Icad, Inc.

Koninklijke Philips Corporation N.v.

Naira Muradyan

Andreas Muehler

A system and method for automated contrast arrival detection in temporally phased images or datasets of tissues effectively determines contrast arrival in regions that are substantially free of arteries. A plurality of tissue voxels in a plurality of temporally phased images are identified as a function of voxel enhancement characteristics associated with discrete tissue voxels. A processor/process computes average enhancement characteristics from the plurality of identified tissue voxels. The average enhancement characteristics are compared with predetermined average enhancement characteristics associated with contrast media arrival phases. Contrast media arrival phases in the temporally phased images are provided based on the comparison.

Automated method for tissue-based contrast media arrival detection for dynamic contrast enhanced MRI

This invention provides an automated system and method for determining contrast media arrival in vessels near tissues of interest that have been imaged using a predetermined imaging system that produces a plurality of temporally phased images. The system and method reliably distinguishes between actual contrast arrival and potential false positives that can render basic automated techniques inoperable or unreliable. In an illustrative embodiment, the system and method for determining a contrast arrival phase in a plurality of temporal phases of a medical image dataset of tissue includes an image pre-processor or process that, for each of at least a subset of the temporal phases of the medical image dataset, with at least a subset of the signal intensity values respectively in each of the temporal phases, computes signal intensity frequency distributions. An arrival phase analysis processor or process then determines the contrast arrival phase as a function of a relative change in the signal intensity frequency distributions between each of the temporal phases. The signal intensity frequency distributions can be characterized as histograms in an illustrative embodiment. The arrival phase can further be used for setting proper parameters in which to post-process temporally phased images using various methods for tissue perfusion analysis.

Automated method for contrast media arrival detection for dynamic contrast enhanced MRI

A method for performing a high-resolution pharmacokinetic analysis for calculation of tissue parameters for a fast-enhancing tissue enables medical personnel to accurately determine pharmacokinetic parameters in fast-enhancing tissues. The method includes obtaining mask image data of the tissue when it is in a steady state condition, obtaining a time series of image data of the tissue when the contrast agent is flowing in the tissue, and increasing a spatial resolution of the time series of image data using the mask image data to obtain a time series of increased spatial resolution image data. The method further includes performing a pharmacokinetic analysis to obtain data including at least one parameter that characterizes the tissue, providing a multi-parameter look-up table derived from a combination of two or more parameters, and providing a display including one parameter or a parametric image, where the parametric image is derived from the look-up table.

Method for tracking of contrast enhancement pattern for pharmacokinetic and parametric analysis in fast-enhancing tissues using high-resolution MRI

A method, system and computer-readable medium of filtering noise pixels and other extraneous data, including saturated fat tissue and air data in image data is provided. Examples of image data may include but are not limited to magnetic resonance imaging data and computed tomography data. The method includes receiving pixel count for each signal intensity value of the image data; determining a signal intensity value, I, corresponding to a pixel count of a greatest number of pixels, N; setting a noise threshold at a signal intensity value, I, corresponding to a pixel count, N, such that Nis determined based on N; and filtering from the image data one or more pixels with signal intensity values below the noise threshold. Nmay be determined such that N=N/3 or close to N/3.

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Bathesda, MD, United States of America

Naira Muradyan