Abstract: A method for navigating a three-dimensional (3D) image includes accessing a 3D image dataset, generating a 3D mesh corresponding to a 3D segmentation result using the 3D image dataset, displaying a 3D surface rendering of the 3D surface mesh, and navigating the 3D image based on a manual input received from a user indicated on the rendered 3D mesh.

Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and decision support in a drug development process. An example method includes a computer-implemented method for assessing drug efficacy, comprising: accessing a first data set related to the performance of a target drug for a given indication; accessing a second data set related to a control for the indication; comparing the data for the target drug and the data for the control on at least one of a plurality of different metrics using a holistic analysis, wherein the at least one metric corresponds to an outcome associated with the indication and generating a corresponding report.

Abstract: A method for extracting a three-dimensional (3D) volume of interest from a three-dimensional (3D) image dataset includes accessing a 3D image dataset that includes a plurality of image slices, enclosing a 3D volume of interest in the 3D image dataset using a 3D mesh, automatically extracting the 3D volume of interest based on the 3D mesh, and generating a 3D image of the extracted 3D volume of interest. A computer and a non-transitory computer readable medium are also described herein.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing reference deviation maps for a plurality of disease types. The reference deviation maps may include subsets of maps associated with severity levels of respective disease types, and a disease severity score may be associated with each severity level. The method may include selecting patient severity levels for multiple disease types based on the subsets of reference deviation maps. Also, the method may include automatically calculating a combined patient disease severity score based at least in part on the disease severity scores associated with the selected patient severity levels, and may include outputting a report based at least in part on the combined patient disease severity score. Additional methods, systems, and manufactures are also disclosed.

Abstract: A method for navigating a three-dimensional (3D) image includes accessing a 3D image dataset, generating a 3D mesh corresponding to a 3D segmentation result using the 3D image dataset, displaying a 3D surface rendering of the 3D image intensities on the 3D mesh, and navigating the 3D image based on a manual input received from a user indicated on the rendered 3D image.

Abstract: A method for extracting a three-dimensional (3D) volume of interest from a three-dimensional (3D) image dataset includes accessing a 3D image dataset that includes a plurality of image slices, enclosing a 3D volume of interest in the 3D image dataset using a 3D mesh, automatically extracting the 3D volume of interest based on the 3D mesh, and generating a 3D image of the extracted 3D volume of interest. A computer and a non-transitory computer readable medium are also described herein.

Abstract: A method for navigating a three-dimensional (3D) image includes accessing a 3D image dataset, generating a 3D mesh corresponding to a 3D segmentation result using the 3D image dataset, displaying a 3D surface rendering of the 3D surface mesh, and navigating the 3D image based on a manual input received from a user indicated on the rendered 3D mesh.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing patient image and non-image deviation scores derived through respective comparisons of patient image and non-image data to standardized image and non-image data. The method may also include processing the image and non-image deviation scores to generate a visual output indicative of differences between the patient image and non-image data, and the standardized image and non-image data, respectively. Further, the method may include displaying the visual output. Additional methods, systems, and manufactures are also disclosed.

Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and decision support in a drug development process. An example method includes a computer-implemented method for assessing drug efficacy, comprising: accessing a first data set related to the performance of a target drug for a given indication; accessing a second data set related to a control for the indication; comparing the data for the target drug and the data for the control on at least one of a plurality of different metrics using a holistic analysis, wherein the at least one metric corresponds to an outcome associated with the indication and generating a corresponding report.

Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and decision support in a drug development process, and safety testing. An example method includes providing a first set of data corresponding to a drug of interest; providing a reference set of drug interaction data, comparing the first set of data to the reference set using a holistic analysis, and reporting the results of the comparison. An example of the holistic analysis and viewing apparatus for drug safety comprises a standardizer to at least one of standardize and normalize drug interaction data related to drug safety; a deviation analyzer to compare the drug interaction data to data corresponding to a drug under review using a holistic analysis, and a reporter for reporting the output of the deviation analyzer.

Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and decision support in a drug development process. An example method includes accessing data related to drug development; pre-processing the data to prepare the data for measurement and analysis; and analyzing the data based on at least one of a plurality of different metrics. Each metric corresponds to a quantified variation between a first data set of results corresponding to a category in the drug development process. The first data set of results is provided for comparison with a second data set of results corresponding to at least one other category in the drug development process. At least some of the plurality of metrics are aggregated to generate a visual representation representing an integrated comparative visualization for the identified category.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing individual patient deviation maps indicative of a structural difference and a functional difference, respectively, of at least one anatomical region of a patient with respect to standardized reference image data. The method may also include generating a composite patient deviation map indicative of both the structural difference and the functional difference based on at least the individual patient deviation maps, and outputting the composite patient deviation map. Additional methods, systems, and manufactures are also disclosed.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing patient deviation data of a structural difference between a patient anatomical feature and a standardized anatomical feature. The method may also include comparing the patient deviation data to reference deviation data sets representative of multiple disease types. Each reference deviation data set may be representative of an expected deviation from the standardized anatomical feature for a particular disease type. The method may further include automatically identifying one or more potential patient disease types based at least in part on the comparison. Additional methods, systems, and manufactures are also disclosed.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing individual patient deviation maps indicative of a structural difference and a functional difference, respectively, of at least one anatomical region of a patient with respect to standardized reference image data. The method may also include generating a composite patient deviation map indicative of both the structural difference and the functional difference based on at least the individual patient deviation maps, and outputting the composite patient deviation map. Additional methods, systems, and manufactures are also disclosed.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing patient image and non-image deviation scores derived through respective comparisons of patient image and non-image data to standardized image and non-image data. The method may also include processing the image and non-image deviation scores to generate a visual output indicative of differences between the patient image and non-image data, and the standardized image and non-image data, respectively. Further, the method may include displaying the visual output. Additional methods, systems, and manufactures are also disclosed.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing reference deviation maps for a plurality of disease types. The reference deviation maps may include subsets of maps associated with severity levels of respective disease types, and a disease severity score may be associated with each severity level. The method may include selecting patient severity levels for multiple disease types based on the subsets of reference deviation maps. Also, the method may include automatically calculating a combined patient disease severity score based at least in part on the disease severity scores associated with the selected patient severity levels, and may include outputting a report based at least in part on the combined patient disease severity score. Additional methods, systems, and manufactures are also disclosed.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing patient deviation data of a structural difference between a patient anatomical feature and a standardized anatomical feature. The method may also include comparing the patient deviation data to reference deviation data sets representative of multiple disease types. Each reference deviation data set may be representative of an expected deviation from the standardized anatomical feature for a particular disease type. The method may further include automatically identifying one or more potential patient disease types based at least in part on the comparison. Additional methods, systems, and manufactures are also disclosed.

Abstract: A penetrating radiation source (14) is disposed on one side of a object (10) which is on a object support (12). A flat panel radiation detector (18) is stationarily disposed on the opposite side of the object (10) than the source (14). A moving system (16, 50, 52) moves the source (14) with respect to the object (10). In each position (Xi, Yi, Zi) of the source (14) a center ray of the x-ray beam strikes the detector (18) at a corresponding location (xi, yi, 0). For each position (Xi, Yi, Zi) of the source (14), the image (xi, yi, 0) of an object located on a focal plane offsets by a vector displacement (Di) relative to a reference position (xo, yo, zo) of the image when the source is at (X0, Y0, Z0). A processor (28) shifts and interpolates each view by the different vector displacements corresponding to each of the focal planes (L1, L2, . . . ) and integrates the images to generate a series of slice image representations which are stored in a volume image memory (30).

Abstract: An object (14) is positioned on an object support (16). A radiation source (10) projects a beam of radiation through a region of interest (12) of the object. A plurality of focal planes (F1, F2, . . . , Fn) mark the center of selected slice images through the region of interest. A gantry (20) rotates the radiation source (10) around a circular trajectory (22) as an encoder (26) monitors a radius (r) of the trajectory and an angular position (&phgr;) of the radiation source (10) around the trajectory (22). A look-up table (40) is addressed with the selected focal plane(s) (F1, F2, . . . , Fn) and (r, &phgr;) to generate a correction or shift value (S1, . . . , Sn.) for each selected focal plane (F1, F2, . . . , Fn). A flat panel detector (18) is read out a plurality of times to generate a plurality of electronic data views as the radiation source rotates.

Abstract: An object (22) is positioned on a patient support (25) between an x-ray source (10) and an x-ray detector assembly (15). The x-ray source (10) is selectively activated to transmit an x-ray beam (20) through an imaging region (32) to the x-ray detector assembly (15). Positioning of the object (22) on the patient support (25) is such that a gap 33 exists in the imaging region (32) through which x-rays may pass unattenuated. An x-ray equalization filter (30) is introduced into the gap 33 and substantially conforms to its shape. The equalization filter (30) comprises a fluid medium disposed in a flexible receptacle. The fluid medium contains x-ray attenuating material suspended in a gel base. Placement of the equalization filter (30) in the gap reduces the number of unattenuated x-rays reaching the x-ray detector assembly (15) thereby enhancing image quality.