Siemens Medical Solutions USA, Inc.

Vrije Universiteit Brussel

Katholieke Universiteit Leuven, Leuven Research & Development

Vladimir Y Panin

Michael E Casey

Mehmet Aykac

Frank Kehren

Deepak Bharkhada

Jicun Hu

Christian J Michel

Bernard Bendriem

Vijay Shah

William Whiteley

Michel Defrise

Maurizio Conti

Harold E Rothfuss

Mu Chen

Johan Nuyts

William Curtis Howe

Harshali Bal

Anne M Smith

Shikui Yan

Brian Kelly

Matthew Baker

Matthias J Schmand

James Luke Corbeil

Charles C Watson

Inki Hong

Andrew Philip Moor

Paul Schleyer

Mark W Lenox

Girish Bal

Mohammadreza Teimoorisichani

Charles H Hayden, Jr

Chuanyu Zhou

Timothy G Gremillion

Matthew Restivo

Hasan Sari

Various systems and computer-implemented methods for background radiation based attenuation correction are disclosed. A first set of nuclear scan data including first scan data associated with a first imaging modality having a long-axial field of view and first background radiation data is received and a first background radiation attenuation map is generated by applying a trained machine-learning model to the first background radiation data. A first set of attenuation corrected scan data is generated by performing attenuation correction of the first scan data based only on the first background radiation attenuation map and a first image is reconstructed from the first set of attenuation corrected scan data. The disclosed background radiation based attenuation correction may be used for longer duration scans, repeat scans, and/or low-dose clinical applications, such as pediatric applications, theranostics, and/or other suitable applications.

System method estimate attenuation correction for repeated scans and low dose scans in long axial FOV PET scanners

A PET system for a PET/MRI machine is disclosed. The PET system includes a PET detector assembly arranged to form a single gap aligned with the high-density support structure assembly and the shielded cable assembly that run along the patient bed in the PET/MRI machine. The PET detector arrangement maximizes the allowable diameter of the PET system within the MR magnet and ensures that the high-density material does not interfere with image acquisition. Further, various image reconstruction techniques compatible with the PET detector arrangement are described.

Arrangement of pet detectors for combined PET/MR systems

Various systems and computer-implemented methods for Radio Frequency (RF) coil attenuation correction are disclosed. PET time-of-flight (TOF) data generated by a PET imaging modality collocated with an MR imaging modality is received. RF coil attenuation data is extracted from the PET TOF data and an initial RF coil attenuation map is generated. A trained model configured to improve a signal to noise ratio of the initial RF coil attenuation map is applied to generate a final RF coil attenuation map. Attenuation correction of the PET TOF data is performed based on the final RF coil attenuation map. An image is reconstructed from attenuation corrected PET TOF data.

Collocated PET and MRI attenuation map estimation for RF coils attenuation correction via machine learning

Systems and methods to estimated mean randoms include acquisition of list mode data describing true coincidences and delay coincidences detected by a positron emission tomography scanner during a scan of an object, determination, for each crystal of the positron emission tomography scanner and for each of a plurality of time periods of the scan, of delay coincidences including the crystal based on the list mode data, determination, each crystal, of determine a singles rate associated with each time period based on the delay coincidences determined for the crystal over the time period, determination, for each time period, of determine estimated mean randoms for each of a plurality of pairs of the crystals based on the singles rate associated with the time period for each crystal of the crystal pair, and reconstruction of an image of the object based on the estimated mean randoms for each time period and the detected true coincidences.

Mean randoms estimation from list mode data

An improved method for time alignment (TA) procedure and crystal efficiency (CE) normalization estimation procedure for a PET scanner system is disclosed. In the TA procedure modeled time-of-flight (TOF) data are compared against the measured TOF data from an axially short cylinder phantom in order to find individual detector's time offsets (TOs). Then the TOs are estimated simultaneously by matching the TOF center of mass between the modeled and measured TOF data. In the CE estimation, TOF reconstruction of CBM data on the axially short cylinder phantom is performed. Alternating between TOF image reconstruction and CE updates eventually lead to the correct estimation of activity and CE component.

Continuous bed motion acquisition with axially short phantom for PET imaging system setup and quality control

Systems and methods to partially-gate PET data include acquisition of first data describing a plurality of coincidences detected during a scan of an object, each of the plurality of coincidences associated with a coincidence time and a line of response, acquisition of a motion signal associated with motion of the object during the scan, determination of lines of response which are associated with a region of the object, determination of time periods of region motion based on the motion signal, modification of the first data to remove coincidences which are associated with the determined lines of response and which are associated with a coincidence time during a time period of region motion, reconstruction of an image of the object based on the modified first data, and display of the image.

Partially-gated PET imaging

Positron emission tomography (PET) with partially known attenuation accounts for the missing attenuation. Since a computed tomography (CT) scan may provide attenuation for less than all the locations used in PET reconstruction, artificial intelligence corrects for the missing attenuation. For example, the unknown attenuation or attenuation correction factors are estimated by the artificial intelligence. The known and estimated attenuations or correction factors are used in the PET reconstruction, providing more uniform PET sensitivity and better accounting for scatter. As another example, the artificial intelligence alters intensity of the activity in some locations to account for reconstruction with missing attenuation information, correcting for sensitivity variation and/or lack of scatter information for some locations.

Estimation of partially missing attenuation in time-of-flight positron emission tomography

Systems and methods to estimate 3D TOF scatter include acquisition of 3D TOF data, determination of 2D TOF data from the first TOF data, determination of first estimated scatter based on the second TOF data, reconstruction of a first estimated image based on the first estimated scatter and the second TOF data, determination of attenuated unscattered true coincidences based on the first estimated image, determination of second estimated scatter based on the first TOF data and the attenuated unscattered true coincidences, and reconstruction of an image of the object based on the first TOF data and the second estimated scatter.

3D scatter distribution estimation

A computer-implemented method for generating a motion corrected image is provided. The method includes receiving listmode data collected by an imaging system; producing two or more histo-image frames or two or more histo-projection frames based on the listmode data; providing the two or more histo-image frames or two or more histo-projection frames to an Artificial Intelligence (AI) system; receiving two or more AI reconstructed images from the AI system based on the two or more histo-image frames or the two or more histo-projection frames; and generating a motion estimation in reconstructed images by using a motion free AI reconstructed image frame as a reference frame.

Systems and methods for motion estimation in PET imaging using AI image reconstructions

An image reconstruction system generates a motion estimation using images that have been reconstructed using AI processing. The system receives listmode data collected by an imaging system and produces two or more histo-images based on the listmode data. The system provides the two or more histo-images to an AI system and receives two or more AI reconstructed images back from the AI system based on the two or more histo-images. The system generates a motion estimation based on the two or more AI reconstructed images.

A system and method include execution of a first scan to acquire a first PET dataset, back-projection of the first PET dataset to generate a first histo-image, input of the first histo-image to a trained neural network, and reception of a first output image from the trained neural network.

PET image reconstruction using TOF data and neural network

A method of conducting a multi-pass dynamic positron emission tomography (PET) scans using continuous bed motion (CBM) mode is disclosed where the method involves acquiring PET sinogram data using CBM mode as the patient bed is moving in a first direction; acquiring PET sonogram data using CBM mode as the patient bed is moving in a second direction that is opposite from the first direction; and reconstructing 3-D PET image from the acquired PET sonogram data.

System and method for whole body continuous bed motion PET scanning with bi-directional data acquisition modes

Systems and methods to determine random coincidence rates include determination of a detector rate for each of a plurality of detectors of a positron emission tomography scanner based on a frame of positron emission tomography data, determination of a sensitivity for each detector crystal of the plurality of detectors, based on the detector rate of the detector including the detector crystal, determination of a singles rate for each detector crystal based on the detector rate of the detector including the detector crystal and the determined sensitivity of the detector crystal, estimation of a mean random coincidence rate for each of a plurality of pairs of the detector crystals based on the singles rate of each detector crystal of each of the plurality of pairs of the detector crystals, correction of the acquired frame of positron emission tomography data based on the estimated mean random coincidence rates, and reconstruction of a positron emission tomography image based on the corrected frame of positron emission tomography data.

Determination of crystal singles rates to estimate mean random coincidence rate

A method for self-time alignment procedure for a PET scanner system is disclosed. Modeled time-of-flight (TOF) data are compared against the measured TOF data in order to find individual detector's time offsets (TOs). Then the TOs are estimated simultaneously by matching the TOF center of mass between the modeled and measured TOF data.

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Knoxville, TN, United States of America

Vladimir Y Panin