Abstract: Disclosed herein are methods and systems for identifying the location of a target region using a tumor identification (ID) profile. A tumor ID profile includes identification parameters that characterize the target region. The tumor ID profile may be used to facilitate the identification of multiple target regions and to evaluate whether it is safe to deliver radiation to the target regions at their updated locations. Also disclosed herein are methods for analyzing a dose distribution to a target region by generating a bounded dose volume histogram (bDVH) based on gamma criteria comprising a distance-to-agreement (DTA) criterion and a dose difference (DD) criterion. In one variation, a gamma-derived bDVH is used in a method for selecting gamma criteria values for evaluating a radiotherapy treatment plan.

Abstract: A quantitative imaging system (100) for automating delivery of a radiopharmaceutical and imaging a patient. The system (100) includes a QI controller (102) that controls the operation of the system. The system (100) includes a producer (104) for creating a radiopharmaceutical and a synthesizer (106) to synthesize the radioisotope with a biologically compatible material to form the radiopharmaceutical. The system (100) includes a dosimeter (110) to measure and timestamp the concentration measurement of the radiopharmaceutical and an injector (112) to deliver the radiopharmaceutical into the patient. The system (100) includes an imaging apparatus (114) to image the patient. The QI controller (102) uses the timestamp of the concentration and decay characteristics to adjust imaging data. The system includes aquantitative analyzer (116) to calculate parametric images and quantitative values from the collected data.

Abstract: A medical image (24) with contrast of one imaging modality is emulated from images with other contrast characteristics. A plurality of input images (20) of a region of interest with the other contrast characteristics is received. A transform (22) which was generated by machine learning is applied to the corresponding plurality of input images (20) to generate a scalar value (38) or a vector value (38?) representing the corresponding voxel (36?) of the medical image (24) to be evaluated.

Abstract: A lung segmentation processor (40) is configured to classify magnetic resonance (MR) images based on noise characteristics. The MR segmenatation processor generates a lung region of interest (ROI) and detailed structure segmentation of the lung from the ROI. The MR segmentation processor performs an iterative normalization and region definition approach that captures the entire lung and the soft tissues within the lung accurately. Accuracy of the segmentation relies on artifact classification coming inherently from MR images. The MR segmentation processor (40) correlates segmented lung internal tissue pixels with the lung density to determine the attenuation coefficients based on the correlation. Lung densities are computed using MR data obtained from imaging sequences that minimize echo and acquisition times. The densities differentiate healthy tissues and lesions, which an attenuation map processor (36) uses to create localized attenuation maps for the lung.

Abstract: A photon detector includes a sensor array of optical sensors disposed in a plane and four substantially identical scintillation crystal bars. Each optical sensor is configured to sense luminescence. Each of the four scintillator crystal bars being a rectangular prism with four side surfaces and first and second end surfaces, each scintillation bar has two side surfaces which each face a side surface of another scintillation bar, and each scintillation crystal bar generating a light scintillation in response to interacting with a received gamma photon. A first layer (80) is disposed in a first plane disposed between and adjacent facing side surfaces of the four substantially identical scintillation crystal bars with a light sharing portion (82) adjacent the first end surface and a reflective portion (84) adjacent the second end surface.

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: A whole body SPECT system (10) includes a patient support (14) and a static gantry (12) which includes a plurality of rings (40a,40b,40c) of radiation detectors (42). The patient support (14) supports a patient and moves the patient in an axial direction (18) through the static gantry (12). One or more processors (20,24,32) connected to the plurality of detectors records strikes of gamma photons in the radiation detectors (42) and reconstruct the recorded strikes of the gamma photons into a whole body image.

Abstract: A method and system for managing imaging data are provided. In one aspect, imaging data is stored in combination with user-generated information relating to the imaging data. In various other aspects, an image file header structure including user-generated information, a software editing tool to record user-generated information, and an imaging display tool to correlate imaging data and user-generated information are provided.

Abstract: A quantitative imaging system (100) for automating delivery of a radiopharmaceutical and imaging a patient. The system (100) includes a QI controller (102) that controls the operation of the system. The system (100) includes a producer (104) for creating a radiopharmaceutical and a synthesizer (106) to synthesize the radioisotope with a biologically compatible material to form the radiopharmaceutical. The system (100) includes a dosimeter (110) to measure and timestamp the concentration measurement of the radiopharmaceutical and an injector (112) to deliver the radiopharmaceutical into the patient. The system (100) includes an imaging apparatus (114) to image the patient. The QI controller (102) uses the timestamp of the concentration and decay characteristics to adjust imaging data. The system includes aquantitative analyzer (116) to calculate parametric images and quantitative values from the collected data.

Abstract: A medical image (24) with contrast of one imaging modality is emulated from images with other contrast characteristics. A plurality of input images (20) of a region of interest with the other contrast characteristics is received. A transform (22) which was generated by machine learning is applied to the corresponding plurality of input images (20) to generate a scalar value (38) or a vector value (38?) representing the corresponding voxel (36?) of the medical image (24) to be evaluated.

Abstract: A photon detector includes a sensor array of optical sensors disposed in a plane and four substantially identical scintillation crystal bars. Each optical sensor is configured to sense luminescence. Each of the four scintillator crystal bars being a rectangular prism with four side surfaces and first and second end surfaces, each scintillation bar has two side surfaces which each face a side surface of another scintillation bar, and each scintillation crystal bar generating a light scintillation in response to interacting with a received gamma photon. A first layer (80) is disposed in a first plane disposed between and adjacent facing side surfaces of the four substantially identical scintillation crystal bars with a light sharing portion (82) adjacent the first end surface and a reflective portion (84) adjacent the second end surface.

Abstract: A medical imaging system includes a data store (16) of reconstruction procedures, a selector (24), a reconstructor (14), a fuser (28), and a display (22). The data store (16) of reconstruction procedures identifies a plurality of reconstruction procedures. The selector (24) selects at least two reconstruction procedures from the data store of reconstruction procedures based on a received input, each reconstruction procedure optimized for one or more image characteristics. The reconstructor (14) concurrently performs the selected at least two reconstruction procedures, each reconstruction procedure generates at least one image (26) from the at least one data store of imaging data (12). The fuser (28) fuses the at least two generated medical images to create a medical diagnostic image which includes characteristics from each generated image (26). The display (22) displays the medical diagnostic image.

Abstract: A medical imaging system includes a view transformation component (210) and a segment combiner (212). The transformation component (210) transforms projection data in each view of a plurality of individual segments, which each includes at least one view. The transformed projection data for substantially similar views across the plurality of individual segments have a common radius of rotation. The segment combiner (212) combines the transformed projection data to produce a single data set that includes the transformed projection data for each of the views of each of the plurality of individual segments.

Abstract: A nuclear imaging chain (100) includes a molecular agent (102), an acquisition system (104), a reconstruction system (106), a detection system (108), and a display system (110). The various components of the imaging chain are optimized according to desired optimization criteria. The optimized characteristics of the imaging chain (100) may include one or more an agent characteristic, an acquisition characteristic (127), a reconstruction characteristic (143), a detection characteristic (159), and a display characteristic.

Abstract: A lung segmentation processor (40) is configured to classify magnetic resonance (MR) images based on noise characteristics. The MR segmenatation processor generates a lung region of interest (ROI) and detailed structure segmentation of the lung from the ROI. The MR segmentation processor performs an iterative normalization and region definition approach that captures the entire lung and the soft tissues within the lung accurately. Accuracy of the segmentation relies on artifact classification coming inherently from MR images. The MR segmentation processor (40) correlates segmented lung internal tissue pixels with the lung density to determine the attenuation coefficients based on the correlation. Lung densities are computed using MR data obtained from imaging sequences that minimize echo and acquisition times. The densities differentiate healthy tissues and lesions, which an attenuation map processor (36) uses to create localized attenuation maps for the lung.

Abstract: A method and apparatus of image reconstruction attenuation correction in PET or SPECT cardiac imaging is provided. A volumetric attenuation imaging scan by an X-ray source may be used to generate a gamma ray attenuation map. The volumetric attenuation imaging scan may be randomized, and may be performed while the imaged subject is breathing.

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: In Positron Emission Tomography, a time window (260) and an energy window (225) are dynamically adjusted, based on an attenuation map, count rate, clinical application, discrimination tailoring, and/or offline discrimination tailoring. Detected radiation events are filtered using the dynamically adjusted energy and time windows into scattered events, random events, and true events. The true events are input to image reconstruction, correction, and error analysis.

Abstract: A presentation component (118) presents information from one or more data source(s) (102) to an assessor for assessment. A receiver operating characteristic (ROC) analyzer 120 uses an ROC analysis technique to evaluate the performance of the assessor. A feedback component (126) provides feedback as to the assessor's performance. A data manipulator (114) facilitates manipulation of the presented data.

Abstract: When imaging a compact structure, such as a calcium deposit in a patient's heart, a slow scan (e.g., less than approximately 6 rpm) CT data acquisition is performed, wherein data is continuously but sparsely acquired during around a 360° revolution around the patient. Arc segments are defined that equate to one heart cycle (e.g., heartbeat) given the patient's heart rate and the speed of the CT gantry. Electrocardiogram signal data is used to identify sets of acquired projection data that correspond to each of a plurality of heart cycle phases during which the heart is relatively still. A sparse reconstruction algorithm is executed on the identified sets of sparse projection data to generate images for each heart cycle phase from the scan data acquired for that phase across all heart cycles.