Koninklijke Philips Corporation N.v.

Koninklijke Philips Electronics N.v.

Yang-Ming Zhu

Steven Michael Cochoff

Andriy Andreyev

Chuanyong Bai

Chi-Hua Tung

Charles Nortmann

Ronald Sukalac

Jinghan Ye

Xiyun Song

Bin Zhang

Shekhar Dwivedi

Xiangyu Wu

L Alan Love

Richard Cheng-Hsiu Chen

Chris A Dauterman

Madhavi Ahuja

Dawn M Maniawski

Zhiqiang Hu

Douglas B McKnight

John Patrick Collins

Hao Lai

Thomas Christopher Bulgrin

Shiyu Xu

Shushen Lin

Yu-Lung Hsieh

Hao Dang

Michael Allen Miller

Hui Wang

Sheng Lu

Ilya Brodskiy

Cheng-Hsiu Chen

Sydney Kaplan

Charles A Nortmann

Steven M Cochoff

An imaging method () includes: acquiring first training images of one or more imaging subjects using a first image acquisition device (); acquiring second training images of the same one or more imaging subjects as the first training images using a second image acquisition device () of the same imaging modality as the first imaging device; and training a neural network (NN) () to transform the first training images into transformed first training images having a minimized value of a difference metric comparing the transformed first training images and the second training images.

Positron emission tomography (PET) system design optimization using deep imaging

In a multi-session imaging study, information from a previous imaging session is stored in a Binary Large Object (BLOB). Current emission imaging data are reconstructed into a non-attenuation corrected (NAC) current emission image. A spatial transform is generated aligning a previous NAC emission image from the BLOB to the current NAC emission image. A previous computed tomography (CT) image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed with attenuation correction using the warped CT image. Alternatively, low dose current emission imaging data and a current CT image are acquired, a spatial transform is generated aligning the previous CT image to the current CT image, a previous attenuation corrected (AC) emission image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed using the current CT image with the warped AC emission image as prior.

Radiation dose reduction and improved consistency between sessions in hybrid imaging studies

Emission imaging data are reconstructed to generate a low dose reconstructed image. Standardized uptake value (SUV) conversion () is applied to convert the low dose reconstructed image to a low dose SUV image. A neural network () is applied to the low dose SUV image to generate an estimated full dose SUV image. Prior to applying the neural network the low dose reconstructed image or the low dose SUV image is filtered using a low pass filter (). The neural network is trained on a set of training low dose SUV images and corresponding training full dose SUV images to transform the training low dose SUV images to match the corresponding training full dose SUV images, using a loss function having a mean square error loss component () and a loss component () that penalizes loss of image texture and/or a loss component () that promotes edge preservation.

Full dose PET image estimation from low-dose PET imaging using deep learning

A non-spectral computed tomography scanner () includes a radiation source () configured to emit x-ray radiation, a detector array () configured to detect x-ray radiation and generate non-spectral data, and a memory () configured to store a spectral image module () that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor () configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

Non-spectral computed tomography (CT) scanner configured to generate spectral volumetric image data

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 diagnostic imaging system retrieves data () from a plurality of accessible data sources, the accessible data sources storing data including physiological data describing a subject to be imaged, a nature of a requested diagnostic image, image preferences of a clinician who requested the diagnostic image, and previously reconstructed images of the requested nature of the subject and/or other subjects, reconstruction parameters and/or sub-routines used to reconstruct the previously reconstructed images. The system analyzes () the retrieved data to automatically generate reconstruction parameters and/or sub-steps specific to the nature of the requested diagnostic image, the subject, and the clinician image preferences. The system controls a display device () to display the generated reconstruction parameters and/or sub-routines to the user for a user selection. The system sets a reconstruction processor system to reconstruct scan data using the selected reconstruction parameters and/or sub-routines.

Irecon: intelligent image reconstruction system with anticipatory execution

An imaging device () includes a positron emission tomography (PET) scanner () including radiation detectors () and coincidence circuitry for detecting electron-positron annihilation events as 511 keV gamma ray pairs defining lines of response (LORs) with each event having a detection time difference At between the 511 keV gamma rays of the pair. At least one processor () is programmed to reconstruct a dataset comprising detected electron-positron annihilation events acquired for a region of interest by the PET scanner to form a reconstructed PET image wherein the reconstruction includes TOF localization of the events along respective LORs using a TOF kernel having a location parameter dependent on At and a TOF kernel width or shape that varies over the region of interest. A display device () is configured to display the reconstructed PET image.

Time-of-flight (TOF) PET image reconstruction using locally modified TOF kernels

When performing nuclear medicine image reconstruction, lesion proxies () are introduced by a clinician and merged with real acquired scan data outside or inside the patient in the patient image. By monitoring the image attributes of the lesion proxies during reconstruction and processing, reconstruction and processing parameters can be dynamically adapted or adjusted in order to optimize image quality and quantitation to improve delivery of precise, personalized medical treatment.

PET image reconstruction and processing using lesion proxies

iRecon: intelligent image reconstruction system with anticipatory execution

A medical imaging system () comprises one or more displays (). A viewer device () generates an interactive user interface screen () on the display (), which viewer device () enables a user to simultaneously inspect selected image data of multiple patients or multiple images.

Multi-modality medical image viewing

Method and apparatus are disclosed for generating a scatter-corrected image from positron emission tomography (PET) or other radioemission imaging data () acquired of an object () in a field of view (). A background portion (B) of the PET imaging data is identified corresponding to a background region (B) of the FOV that is outside of the object. An outside-FOV activity estimate () for at least one spatial region outside of the FOV and into which the object extends is adjusted (e.g. iterative or several randomly selected estimates) to optimize a simulated scatter distribution for the combination of the PET imaging data and the outside FOV activity estimate to match the background portion (B) of the PET imaging data. The PET imaging data are reconstructed to generate a scatter-corrected PET image of the object in the FOV using the optimized simulated scatter distribution.

Solving outside-field of view scatter correction problem in positron emission tomography via digital experimentation

A method and system for removing an object support from imaging data such as CT imaging data are provided. The automatic or semi-automatic removal process comprises identifying and locating the top edge of the object support in sagittal imaging plane data, and then removing the object support from transverse or volumetric imaging data.

Removing an object support from imaging data

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Solon, OH, United States of America

Yang-Ming Zhu