Abstract: A non-transitory computer readable medium storing instructions readable and executable by an imaging workstation (14) including at least one electronic processor (16) to perform a dataset generation method (100) operating on emission imaging data acquired of a patient for one or more axial frames at a corresponding one or more bed positions, the method comprising: (a) identifying a frame of interest from the one or more axial frames; (b) generating simulated lesion data by simulating emission imaging data for the frame of interest of at least one simulated lesion placed in the frame of interest; (c) generating simulated frame emission imaging data by simulating emission imaging data for the frame of interest of the patient; (d) determining a normalization factor comprising a ratio of the value of a quantitative metric for the simulated patient data and the value of the quantitative metric for the emission imaging data acquired of the same patient for the frame of interest; and (e) generating a hybrid data set

Abstract: An imaging apparatus includes a nuclear medicine imaging device (10), a patient table (14), and a table controller (18) comprising an electronic processor and actuators configured to position the patient table along an axial direction and in a transverse plane that is transverse to the axial direction. An automatic positioning engine (40) comprises an electronic processor (42) programmed to determine an optimal position of the patient table in the transverse plane for imaging a target of interest in a patient based on a prior image (20, 34) of the patient. The table controller operates the patient table to position the patient table in accord with the determined optimal position of the patient table.

Abstract: A machine learning guided image segmentation process is performed by an electronic processor (10). Image segmentation (22) is performed to generate an initial segmented representation (50) of an anatomical structure in the medical image. Parameters of a geometric shape are fitted (52) to the anatomical structure in the medical image to produce initial fitted shape parameters (54). A classification is assigned for the anatomical structure in the medical image using at least one classifier (60) operating on the initial fitted shape parameters and the initial segmented representation of the anatomical structure. A final segmented representation (72) of the anatomical structure in the medical image is generated by operations including repeating (70) the image segmentation using the classification as prior knowledge. In illustrative embodiments, the anatomical structure is a heart and the geometric shape is an ellipsoid.

Abstract: A PET detector array (8) comprising detector pixels acquires PET detection counts along lines of response (LORs). The counts are reconstructed to generate a reconstructed PET image (36, 46). The reconstructing is corrected for missing LORs which are missing due to dead detector pixels of the PET detector array. The correction may be by estimating counts along the missing LORs (60) by interpolating counts along LORs (66) neighboring the missing LORs. The interpolation may be iterative to handle contiguous groups of missing detector pixels. The correction may be by computing a sensitivity matrix having matrix elements corresponding to image elements (80, 82) of the reconstructed PET image. In this case, each matrix element is computed as a summation over all LORs intersecting the corresponding image element excepting the missing LORs. The computed sensitivity matrix is used in the reconstructing.

Abstract: In positron emission tomography (PET) imaging, PET imaging data (22) having TOF localization is reconstructed. TOF image reconstruction (30) is performed on the PET imaging data to produce a TOF reconstructed image (32). The TOF image reconstruction utilizes the TOF localization of the PET imaging data. Non-TOF image reconstruction (40) is also performed on the PET imaging data to produce a non-TOF reconstructed image (42). The non-TOF image reconstruction does not utilize the TOF localization of the PET imaging data. A comparison image (50) is computed which is indicative of differences between the TOF reconstructed image and the non TOF reconstructed image. An adjustment (54) is determined for the TOF image reconstruction based on the comparison image, such as alignment correction of an attenuation map (18), and the TOF image reconstruction is repeated on the PET imaging data with the determined adjustment to produce an adjusted TOF reconstructed image.

Abstract: A positron emission tomography (PET) detector array includes an enclosing radiation detector array (10) comprising radiation detector elements (14, 16) effective for detecting 511 keV radiation emanating from inside the radiation detector array. The radiation detector pixels of the cylindrical radiation detector array include both higher speed radiation detector elements (14) and lower speed radiation detector elements (16). The lower speed radiation detector pixels have a temporal resolution that is coarser than a temporal resolution of the higher speed radiation detector pixels.

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: A nuclear medicine image reconstruction method generates a reconstructed image (44) by performing iterative image in reconstruction (30, 130) on nuclear medicine imaging data (22). The iterative image reconstruction produces a sequence of update images (34, 36, 134, 136). During the iterative image reconstruction, a standardized uptake value (SUV) transform (40) is applied to convert an update image (34, 36) to an update SUV image (42, 46). The SUV transform scales values of voxels of the update image to SUV values using scaling factors including at least a body size metric and a dose metric. During the iterative image reconstruction, at least one parameter used in an image update of the iterative image reconstruction is adjusted using the update SUV image. For example, a parameter of a prior or filter (38) incorporated into an image reconstruction update step (32) or used in filtering of an update image (36) may be adjusted.

Abstract: A system (10) and a method (100) iteratively reconstruct an image of a target volume of a subject. In each iteration of a plurality of iterations, an estimate image of the target volume (54) is forward projected (58) and compared (62) to received event data (44) to determine a discrepancy (64). The discrepancy (64) is back projected (66) and the back projection (68) updates (70) the estimate image (54). In at least one iteration, the estimate image (54) is filtered (52) in the image domain prior to being back projected.

Abstract: Iterative reconstruction (20) of imaging data is performed to generate a sequence of update images (22) terminating at a reconstructed image. During the iterative reconstruction, at least one of an update image and a parameter of the iterative reconstruction is adjusted using an adjustment process separate from the iterative reconstruction. In some embodiments using an edge-preserving regularization prior (26), the adjustment process (30) adjusts an edge preservation threshold to reduce gradient steepness above which edge preservation applies for later iterations compared with earlier iterations. In some embodiments, the adjustment process includes determining (36, 38) for each pixel, voxel, or region of a current update image whether its evolution prior to the current update image 22) satisfies an artifact feature criterion. A local noise suppression operation (40) is performed on the pixel, voxel, or region if the evolution satisfies the artifact feature criterion and is not performed otherwise.

Abstract: Image processing performed by a computer (22) includes iterative image reconstruction or refinement (26, 56) that produces a series of update images ending in an iteratively reconstructed or refined image. A difference image (34, 64) is computed between a first update image (30, 60) and a second update image (32, 62) of the series. The difference image is converted to a feature image (40) and is used in the iterative processing (26, 56) or in post-processing (44) performed on the iteratively reconstructed or refined images or images from different reconstruction or refinement techniques. In another embodiment, first and second image reconstructions (81, 83) are performed to generate respective first and second reconstructed images (80, 82). A difference image (84) is computed between two images each selected from the group: the first reconstructed image, an update image of the first reconstruction, the second reconstructed image, and an update image of the second reconstruction.

Abstract: The present invention relates to the field of petroleum extraction equipment, and discloses a coiled tubing unit, which comprises a vehicle body (T), a control cab (30), a coiled tubing reel (10) configured to wind coiled tubing, and a power skid (20) configured to supply power to the coiled tubing reel (10) and transported separately, wherein, the coiled tubing reel (10) and the control cab (30) are mounted on the vehicle body (T). With the coiled tubing unit provided in the present invention, the total length and total weight of the vehicle body on which the coiled tubing reel is mounted are smaller and can meet the requirements for transportation in regions where smaller vehicle dimensions and weight are specified for transportation when coiled tubing in large diameter and/or great length is transported, and the coiled tubing unit is adaptive to the operating habits, and can be deployed flexibly so that it can be used in a well field where the space is limited.

Abstract: A medical imaging system (10) includes a nuclear imaging system (62), a timing optimization unit (40), a magnetic resonance (MR) scanner (12), an MR reconstruction unit (38), and an attenuation map unit (50). The nuclear imaging system (62) receives nuclear decay data and generates at least one nuclear image (64) of a first resolution based on the received nuclear decay data of an imaged subject (16) and an attenuation map (52). The timing optimization unit (40) which selects a first and a second echo time for a modified Dixon (mDixon) pulse sequence and a sufficient number of repetition times (TRs) to generate an image of the subject (16) of at least a first resolution, with the phase angle difference between water and fat at the first and the second echo time being unequal to 0° and 180°. The MR scanner (12) applies the sequence to the subject (16) and receives MR data (32) from the subject. The MR reconstruction unit (38) reconstructs at least one MR image (44) based on the MR data (32).

Abstract: A medical imaging system (10) includes a magnetic resonance (MR) scanner (12), and a MR reconstruction unit (34). The MR scanner (12) applies a multi-echo ultra-short TE (UTE) with mDixon pulse sequence to a subject (16) and receives MR data (33) representing at least a portion of the subject. The MR reconstruction unit (34) reconstructs a Free Induction Decay (FID) image (120), and one or more echo magnitude images (122), one or more phase images (39), an in-phase image (39), a water image (39), and a fat image (39) from the received MR data (33).

Abstract: The following relates to noise filtering in nuclear imaging systems. In one aspect, a fully automatic noise filtering system is provided in a nuclear imaging device. In some embodiments, a filter parameter selection engine is applied to an image environment of a medical image to calculate an image filter configuration for the medical image wherein the image environment includes values for one or more of an imaging subject of the medical image, an imaging device used to acquire the medical image, and a medical context of the medical image.

Abstract: A radioemission scanner (12) is operated to acquire tomographic radioemission data of a radiopharmaceutical in a subject in an imaging field of view (FOV). An imaging system is operated to acquire extension imaging data of the subject in an extended FOV disposed outside of and adjacent the imaging FOV along an axial direction (18). A distribution of the radiopharmaceutical in the subject in the extended FOV is estimated based on the extension imaging data, and further based on a database (32) of reference subjects. The tomographic radioemission data are reconstructed to generate a reconstructed image (26) of the subject in the imaging FOV. The reconstruction includes correcting the reconstructed image for scatter from the extended FOV into the imaging FOV based on the estimated distribution of the radiopharmaceutical in the subject in the extended FOV.

Abstract: A system (10) and a method (100) iteratively reconstruct an image of a target volume of a subject. In each iteration of a plurality of iterations, an estimate image of the target volume (54) is forward projected (58) and compared (62) to received event data (44) to determine a discrepancy (64). The discrepancy (64) is back projected (66) and the back projection (68) updates (70) the estimate image (54). In at least one iteration, the estimate image (54) is filtered (52) in the image domain prior to being back projected.

Abstract: A medical imaging system (10) includes a nuclear imaging system (62), a timing optimization unit (40), a magnetic resonance (MR) scanner (12), an MR reconstruction unit (38), and an attenuation map unit (50). The nuclear imaging system (62) receives nuclear decay data and generates at least one nuclear image (64) of a first resolution based on the received nuclear decay data of an imaged subject (16) and an attenuation map (52). The timing optimization unit (40) which selects a first and a second echo time for a modified Dixon (mDixon) pulse sequence and a sufficient number of repetition times (TRs) to generate an image of the subject (16) of at least a first resolution, with the phase angle difference between water and fat at the first and the second echo time being unequal to 0° and 180°. The MR scanner (12) applies the sequence to the subject (16) and receives MR data (32) from the subject. The MR reconstruction unit (38) reconstructs at least one MR image (44) based on the MR data (32).

Abstract: A medical imaging system (10) includes a magnetic resonance (MR) scanner (12), and a MR reconstruction unit (34). The MR scanner (12) applies a multi-echo ultra-short TE (UTE) with mDixon pulse sequence to a subject (16) and receives MR data (33) representing at least a portion of the subject. The MR reconstruction unit (34) reconstructs a Free Induction Decay (FID) image (120), and one or more echo magnitude images (122), one or more phase images (39), an in-phase image (39), a water image (39), and a fat image (39) from the received MR data (33).

Abstract: An apparatus for generating assignments between image regions of an image of an object and element classes includes an assigning unit (13) for assigning element classes to image regions of an element image of the object, which is indicative of a distribution of the element classes, depending on region and/or boundary features, which are determined depending on image values of a provided object image and provided first preliminary assignments. Thus, the resulting element image with the assignments to the element classes is not necessarily based on the provided object image only, but can also be based on the provided preliminary assignments. If the quality of the assignments defined by the element image would be restricted due to restrictions of the provided object image, these restrictions of the provided image can therefore be compensated by the preliminary assignments such that the quality of the resulting element image can be improved.