A method and device for magnetic resonance imaging data acquisition guided by physiologic feedback

US2017332981A1 · US · A1

Patent metadata
FieldValue
Publication numberUS-2017332981-A1
Application numberUS-201515522098-A
CountryUS
Kind codeA1
Filing dateOct 27, 2015
Priority dateOct 31, 2014
Publication dateNov 23, 2017
Grant date

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Abstract

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An adaptive real-time radial k-space sampling trajectory (ARKS) can respond to a physiologic feedback signal to reduce motion effects and ensure sampling uniformity. In this adaptive k-space sampling strategy, the most recent signals from an ECG waveform can be continuously matched to the previous signal history, new radial k-space locations c were determined, and these MR signals combined using multi-shot or single-shot radial acquisition schemes. The disclosed methods allow for improved

First claim

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1 . A method of making a Magnetic Resonance Imaging (MRI) image of a body part from MRI data, comprising: obtaining physiologic data; analyzing the physiologic data to determine at least one previous time that the body part was in a similar position to the current position; analyzing MRI hardware-controllable settings used at the at least one previous time to determine new MRI hardware-controllable settings; scanning the body part with the new MRI hardware-controllable settings to produce new MRI data; and producing the MRI image using the new MRI data. 2 . The method of claim 1 wherein the physiologic data is a heart signal. 3 . The method of claim 1 , wherein the physiologic data is an electrocardiogram (ECG). 4 . The method of claim 1 , wherein an autocorrelation on the physiologic data is done to determine the at least one previous time. 5 . The method of claim 1 , wherein the MRI hardware-controllable settings are settings to control the magnetic fields and radiofrequency pulses of the MRI and correspond to k-space values of the time domain MRI signal. 6 . The method of claim 5 , wherein the new MRI hardware-controllable settings are such that the k-space values for the new MRI hardware-controllable settings differ from the k-space values for MRI hardware-controllable settings. 7 . The method of claim 1 , wherein the new MRI data along with previous MRI data from the at least one previous time is used to produce the MRI image. 8 . The method of, wherein the MRI image is a frame in an MRI cine. 9 . A Magnetic Resonance Imaging (MRI) device configured to make an MRI image of a body part from MRI data, comprising: a sensor to obtain physiologic data; a controller to analyze the physiologic data to determine at least one previous time that the body part was in a similar position to the current position, the controller analyzing MRI hardware-controllable settings used at the at least one previous time to determine new MRI hardware-controllable settings; a MRI scanner to scan the body part with the new MRI hardware-controllable settings so as to produce new MRI data; and an image processor to produce the MRI image using the new MRI data. 10 . The MRI device of claim 9 , wherein the sensor is a heart sensor and the physiologic data is a heart signal. 11 . The MRI device of claim 9 , wherein the physiologic data is an electrocardiogram (ECG). 12 . The MRI device of claim 9 , wherein the controller does an autocorrelation on the physiologic data is done to determine the at least one previous time. 13 . The MRI device of claim 9 , wherein the MRI hardware-controllable settings are settings to control the magnetic fields and radiofrequency pulses of the MRI scanner and correspond to k-space values of the time domain MRI signal. 14 . The MRI device of claim 9 , wherein the new MRI hardware-controllable settings are such that the k-space values for the new MRI hardware-controllable settings differ from the k-space values for MRI hardware-controllable settings. 15 . The MRI device of claim 9 , wherein the new MRI data along with previous MRI data from the at least one previous time is used to produce the MRI image. 16 . The MRI device of claim 9 , wherein the MRI image is a frame in an MRI cine. 17 . A method of making an Magnetic Resonance Imaging (MRI) image of a heart from MRI data comprising: obtaining physiologic data; analyzing the physiologic data to determine at least one previous time that the heart was in a similar position to the current position; analyzing MRI hardware-controllable settings used at the at least one previous time to determine new MRI hardware-controllable settings; scanning the heart with the new MRI hardware-controllable settings to produce new MRI data; and producing the MRI image of the heart using the new MRI data. 18 . The method of claim 17 , wherein an autocorrelation on the physiologic data is done to determine the at least one previous time. 19 . The method of claim 17 , wherein the MRI hardware-controllable settings are settings to control the magnetic fields and radiofrequency pulses of the MRI and correspond to k-space values of the time domain MRI signal. 20 . The method of claim 17 , wherein the new MRI data along with previous MRI data from the at least one previous time is used to produce the MRI image.

Assignees

Inventors

Classifications

  • for the heart · CPC title

  • A61B5/7289Primary

    Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition · CPC title

  • Cine imaging · CPC title

  • involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

  • using a non-Cartesian trajectory · CPC title

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What does patent US2017332981A1 cover?
An adaptive real-time radial k-space sampling trajectory (ARKS) can respond to a physiologic feedback signal to reduce motion effects and ensure sampling uniformity. In this adaptive k-space sampling strategy, the most recent signals from an ECG waveform can be continuously matched to the previous signal history, new radial k-space locations c were determined, and these MR signals combined usin…
Who is the assignee on this patent?
Univ Pennsylvania
What technology area does this patent fall under?
Primary CPC classification A61B5/7289. Mapped technology areas include Human Necessities.
When was this patent published?
Publication date Thu Nov 23 2017 00:00:00 GMT+0000 (Coordinated Universal Time) (A1). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).