WO2014203038A1 - Système et procédé pour mettre en œuvre un calcul de réservoir dans un dispositif d'imagerie par résonance magnétique à l'aide de techniques d'élastographie - Google Patents

Système et procédé pour mettre en œuvre un calcul de réservoir dans un dispositif d'imagerie par résonance magnétique à l'aide de techniques d'élastographie Download PDF

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Publication number
WO2014203038A1
WO2014203038A1 PCT/IB2013/055041 IB2013055041W WO2014203038A1 WO 2014203038 A1 WO2014203038 A1 WO 2014203038A1 IB 2013055041 W IB2013055041 W IB 2013055041W WO 2014203038 A1 WO2014203038 A1 WO 2014203038A1
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gel
computing
reservoir
recurrent neural
physical
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PCT/IB2013/055041
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English (en)
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Ozgur Yilmaz
Volkan ACIKEL
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Aselsan Elektronik Sanayi Ve Ticaret Anonim Sirketi
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Priority to PCT/IB2013/055041 priority Critical patent/WO2014203038A1/fr
Publication of WO2014203038A1 publication Critical patent/WO2014203038A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/067—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using optical means
    • G06N3/0675—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using optical means using electro-optical, acousto-optical or opto-electronic means
    • 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
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
    • G01R33/56358—Elastography

Definitions

  • the present invention relates to a system and method for implementing a specific class of a recurrent neural network algorithm called reservoir computing using magnetic resonance imaging device and its principles in elastography.
  • RNNs Recurrent Neural Networks
  • RNNs are connectionist computational models that utilize distributed representation and nonlinear dynamics of its units. Information in RNNs is propagated and processed in time through the states of its hidden units, which make them appropriate tools for sequential information processing.
  • RNNs stochastic energy based with symmetric connections
  • deterministic with directed connections There are two broad types of RNNs: stochastic energy based with symmetric connections, and deterministic with directed connections.
  • RNNs are known to be Turing complete computational models (Siegelmann and Sontag, 1995) and universal approximators of dynamical systems (Funahashi and Nakamura, 1993). They are especially appealing for problems that require remembering long-range statistical relationships such as speech, natural language processing, video processing, financial data analysis etc. Additionally, RNNs are shown to be very successful generative models for data completion tasks (Salakhutdinov and Hinton, 2012).
  • Previously Fernando and Sojakka implemented reservoir computing in a system consisting of a bucket of water and a camera mounted on top of the water surface.
  • the water waves act as the reservoir of dynamical activity that maps the input onto a high dimensional nonlinear space.
  • Using diffusive wave front interactions on water surface produces the necessary nonlinearity in the reservoir.
  • the water reservoir is vibrated by multiple mechanical actuators mounted on the bucket surfaces that act as the input device. It is necessary to encode the set of actual inputs (speech signals, images, financial data etc.) into mechanical actuator commands.
  • the camera captures the water wave evolution caused by actuator vibration, and uses the image properties (edge strength in a N*M grid) as the output of the reservoir.
  • the output vector of the reservoir is then used to train the network using classification methods for speech recognition task ("one" vs "zero” speech recognition).
  • Adamatzky (2001 and 2002) previously analyzed usage of various nonlinear media for computing. Walmsley (2001) and Duport et al. (2012) and Paquot et al. (2012) proposed optical devices and Adamatzky (2004) proposed chemical reactions for implementing nonlinear medium.
  • US patent 7,392,230 B2 proposed a method for implementing reservoir system using nanotechnology, i.e. molecular interactions modulated by electrical input.
  • Patents US 20130060772 Al and US 8301628 B2 suggest using an echo state network for ontology generation
  • patent EP 2389669 Al proposes using reservoir computing based neural network for geodatabase information processing.
  • Muthupillai et al. proposed a method for magnetic resonance (MR) imaging elastography, in which the tissue stiffness properties are measured using a setup in an MR machine.
  • the tissue is mechanically vibrated using actuators and the acoustic strain waves that propagate in the tissue are captured in MR image using special type of magnetic gradients called "motion sensitizing gradients".
  • the method output is a 3D MR image volume of the acoustic waves that vibrate the tissue.
  • the object of the invention is to provide a system for implementing reservoir computing in a magnetic resonance imaging device and utilizing both acoustic and magnetic wavefront interactions, and the inhomogeneity in the phantom gel as the nonlinearity in the medium.
  • Another object of the invention is to provide a method for preparing the physical medium of an inhomogeneous gel volume that is specifically tailored for the task of the reservoir computer. This method is very similar to unsupervised pre- training of neural networks with unlabeled data (Hinton et al., 2006). Detailed description of the invention
  • the physical construct (100) of the reservoir computing device is shown and it is composed of a block of gel (101), inhomogeneity introduced in the gel (102), mechanical actuators that vibrate the gel (103), an MR imaging machine (104) and a computing device (105) for processing and communicating reservoir data.
  • the gel is an agarose compound, however it can be chosen as any semisolid gel that can be vibrated by the actuators. Any shape can be used for the container of the gel, however using a cube enables systematic placement of mechanical actuators (103).
  • the inhomogeneity in the gel can be a combination of a non-metal object of any shape that distorts the acoustic wave propagation, and stiffness inhomogeneity due to varying concentration of gel ingredient.
  • An object can be inserted into the gel during preparation, or the inhomogeneity can be introduced in a region by using a different concentration of gel ingredient which creates different stiffness in that region, or both.
  • the actuators are located on 5 sides of the cube (bottom side is omitted). The actuators are able to create point source or planar acoustic waves, depending on the input code generated in encoding stage (202).
  • Actuators can be any type available in elastography methods: electromechanical driver, piezoelectric stack driver, focused-ultrasound based or acoustic speaker based.
  • MR imaging machine (104) is the both one of the input (via motion sensitizing gradient) devices and the output device in the system that can be used for phase-contrast MRI (Muthupillai et al., 1996). This technique provides the 3D volume images of propagating acoustic waves in the gel.
  • a computing device (105) is connected to the system to receive input from the outside world (201), to provide processing medium for encoding (202) and decoding (205) stages, to process (206) the reservoir output for a specific task (classification etc.) and to communicate the output (207) of the system to the outside world.
  • the reservoir computing system receives the input data (201).
  • the encoding stage (202) translates the input into a code that drives the mechanical actuators and modulates the motion sensitizing magnetic gradient.
  • input data is transformed into a set of instructions that drives the physical system.
  • the physical system is excited (203) according to these instructions generated in the previous stage and the phase-contrast MR imaging (204) is performed that gives an image volume of the gel.
  • the MR image volume of the gel is decoded (205). In the decoding stage, the MR image volume is converted into a data vector that represents the state of the complex acoustic wave patterns in the gel.
  • the decoded data vector is then further processed (206) according to the task at hand (eg. recognition, data compression, clustering etc.).
  • the stages (203) and (204) take place in the physical construct of the system whereas the rest of the stages are part of software implemented in the computing device (105).
  • This computer accesses input data (201) and fetches MR image volume (204) for decoding (205), and then applies processing algorithms (206) for the assigned task.
  • ⁇ (r) — fi . ⁇ ) s ⁇ k . r -f- )
  • the MR image is dependent on the gyromagnetic ratio ( ⁇ ) of the material at location r, the angular frequency ( ⁇ ), the wavenumber (k), initial phase offset (a) and peak displacement ( ⁇ ) of the mechanical excitation and the magnetic gradient (G).
  • G can be a temporally periodic signal:
  • V There are total of at least 14 (V) variables for encoding. Using these variables, the input data (201) needs to be converted into a specific set of system instructions and the system is excited (203) with these instructions.
  • actuator locations represented different frequency channels of the speech data, and time varying peak displacement of the waves represented the magnitude of the specific frequency channel.
  • the steps of the encoding stage (202) are shown in Figure 3.
  • the input data is first pre-processed (301), and this can be a sequence of operations (filter, whiten, transformations such as fourier or wavelet, dimensionality reduction etc.) that modifies and transforms the data to make it more appropriate for the subsequent stages.
  • the data At the end of pre-processing, the data have an inherent number of dimensions, K.
  • dimensionality of the input data (201) increases it takes more time to encode the input, and the complexity of the mapping algorithm as well as the need for dimensionality reducing pre-processing steps (i.e. principle component analysis, wavelet transform etc.) also increases.
  • X K is the k th dimension of the vector.
  • mapping stage a function maps the component of the input (can be real, integer or binary) onto the value of m th reservoir variable at epoch e, 3 ⁇ 4:
  • the steps of decoding stage (205) are shown in Figure 4.
  • the MR image volume passed from the imaging stage (204) is first post-processed (401) with a sequence of algorithms such as high pass filtering and edge extraction. This stage filters out low spatial frequency information and enhances the wave propagation information in the image.
  • the image volume can be divided into a coarser grid (M by N by P) than the MR image volume (many voxels falls inside a grid).
  • a single feature or a set of features (length Q) are computed for each grid cell.
  • the features can be average, standard deviation, or any spatial transformation output which can be applied to a neighborhood of voxels inside a grid cell.
  • the feature values for each grid cell are concatenated to form a data vector (403) of size (M*N*P*Q). This vector is the reservoir output for a given input.
  • the output vector of the reservoir is used as an input to another algorithm designed for a specific task such as classification, clustering, dimensionality reduction, data completion etc.
  • a specific task such as classification, clustering, dimensionality reduction, data completion etc.
  • the output of the overall system is computed from this stage but the specifics are irrelevant for this patent.
  • the acoustic waves that are captured by the MR imaging device need to be harnessing the nonlinearity provided by diffusive wavefront interactions and inhomogeneity in the gel.
  • the nonlinearity in the reservoir maps the input onto a high dimensional nonlinear manifold.
  • a series of simulations are performed in 2D acoustic waves.
  • Figure 5 gives an instance of waveforms generated by 3 different scenarios. In (501), 4 point acoustic sources with different phases are used in a homogeneous medium and waves seem to exhibit very complex patterns. A single point source and a very stiff line object are used in (502), and this scenario shows complex diffraction patterns.
  • stiffness inhomogeneity can be used.
  • Using a different concentration of gel ingredient creates different stiffness in a region, and inhomogeneity in stiffness can be achieved by mixing gel ingredient (603) without stirring the hot solution.
  • Data is composed of many instances (i.e. images), and the system instructions (303) of each one of the instances can be computed offline and stored (605) in the computing device.
  • annealing phase (604) the hot gel is excited (acoustic waves) (203) with the system instructions of the whole data (605) one instance at a time, continuously, repeatedly and in random instance order until the gel becomes solid.
  • the acoustic waves applied onto the hot gel stir and mix the gel guiding gel ingredient diffusion and move the inserted objects.
  • the inhomogeneities are created according to the applied excitation.
  • the diffusion/motion process minimizes the total energy of the gel, optimizing it for the data.
  • object insertion (602) and gel ingredient mixing (603) can be selected as the only source of inhomogeneity, or can be applied together.

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  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
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Abstract

La présente invention concerne une mise en œuvre physique d'un réseau neuronal récurrent, qui comprend un milieu de calcul de réservoir, un dispositif d'imagerie par résonance magnétique et un dispositif de calcul/stockage. Une substance de gel non homogène située dans un dispositif d'imagerie par résonance magnétique, des générateurs d'ondes acoustiques reliés au gel et aux gradients magnétiques dans le dispositif d'imagerie par résonance magnétique agissent en tant que réservoir physique de calcul. L'entrée est codée dans les ondes acoustiques et magnétiques générées, et des interactions d'ondes fournissent les opérations non linéaires pour un calcul. La sortie non linéaire du réservoir est lue par le dispositif d'imagerie par résonance magnétique (MR). Le gel non homogène est préparé par l'intermédiaire d'une procédure de recuit non surveillée qui optimise la représentation du milieu de réservoir pour un ensemble de données spécifique.
PCT/IB2013/055041 2013-06-19 2013-06-19 Système et procédé pour mettre en œuvre un calcul de réservoir dans un dispositif d'imagerie par résonance magnétique à l'aide de techniques d'élastographie WO2014203038A1 (fr)

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JP2018524711A (ja) * 2015-06-19 2018-08-30 株式会社Ｐｒｅｆｅｒｒｅｄ Ｎｅｔｗｏｒｋｓ クロスドメイン時系列データ変換装置、方法、およびシステム
WO2018212201A1 (fr) * 2017-05-15 2018-11-22 国立大学法人大阪大学 Dispositif de traitement d'informations et procédé de traitement d'informations
JP2019134100A (ja) * 2018-01-31 2019-08-08 国立大学法人 東京大学 情報処理デバイス
CN110892421A (zh) * 2017-05-29 2020-03-17 根特大学 基于混合波的计算
WO2021067358A1 (fr) * 2019-10-01 2021-04-08 Ohio State Innovation Foundation Optimisation d'ordinateurs de réservoir pour la mise en œuvre matérielle
WO2021084768A1 (fr) * 2019-10-29 2021-05-06 Tdk株式会社 Élément de réservoir et dispositif neuromorphique
US11295198B2 (en) 2017-10-26 2022-04-05 International Business Machines Corporation Implementation model of self-organizing reservoir based on lorentzian nonlinearity
US11397895B2 (en) 2019-04-24 2022-07-26 X Development Llc Neural network inference within physical domain via inverse design tool
CN116441554A (zh) * 2023-04-19 2023-07-18 珠海凤泽信息科技有限公司 一种基于强化学习的金纳米棒AuNRs合成方法、系统

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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018524711A (ja) * 2015-06-19 2018-08-30 株式会社Ｐｒｅｆｅｒｒｅｄ Ｎｅｔｗｏｒｋｓ クロスドメイン時系列データ変換装置、方法、およびシステム
WO2018212201A1 (fr) * 2017-05-15 2018-11-22 国立大学法人大阪大学 Dispositif de traitement d'informations et procédé de traitement d'informations
JPWO2018212201A1 (ja) * 2017-05-15 2020-03-12 国立大学法人大阪大学 情報処理装置及び情報処理方法
JP7108987B2 (ja) 2017-05-15 2022-07-29 国立大学法人大阪大学 情報処理装置及び情報処理方法
CN110892421A (zh) * 2017-05-29 2020-03-17 根特大学 基于混合波的计算
US11295198B2 (en) 2017-10-26 2022-04-05 International Business Machines Corporation Implementation model of self-organizing reservoir based on lorentzian nonlinearity
JP2019134100A (ja) * 2018-01-31 2019-08-08 国立大学法人 東京大学 情報処理デバイス
WO2019151254A1 (fr) * 2018-01-31 2019-08-08 国立大学法人東京大学 Dispositif de traitement d'informations
JP7109046B2 (ja) 2018-01-31 2022-07-29 国立大学法人 東京大学 情報処理デバイス
US11397895B2 (en) 2019-04-24 2022-07-26 X Development Llc Neural network inference within physical domain via inverse design tool
WO2021067358A1 (fr) * 2019-10-01 2021-04-08 Ohio State Innovation Foundation Optimisation d'ordinateurs de réservoir pour la mise en œuvre matérielle
WO2021084768A1 (fr) * 2019-10-29 2021-05-06 Tdk株式会社 Élément de réservoir et dispositif neuromorphique
CN116441554A (zh) * 2023-04-19 2023-07-18 珠海凤泽信息科技有限公司 一种基于强化学习的金纳米棒AuNRs合成方法、系统

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