Abstract: A method is provided for follow-up functional imaging after obtaining a first functional image data set of a patient. The method includes obtaining a second functional image data set of the patient at a follow-up time subsequent to the obtaining of the first functional image data set. The method also includes generating a local change map using the first functional image data set and the second functional image data set. Further, the method includes generating a mutual structural similarity map using the first functional image data set and the second functional image data set. Also, the method includes generating a significant-response map using the local change map and the mutual structural similarity map. The method also includes displaying the significant-response map.

Abstract: The current application relates to an optimization procedure where the noise reduction strength is incrementally increased and applied in the noise reduction scheme. A non-linear quantitative map is then computed followed by the quantitative bias estimation. The optimization conditions are then checked and the noise reduction “strength” is increased if the bias difference is higher than a predefined threshold.

Abstract: A system is provided that includes a gantry, detector units, and at least one processor. The gantry defines a bore. The plural detector units are mounted to the gantry and configured to rotate as a group with the gantry in rotational steps. Each detector unit is configured to acquire imaging information while sweeping about a corresponding axis. The at least one processor is configured to determine a region of interest (ROI) of an object; identify a set of detector units; for the identified set of detector units, determine a sweeping configuration that results in a predetermined percentage of projection pixels receiving information from 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: A method includes obtaining contrast enhanced perfusion imaging data of at least two vessel regions, one downstream from the other, and at least one tissue of interest, which receives blood from the circulatory system, of a scanned subject. The method further includes determining a blood flow time difference between contrast material peaks of the at least two vessel regions based on the image data. The method further includes determining an absolute perfusion of the tissue of interest based on the image data. The method further includes computing a standardized perfusion value based on the absolute perfusion and the time difference. The method further includes displaying the standardized perfusion value.

Abstract: An image processing system (IPS) and related method. The system comprises an input interface (IN) for receiving an earlier input image (Va) and a later input image (Vb) acquired of an object (OB) whilst a fluid is present within the object (OB). A differentiator (?) is configured to form a difference image (Vd) from the at least two input images (Va,Vb). An image structure identifier (ID) is operable to identify one or more locations in the difference image. Based on a respective feature descriptor that describes a respective neighborhood around said one or more locations. An output interface (OUT) outputs a feature map (Vm) that includes the one or more locations.

Abstract: A method includes combining a plurality of different types of spectral images into a first single blended image based on a first blend point of a first spectral image data blend map. The first blend point includes a first set of weight values. The first set of weight values includes a weight value for each of the plurality of different types of spectral images. The plurality of different types of spectral images is combined based on the first set of weight values. The method further comprising displaying the first single blended image via a display device.

Abstract: Systems and methods described herein generally relate to improved medical diagnostics for nuclear medicine (NM) imaging. The systems and methods define an uncertainty model of a medical imaging system. The uncertainty model is based on natural-statistics distribution of the medical imaging system response. The systems and methods acquire image data of a patient from the medical imaging system, and calculate an uncertainty map of the patient based on the uncertainty model and the image data. The uncertainty map represents a collection of realizations that are generated based on the image data and the uncertainty model. The systems and methods apply a classification or ranking algorithm to the image data and the uncertainty map to calculate image data classification or ranking of the patient including confidence values.

Abstract: A method is provided for follow-up functional imaging after obtaining a first functional image data set of a patient. The method includes obtaining a second functional image data set of the patient at a follow-up time subsequent to the obtaining of the first functional image data set. The method also includes generating a local change map using the first functional image data set and the second functional image data set. Further, the method includes generating a mutual structural similarity map using the first functional image data set and the second functional image data set. Also, the method includes generating a significant-response map using the local change map and the mutual structural similarity map. The method also includes displaying the significant-response map.

Abstract: A method includes obtaining volumetric image data generated by an imaging system, generating an uncertainty for each voxel of the volumetric image data, and generating an evaluation volume with volumetric image data based on the generated uncertainty. The method further includes receiving an input identifying a region and/or volume of interest in the evaluation volume, receiving an intended diagnostic type, receiving an evaluation probability level of interest, and receiving an effect direction of interest. The method further includes deforming the evaluation volume to create an artificial volume that reflects an effect of the uncertainty on the intended diagnostic type based on the evaluation probability level of interest and the effect direction of interest. The method further includes visually displaying the deformed evaluation volume.

Abstract: Systems and methods described herein generally relate to improved medical diagnostics for nuclear medicine (NM) imaging. The systems and methods define an uncertainty model of a medical imaging system. The uncertainty model is based on natural-statistics distribution of the medical imaging system response. The systems and methods acquire image data of a patient from the medical imaging system, and calculate an uncertainty map of the patient based on the uncertainty model and the image data. The uncertainty map represents a collection of realizations that are generated based on the image data and the uncertainty model. The systems and methods apply a classification or ranking algorithm to the image data and the uncertainty map to calculate image data classification or ranking of the patient including confidence values.

Abstract: Method for generating contrast agent concentration map from a non-contrast enhanced Computed Tomography scan, a contrast enhanced Computed Tomography scan and corresponding spectral Computed Tomography data, comprising: a. Generating at least two different primary contrast agent concentration maps out of the non-contrast enhanced Computed Tomography scan, the contrast enhanced Computed Tomography scan and the spectral Computed Tomography data, b. Performing a local quality analysis of each primary contrast agent concentration map c. Determining local volumetric weights for each primary contrast agent concentration map based on the local quality analysis, and d. Generating a secondary contrast agent concentration map based on the two primary contrast agent concentration maps and on their corresponding local volumetric weights.

Abstract: The invention relates to a method for a medical image analysis, comprising the steps: Performing a first CT scan of a region of interest of a subject resulting in a first image with a first resolution; Applying a medical image processing method to the first image resulting in first values representing a first analysis of the region of interest of the subject; Determining a range of interest of the subject based on the first values; Performing a second CT scan of the range of interest of the subject resulting in a second image with a second resolution, wherein the second resolution is higher than the first resolution; and Applying the medical imaging processing method to the second image resulting in second values representing a second analysis of the range of interest of the subject. Further, the invention relates to a system for medical image analysis.

Abstract: The current application relates to an optimization procedure where the noise reduction strength is incrementally increased and applied in the noise reduction scheme. A non-linear quantitative map is then computed followed by the quantitative bias estimation. The optimization conditions are then checked and the noise reduction “strength” is increased if the bias difference is higher than a predefined threshold.

Abstract: A quantitative measurement system includes a quantitative imaging biomarker calibrator (116). The quantitative imaging biomarker calibrator (116) receives one or more pre-calibrated quantitative measurements of imaging data obtained according to a global features analysis (230) and a class-combination (232) of a biological target, an indicated biology, an imaging acquisition modality and a protocol, a data processing technique, and a contrast agent. The quantitative imaging biomarker calibrator (116) applies an identified function (234) to the one or more pre-calibrated quantitative measurements to compute the one or more calibrated quantitative measurements based on a target class-combination (236) which is different from the class-combination (232).

Abstract: Method for generating contrast agent concentration map from a non-contrast enhanced Computed Tomography scan, a contrast enhanced Computed Tomography scan and corresponding spectral Computed Tomography data, comprising: a. Generating at least two different primary contrast agent concentration maps out of the non-contrast enhanced Computed Tomography scan, the contrast enhanced Computed Tomography scan and the spectral Computed Tomography data, b. Performing a local quality analysis of each primary contrast agent concentration map c. Determining local volumetric weights for each primary contrast agent concentration map based on the local quality analysis, and d. Generating a secondary contrast agent concentration map based on the two primary contrast agent concentration maps and on their corresponding local volumetric weights.

Abstract: A method includes obtaining at least a first energy dependent spectral image volume and a second different energy dependent spectral image volume from reconstructed spectral image data. The method further includes generating a multi-dimensional spectral diagram that maps, for each voxel, a value of the first energy dependent spectral image volume to a corresponding value of the second energy dependent spectral image volume. The method further includes generating a set of spectral texture analysis weights from the multi-dimensional spectral diagram. The method further includes retrieving a set of texture analysis functions, which are generated as a function of voxel intensity and voxel gradient value from a co-occurrence matrix histogram. The method further includes generating a texture analysis map through a texture analysis of the reconstructed spectral image data with the set of texture analysis functions and the set of spectral texture analysis weights and visually presenting the texture analysis map.

Abstract: A method includes determining a permeability metric of vascular tissue of interest based on a first time enhancement curve and second time enhancement curve corresponding to a first contrast material and a second contrast material flowing through the vascular tissue of interest and generating a signal indicative thereof.

Abstract: A method includes fusing at least three images together into a single fused image, wherein at least one of the three images includes a binary-pattern representation image. A system includes an image processing system (100) that combines an anatomical image, a functional image and a binary-pattern representation image into a single image. A computer readable storage medium encoded with computer executable instructions, which, when executed by a processor of a computer, cause the processor to combine an anatomical image, a functional image, and a binary-pattern representation of a different functional image into a single image such that the anatomical image and the functional image are visible in interspaces between binary points of the binary-pattern representation of the functional image.

Abstract: An imaging system includes a radiation source (110) that emits radiation that traverses an examination region and a detector (116) that detects radiation traversing the examination region and a subject disposed therein, and produces a signal indicative of the energy of the detected radiation. A data selector (122) energy discriminates the signal based on an energy spectra setting corresponding to first and second spectral characteristics of a contrast agent administered to the subject, wherein the contrast agent has a first attenuation spectral characteristic when attached to the target and a second different spectral characteristic when not attached to the target. A reconstructor (134) reconstructs the signal based on the first and second spectral characteristics and generates volumetric image data indicative of the target.

Abstract: A system includes a structural imaging acquisition unit, a functional imaging acquisition unit, and one or more processors. The structural imaging acquisition unit is configured to perform a structural scan to acquire structural imaging information of a patient. The functional imaging acquisition unit is configured to perform a functional scan to acquire functional imaging information of a patient.