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WO2008148250A1 - Procédé de débruitage par reconstruction de signaux à partir de données de spectre partiel - Google Patents

Procédé de débruitage par reconstruction de signaux à partir de données de spectre partiel Download PDF

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Publication number
WO2008148250A1
WO2008148250A1 PCT/CN2007/001898 CN2007001898W WO2008148250A1 WO 2008148250 A1 WO2008148250 A1 WO 2008148250A1 CN 2007001898 W CN2007001898 W CN 2007001898W WO 2008148250 A1 WO2008148250 A1 WO 2008148250A1
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WIPO (PCT)
Prior art keywords
signal
singular
data
denoising
complex
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PCT/CN2007/001898
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English (en)
Chinese (zh)
Inventor
Jianhua Luo
Yuemin Zhu
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Jianhua Luo
Yuemin Zhu
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Publication of WO2008148250A1 publication Critical patent/WO2008148250A1/fr

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/10—Image enhancement or restoration using non-spatial domain filtering
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30016—Brain

Definitions

  • the invention relates to the technical field of medical imaging detection, in particular to the field of noise removal of a magnetic resonance imaging fidelity signal, in particular to a signal denoising method based on partial frequency data signal reconstruction. Background technique
  • the magnetic resonance signal space (raw data space) is called K space, which is Fourier.
  • Transform space K space sampled signal after Fourier inverse transform and then modulo, that is, to obtain nuclear magnetic resonance (MR) image.
  • the usual image signals contain various noises, and the noise can be divided into additive noise and multiplicative noise.
  • the multiplicative noise is proportional to the size of the contaminated signal, and the additive noise is independent of the size of the contaminated signal.
  • the observation signal with additive noise / (X) mathematical model can be expressed as:
  • the first category multiple observation signal averaging method.
  • the main idea is that the elements of the #-sequence can be considered as independent, uniformly distributed, zero-mean, stationary random variables. In this way, when the observed signal/(X) sequence acquired multiple times is superimposed and averaged, the random noise (x) will cancel each other weakly, thereby achieving the purpose of denoising.
  • This method is currently recognized as a method of fidelity denoising and is widely used in medical equipment. But this method has the following drawbacks:
  • the second category single observation signal neighborhood estimation method.
  • the basic idea of this type of method is based on the assumption that /(X) can also be considered as approximately independent, identically distributed, zero-mean, stationary random variables in local small neighborhoods. This allows you to denoise / (X) with local spatial neighborhood estimates (such as mean, median, and fitted values). But in the vast majority of cases, Such methods often result in loss of signal detail and distortion. To this end, people have proposed a signal space i or method to improve signal fidelity (see literature: Charles, D.; Davies, ER, Distance-weighted median filters and their application to colour images, Visual Information Engineering, 2003. VIE 2003.
  • the third category single observation signal transform domain coefficient separation method.
  • the basic assumptions of this type of method are: The noise pollution signal can be divided into signal transform domain coefficients and noise transform domain coefficients in the transform domain.
  • the noise transform domain coefficients can be zeroed, and then the inverse transform method is used to reconstruct the noiseless signal.
  • Common methods are: Fourier transform, wavelet transform (see literature: Yunyi Yan; Baolong Guo; Wei Ni, Image Denoising: An Approach Based on Wavelet Neural Network and Improved Median Filtering, Intelligent Control and Automation, 2006. WCICA 2006.
  • the object of the present invention is to overcome the above shortcomings in the prior art, and to provide a high frequency signal loss or signal distortion in the process of signal denoising, and an average denoising method for multiple observation signals and a single observation signal.
  • the superiority of noise method easy signal acquisition, effective image noise removal, accurate display of original magnetic resonance image, high efficiency and practicality, stable and reliable working performance, and wide application range of signal denoising based on partial frequency data signal reconstruction.
  • the signal denoising method based on partial frequency data reconstruction includes the following steps:
  • the complex singularity analysis model in the signal denoising method based on partial frequency data signal reconstruction is:
  • the model parameter estimation in the signal denoising method based on partial frequency and rich data signal reconstruction includes the following steps:
  • G z (k) G(k)R s _ e (k);
  • the reconstruction of the observed signal in the signal denoising method based on the partial frequency data reconstruction may be: based on the result of the model parameter estimation ⁇ ,, ,..., ⁇ , reconstructing according to the following formula
  • the signal denoising method using the multiple observation signal averaging method in the signal denoising method based on partial spectral data signal reconstruction includes the following steps:
  • a signal denoising method based on partial frequency speech data signal reconstruction using the invention is used for actual magnetic resonance image denoising.
  • the method steps are: extracting part of the spatial spectrum data from the complete ⁇ space, and reconstructing a plurality of observation signals by the complex singularity analysis method according to the partial ⁇ spatial frequency data, and the observation signals and the original observation signals respectively have Different random noises and the same true noise-free signal are then used to remove noise using multiple observation signal averaging. Therefore, this method has the advantages of multiple observation signal average denoising method and single observation signal denoising method at the same time, and ingeniously avoids the problem that the multiple observation signal average denoising method is difficult to acquire the observation signal of the same real signal.
  • the signal distortion problem of the single observation signal denoising method is overcome, so that the image signal noise can be effectively removed and the signal-to-noise ratio is improved under the condition of ensuring high signal-to-noise ratio, high resolution and high precision of the image. It provides high-quality and reliable image information for medical MRI detection.
  • the method of the invention is efficient and practical, stable and reliable in work performance, and has wide application range, which brings great convenience to people's work and life, and also It has laid a solid theoretical and practical foundation for the further development of medical technology and the widespread application.
  • FIG. 1 is a schematic diagram of a ⁇ c + N/ 2) + 0 + W/ 2) function curve in a signal denoising method based on partial frequency chirp data signal reconstruction according to the present invention.
  • FIG. 2 is a schematic diagram of comparison of noise 0) and its convolution + ⁇ ( ⁇ )] * ns(x) in a signal denoising method based on partial frequency data signal reconstruction according to the present invention.
  • FIG. 3 is a schematic diagram showing the working principle of a signal denoising method based on partial frequency "resolution data signal reconstruction” according to the present invention.
  • FIGS. 4a and 4b are respectively before and after the image denoising method using the present invention in an object simulated magnetic resonance imaging test. Image comparison diagram.
  • Figures 5a and 5b are schematic views showing the comparison of the enlarged images of the coordinates (x, y) in Figs. 4a and 4b in the range of 240 ⁇ x ⁇ 320, 360 ⁇ y ⁇ 440, respectively.
  • Fig. 6 is a schematic diagram showing the comparison of the gradation curves of the pixels of the 205th row of Figs. 4a and 4b.
  • Figures 7a and 7b are respectively a schematic diagram of image comparison before and after the image denoising method of the 88th sagittal plane in the actual human magnetic resonance imaging test using the present invention.
  • Figures 7c and 7d show the image denoising of the coronal surface of the crown in the actual human magnetic resonance imaging test.
  • Figures 7e and 7f are respectively a schematic diagram of image comparison before and after the image denoising method of the 88th cross section of the roll in the actual human magnetic resonance imaging test. detailed description
  • the invention collects complete k-space data from the actual magnetic resonance equipment, and then extracts some frequency data from the phase, and the phase encoding range is -N/2 ⁇ N/2 - 1 , where N is the phase encoding number of the complete K-space data.
  • CSSA method complex singularity error analysis imaging method
  • DSRPSD partial spectral data signal reconstruction denoising method
  • Definition 1 Given a real or complex digital signal, the point where the difference is not zero is a singular point, the difference value at the singular point is a singular value, and the singular value can be a real number or a complex number. .
  • the complex digital signal g(x) can be Q singular functions
  • Complex linear functional representation of ⁇ w (X), w (x),..., w 1 ⁇ 2 (x) ⁇ : g(x) ⁇ a q w bq (x), x 0,l,...,Nl ... (1) where, , ... ⁇ is.
  • a singular point a complex singular value on .., ⁇ .
  • the next step is an estimate of the model parameters. If only part of the spectral data is known, and the real image is not known, the singular points and singular values obtained directly by the difference method are impossible. However, the missing part of the partial frequency data can be zero-padded, and the inverse Fourier transform can be performed to obtain an approximate image. Thus, the approximate image can be used to estimate the singularity, and then the singular point and the complex singular value are determined by the method of solving the singular equations. '
  • G 2 (k) G(k)R s _ e (k) (5)
  • . — e (yt) is a rectangular function, defined as follows:
  • the signal used to estimate the phase can be expressed as:
  • a pseudo-inverse matrix method can be used to obtain a minimum error solution, and L complex singular values ⁇ ⁇ , ⁇ 2 ,..., ⁇ are obtained.
  • the set of singular points is the corresponding singular value.
  • ⁇ a x , a 2 ,...,a Q ⁇ the observed signal /0
  • the real signal g(x) have the same set of singular points, and /0) corresponding to the singular point introduced by noise.
  • the singular value becomes ⁇ +" ⁇ 3 ⁇ 4), ⁇ 2 +ra(b 2 ), . ⁇ ., i3 ⁇ 4 , then: ⁇ '
  • the first term of the above formula is the reconstruction of the partial spectral data with zero-inverted Fourier transform
  • the second term is the singular analytic reconstruction of the partial frequency-divided data.
  • the denoising method has the advantages of multiple observation signal average denoising method and single observation signal denoising method, and avoids the problem of signal acquisition of multiple observation signal average denoising method.
  • Figure 2 is a comparison of the noise ra(x) and its convolution + ⁇ 0)] * ns ⁇ x). According to the periodic convolution property, the standard deviation relationship of 3 ⁇ 4S(X) and is:
  • the signal denoising method based on partial frequency speech data signal reconstruction of the present invention comprises the following steps: (1) extracting various partial spectrum data G according to different frequency bands from a complete signal spectrum space ( k);
  • the reconstruction processing includes the following steps:
  • G(
  • the model parameter estimation includes the following steps:
  • G z (k) G(k)R s _ e (k);
  • G(J ⁇ ) is the signal of g(x)
  • X 0, 1, .. ⁇ , N - 1
  • K -N/2-l,...,N/2 -l ⁇
  • the reconstructed plurality of observed signals g(x) sequences are superimposed and averaged as the final magnetic resonance complex image signal.
  • the linear functional representation of the singular function, the partial frequency data signal reconstruction method can be applied.
  • K-space data it can be reconstructed from x , two directions, and then each is averaged to achieve the denoising effect.
  • the reciprocal values from the two directions can also suppress the adverse effects of the linear stripes.
  • a line graph is a gray scale with a position change curve on a certain row or column of an image, which can easily make a difference in gray scale between images.
  • Experiment 1 Actual water model magnetic resonance imaging.
  • the image is a model for testing the imaging resolution. The characteristics of this image are:
  • FIGS. 4a and 4b are respectively a schematic diagram of comparison before and after image enhancement of the magnetic resonance model. It can be seen that Figure 4b has substantially removed the noise and is clearer than Figure 4a.
  • the signal-to-noise ratios of Figures 4a and 4b are 24.05 and 36.54, respectively.
  • Figure 5a and Figure 5b is a raster enlarged image of coordinates (x, y) in Figs. 4a and 4b, respectively, in the range of 240 ⁇ x ⁇ 320, 360 ⁇ y ⁇ 440. From the comparison of the fence images in Fig. 5a and Fig. 5b, it is found that the partial frequency data signal reconstruction denoising method can not only make the spatial resolution not weaken, but slightly increase.
  • Figure 6 is a comparison of the line drawing of line 205 of Figures 4a and 4b.
  • the two curves in Fig. 6 are line graphs of the 205th row of pixels of the images of Figs. 4a and 4b, respectively. It can be seen from the figure that the curve 2 corresponding to FIG. 4b is flat and smooth at the noise and denoised with respect to the curve 1 corresponding to FIG. 4a, and remains intact at the grating.
  • the method of the present invention is described as having the function of denoising to protect image details.
  • Figures 7a, 7c and 7e are both unnoised images
  • Figures 7b, 7d and 7f are both denoised images.
  • the image effects of Figs. 7b, 7d, and 7f appear more clear than those of Figs. 7a, 7c, and 7e, indicating that the method of the present invention has a good denoising effect on images of various structures.
  • the signal denoising method based on the partial frequency data signal reconstruction is adopted, because it collects complete K-space data from the actual magnetic resonance equipment, and then takes part of the K-space frequency data, and passes the part K-space spectrum data according to the part.
  • the complex singularity analysis method (CSSA method) reconstructs multiple observation signals, and these observation signals have different random noise and the same true noiseless signal from the original observation signals, and then use the multiple observation signal averaging method to remove noise.
  • CSSA method complex singularity analysis method
  • the signal distortion problem of single observation signal denoising method which saves the scanning time under the condition of high signal-to-noise ratio, high resolution and high precision of the image, and can effectively remove image noise for medical nuclear magnetic resonance Imaging inspection provides high quality Reliable image information;
  • the method of the invention is efficient and practical, the work performance is stable and reliable, and the scope of application is wide, which brings great convenience to people's work and life, and also further develops medical technology and technology. Wide-ranging universal application has laid a solid theoretical and practical foundation.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)
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Abstract

Cette invention concerne un procédé de débruitage par reconstruction de signaux à partir de données de spectre partiel, lequel procédé comprend les étapes qui consistent à extraire différentes données de spectre partiel à partir d'un espace de spectre de signaux intact en fonction de différentes gammes de fréquences, à reconstruire une pluralité de ces signaux d'observation à partir des données de spectre partiel au moyen de l'analyse de spectre singulier complexe, puis en fonction de ces multiples signaux d'observation reconstruits, à procéder au débruitage des signaux par pondération des multiples signaux d'observation. Ce mode de réalisation présente l'avantage de combiner le débruitage par pondération des signaux d'observation et le débruitage simple des signaux d'observation, ainsi, le temps de balayage est réduit, le bruit de l'image est efficacement éliminé et le rapport signal sur bruit est amélioré.
PCT/CN2007/001898 2007-06-08 2007-06-15 Procédé de débruitage par reconstruction de signaux à partir de données de spectre partiel WO2008148250A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2540534C2 (ru) * 2013-04-29 2015-02-10 Федеральное государственное бюджетное учреждение науки Институт физиологии им. И.П. Павлова Российской академии наук (ФГБУН ИФ РАН) Способ оценки функционального состояния центральной нервной системы человека
CN113598728A (zh) * 2021-08-31 2021-11-05 嘉兴温芯智能科技有限公司 生理信号的降噪方法、监测方法、监测装置及可穿戴设备
CN115291151A (zh) * 2022-09-28 2022-11-04 中国科学院精密测量科学与技术创新研究院 一种基于低相关分段的高精度磁共振信号频率测量方法

Families Citing this family (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101067650A (zh) * 2007-06-08 2007-11-07 骆建华 基于部分频谱数据信号重构的信号去噪方法
KR101320963B1 (ko) 2009-03-31 2013-10-23 후아웨이 테크놀러지 컴퍼니 리미티드 신호 잡음 제거 방법, 신호 잡음 제거 장치, 및 오디오 디코딩 시스템
CN102456216A (zh) * 2010-11-03 2012-05-16 北京普源精电科技有限公司 频谱分析仪的图像数据构造方法及装置
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US10514431B2 (en) * 2015-04-30 2019-12-24 Koninklijke Philips N.V. Method and apparatus for magnetic resonance imaging with RF noise
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US10949951B2 (en) * 2018-08-23 2021-03-16 General Electric Company Patient-specific deep learning image denoising methods and systems
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CN110432885A (zh) * 2019-09-11 2019-11-12 东北大学 一种光电容积脉搏波噪声去除方法
CN111610359B (zh) * 2020-05-29 2021-05-14 上海挚达科技发展有限公司 一种充电桩控制引导电路电压采集的滤波方法
CN113295928A (zh) * 2021-05-24 2021-08-24 国网天津市电力公司电力科学研究院 一种数显式电介质电导率测试仪增强方法及系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1198530A (zh) * 1998-04-20 1998-11-11 骆建华 奇异谱分析截断频谱信号重建方法及其应用
US20020099306A1 (en) * 2000-03-17 2002-07-25 Etymotic Research, Inc. Stimulus analysis system and method having adaptive artifact rejection
WO2006116265A2 (fr) * 2005-04-25 2006-11-02 University Of Rochester Procede et appareil de debruitage global pour l'imagerie tomographique a faisceau conique et a faisceau eventail
CN101067650A (zh) * 2007-06-08 2007-11-07 骆建华 基于部分频谱数据信号重构的信号去噪方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1198530A (zh) * 1998-04-20 1998-11-11 骆建华 奇异谱分析截断频谱信号重建方法及其应用
US20020099306A1 (en) * 2000-03-17 2002-07-25 Etymotic Research, Inc. Stimulus analysis system and method having adaptive artifact rejection
WO2006116265A2 (fr) * 2005-04-25 2006-11-02 University Of Rochester Procede et appareil de debruitage global pour l'imagerie tomographique a faisceau conique et a faisceau eventail
CN101067650A (zh) * 2007-06-08 2007-11-07 骆建华 基于部分频谱数据信号重构的信号去噪方法

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2540534C2 (ru) * 2013-04-29 2015-02-10 Федеральное государственное бюджетное учреждение науки Институт физиологии им. И.П. Павлова Российской академии наук (ФГБУН ИФ РАН) Способ оценки функционального состояния центральной нервной системы человека
CN113598728A (zh) * 2021-08-31 2021-11-05 嘉兴温芯智能科技有限公司 生理信号的降噪方法、监测方法、监测装置及可穿戴设备
CN113598728B (zh) * 2021-08-31 2024-05-07 嘉兴温芯智能科技有限公司 生理信号的降噪方法、监测方法、监测装置及可穿戴设备
CN115291151A (zh) * 2022-09-28 2022-11-04 中国科学院精密测量科学与技术创新研究院 一种基于低相关分段的高精度磁共振信号频率测量方法

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