Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Duke University

Brian Dale

Marcel Dominik Nickel

Berthold Kiefer

Stephan Kannengiesser

Xiaodong Zhong

Mustafa R Bashir

A magnetic resonance imaging system and method are provided for improved determination of noise bias effects in calculating fitted parameters for quantitative MRI procedures. The system and method includes selecting a range for the SNR and fitted parameter values, and for each of a plurality of base pairs of these values and for a plurality of b values, adding a random noise term to the real and imaginary components of a plurality of corresponding signal terms, fitting magnitudes of the resulting 'noisy' signals to determine a 'noisy' fitted parameter value, and compare the &#x201c;noisy&#x201d; and base fitted parameter values to determine a noise-based error for each pair of base values. The noise-based errors can be used to generate an error map, modify imaging parameters to reduce such errors, or correct fitted parameters directly.

Systems and methods for predicting errors and optimizing protocols in quantitative magnetic resonance imaging

Disclosed herein is a framework for identifying signal components in image data. In accordance with one aspect, the framework receives multiple measured signal values corresponding to respective quantified signal components in image data. The framework determines at least one first measure of fit map of a signal model based on the measured signal values. The measured signal values may be swapped to generate swapped signal values. At least one second measure of fit map of the signal model may be determined based on the swapped signal values. The multiple signal components may then be identified by comparing the first and second measure of fit maps.

Signal component identification using medical imaging

In a method to correct noise effects in magnetic resonance (MR) images, which is executed in a processor (computer), the processor executes a fitting algorithm in order to calculate initial values for each of selected variables in signal model that models noise effects in a modeled, noise-containing MR image. The processor then iteratively executes the same or a different fitting algorithm, in order to generate final values for each of the selected variables. The processor is provided with an actual, acquired MR image that contains noise, and the processor uses the final values of the selected variables to calculate synthetic signal intensities in the MR image, thereby producing a synthetic MR image with no noise bias effects of errors. This synthetic image is made available in electronic form at an output of the processor, as a data file.

Method and apparatus to correct noise effects in quantitative techniques in magnetic resonance imaging

A method for determining a concentration of a contrast agent in imaging data includes calculating a pre-contrast attenuation map of a region of interest of a patient. Following an administering of a contrast agent to the patient, a post-contrast attenuation map of the region of interest of the patient is calculated. An increase in attenuation value is determined based on the pre-contrast attenuation map and the post-contrast attenuation map and a contrast agent concentration map is calculated for the region of interest based on the increase in attenuation value.

Methods and systems for determining the concentration of a contrast agent

A computer-implemented method for concurrently estimating the amount of fat and iron in anatomical tissue from magnetic resonance (MR) signal data includes receiving a test signal representative of the anatomical tissue acquired using a MR pulse sequence type. A repository of reference signal data is generated. The repository comprises a plurality of reference signals derived by an MR signal simulation for a plurality of different transverse relaxation rates, a plurality of different fat fractions, and the MR pulse sequence type. A first reference signal is identified in the plurality of reference signals. The first reference signal provides a best match to the test signal based on one or more matching criteria. The repository is searched to determine a first transverse relaxation rate and a first fat fraction associated with the first reference signal. Then, the amount of fat and iron in the anatomical tissue is estimated based on the first transverse relaxation rate and the first fat fraction.

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Morrisville, NC, United States of America

Brian Dale