Abstract: Imaging data (20) are acquired by a PET scanner (6) or other imaging device. Iterative image reconstruction of the imaging data is performed to generate a reconstructed image (22). The iterative image reconstruction includes performing an update step (24) that includes an edge preserving prior (28) having a spatially varying edge preservation threshold (30) whose value at each image voxel depends on a noise metric (32) in a local neighborhood of the image voxel. The noise metric may be computed as an aggregation of the intensities of neighborhood image voxels of the reconstructed image in the local neighborhood of the image voxel. The edge preserving prior may be a Relative Difference Prior (RDP). For further noise suppression, during the iterative image reconstruction image values of image features of the reconstructed image that have spatial extent smaller than a threshold (38) may be reduced.

Abstract: Data processing systems (DPS) and related methods for nuclear medicine imaging. At an input interface (IN), first projection data (?), or a first image (V) reconstructable from the first projection data, is received. The first projection data is associated with a first waiting period (?T*). The first waiting period indicates the time period from administration of a tracer agent to a start of acquisition by a nuclear medicine imaging apparatus (IA) of the projection date. A trained machine learning module (MLM) estimates, based on the first projection data (?) or on the first image (V), a second projection data (??) or a second image (V?) associable with a second waiting period (?T), longer than the first waiting period (?T*). Nuclear imaging can thus be conducted quicker. Similar machine learning based data processing systems and related methods are also envisaged to reduce acquisition time periods or the time it takes to reconstruct imagery.

Abstract: A method and system are provided for reconstructing a motion-compensated nuclear image of a subject, as well as an arrangement for method. The reconstruction method comprises receiving nuclear image data the acquiring a nuclear image, and a computer program for carrying out the for multiple motion states, reconstructing the data into an image for each motion state, and calculating a deformation vector field for each state for mapping the image onto a reference motion state. Calculating the deformation vector field comprises providing an initial vector field, defining at least one rigid region of the subject, incorporating that rigid region into the initial vector field, and calculating the deformation vector field with the incorporated rigid region. The method further comprises mapping the reconstructed image of each motion state onto the reference state using the deformation vector fields; and combining the mapped images into a motion-compensated nuclear image.

Abstract: A system (PP) and related methods for supporting nuclear imaging such as PET or other. The system comprises an input interface (IN) for receiving event data that represents an interaction event of gamma-radiation with a pixelated scintillator (SC) of a nuclear imaging apparatus (NIA). A pre-trained machine learning component (MLC) estimates a point spread function, PSF, for the said event. An output interface (OUT) outputs a representation of the PSF. The PSF may be used in emission image reconstruction for improved spatial resolution.

Abstract: The invention relates to a system and a method for assisting in attenuation correction of gated PET data of a moving object (2). In the system, an evaluation unit (15) is configured to (i) receive a CT image of the object (2) and to segment the CT image into a plurality of CT sub-images, each CT sub-image correspond to an axial segment of an imaged volume, (ii) to determine for each CT sub-image a gate including PET data having a greatest correspondence with the CT sub-image, (iii) to construct, for each CT sub-image, a PET sub-image from the PET data included in the gate determined for the CT sub-image, the PET sub-image substantially corresponding to the same axial segment of the imaged volume as the CT sub-image, and (iv) to combine the PET sub-images to form a PET reference image of the object (2).

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (181, 182, 20) to perform an imaging method (100). The method includes: reconstructing emission imaging data to generate an emission image of a lesion; converting intensity values of the emission image to at least one standardized uptake value (SUV value) for the lesion; processing input data using a regression neural network (NN) (28) to output an SUV correction factor for the lesion, wherein the input data includes at least two of (i) image data comprising the emission image or a feature vector representing the emission image, (ii) the at least one SUV value, (iii) a size of the lesion, and (iv) reconstruction parameters used in the reconstructing; and controlling a display device (24) to display at least one of (I) the SUV correction factor and (II) a corrected SUV value generated by applying the SUV correction factor to the at least one SUV value.

Abstract: A respiratory motion signal generation method operates on emission data (22) of an imaging subject in an imaging field of view (FOV) acquired by a positron emission tomography (PET) or single photon emission computed tomography (SPECT) imaging device (10). An array of regions (32) is defined in the imaging FOV without reference to anatomy of the imaging subject. For each region of the array of regions defined in the imaging FOV, an activity position versus time curve (54) is computed from the emission data acquired by the PET or SPECT imaging device. Frequency-selective filtering of the activity position versus time curves is performed to generate filtered activity position versus time curves. At least one motion signal (66) is generated by combining the filtered activity position versus time curves of at least a selected sub-set of the regions.

Abstract: A time of flight (TOF) positron emission tomography (PET) image (38) is generated from TOF PET imaging data (10) acquired of a subject using a TOF PET imaging data acquisition device (6). Iterative image reconstruction (30) of the TOF PET imaging data is performed with TOF localization of counts along respective lines of response (LORs) to iteratively update a reconstructed image (32). Values for at least one regularization or filtering parameter are assigned to the TOF PET imaging data or to voxels of the reconstructed image based on an estimated TOF localization resolution for the TOF PET imaging data or voxels. Regularization (34) or filtering (36) of the reconstructed image is performed using the assigned values for the at least one regularization or filtering parameter. In some embodiments, the varying TOF localization resolution for the TOF PET imaging data or voxels is estimated based on related acquisition characteristics such as count rates or operating temperature of the detectors.

Abstract: The invention relates to a system and a method for assisting in attenuation correction of gated PET data of a moving object (2). In the system, an evaluation unit (15) is configured to (i) receive a CT image of the object (2) and to segment the CT image into a plurality of CT sub-images, each CT sub-image correspond to an axial segment of an imaged volume, (ii) to determine for each CT sub-image a gate including PET data having a greatest correspondence with the CT sub-image, (iii) to construct, for each CT sub-image, a PET sub-image from the PET data included in the gate determined for the CT sub-image, the PET sub-image substantially corresponding to the same axial segment of the imaged volume as the CT sub-image, and (iv) to combine the PET sub-images to form a PET reference image of the object (2).

Abstract: Patient imaging systems, such as PET imaging systems, for example, may suffer from the introduction of artificially introduced noise. This noise is, typically, introduced during iterations of reconstruction algorithms, such as the least-squares algorithms, which attempts to recreate a 2D or a 3D image from raw acquisition information. The noise appears as “hot-spots” in the reconstructed image. Approaches to address these artefacts use filtering approaches. Typically, a least-squares reconstruction is supplemented with a penalty term, an approach known as “Relative Difference Penalty”. The penalty parameter causes the reconstruction algorithm to filter more or less strongly at certain regions of the reconstruction. The present application proposes an approach which supplements the penalty term with continuous probability information about the likelihood of an edge being present in a portion of an image.

Abstract: A respiratory motion signal generation method operates on emission data (22) of an imaging subject in an imaging field of view (FOV) acquired by a positron emission tomography (PET) or single photon emission computed tomography (SPECT) imaging device (10). An array of regions (32) is defined in the imaging FOV without reference to anatomy of the imaging subject. For each region of the array of regions defined in the imaging FOV, an activity position versus time curve (54) is computed from the emission data acquired by the PET or SPECT imaging device. Frequency-selective filtering of the activity position versus time curves is performed to generate filtered activity position versus time curves. At least one motion signal (66) is generated by combining the filtered activity position versus time curves of at least a selected sub-set of the regions.

Abstract: Imaging data (20) are acquired by a PET scanner (6) or other imaging device. Iterative image reconstruction of the imaging data is performed to generate a reconstructed image (22). The iterative image reconstruction includes performing an update step (24) that includes an edge preserving prior (28) having a spatially varying edge preservation threshold (30) whose value at each image voxel depends on a noise metric (32) in a local neighborhood of the image voxel. The noise metric may be computed as an aggregation of the intensities of neighborhood image voxels of the reconstructed image in the local neighborhood of the image voxel. The edge preserving prior may be a Relative Difference Prior (RDP). For further noise suppression, during the iterative image reconstruction image values of image features of the reconstructed image that have spatial extent smaller than a threshold (38) may be reduced.

Abstract: A time of flight (TOF) positron emission tomography (PET) image (38) is generated from TOF PET imaging data (10) acquired of a subject using a TOF PET imaging data acquisition device (6). Iterative image reconstruction (30) of the TOF PET imaging data is performed with TOF localization of counts along respective lines of response (LORs) to iteratively update a reconstructed image (32). Values for at least one regularization or filtering parameter are assigned to the TOF PET imaging data or to voxels of the reconstructed image based on an estimated TOF localization resolution for the TOF PET imaging data or voxels. Regularization (34) or filtering (36) of the reconstructed image is performed using the assigned values for the at least one regularization or filtering parameter. In some embodiments, the varying TOF localization resolution for the TOF PET imaging data or voxels is estimated based on related N acquisition characteristics such as count rates or operating temperature of the detectors.

Abstract: Patient imaging systems, such as PET imaging systems, for example, may suffer from the introduction of artificially introduced noise. This noise is, typically, introduced during iterations of reconstruction algorithms, such as the least-squares algorithms, which attempts to recreate a 2D or a 3D image from raw acquisition information. The noise appears as “hot-spots” in the reconstructed image. Approaches to address these artefacts use filtering approaches. Typically, a least-squares reconstruction is supplemented with a penalty term, an approach known as “Relative Difference Penalty”. The penalty parameter causes the reconstruction algorithm to filter more or less strongly at certain regions of the reconstruction. The present application proposes an approach which supplements the penalty term with continuous probability information about the likelihood of an edge being present in a portion of an image.

Abstract: An imaging system includes a magnetic resonance portion that produces an electric field and a second imaging portion, including a detector with a two dimensional array of detector tiles, wherein adjacent tiles along an axial direction are spaced apart by an electrically conductive material, which shields the tiles from the electric field. An imaging system includes a first imaging portion having a detector, which includes an array of scintillation crystals and a photo-sensor coupled to the array of scintillation crystals, wherein an output of the photo-sensor includes a unique ratio of information that identifies each crystal.

Abstract: An imaging system includes a magnetic resonance portion that produces an electric field and a second imaging portion, including a detector with a two dimensional array of detector tiles, wherein adjacent tiles along an axial direction are spaced apart by an electrically conductive material, which shields the tiles from the electric field. An imaging system includes a first imaging portion having a detector, which includes an array of scintillation crystals and a photo-sensor coupled to the array of scintillation crystals, wherein an output of the photo-sensor includes a unique ratio of information that identifies each crystal.

Abstract: A magnetic resonance (MR) image segmentation processor (32) is configured to identify one or more geometrical regions of a subject using an MR image of the subject. An emission data reconstruction processor (40) is configured to generate an attenuation map (54) of the subject by assigning initial attenuation values (52) to the geometrical regions of the subject, and to: (i) process (56) emission data acquired from the subject to generate an emission image (58) of the subject, the processing employing the attenuation map of the subject; (ii) update (60) the attenuation map based on corrections calculated using the emission image of the subject; and (iii) iterate operations (i) and (ii) to iteratively generate a reconstructed emission image of the subject.

Abstract: A magnetic resonance (MR) image segmentation processor (32) is configured to identify one or more geometrical regions of a subject using an MR image of the subject. An emission data reconstruction processor (40) is configured to generate an attenuation map (54) of the subject by assigning initial attenuation values (52) to the geometrical regions of the subject, and to: (i) process (56) emission data acquired from the subject to generate an emission image (58) of the subject, the processing employing the attenuation map of the subject; (ii) update (60) the attenuation map based on corrections calculated using the emission image of the subject; and (iii) iterate operations (i) and (ii) to iteratively generate a reconstructed emission image of the subject.