Abstract: A PET imaging system includes BGO crystals that generate Cherenkov photons in response to excitation by photons from positron annihilation events and photosensors configured to detect the Cherenkov photons and generate signals indicative thereof, an LOR determiner configured to identify, based on the signals, coincident photon pairs along each LOR, a TOF determiner configured to determine a timing resolution for each of the coincident photon pair along each LOR, an accuracy determiner configured to bin the LORs, based on the timing resolutions, into at least higher accuracy TOF LORs and lower accuracy TOF LORs, a reconstructor configured to reconstruct the higher accuracy TOF LORs using models and generate volumetric image data, and a model adjuster configured to adjust at least one model of the models based on the volumetric image data. The reconstructor is configured to reconstruct at least a subset of the LORs using the updated models.

Abstract: Various methods and systems are provided for automatic quality control of image reconstruction. In one example, a method comprises obtaining medical image data, reconstructing the medical image data with a baseline reconstruction algorithm to generate one or more baseline reconstruction images and an enhanced reconstruction algorithm to generate one or more enhanced reconstruction images, detecting and localizing a set of features of interest within the one or more baseline reconstruction images, determining image values for each of the features of interest, comparing image values of the one or more baseline reconstruction images to corresponding image values of the one or more enhanced reconstruction images to determine one or more statistical characteristics, comparing the one or more statistical characteristics to predetermined criteria to determine deviations, and automatically modifying one or more parameters of the enhanced reconstruction algorithm based on the deviations.

Abstract: A system includes a spatial mismatch correction module configured to receive functional emission data, anatomical image data, and functional image data reconstructed based on the functional emission data and attenuation corrected based on the anatomical image data. The system further includes a data set provider configured to provide a first data set and a second data set, which are spatially mismatched. The system further includes a voxel of interest identifier configured to identify voxels or regions of reconstruction inconsistency due to a spatial mismatch between true attenuation values and attenuation values derived from the anatomical image data based on relations between the first and second data sets.

Abstract: Various methods and systems are provided for automatic quality control of image reconstruction. In one example, a method comprises obtaining medical image data, reconstructing the medical image data with a baseline reconstruction algorithm to generate one or more baseline reconstruction images and an enhanced reconstruction algorithm to generate one or more enhanced reconstruction images, detecting and localizing a set of features of interest within the one or more baseline reconstruction images, determining image values for each of the features of interest, comparing image values of the one or more baseline reconstruction images to corresponding image values of the one or more enhanced reconstruction images to determine one or more statistical characteristics, comparing the one or more statistical characteristics to predetermined criteria to determine deviations, and automatically modifying one or more parameters of the enhanced reconstruction algorithm based on the deviations.

Abstract: A computing system includes computer readable medium and a processor. The computer readable medium includes a computer readable medium including a positron emission tomography (PET)-to-single photon emission tomography (SPECT) module with instructions for generating simulated SPECT image data based on first PET image data. The PET image data is acquired prior to a treatment course of a treatment regimen with a SPECT-based treatment/imaging radiopharmaceutical. The processor is configured to obtain the first PET image data, system parameters of a SPECT-CT imaging system utilized to generate corresponding SPECT image data for the treatment regimen and scan parameters utilized by the SPECT-CT imaging system to acquire the corresponding SPECT image data. The processor is further configured to execute the instructions to generate the simulated SPECT image data based on the PET image data and the system parameters and the scan parameters.

Abstract: Methods and systems are herein provided for image registration of PET and CT image volumes. In one example, a method comprises acquiring a first positron emission tomography (PET) image volume of a patient injected with a first radiotracer, wherein the first PET image volume is acquired with a PET imaging system; acquiring a second PET image volume of the patient with the PET imaging system during transit of a bolus of a second radiotracer; acquiring a computed tomography (CT) angiography image volume of the patient; registering the first PET image volume to the second PET image volume to generate a first deformation field; registering the second PET image volume to the CT angiography image volume to generate a second deformation field; generating a final registered image volume based on a combination of the first and second deformation fields; and outputting the final registered image volume for display.

Abstract: A method includes obtaining emission-tomography functional image data and a corresponding reconstructed anatomical image volume including at least one organ having natural motion; pre-determining a dedicated model for spatial mismatch correction of the at least one organ; performing initial image reconstruction of the emission-tomography functional image data to generate a reconstructed emission-tomography functional image volume utilizing attenuation correction based on the corresponding reconstructed anatomical image volume; and identifying relevant anatomical regions, within both image volumes, where functional image quality may be affected by the natural motion of the at least one organ.

Abstract: A method includes obtaining functional and anatomical image data sets from a subject acquired at different dates. The method includes receiving a volumetric coordinate of interest in a specified functional and anatomical image data set. The method includes generating a 3D feature matching map for at least one functional feature layer type and for at least one anatomical feature layer type for each non-specified functional and anatomical image data set relative to the specified functional and anatomical image data set utilizing the volumetric coordinate of interest. The method includes generating a best matching coordinate and a corresponding confidence metric value for each 3D feature matching map.

Abstract: A computer-implemented method for determining a flow rate for a given vessel includes obtaining, via a processor, dynamic three-dimensional (3D) images of a subject utilizing nuclear medicine imaging. The method also includes obtaining, via the processor, injection parameters for a radiotracer bolus injected into the subject via an automated injector. The method further includes generating, via the processor, time activity curves (TACs) for the radiotracer bolus from the 3D images. The method even further includes estimating, via the processor, the flow rate for the given vessel based on a morphology of the one or more TACs and the injection parameters.

Abstract: Various methods and systems are provided for selective recombination of reconstructed nuclear medicine (NM) image regions. The method may include acquiring and sorting imaging scan data that includes data elements having different accuracy properties into two or more different groups using an anticipated accuracy metric, reconstructing a first initial image volume from a first group having a highest anticipated accuracy metric of the two or more different groups, reconstructing a second initial image volume from a combined data group comprising data of the first group and a second group of the two or more different groups, wherein the second group has a lower anticipated accuracy metric compared to the first group, and generating an optimized image volume using corresponding image values from the first initial image volume, the second initial image volume, and an output of a high spatial frequencies detection (HSFD) operator applied to the first initial image volume.

Abstract: A method and workstation for combining multiple functional imaging datasets into a joint-visualization image. In one aspect, a method of functional imaging includes accessing a plurality of functional imaging datasets acquired from a volume-of-interest, wherein each of the plurality of functional imaging datasets is a different one of a plurality of functional imaging data types. The method includes registering the plurality of functional imaging datasets and determining a visualization priority for each of a plurality of pixels in a joint-visualization image based on a logical comparison of corresponding information in each of the plurality of functional imaging datasets. The method includes generating the joint-visualization image based on the visualization priority and at least a portion of each of the plurality of functional imaging datasets, wherein each of a plurality of pixels in the joint-visualization image represents only a single one of the plurality of functional imaging data types.

Abstract: Techniques are provided for improving image data quality, such as in functional imaging follow-up studies, using reconstruction, post-processing, and/or deep-learning enhancement approaches in a way that automatically improves analysis fidelity, such as lesion tracking fidelity. The disclosed approaches may be useful in improving the performance of automatic analysis methods as well as in facilitating reviews performed by clinician.

Abstract: A computer-implemented method for determining a flow rate for a given vessel includes obtaining, via a processor, dynamic three-dimensional (3D) images of a subject utilizing nuclear medicine imaging. The method also includes obtaining, via the processor, injection parameters for a radiotracer bolus injected into the subject via an automated injector. The method further includes generating, via the processor, time activity curves (TACs) for the radiotracer bolus from the 3D images. The method even further includes estimating, via the processor, the flow rate for the given vessel based on a morphology of the one or more TACs and the injection parameters.

Abstract: Various methods and systems are provided for automatic quality control of image reconstruction. In one example, a method comprises obtaining medical image data, reconstructing the medical image data with a baseline reconstruction algorithm to generate one or more baseline reconstruction images and an enhanced reconstruction algorithm to generate one or more enhanced reconstruction images, detecting and localizing a set of features of interest within the one or more baseline reconstruction images, determining image values for each of the features of interest, comparing image values of the one or more baseline reconstruction images to corresponding image values of the one or more enhanced reconstruction images to determine one or more statistical characteristics, comparing the one or more statistical characteristics to predetermined criteria to determine deviations, and automatically modifying one or more parameters of the enhanced reconstruction algorithm based on the deviations.

Abstract: A method includes applying both a first dedicated functional-anatomical registration scheme to a first volume of interest to deform the first volume of interest and a second dedicated functional-anatomical registration scheme to a second volume of interest to deform the second volume of interest, wherein the first volume of interest at least partially encompasses the second volume of interest. The method includes identifying or segmenting relevant organs or anatomical structures related to a first group and a second group in the first volume of interest and the second volume of interest, respectively; generating a spatially smooth-transition weight mask that gives higher weight to image data corresponding to the identified or segmented relevant organs or anatomical structures related to the first group and the second group; and generating a final cohesive registered image volume from the first image volume and the second image volume utilizing the spatially smooth-transition weight mask.

Abstract: A computing system includes computer readable medium and a processor. The computer readable medium includes a computer readable medium including a positron emission tomography (PET)-to-single photon emission tomography (SPECT) module with instructions for generating simulated SPECT image data based on first PET image data. The PET image data is acquired prior to a treatment course of a treatment regimen with a SPECT-based treatment/imaging radiopharmaceutical. The processor is configured to obtain the first PET image data, system parameters of a SPECT-CT imaging system utilized to generate corresponding SPECT image data for the treatment regimen and scan parameters utilized by the SPECT-CT imaging system to acquire the corresponding SPECT image data. The processor is further configured to execute the instructions to generate the simulated SPECT image data based on the PET image data and the system parameters and the scan parameters.

Abstract: A method includes obtaining emission-tomography functional image data and a corresponding reconstructed anatomical image volume including at least one organ having natural motion; pre-determining a dedicated model for spatial mismatch correction of the at least one organ; performing initial image reconstruction of the emission-tomography functional image data to generate a reconstructed emission-tomography functional image volume utilizing attenuation correction based on the corresponding reconstructed anatomical image volume; and identifying relevant anatomical regions, within both image volumes, where functional image quality may be affected by the natural motion of the at least one organ.

Abstract: A nuclear medicine (NM) multi-head imaging system is provided that includes a gantry, detector units, and at least one processor. The gantry defines a bore. The detector units are mounted to the gantry and configured to rotate as a group with the gantry around the bore in rotational steps, with each detector unit configured to sweep about a corresponding axis and acquire imaging information while sweeping about the corresponding axis. The at least one processor is coupled to the detector units and configured to determine a region of interest (ROI) of the object to be imaged; determine a sweeping configuration based on the size of the ROI; determine a rotational movement configuration for the gantry using the determined sweeping configuration; and control the gantry and the set of detector units to utilize the determined rotational movement and sweeping configurations during acquisition of imaging information.

Abstract: Techniques are provided for improving image data quality, such as in functional imaging follow-up studies, using reconstruction, post-processing, and/or deep-learning enhancement approaches in a way that automatically improves analysis fidelity, such as lesion tracking fidelity. The disclosed approaches may be useful in improving the performance of automatic analysis methods as well as in facilitating reviews performed by clinician.

Abstract: A method and workstation for combining multiple functional imaging datasets into a joint-visualization image. In one aspect, a method of functional imaging includes accessing a plurality of functional imaging datasets acquired from a volume-of-interest, wherein each of the plurality of functional imaging datasets is a different one of a plurality of functional imaging data types. The method includes registering the plurality of functional imaging datasets and determining a visualization priority for each of a plurality of pixels in a joint-visualization image based on a logical comparison of corresponding information in each of the plurality of functional imaging datasets. The method includes generating the joint-visualization image based on the visualization priority and at least a portion of each of the plurality of functional imaging datasets, wherein each of a plurality of pixels in the joint-visualization image represents only a single one of the plurality of functional imaging data types.