Siemens Medical Solutions USA, Inc.

Paul Schleyer

Inki Hong

Dieter Ritter

Ziad Burbar

Deepak Bharkhada

Harold E Rothfuss

Mohammadreza Teimoorisichani

Vladimir Y Panin

Matthias J Schmand

James Luke Corbeil

Matthew Mitchell

Judson P Jones

Sebastian Fuerst

Emily Anaya

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

PET imaging () accounts for attenuation by MR hardware (). A camera () captures the MR hardware () as positioned on or by the patient (). For example, MR local coils to be or as positioned between the emission sources in the patient () and the PET detector are optically imaged (). Image processing is used to determine () the position of the MR hardware (). The attenuation of the MR hardware () is accounted for in attenuation correction for PET imaging () based on the determined position.

Magnetic resonance hardware-contributed attenuation in positron emission tomography

A framework for gantry alignment of a multimodality medical scanner. First image data of a non-radioactive structure is acquired by using intrinsic radiation emitted by scintillator crystals of detectors in a first gantry of the multimodality medical scanner. Second image data of the non-radioactive structure is acquired using a second gantry for another modality of the multimodality medical scanner. Image reconstruction may be performed based on the first and second image data of the non-radioactive structure to generate first and second reconstructed image volumes. A gantry alignment transformation that aligns the first and second reconstructed image volumes may then be determined.

Gantry alignment of a medical scanner

A system and method include localization of a first frame of positron emission tomography data acquired by an imaging device to a first frame of Cartesian data, generation of a first Cartesian image volume based on the first frame of Cartesian data, display of the first Cartesian image volume, localization of a second frame of positron emission tomography data acquired by the imaging device to a second frame of Cartesian data, generation of a second Cartesian image volume based on the second frame of Cartesian data, and display of the combined Cartesian image volume.

Live display of PET image data

A system and method include association of imaging event data to one of a plurality of bins based on a time associated with the imaging event data, determination that the time periods of a first bin and the time periods of a second bin are adjacent-in-time, determination of whether a spatial characteristic of the imaging event data of the first bin is within a predetermined threshold of the spatial characteristic of the imaging event data of the second bin, and, based on the determination, reconstruction of one or more images based on the imaging event data of the first bin and the second bin.

Time and position based binning of image data

Disclosed herein are novel techniques that address blurriness in medical images resulting from motion of a rigid body, such as a patient, relative to the medical scanning equipment by using a motion-correction algorithm for 3D medical images using to two-dimensional projections.

Summing tree structural motion-correction algorithm for medical images using 3D to 2D projection

Systems and methods include acquisition of magnetic resonance data of a subject disposed in a first position, acquisition of positron emission tomography data of imaging hardware and of the subject disposed substantially in the first position, generation of a subject attenuation correction map of the subject based on the magnetic resonance data, determination of an imaging hardware attenuation correction map associated with the imaging hardware, determination of a target location and orientation of the imaging hardware attenuation correction map with respect to the positron emission tomography data and based on the positron emission tomography data and on the subject attenuation correction map, and application of attenuation correction to the positron emission tomography data based on the imaging hardware attenuation correction map in the target location and orientation and the subject attenuation correction map to generate attenuation-corrected positron emission tomography data.

Pet attenuation map alignment

Embodiments provide a computer-implemented method of deriving a periodic motion signal from imaging data for continuous bed motion acquisition, including: acquiring a time series of three dimensional image volumes; estimating a first motion signal through a measurement of distribution of each three dimensional image volume; dividing the time-series of three dimensional image volumes into a plurality of axial sections overlapping each other by a predetermined amount; performing a spectral analysis on each axial section to locate a plurality of three dimensional image volumes which are subject to a periodic motion; performing a phase optimization on each axial section to obtain a three dimensional mask; estimating a second motion signal through the three dimensional mask and the time-series of three dimensional image volumes; and estimating a final motion signal based on the first motion signal and the second motion signal.

Motion signal derived from imaging data

A framework for motion correction in medical image data. In accordance with one aspect, one or more anatomical ranges where motion is expected are identified in a localizer image of a subject. Image reconstruction with motion correction may be performed based on medical image data within the one or more anatomical ranges to generate motion corrected image data. The motion corrected image data may then be combined with non-motion corrected image data to generate final image data.

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Knoxville, TN, United States of America

Paul Schleyer