Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/28—Details of apparatus provided for in groups G01R33/44 - G01R33/64
  • G01R33/38—Systems for generation, homogenisation or stabilisation of the main or gradient magnetic field
  • G01R33/385—Systems for generation, homogenisation or stabilisation of the main or gradient magnetic field using gradient magnetic field coils
  • G01R33/3854—Systems for generation, homogenisation or stabilisation of the main or gradient magnetic field using gradient magnetic field coils means for active and/or passive vibration damping or acoustical noise suppression in gradient magnet coil systems
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/28—Details of apparatus provided for in groups G01R33/44 - G01R33/64
  • G01R33/283—Intercom or optical viewing arrangements, structurally associated with NMR apparatus
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
  • G01R33/48—NMR imaging systems
  • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
  • G01R33/543—Control of the operation of the MR system, e.g. setting of acquisition parameters prior to or during MR data acquisition, dynamic shimming, use of one or more scout images for scan plane prescription
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
  • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
  • G10K11/1781—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions
  • G10K11/17821—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the input signals only
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
  • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
  • G10K11/1787—General system configurations
  • G10K11/17873—General system configurations using a reference signal without an error signal, e.g. pure feedforward
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
  • G10K2210/10—Applications
  • G10K2210/116—Medical; Dental
  • G10K2210/1161—NMR or MRI
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
  • G10K2210/30—Means
  • G10K2210/301—Computational
  • G10K2210/3038—Neural networks
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
  • G10K2210/30—Means
  • G10K2210/301—Computational
  • G10K2210/3047—Prediction, e.g. of future values of noise

Definitions

• embodiments may provide for a system that generates anti-noise by means of a sound transducer to compensate for noise experienced at the ears of a subject present in the magnetic resonance imaging system, the noise resulting from operation of the magnetic resonance imaging system.
• a sound transducer to compensate for noise experienced at the ears of a subject present in the magnetic resonance imaging system, the noise resulting from operation of the magnetic resonance imaging system.
• Embodiments may provide a reliable prediction by using two approaches: first, one does not only rely on a sole dependency of the noise on the gradient coil pulse commands, but rather refers to both the gradient coil pulse commands and at least one further parameter describing an imaging of the subject. Secondly, a trained machine learning system is used to predict the noise, i.e., to provide information as to which anti -noise to use.
• Execution of the machine executable instructions causes the processor to receive a selection input for selecting, out of the gradient coil pulse commands, a first set of gradient coil pulse commands. Execution of the machine executable instructions further causes the processor to provide the selected gradient coil pulse commands and at least one value relating to a respective at least one further parameter describing an imaging of the subject to a trained machine learning system external to the processor. Execution of the machine executable instructions further causes the processor to - in response to the providing - , receive from the machine learning system information as to anti-noise to be generated by a sound transducer to compensate for noise experienced at the subject’s ears and resulting from operation of the magnetic resonance imaging system with the first set of gradient coil pulse commands and under the constraints of the at least one value.
• the trained machine learning model used with the magnetic resonance imaging system is suitably obtained.
• the medical system may here be as well a magnetic resonance imaging system, wherein the transducer here has a function of a microphone.
• the medical system may itself provide the trainig data used in the training of the machine learning model.
• Using the trained machine learning system may include providing selected gradient coil pulse commands and at least one value relating to a respective at least one further parameter describing an imaging of the subject to a trained machine learning system, and in response to the providing, receiving from the machine learning system information as to anti -noise to be generated by a sound transducer to compensate for noise experienced at the subject’s ears and resulting from operation of the magnetic resonance imaging system with the first set of gradient coil pulse commands and under the constraints of the at least one value.
• a computer readable signal medium may include a propagated data signal with computer executable code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof.
• a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
• Fig, 8a shows a flow diagram illustrating an example of a method of generating anti noise
• the machine -executable instructions 140 provide a means for the processor 130 to control the magnetic resonance imaging system 100.
• the machine -executable instructions 140 may also enable the processor 130 to perform various data analysis and image reconstruction tasks.
• the machine-executable instructions 140 may also enable the processor 130 to control the user interface 132 in a manner as to offer inputs to be made, for instance by asking for inputs (by a mask layer with a display user interface, or by audio commands at an audio user interface), namely by enabling a selection out of predetermined units or a numerical input.
• the machine -executable instructions 140 may then further enable the processor 130 to receive these inputs and to cause the input data to be stored in the memory 134.
• the memory 134 is as well shown as containing multiple preparatory scan commands 142. The memory 134 is then shown as containing selected preparatory scan commands 144.
• An operator might be able to select from values 164 indicating “head” (e.g., “1”), “neck” (“2”), “thorax” (“3”), “abdomen” (“4”), “pelvis” (“5”), “foot” (“6”), “extremities” (“7”), one of these and store the selected value 166 in the memory.
• the memory is further shown to store further values 168 (e.g., numerical values) that are freely, without a major limitation, input via the user interface 132. Such further values may include those indicating the age, height and/or weight of the subject 118.
• Fig. 3 schematically illustrates the operation principle of noise cancellation at an earplug 170.
• the impinging noise here represented by the sound wave 172 is compensated by superimposition of the same wave that is, however, inverted to form inverted wave 174.
• “Inverting” means that the wave is phase-shifted by 180 °.
• superimposition of the wave 172 and the inverted wave 174 means that the noise is cancelled at 176.
• the noise is recorded and inverted, the anti -noise is in the present embodiment predicted.
• FIG. 8c is a flowchart which illustrates a method of generating anti-noise wherein the machine learning system is already trained, i.e., already provided as trained, and such method might be performed with the magnetic resonance imaging system of Fig.1 or Fig. 2.
• the method of Fig, 8a might be performed with a magnetic resonance system having both the loudspeakers as in Fig. 1 or Fig. 2 and the microphone of Fig. 5).
• Examples may possibly comprise one or more of the following features:
• a system for machine learning, trained or to be trained.
• a system for presenting the ant-noise to the subject for instance a loudspeaker, headphones, a headset, or earphones with a shielding against the magnetic fields in the magnetic resonance imaging system.
• a computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

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• Physics & Mathematics (AREA)
• Engineering & Computer Science (AREA)
• Condensed Matter Physics & Semiconductors (AREA)
• General Physics & Mathematics (AREA)
• Acoustics & Sound (AREA)
• Multimedia (AREA)
• Signal Processing (AREA)
• High Energy & Nuclear Physics (AREA)
• Magnetic Resonance Imaging Apparatus (AREA)

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• 2021
  • 2021-07-26 EP EP21187716.2A patent/EP4124876A1/en not_active Withdrawn
• 2022
  • 2022-07-15 EP EP22751072.4A patent/EP4377708A1/en active Pending
  • 2022-07-15 CN CN202280052568.0A patent/CN117795361A/zh active Pending
  • 2022-07-15 US US18/291,597 patent/US12487303B2/en active Active
  • 2022-07-15 WO PCT/EP2022/069927 patent/WO2023006456A1/en not_active Ceased
  • 2022-07-15 JP JP2024504513A patent/JP2024529948A/ja active Pending

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