Abstract: Reconstruction quality is assessed in medical imaging. An amount of local non-uniformity in a distribution of a statistical measure (e.g., MCDF) is determined. The amount indicates a level of reconstruction quality. A more easily understood amount rather than a rendering of MCDF and/or the amount being a function of local artifacts aids a radiologist in recognizing reconstruction quality and determining whether different reconstruction is warranted.

Abstract: A set of set of first modality data is received including at least one view comprising a plurality of gates. The set of first modality data is received from a first imaging modality of an imaging system. A set of second modality data is received from a second imaging modality of the imaging system. A motion corrected model of the set of first modality data is generated by forward projecting the set of first modality data including a motion estimate. An update factor for each of the plurality of views is generated by comparing at least one of the plurality of gates to the motion corrected model. The motion corrected model is updated by the update factor to generate a motion corrected image.

Abstract: 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.

Abstract: 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.

Abstract: Projection data are acquired for a portion of the body of a patient at multiple views using one or more detectors, the projection data including multiple two dimensional (2D) projections. A 3D image is initialized. For each view among the plurality of views, the 3D image is transformed using a view transformation corresponding to said view to generate an initial transformed image corresponding to said view, and multiple iterations of an MLEM process are performed based on at least the initial transformed image and the projection data. The MLEM process is initialized with the initial transformed image. The 3D image is updated based on an output of the MLEM process.

Abstract: Single photon emission computed tomography (SPECT) is performed with multiple emission energies. For quantitative or qualitative SPECT, the image formation process for emissions at different energy ranges is modeled (44, 46, 48, 50) separately. Different scatter, different attenuation, and/or different collimator-detector response models corresponding to different energy ranges are used in the reconstruction.

Abstract: A method for computer-assisted modeling of a technical system is disclosed. At multiple different operating points, the technical system is described by a first state vector with first state variable(s) and by a second state vector with second state variable(s). A neural network comprising a special form of a feed-forward network is used for the computer-assisted modeling of said system. The feed-forward network includes at least one bridging connector that connects a neural layer with an output layer, thereby bridging at least one hidden layer, which allows the training of networks with multiple hidden layers in a simple manner with known learning methods, e.g., the gradient descent method. The method may be used for modeling a gas turbine system, in which a neural network trained using the method may be used to estimate or predict nitrogen oxide or carbon monoxide emissions or parameters relating to combustion chamber vibrations.

Abstract: Systematic error is estimated as a function of location in quantitative functional imaging. The systematic error is estimated by perturbing the system matrix. The values of one or more variables of the system matrix are altered, and the reconstruction repeated. The differences in the resulting object or image data of the different reconstructions provide systematic error information as a function of location.

Abstract: Segmentation is provided in multi-modal reconstruction of functional information. Rather than using HU values for segmentation, the HU values are converted into linear attenuation coefficients, such as in a ?-map (Mu map). The conversion uses different functions for different ranges of the HU values. Linear attenuation coefficients are less likely subject to variation by patient, protocol, or scanner. The resulting segmentation may be more consistent across various clinical settings, providing for more accurate multi-modal reconstruction.

Abstract: First and second images obtained from first and second imaging modalities, respectively, are set as a target image and an object image, respectively. The object image is segmented into one or more anatomic segments. Each segment is associated with a respective anatomic class. At least one attribute is assigned to at least one of the anatomic segments based on the anatomic class corresponding to said at least one anatomic segment. A registration is performed with the object image and the target image, wherein the registration is constrained by the assigned attribute(s).

Abstract: A method of clinical collaboration between a clinical site and an analysis site includes receiving scan data from a scanner via a first reconstruction computer system at the clinical site, implementing a reconstruction procedure on the received scan data using a second reconstruction computer system at the analysis site and configured in accordance with a reconstruction configuration parameter, the analysis site being remote from the clinical site, and transmitting data indicative of the reconstruction configuration parameter to the first reconstruction computer system to configure the first reconstruction computer system in accordance with the reconstruction configuration parameter.

Abstract: 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.

Abstract: The point spread function of a multi-channel collimator is modeled in photon imaging. The geometric aperture used in the point spread function is expanded to account for penetration, scattering, and/or imperfections of the collimator. The aperture is broadened using a weight that is a function of the distance of the source from the collimator. Rather than scaling the point spread function itself, the geometric aperture used in the point spread function is scaled to an effective aperture. The distance and geometric constraints may be used to determine the geometric aperture, but an additional broadening occurs as a function of the distance to account for non-ideal photon paths of travel. This additional broadening may improve the fidelity with respect to measured data relative to purely geometric or Gaussian models.

Abstract: Systematic error is estimated as a function of location in quantitative functional imaging. The systematic error is estimated by perturbing the system matrix. The values of one or more variables of the system matrix are altered, and the reconstruction repeated. The differences in the resulting object or image data of the different reconstructions provide systematic error information as a function of location.

Abstract: First and second images obtained from first and second imaging modalities, respectively, are set as a target image and an object image, respectively. The object image is segmented into one or more anatomic segments. Each segment is associated with a respective anatomic class. At least one attribute is assigned to at least one of the anatomic segments based on the anatomic class corresponding to said at least one anatomic segment. A registration is performed with the object image and the target image, wherein the registration is constrained by the assigned attribute(s).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.