Siemens Medical Solutions Usa, Inc.

University of California

Imagerecon LLC

Pixon, LLC

Amos Yahil

Alexander Hans Vija

A Hans Vija

Richard C Puetter

Jun Ma

Xing Rong

Robert Piña

A system to generate images based on imaging data of a portion of a body and physiological event data associated with a physiological process of the body. The system is to identify a plurality of physiological cycles based on the physiological event data, determine a duration of each of the plurality of physiological cycles, determine a representative duration based on the durations of each of the plurality of physiological cycles, identify a first plurality of the plurality of physiological cycles based on a difference between the durations of the first plurality of physiological cycles and the representative duration, identify a second plurality of the plurality of physiological cycles different from the first plurality of the plurality of physiological cycles, determine a predetermined number of portions of each of the second plurality of the plurality of physiological cycles, accumulate imaging data acquired during respective portions of each of the second plurality of the plurality of physiological cycles to determine a set of accumulated imaging data for each of the predetermined number of portions, and generate a plurality of images, each of the plurality of images being generated based on a respective one of the sets of accumulated imaging data.

Gating of medical imaging data

In quantitative SPECT with multiple photopeaks, the combination for the multiple photopeaks is performed within or as part of reconstruction rather than post-reconstruction. Reconstruction is performed iteratively, so the combination for the multiple photopeaks is performed within the iteration loop of the reconstruction, such as combining back projected feedback of the different photopeaks for updating the volume.

Reconstruction with multiple photopeaks in quantitative single photon emission computed tomography

Multiple sets of projection data are provided for respective ones of a first set of volumes. An initial image is generated for a second volume larger than each volume in the first set of volumes. Based on at least the initial image and the sets of projection data, an image of the second volume is reconstructed using multiple iterations of a single iterative reconstruction process.

Image reconstruction for a volume based on projection data sets

A method and system are provided for constructing a model of a target object in a computer processor by receiving an input signal from a source, the input signal containing data describing the target object and a plurality of parameters, the input signal having a noise portion; selecting a group of initial parameters, estimating a nonparametric probability distribution function (pdf) comprising a linear combination of a set of square-integrable basis functions, computing a quadratic likelihood functional (QLF), evaluating a fit of the initial parameters to the data based on the QLF, iteratively optimizing the QLF by selecting a new group of parameters and evaluating the fit of the new group of parameters into a predetermined condition is achieved. Once an acceptable fit is achieved, an output of a model of the target object constructed using optimized parameters can be displayed.

Method and system for statistical modeling of data using a quadratic likelihood functional

A point response function (PRF) is estimated in SPECT. A 3D PRF is based on measured emissions from a point source at different distances from the detector. Rather than sampling every location in space, the 2D PRFs at a sub-set of distances are sampled. The 3D PRF is then modeled with a representative 2D PRF and a scale as a function of distance. For a given distance from the detector, the 2D PRF to be applied is formed by scaling the representative 2D PRF using the scale for that distance.

Self-similar, transversely shift-invariant point response function in single photon emission computed tomography

Computer-implemented methods of reconstructing an image object for a measured object in object space from image data in data space cause a computer system to execute instructions for providing zonal information separating the object space into at least two zones, providing at least two zonal image objects, each zonal image object being associated with one of the at least two zones, performing a zonal smoothing operation on at least one of the zonal image objects, thereby generating at least one smoothed zonal image object, reconstructing the image object on the basis of the at least one smoothed zonal image object, and outputting the image object.

Multimodal image reconstruction with zonal smoothing

Emission tomography is registered with computed tomography or other modality in reconstruction. The anatomical information is used in the emission tomography reconstruction. In addition to an initial registration to use the anatomical information in the reconstruction, the registration is refined one or more times during other iterations refining the reconstruction of the emission volume. The registration is performed as part of the reconstruction. This multi-modal reconstruction may result in an emission tomography volume better aligned with the anatomical information.

Registration of emission tomography and computed tomography

Methods and apparatuses for quality control in image space for processing with an input data set are disclosed. A method includes providing an image object, including multiple voxels, and an input data set. A data model is determined from the image object. A cumulative distribution function (CDF) for the input data set is determined from the data model and the input data set based on a plurality of projections. The CDF is transformed to an image cumulative distribution function (ICDF) in object space. The ICDF represents a number of standard deviations associated with each voxel of the image object. The output of the ICDF is displayed. A nuclear imaging system and a computer readable storage medium are also disclosed. Techniques disclosed herein facilitate efficient quality control for tomographic image reconstruction.

Method and apparatus for using image cumulative distribution function for tomographic reconstruction quality control

A method for iterative reconstruction of an object containing noise using the pixon method determines the pixon map from a variable that is used to update the object in the iteration. The updating variable is based on an optimized merit function, and smoothes the updating variable during each iteration. The updating variable depends on the reconstruction method, but is typically a gradient of a merit function or a multiplicative update factor. The updated object can optionally also be further smoothed at the end of the iteration, using the pixon map determined during the iteration.

Method to determine a pixon map in iterative image reconstruction and spectral analysis

Computer-implemented methods of reconstructing an image object for a measured object in object space from image data in data space include causing a computer to execute instructions for providing zonal information separating the object space into at least two zones, providing at least two zonal image objects, each zonal image object being associated with one of the at least two zones, reconstructing the image object using a data model derived from forward projecting the zonal image objects into data space, wherein the contribution of each zonal image object to the data model is weighted according to a zonal scaling factor, and outputting the image object.

Multimodal image reconstruction

An iterative reconstruction method to reconstruct an object includes determining, in a series of iteration steps, updated objects, wherein each iteration step includes determining a data model from an input object, and determining a stop-criterion of the data model on the basis of a chi-square-gamma statistic. The method further includes determining that the stop-criterion of the data model has transitioned from being outside the limitation of a preset threshold value to being inside the limitation, ending the iterations, and selecting one of the updated objects to be the reconstructed object.

Controlling the number of iterations in image reconstruction

A curvature-preserving filter having a null covariance matrix is applied to an input image to produce a denoised output image for output to a graphic display device or to a machine analysis tool. In one embodiment, the input image is a small kernel consisting of a limited number of pixels and the filter is applied to the input image by direct summation. In another embodiment, a digital image is input into an image processor that executes a Fourier transform to produce a Fourier-transformed signal. The curvature-preserving filter is applied to the Fourier-transformed signal in Fourier space to produce a denoised signal, then the denoised signal is transformed by an inverse Fourier transform to generate a denoised output image In an alternate embodiment, the filter further produces a deblurred signal by including an inverse point-response function.

Filters for denoising digital images with minimized blur and shape distortion and controlled deblurring

A computer-implemented method of presenting an image of an object includes causing a computer to execute instructions for providing a signal distribution of values N, generating a transformed distribution by calculating, for each value N, a transformed value X=&#x221a;{square root over (N+&#x215c;)}, and outputting the transformed distribution.

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Stony Brook, NY, United States of America

Amos Yahil