Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

The University of Sydney

Katholieke Universiteit Leuven, Ku Leuven R&d

Vrije Universiteit Brussel

Western Sydney Local Health District

Katholieke Universiteit Leuven, Leuven Research & Development

Johan Nuyts

Vladimir Y Panin

Christian J Michel

Anna Jerebko

Frank Kehren

Michel Defrise

Girish Bal

Tao Sun

Jung-Ha Kim

Koen Michielsen

Roger Fulton

A method for the reduction of motion artifacts in CT imaging, the method including the steps of: (a) reconstructing from the raw CT data an initial estimate of the object of interest; (b) estimating the pose of the object at each projection angle; (c) undertaking a motion-corrected reconstruction from the measured projections, accounting for the pose changes estimated in (b) by employing a modified source/detector orbit that accounts for the object motion; (d) iterating steps (b)-(c) until a predetermined convergence criterion is met; and (e) making a final reconstruction of the required image size using the pose estimates obtained in the previous steps.

Method and apparatus for motion correction in CT imaging

Reconstruction in positron emission tomography is performed with partially known attenuation. A PET-CT scanner is used to generate a PET image with time of flight emission information. To limit x-ray dose while providing increased sensitivity at the ends of the CT volume in the PET image, attenuation coefficients for oblique LORs passing outside the CT volume are determined from the time of flight emission information. The attenuation coefficients for LORs within the CT volume are derived from the CT data. An objective function may be maximized for the emission distribution without reconstructing the attenuation distribution.

Reconstruction with partially known attenuation information in time of flight positron emission tomography

The DCC (Data Consistency Condition) algorithm is used in combination with MLAA (Maximum Likelihood reconstruction of Attenuation and Activity) to generate extended attenuation correction maps for nuclear medicine imaging studies. MLAA and DCC are complementary algorithms that can be used to determine the accuracy of the mu-map based on PET data. MLAA helps to estimate the mu-values based on the biodistribution of the tracer while DCC checks if the consistency conditions are met for a given mu-map. These methods are combined to get a better estimation of the mu-values. In gated MR/PET cardiac studies, the PET data is framed into multiple gates and a series of MR based mu-maps corresponding to each gate is generated. The PET data from all gates is combined. Once the extended mu-map is generated the central region is replaced with the MR based mu-map corresponding to that particular gate. On the other hand, in dynamic PET studies the uptake in the patient's arms reaches a steady state only after the tracer distributes throughout the body. Hence, for dynamic scans, the projection data of all frames is summed and used to generate the MLAA based extended mu-map for all frames.

Generating attenuation correction maps for combined modality imaging studies and improving generated attenuation correction maps using MLAA and DCC algorithms

The present invention is a method of generating a best estimate of an image attenuation map derived from a truncated image attenuation map and PET emissions data for the object being imaged by a morphological imaging modality. The method involves a plurality of steps beginning with the recordation and processing of PET emissions data. Next, the morphological imaging modality records image data which is processed to determine an attenuation map. The attenuation map, for image modalities such as CT and MR scanning systems integrated with PET, is truncated, resulting in a truncated attenuation map image. Pixels for which attenuation data needs to be determined are identified and attenuation coefficients for these pixels are estimated and combined with the truncated attenuation map to generate a full initial attenuation map for the image, which is iteratively processed together with the PET emission data until the improvement change in the emission image reaches a defined threshold improvement level.

Completion of truncated attenuation maps using maximum likelihood estimation of attenuation and activity (MLAA)

A method for digitally reconstructing a 3-dimensional tomosynthesis image by iterative reconstruction, a reconstructor, and a computer program product method are capable of plane-by-plane iterative reconstruction for digital breast tomosynthesis. The reconstruction process is based on a grouped coordinate ascent algorithm where the volume is split into a plurality of patches, wherein all patches are parallel to a surface of a detector. Splitting the volume allows implementing a modified model for image acquisition where the physical movement of the x-ray source is taken into account because each of the patches is updated separately and sequentially. In addition the splitting allows an efficient implementation on a graphical processing unit by reducing memory requirements.

Plane-by-plane iterative reconstruction for digital breast tomosynthesis

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Heverlee, Belgium

Johan Nuyts