Koninklijke Philips Corporation N.v.

University of Massachusetts

Xiyun Song

Jinghan Ye

Andriy Andreyev

Zhiqiang Hu

Chuanyong Bai

Bin Zhang

Lingxiong Shao

Shekhar Dwivedi

Yu-Lung Hsieh

Chi-Hua Tung

Yang-Ming Zhu

Michael Allen Miller

Eberhard Sebastian Hansis

Douglas B McKnight

Manoj Narayanan

Zuo Zhao

Shushen Lin

Yanfei Mao

Teimuraz Bandzava

Tobias Klinder

Joerg Bredno

Cristian Lorenz

Thomas Leroy Laurence

Horace H Hines

Sven Kabus

Sven Prevrhal

Andreas Goedicke

Sharon Xiaorong Wang

Thomas Christopher Bulgrin

Andre Frank Salomon

Angela Da Silva

Mary K Durbin

Jingkun Hu

Jiong Wang

Ravindra Mohan Manjeshwar

Leonid Romanov

Satrajit Misra

Alicia Wong

Sudhir K Mahakali

Michael A King

Joyeeta Mitra Mukherjee

Ilya Brodskiy

James Gurian

Gregory Doughty

Changhong Dai

Varun Verma

Sudhir K Mahakai

An image reconstruction method includes: determining singles rates of radiation detectors in a frame of imaging data detected by the radiation detectors; determining an energy correction factor (N) for each radiation detector based on an energy spectrum distribution of gamma rays incident on the radiation detector during acquisition of the frame of imaging data; determining a singles live time correction factor for each radiation detector from the singles rate and the energy correction factor; determining a system coincidence live time correction factor from the system singles rate; for each line of response (LOR) connecting pairs of radiation detectors, determining a live time correction factor for the LOR from the determined singles live time correction factors of the pair of radiation detectors connected by the LOR and the determined system coincidence live time correction factor; and reconstructing the frame of imaging data using the determined LOR live time correction factors.

Dead-time correction method in quantitative positron emission tomography (PET) reconstruction for various objects and radioactivity distributions

A non-transitory computer-readable medium stores instructions readable and executable by a workstation () including at least one electronic processor () to perform an imaging method (). The method includes: receiving imaging data on a frame by frame basis for frames along an axial direction with neighboring frames overlapping along the axial direction wherein the frames include at least a volume (k) and a succeeding volume (k+1) at least partially overlapping the volume (k) along the axial direction; and generating an image of the volume (k) using an iterative image reconstruction process in which an iteration of the iterative image reconstruction process includes: computing a local penalty function for suppressing noise over the volume (k) including reducing the value of the local penalty function in an overlap region; generating an update image of the volume (k) using imaging data from the volume (k) and further using the local penalty function.

Edge preserving penalized reconstruction for step-and-shoot and motion compensated positron emission tomography (PET) studies

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 stores instructions readable and executable by a workstation () including at least one electronic processor () to perform an image reconstruction method () to reconstruct list mode data acquired over a frame acquisition time using a plurality of radiation detectors () in which the events of the list mode data is timestamped. The method includes: for the sub-frame bins of a plurality of sub-frame bins into which the frame acquisition time is divided, determining a sub-frame singles rates map for the plurality of radiation detectors from the list mode data whose time stamps reside in the sub-frame bin; determining a singles rate for the singles events of the list mode data using the sub-frame singles rates maps wherein the singles rates for the singles events are determined at a temporal resolution that is finer than the frame acquisition time; determining correction factors for the list mode data using the determined singles rates for the singles events of the list mode data; and reconstructing the list mode data of the frame acquisition time using the determined correction factors to generate a reconstructed image for the frame acquisition time.

Correction method for quantification accuracy improvement in list mode reconstruction

A non-transitory storage medium stores instructions readable and executable by an electronic processor () to perform a method () for estimating singles rates for detectors () of a detector array () of a positron emission tomography (PET) imaging device (). The method includes: obtaining measured delayed coincidence rates for lines of response (LORs) defined by pairs of the detectors of the detector array; and generating estimated singles rates for the detectors of the detector array by an iterative optimization in which each iteration performs an update of estimates of the singles rates for the detectors by operations including: forward projecting the estimates of the singles rates for the detectors to generate estimates of delayed coincidence rates for the LORs; generating quantitative comparisons between the measured delayed coincidence rates for the LORs and the generated estimates of the delayed coincidence rates for the LORs; backprojecting the quantitative comparisons to generate corrections for the singles rates for the detectors; and updating the estimates of the singles rates for the detectors using the generated corrections. The estimated singles rates for the detectors of the detectors array comprise the estimates of the singles rates for the detectors output by the iterative optimization when the iterative optimization satisfies a stopping criterion. This process jointly optimizes the estimated singles rates and randoms estimation.

Relaxed iterative maximum-likelihood expectation maximization for positron emission tomography random coincidence estimation

A non-transitory computer readable medium storing instructions readable and executable by an imaging workstation () including at least one electronic processor () to perform a dataset generation method () operating on emission imaging data acquired of a patient for one or more axial frames at a corresponding one or more bed positions, the method comprising: (a) identifying a frame of interest from the one or more axial frames; (b) generating simulated lesion data by simulating emission imaging data for the frame of interest of at least one simulated lesion placed in the frame of interest; (c) generating simulated frame emission imaging data by simulating emission imaging data for the frame of interest of the patient; (d) determining a normalization factor comprising a ratio of the value of a quantitative metric for the simulated patient data and the value of the quantitative metric for the emission imaging data acquired of the same patient for the frame of interest; and (e) generating a hybrid data set comprising the emission imaging data acquired of the patient for the one or more axial frames at the corresponding one or more bed positions with the frame of interest replaced by a combination of the simulated lesion data scaled by the normalization factor and the emission imaging data acquired of the patient for the frame of interest.

Generation of accurate hybrid datasets for quantitative molecular imaging

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: generating, from received imaging data, a plurality of intermediate images reconstructed without scatter correction from data partitioned into different energy windows; generating a fraction of true counts and a fraction of scatter events in the generated intermediate images; generating a final reconstructed image from the intermediate images, the fraction of true counts in the intermediate images, and the fraction of scatter counts in the intermediate images; and at least one of controlling the non-transitory computer readable medium to store the final image and control a display device () to display the final image.

Scatter correction for positron emission tomography (PET)

A non-transitory storage medium storing instructions readable and executable by an imaging workstation () including at least one electronic processor () to perform an image reconstruction method (). The method includes: receiving emission imaging data () from an image acquisition device () wherein the emission imaging data has been filtered using an acquisition energy passband (); generating filtered imaging data by filtering the emission imaging data with a second energy passband () that is narrower than an acquisition energy passband; reconstructing the filtered imaging data to generate an intermediate image; estimating one or more scatter correction factors (SCFs) from the intermediate image; and reconstructing the emission imaging data corrected with the estimated SCFs to generate a reconstructed image.

Scatter correction using emission image estimate reconstructed from narrow energy window counts in positron emission tomography

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

Image processing performed by a computer () includes iterative image reconstruction or refinement () that produces a series of update images ending in an iteratively reconstructed or refined image. A difference image () is computed between a first update image () and a second update image () of the series. The difference image is converted to a feature image () and is used in the iterative processing () or in post-processing () performed on the iteratively reconstructed or refined images or images from different reconstruction or refinement techniques. In another embodiment, first and second image reconstructions () are performed to generate respective first and second reconstructed images (). A difference image () is computed between two images each selected from the group: the first reconstructed image, an update image of the first reconstruction, the second reconstructed image, and an update image of the second reconstruction. A feature image is generated from the difference image and used to combine the first and second reconstructed images.

Feature-based image processing using feature images extracted from different iterations

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

In positron emission tomography (PET) imaging, PET imaging data () having TOF localization is reconstructed. TOF image reconstruction () is performed on the PET imaging data to produce a TOF reconstructed image (). The TOF image reconstruction utilizes the TOF localization of the PET imaging data. Non-TOF image reconstruction () is also performed on the PET imaging data to produce a non-TOF reconstructed image (). The non-TOF image reconstruction does not utilize the TOF localization of the PET imaging data. A comparison image () is computed which is indicative of differences between the TOF reconstructed image and the non TOF reconstructed image. An adjustment () is determined for the TOF image reconstruction based on the comparison image, such as alignment correction of an attenuation map (), and the TOF image reconstruction is repeated on the PET imaging data with the determined adjustment to produce an adjusted TOF reconstructed image.

Using time-of-flight to detect and correct misalignment in PET/CT imaging

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

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

A system () and a method () iteratively reconstruct an image of a target volume of a subject. In each iteration of a plurality of iterations, an estimate image of the target volume () is forward projected () and compared () to received event data () to determine a discrepancy (). The discrepancy () is back projected () and the back projection () updates () the estimate image (). In at least one iteration, the estimate image () is filtered () in the image domain prior to being back projected.

In-reconstruction filtering for positron emission tomography (PET) list mode iterative reconstruction

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Cupertino, CA, United States of America

Xiyun Song