Koninklijke Philips Corporation N.v.

Shushen Lin

Chuanyong Bai

Andriy Andreyev

Bin Zhang

Zhiqiang Hu

Jinghan Ye

Xiyun Song

Michael Allen Miller

Yang-Ming Zhu

Shekhar Dwivedi

Douglas B McKnight

Thomas Christopher Bulgrin

Yu-Lung Hsieh

Ilya Brodskiy

Kushal Shah

A device () for measuring respiration of a patient includes a positron emission tomography (PET) or single photon emission computed tomography (SPECT) imaging device (). At least one electronic processor () is programmed to: extract a first respiration data signal () from emission imaging data of a patient acquired by the PET or SPECT imaging device; extract a second respiration data signal () from a photoplethysmograph (PPG) signal of the patient; and combine the first and second extracted respiration data signals to generate a respiration signal () indicative of respiration of the patient.

Respiratory gating using pulse oximeters for tomographic imaging

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 nuclear medicine image reconstruction method generates a reconstructed image () by performing iterative image in reconstruction () on nuclear medicine imaging data (). The iterative image reconstruction produces a sequence of update images (). During the iterative image reconstruction, a standardized uptake value (SUV) transform () is applied to convert an update image () to an update SUV image (). The SUV transform scales values of voxels of the update image to SUV values using scaling factors including at least a body size metric and a dose metric. During the iterative image reconstruction, at least one parameter used in an image update of the iterative image reconstruction is adjusted using the update SUV image. For example, a parameter of a prior or filter () incorporated into an image reconstruction update step () or used in filtering of an update image () may be adjusted.

Standardized uptake value (SUV) guided reconstruction control for improved outcome robustness in positron emission tomography (PET) imaging

A PET detector array () comprising detector pixels acquires PET detection counts along lines of response (LORs). The counts are reconstructed to generate a reconstructed PET image (). The reconstructing is corrected for missing LORs which are missing due to dead detector pixels of the PET detector array. The correction may be by estimating counts along the missing LORs () by interpolating counts along LORs () neighboring the missing LORs. The interpolation may be iterative to handle contiguous groups of missing detector pixels. The correction may be by computing a sensitivity matrix having matrix elements corresponding to image elements () of the reconstructed PET image. In this case, each matrix element is computed as a summation over all LORs intersecting the corresponding image element excepting the missing LORs. The computed sensitivity matrix is used in the reconstructing.

Dead pixel correction for digital PET reconstruction

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Cleveland, OH, United States of America

Shushen Lin