christopher Bulgrin

Andre Frank Salomon

Shushen Lin

James Gurian

Ilya Brodskiy

A non-transitory computer-readable medium stores instructions readable and executable by a workstation () including at least one electronic processor () to perform an image reconstruction method (). The method includes: determining a weighting parameter () of an edge-preserving regularization or penalty of a regularized image reconstruction of an image acquisition device () for an imaging data set obtained by the image acquisition device; determining an edge sensitivity parameter (&#x3b3;) of the edge-preserving algorithm for the imaging data set obtained by the image acquisition device; and reconstructing the imaging data set obtained by the image acquisition device to generate a reconstructed image by applying the regularized image reconstruction including the edge-preserving regularization or penalty with the determined weighting and edge sensitivity parameters to the imaging data set obtained by the image acquisition device.

Image reconstruction employing tailored edge preserving regularization

A non-transitory computer-readable medium storing instructions readable and executable by a workstation () including at least one electronic processor () to perform a quality control (QC) method (). The method includes: receiving a current QC data set acquired by a pixelated detector () and one or more prior QC data sets acquired by the pixelated detector; determining stability levels of detector pixels () of the pixelated detector over time from the current QC data set and the one or more prior QC data sets; labeling a detector pixel of the pixelated detector as dead when the stability level determined for the detector pixel is outside of a stability threshold range; and displaying, on a display device () operatively connected with the workstation, an identification () of the detector pixels labelled as dead.

Handling detector pixel performance variation in digital positron emission tomography

A time of flight (TOF) positron emission tomography (PET) image () is generated from TOF PET imaging data () acquired of a subject using a TOF PET imaging data acquisition device (). Iterative image reconstruction () of the TOF PET imaging data is performed with TOF localization of counts along respective lines of response (LORs) to iteratively update a reconstructed image (). Values for at least one regularization or filtering parameter are assigned to the TOF PET imaging data or to voxels of the reconstructed image based on an estimated TOF localization resolution for the TOF PET imaging data or voxels. Regularization () or filtering () of the reconstructed image is performed using the assigned values for the at least one regularization or filtering parameter. In some embodiments, the varying TOF localization resolution for the TOF PET imaging data or voxels is estimated based on related acquisition characteristics such as count rates or operating temperature of the detectors.

Time-of-flight resolution-adaptive image regularization and filtering in positron emission tomography

A radioemission scanner () is operated to acquire tomographic radioemission data of a radiopharmaceutical in a subject in an imaging field of view (FOV). An imaging system is operated to acquire extension imaging data of the subject in an extended FOV disposed outside of and adjacent the imaging FOV along an axial direction (). A distribution of the radiopharmaceutical in the subject in the extended FOV is estimated based on the extension imaging data, and further based on a database () of reference subjects. The tomographic radioemission data are reconstructed to generate a reconstructed image () of the subject in the imaging FOV. The reconstruction includes correcting the reconstructed image for scatter from the extended FOV into the imaging FOV based on the estimated distribution of the radiopharmaceutical in the subject in the extended FOV.

Outside-FOV activity estimation using surview and prior patient data in positron emission tomography

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Aurora, OH, United States of America

Yu-Lung Hsieh