Koninklijke Philips Corporation N.v.

The University of Chicago

Philips Medical Systems (cleveland), Inc.

Chi-Hua Tung

Bin Zhang

Zhiqiang Hu

Yang-Ming Zhu

John Patrick Collins

Daniel Gagnon

Chuanyong Bai

Jinghan Ye

Andriy Andreyev

Xiyun Song

Shekhar Dwivedi

Amy Perkins

Joerg Bredno

Lingxiong Shao

Eberhard Sebastian Hansis

Sven Prevrhal

Xiaochuan Pan

Douglas B McKnight

Patrick Olivier

Zheng Zhang

Dane Pittock

Navdeep Ojha

Nathan Serafino

Changhong Dai

Varun Verma

Debashish Pal

Tianrui Guo

Jeffrey Kaste

A device () for performing an amyloid assessment includes a radiation detector assembly () including at least one radiation detector (). At least one electronic processor () is programmed to: detect radiation counts over a data acquisition time interval using the radiation detector assembly; compute at least one current count metric from the detected radiation counts; store the at least one current count metric associated with a current test date in a non-transitory storage medium (); and determine an amyloid metric based on a comparison of the at least one current count metric with a count metric stored in the non-transitory storage medium associated with an earlier test date.

Method and apparatus for amyloid screening

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

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)

In an emission imaging method, emission imaging data are acquired for a subject using an emission imaging scanner () including radiation detectors (). The emission imaging data are reconstructed to generate a reconstructed image by executing a constrained optimization program including a measure of data fidelity between the acquired emission imaging data an a reconstruct-image transformed by a data model of the imaging scanner to emission imaging data. During the reconstructing, each iteration of the constrained optimization program is constrained by an image variability constraint. The reconstructed image is displayed the reconstructed image on a display device. The emission imaging may be positron emission tomography (PET) imaging data, optionally acquired using a sparse detector array. The image variability constraint may be a constraint that an image total variation (image TV) of a latent image defined using a Gaussian blurring matrix be less than a maximum value.

Optimization-based reconstruction with an image-total-variation constraint in PET

An imaging data set () comprising detected counts along lines of response (LORs) is reconstructed () to generate a full-volume image at a standard resolution. A region selection graphical user interface (GUI) () is provided via which a user-chosen region of interest (ROI) is defined in the full-volume image, and this is automatically adjusted by identifying an anatomical feature corresponding to the user-chosen ROI and adjusting the user-chosen ROI to improve alignment with that feature. A sub-set () of the counts of the imaging data set is selected () for reconstructing the ROI, and only the selected sub-set is reconstructed () to generate a ROI image () representing the ROI at a higher resolution than the standard resolution. A fraction of the sub-set of counts may be reconstructed using different reconstruction algorithms () to generate corresponding sample ROI images, and a reconstruction algorithm selection graphical user interface () employs these sample ROI images.

Interactive targeted ultrafast reconstruction in emission and transmission tomography

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

A nuclear scanner includes an annular support structure () which supports a plurality of radiation detector units (), each detector unit including crystals (), tiles () containing an array of crystals, or modules () of tiles. The detector units define annular ranks of crystals, and the annular ranks of crystals define spaces between the ranks. In another embodiment, the crystals define axial spaces between crystals. Separate rings of crystals have axial spaces that are staggered such that no area of the imaging region is missed. The spaces between the detector units may be adjusted to form uniform or non-uniform spacing. Moving the patient through the annular support structure compensates for reduced sampling under the spaces between ranks.

Pet system with crystal or detector unit spacing

A database () stores image recipient reconstruction profiles each comprising image reconstruction parameter values. An image reconstruction module () is configured to reconstruct medical imaging data to generate a reconstructed image. An image reconstruction setup module () is configured to retrieve an image recipient reconstruction profile from the database () for an intended image recipient associated with a set of medical imaging data and to invoke the image reconstruction module () to reconstruct the set of medical imaging data using image reconstruction parameter values of the retrieved image recipient reconstruction profile to generate a reconstructed image for the intended image recipient. A feedback acquisition module () is configured to acquire feedback from the intended image recipient pertaining to the reconstructed image for the intended image recipient. A profile updating module () is configured to update the image recipient reconstruction profile of the intended image recipient based on the acquired feedback.

Medical imaging reconstruction optimized for recipient

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

iRecon: intelligent image reconstruction system with anticipatory execution

A medical imaging system includes a data store () of reconstruction procedures, a selector (), a reconstructor (), a fuser (), and a display (). The data store () of reconstruction procedures identifies a plurality of reconstruction procedures. The selector () selects at least two reconstruction procedures from the data store of reconstruction procedures based on a received input, each reconstruction procedure optimized for one or more image characteristics. The reconstructor () concurrently performs the selected at least two reconstruction procedures, each reconstruction procedure generates at least one image () from the at least one data store of imaging data (). The fuser () fuses the at least two generated medical images to create a medical diagnostic image which includes characteristics from each generated image (). The display () displays the medical diagnostic image.

Clinically driven image fusion

A positron emission tomography (PET) system includes a memory (), a subject support (), a categorizing unit (), and a reconstruction unit (). The memory () continuously records detected coincident event pairs detected by PET detectors (). The subject support () supports a subject and moves in a continuous movement through a field of view () of the PET detectors (). The categorizing unit () categorizes the recorded coincident pairs into each of a plurality of spatially defined virtual frame (). The reconstruction unit () reconstructs the categorized coincident pairs of each virtual frame into a frame image and combines the frame images into a common elongated image.

Virtual frames for distributed list-mode time-of-flight reconstruction with continuous bed movement

A hybrid imaging system includes a first imaging system configured to acquire anatomical data of a first field of view of an anatomical structure. A second imaging system configured to acquire functional data of the anatomical structure, the second imaging system acquiring functional data in a two-pass list-mode acquisition scheme. A reconstruction processor configured to reconstruct the functional data based on attenuation data into an attenuation corrected image and reconstruct the anatomical data into one or more high resolution images of one or more regions of interest.

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Aurora, OH, United States of America

Chi-Hua Tung