SYSTEM AND METHOD FOR RF BASED FREQUENCY ENCODING USING INJECTION TRANSFORMERS FOR SIMULTANEOUS TRANSMIT AND RECEIVE

    公开(公告)号:US20240385268A1

    公开(公告)日:2024-11-21

    申请号:US18665093

    申请日:2024-05-15

    Abstract: A system for RF based frequency encoding utilizing a Bloch-Siegert shift, includes a controller, an RF encoding system, and an injection transformer simultaneous transmit and receive filter. The controller generates RF excitation pulses, RF based frequency encoding pulses, and a cancellation signal. The RF encoding system includes one or more RF coils configured to transmit the RF excitation pulses and RF based frequency encoding pulses, and to receive an MR signal from the subject where the MR signal includes a leakage signal induced by the RF based frequency encoding pulses. The injection transformer simultaneous transmit and receive filter is in signal communication with the controller and the RF encoding system. The injection transformer simultaneous transmit receive filter is configured to receive the cancellation signal, the MR signal including the leakage signal, and to cancel the leakage signal from the received MR signal to generate a filtered MR signal.

    System and method for B1-selective spatial encoding using magnetic resonance

    公开(公告)号:US11841413B2

    公开(公告)日:2023-12-12

    申请号:US17758138

    申请日:2020-12-30

    CPC classification number: G01R33/56554 G01R33/4831 G01R33/4833

    Abstract: The present application provides a system and method for using a nuclear magnetic resonance (NMR) system. The method includes performing a pulse sequence using the NMR system that spatially encodes NMR signal evolutions to be acquired from a subject using an aggregated radio-frequency (B1) field incoherence and resolving the NMR signal evolutions acquired from the subject using at least one of a dictionary of known magnetic resonance fingerprinting (MRF) signal evolutions to determine matches in the NMR signal evolutions to the known MRF signal evolutions or an optimization process. The method also includes generating at least two spatially-resolved measurements indicating quantitative tissue parameters of the subject in at least two locations.

    System and method for proton density mapping and receiver bias correction using magnetic resonance fingerprinting (MRF)

    公开(公告)号:US11079448B2

    公开(公告)日:2021-08-03

    申请号:US16461549

    申请日:2017-11-15

    Abstract: A system and method is provided for correcting receiver bias during quantitative proton density mapping with magnetic resonance fingerprinting (MRF). The method comprises acquiring MRF data from a region of interest in a subject by performing a pulse sequence using a series of varied sequence blocks to elicit a series of signal evolutions. The method further comprises comparing the MRF data to a MRF dictionary to simultaneously map proton density and another tissue property from the region of interest, the proton density map having a proton density signal and a receiver sensitivity profile signal. The method also includes quantifying the proton density signal and the receiver sensitivity profile signal using parameters provided by the proton density map and the tissue property map, and generating a quantitative map from the region of interest based on the proton density signal.

    SYSTEM AND METHOD FOR VISUALIZATION AND SEGMENTATION OF TISSUE USING A BAYESIAN ESTIMATION OF MULTICOMPONENT RELAXATION VALUES IN MAGNETIC RESONANCE FINGERPRINTING

    公开(公告)号:US20190353732A1

    公开(公告)日:2019-11-21

    申请号:US16416666

    申请日:2019-05-20

    Abstract: A method for magnetic resonance fingerprinting (MRF), including accessing MRF data and a dictionary of signal evolutions. A plurality of regions-of-interest (ROIs) are selected in the MRF data. A first series of tissue parameter estimates is generated from the MRF data in the ROIs using the dictionary and a multicomponent Bayesian framework. From the first series of tissue parameter estimates, probability distributions are computed for different tissue types. The method further includes creating a reduced dictionary by removing entries from the dictionary having tissue parameter values not contained within the computed probability distributions. A second series of tissue parameter estimates is generated from the MRF data using the reduced dictionary and a multicomponent Bayesian framework. The method also includes generating a tissue probability map for each different tissue type from the second series of tissue parameter estimates.

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