Abstract: Systems and methods are provided for generating a combined receipt in a distributed ledger system implemented by replicas of a network. The replicas maintain a distributed ledger comprising a plurality of executed transactions authenticated using a hash tree having a hash root. Some or all of the replicas cryptographically sign the hash root. A combined receipt for a first transaction and second transaction of a plurality of executed transactions is generated by determining path information comprising a minimum set of values required to generate the hash root from either the first transaction or the second transaction given the first transaction and the second transaction. The combined receipt for the first and second transactions comprises: i) the determined path information; and ii) signatures of one or more of the replicas which signed the hash root.

Abstract: Structures are delineated in medical or other images. First, various tissue types present in the image are statistically described using a maximum likelihood classifier. Second, the tissue of interest is described using an exemplar, which is derived either from an anatomical atlas or from user input. Third, the structure of interest is morphologically described. The process can be iterated until a desired level of accuracy is achieved.

Abstract: A phantom has a casing in which vials are arranged, preferably in rows and columns. The vials are filled with solutions of a substance which appears in the imaging modality to be tested. The solutions are of different concentrations; for example, the concentration can increase row by row. The solutions can contain two substances which appear in the imaging modality, in which case the concentration can increase row by row for one and column by column for the other. The phantom can be used to test the linearity of the response of a DCE-MRI or other medical imaging device and to determine whether the fault lies with the coil or the pulse sequence.

Abstract: In a sequence of medical image data showing tumors and blood vessels, a plasma signal is optimized to avoid flow artifacts by receiving a user input of a blood region and using the user input to seed an automated search. Each voxel is scored by time point of maximum intake, slope at maximum intake, peak value and conformance to a gamma variate curve, and the voxels with the highest scores are included in the ideal plasma region of interest. Uptake curves for both tumors and plasma are determined and used to estimate a volume transfer constant.

Abstract: In a human or animal organ or other region of interest, specific objects, such as liver metastases and brain lesions, serve as indicators, or biomarkers, of disease. In a three-dimensional image of the organ, the biomarkers are identified and quantified both before and after a surgical implant is implanted, and their reaction to the surgical implant is observed. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.

Abstract: Abnormal regions in volumetric image sets are detected and delineated through the following technique. Noise is suppressed in the original data. The background is classified into one or more background classes. An exemplar is identified. Essentially similar structures throughout the volume are identified; a directed clustering technique has been developed for doing so. Quantitative information (lesion volume, shape, etc.) is extracted and output to database.

Abstract: In a clinical trial using dceMRI, the assessment of tumor perfusion has problems of noise and reproducibility. To address those problems, an end-to-end method develops and enforces a standard imaging protocol, ensures site compliance both by pre-qualification and throughout the trial, ensures that the scanners function properly both at the outset and throughout the trial, develops an analysis process with automation and quality control to prevent human error, and provides analysis software to perform the assessment and to provide an electronic audit trail.

Abstract: In a sequence of medical image data showing tumors and blood vessels, a plasma signal is optimized to avoid flow artifacts by receiving a user input of a blood region and using the user input to seed an automated search. Each voxel is scored by time point of maximum intake, slope at maximum intake, peak value and conformance to a gamma variate curve, and the voxels with the highest scores are included in the ideal plasma region of interest. Uptake curves for both tumors and plasma are determined and used to estimate a volume transfer constant.

Abstract: An automated process classifies MRI or other medical imaging data as belonging to neurological structures. A training part of the process uses a baseline data set with accompanying classification map, as well as an arbitrary number of training data sets, also with accompanying classification maps. Each training data set is registered to the baseline data set using a warp-based automated registration algorithm. The resulting probability map, mean values and covariance matrices are used to classify structures in an image data set using a maximum likelihood criterion.

Abstract: Structures are delineated in medical or other images. First, various tissue types present in the image are statistically described using a maximum likelihood classifier. Second, the tissue of interest is described using an exemplar, which is derived either from an anatomical atlas or from user input. Third, the structure of interest is morphologically described. The process can be iterated until a desired level of accuracy is achieved.

Abstract: In a human or animal organ or other region of interest, specific objects, such as liver metastases and brain lesions, serve as indicators, or biomarkers, of disease. In a three-dimensional image of the organ, the biomarkers are identified and quantified both before and after a stimulus is applied, and their reaction to the stimulus is observed. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.

Abstract: An automated process classifies MRI or other medical imaging data as belonging to neurological structures. A training part of the process uses a baseline data set with accompanying classification map, as well as an arbitrary number of training data sets, also with accompanying classification maps. Each training data set is registered to the baseline data set using a warp-based automated registration algorithm. The resulting probability map, mean values and covariance matrices are used to classify structures in an image data set using a maximum likelihood criterion.

Abstract: Abnormal regions in volumetric image sets are detected and delineated through the following technique. Noise is suppressed in the original data. The background is classified into one or more background classes. An exemplar is identified. Essentially similar structures throughout the volume are identified; a directed clustering technique has been developed for doing so. Quantitative information (lesion volume, shape, etc.) is extracted and output to database.

Abstract: In a human or animal organ or other region of interest, specific objects, such as liver metastases and brain lesions, serve as indicators, or biomarkers, of disease. In a three-dimensional image of the organ, the biomarkers are identified and quantified. Multiple three-dimensional images can be taken over time, in which the biomarkers can be tracked over time. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images. Regions of normal and abnormal parameters within the 3D biomarker structure are identified. The information is used to highlight or visualize abnormal regions on the original 2D tomographic images.

Abstract: Abnormal regions in volumetric image sets are detected and delineated through the following technique. Noise is suppressed in the original data. The background is classified into one or more background classes. An exemplar is identified. Essentially similar structures throughout the volume are identified; a directed clustering technique has been developed for doing so. Quantitative information (lesion volume, shape, etc.) is extracted and output to database.

Abstract: Abnormal regions in volumetric image sets are detected and delineated through the following technique. Noise is suppressed in the original data. The background is classified into one or more background classes. An exemplar is identified. Essentially similar structures throughout the volume are identified; a directed clustering technique has been developed for doing so. Quantitative information (lesion volume, shape, etc.) is extracted and output to database.

Abstract: In a human or animal organ or other region of interest, specific objects, such as liver metastases and brain lesions, serve as indicators, or biomarkers, of disease. In a three-dimensional image of the organ, the biomarkers are identified and quantified both before and after a stimulus is applied, and their reaction to the stimulus is observed. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.

Abstract: In a human or animal brain or other nerve tissue, specific objects or conditions, such as brain lesions and plaques, serve as indicators, or biomarkers, of neurological disease. In a three-dimensional image of the region of interest, the biomarkers are identified and quantified. Multiple three-dimensional images can be taken over time, in which the biomarkers can be tracked over time. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.

Abstract: In a solid tumor or other cancerous tissue in a human or animal patient, specific objects or conditions serve as indicators, or biomarkers, of cancer and its progress. In a three-dimensional image of the region of interest, the biomarkers are identified and quantified. Multiple three-dimensional images can be taken over time, in which the biomarkers can be tracked over time. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.

Abstract: In a human or animal organ or other region of interest, specific objects, such as liver metastases and brain lesions, serve as indicators, or biomarkers, of disease. In a three-dimensional image of the organ, the biomarkers are identified and quantified. Multiple three-dimensional images can be taken over time, in which the biomarkers can be tracked over time. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.