WO2020082934A1 - 一种抑制伪影的光声图像重建方法 - Google Patents

一种抑制伪影的光声图像重建方法 Download PDF

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WO2020082934A1
WO2020082934A1 PCT/CN2019/106051 CN2019106051W WO2020082934A1 WO 2020082934 A1 WO2020082934 A1 WO 2020082934A1 CN 2019106051 W CN2019106051 W CN 2019106051W WO 2020082934 A1 WO2020082934 A1 WO 2020082934A1
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signal
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image reconstruction
sensor
suppression
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袁杰
马翔
朱昀浩
郭成雯
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南京大学
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
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  • the invention belongs to the fields of optics, acoustics, biomedicine, signal processing and image processing, and particularly relates to a photoacoustic image reconstruction method for suppressing artifacts.
  • Non-destructive image reconstruction methods are needed in various fields such as medical, military, and material monitoring.
  • photoacoustic imaging as a new imaging method, reflects the light absorption distribution of substances in the imaging area.
  • the current mainstream real-time photoacoustic imaging method is a basic delay summation method, which is simple and fast.
  • the delay-sum method will bring obvious artifacts in the picture and affect the quality of the picture. Therefore, it is very important to reduce the artifacts in the reconstruction method while maintaining the real-time nature of the method.
  • the technical problem to be solved by the present invention is to remove the artifacts in the photoacoustic image by a time-based multiple delay summation method.
  • the present invention discloses a photoacoustic image reconstruction method for suppressing artifacts, including the following steps:
  • Step 1 Obtain relevant parameters of the system and collect photoacoustic signals for the imaging area
  • Step 2 Select the appropriate pixel size and divide the imaging area into multiple pixels
  • Step 3 Calculate multiple delay sums for the pixels one by one in time sequence, recover the waveform of the pixel signal, and calculate the signal envelope;
  • Step 4 perform signal suppression on the recovered signal envelope of each pixel, suppress the artifact signal in the signal, and retain the real signal;
  • Step 5 the real signal value in the suppression signal is taken as the gray value of the pixel in the image, and stored in the picture, and the image reconstruction is finally completed.
  • the relevant parameters of the acquisition system include the sampling frequency f s of the sensor, the number of sensor units N, and the actual physical location of each sensor unit is After acquiring the sensor parameters, control the laser to illuminate the imaging area, and use the sensor to collect the signal of each sensor unit
  • step 2 the selected pixel size is ⁇ r, and the imaging area is divided into corresponding multiple pixel blocks according to the selected pixel size, for a total of M pixels, and the actual physical position of each pixel is
  • step 4 the artifact component in the signal is suppressed: first, the maximum value of the signal envelope corresponding to the pixel is located choose the appropriate suppression coefficient k, the suppression coefficient k is used to control the intensity of signal suppression, and calculate the suppressed signal
  • step 5 the true signal value of the suppression signal at time zero is selected As the gray value of the pixel in the image, store it in the image; calculate the suppressed signal for each pixel Finally, the reconstruction of the image is completed.
  • the laser is irradiated to the imaging area, and at the same time, the sensor collects the generated photoacoustic signal to reconstruct the image.
  • the calculation of the delay only requires the position of the sensor unit, and there is no special requirement on the shape of the sensor and the distribution of the unit.
  • the suppression coefficient k is used to control the degree of suppression. The greater the k, the stronger the suppression effect of noise and artifacts.
  • the calculation involves only basic operations and fast Fourier transform.
  • the calculation of each pixel is independent of each other. It has extremely high parallelizability and is suitable for real-time imaging.
  • the reconstruction method is applicable to both 2D and 3D reconstruction.
  • Figure 1 is a schematic diagram of signal acquisition.
  • Figure 2 is a schematic diagram of a linear sensor.
  • 3 is a schematic diagram of signal extraction envelope and suppression.
  • the technical problem to be solved by the present invention is to remove the artifacts in the photoacoustic image by a time-based multiple delay summation method.
  • the present invention discloses a photoacoustic image reconstruction method for suppressing artifacts, including the following steps:
  • Step 1 Obtain relevant parameters of the system and collect photoacoustic signals for the imaging area
  • Step 2 Select the appropriate pixel size and divide the imaging area into multiple pixels
  • Step 3 Calculate multiple delay sums for the pixels one by one in time sequence, recover the waveform of the pixel signal, and calculate the signal envelope;
  • Step 4 perform signal suppression on the recovered signal envelope of each pixel, suppress the artifact signal in the signal, and retain the real signal;
  • Step 5 the real signal value in the suppression signal is taken as the gray value of the pixel in the image, and stored in the picture, and the image reconstruction is finally completed.
  • the relevant parameters of the acquisition system include the sampling frequency f s of the sensor, the number of sensor units N, and the actual physical location of each sensor unit is
  • the imaging area is a 25mm ⁇ 25mm square two-dimensional area under the linear sensor, and the sensor is used to control the laser to illuminate the middle part of the imaging area to collect the signal of each sensor unit
  • c 0 is the speed of sound, which is related to the current medium and temperature.
  • the imaging area is located in water and the temperature is 20 ° C.
  • the speed of sound is 1.482 mm / us; use the calculated delay to find the corresponding in the sensor unit And restore the beamforming signal of the pixel
  • the signal envelope is extracted using Hilbert transform
  • the Hilbert transform can be realized by Fourier transform.
  • the signal is a discrete sampling point, it can be realized by fast Fourier transform, and the processing time can fully meet real-time imaging; , Only intercept The signal starts from ⁇ (m, n) 128 points forward and backward, and the envelope is calculated for these 256 points.
  • step 4 the artifact component in the signal is suppressed: first, the maximum value of the signal envelope corresponding to the pixel is located In this embodiment, the default maximum value appears near time 0, that is, near the center position of 128 points among 256 signal points, so the maximum value is searched from 128 points to both sides.
  • step 5 the true signal value of the suppression signal at time zero is selected Stored in the image as the gray value of the pixel in the image; the suppressed signal is calculated for each pixel in a total of 6,2500 pixels For pixels where artifacts and noise are located, the maximum value of the signal deviates from time zero, so the suppressed signal will be suppressed at time zero, and the signals of pixels that are not artifacts and noise will not be suppressed at time zero , To finally complete the reconstruction of the image and the suppression of noise and artifacts.
  • the calculation of the delay only requires the position of the sensor unit, and there is no special requirement on the shape of the sensor and the distribution of the unit. Therefore, in addition to the linear sensor in this embodiment, other sensors such as a ring sensor and a three-dimensional sensor are applicable.
  • the suppression coefficient k is used to control the degree of suppression. The greater the k, the stronger the suppression effect of noise and artifacts.
  • the above-mentioned multiple delay summation method can add the sensor directivity coefficient and distance coefficient for precise calculation.
  • the calculation of each pixel in this method does not depend on each other, so that a high degree of parallelization can be achieved, and the time to reconstruct the picture can be greatly reduced.
  • the theoretical minimum calculation time does not depend on the number of pixels after parallelizing each pixel, so it can be performed Real-time, fast imaging.
  • the calculation involves only basic operations and fast Fourier transform.
  • the calculation of each pixel is independent of each other. It has extremely high parallelizability and is suitable for real-time imaging.
  • the present invention proposes a photoacoustic image reconstruction method that suppresses artifacts. It should be pointed out that the required laser and ultrasonic device forms do not limit this patent; the number of ultrasonic sensor units used and the location of each unit No limitation is imposed on this patent; the relative positions of the sensor, laser, and imaging area do not limit this patent; the software and hardware implementation of the method described do not limit this patent. It should be pointed out that, for those of ordinary skill in the art, several improvements and modifications can be made without departing from the principles of the invention, and these should also be regarded as the scope of protection of the present invention. In addition, each component that is not clear in this embodiment can be implemented by the existing technology.

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Abstract

本发明公开了一种抑制伪影的光声图像重建方法，包括：获取系统的相关参数；选取合适的像素尺寸，将成像区域分割成多个像素；对每个像素遍历，按照时序顺序计算多重的延时求和，恢复出信号的波形；对每个像素点恢复出的信号计算信号包络，进行信号抑制，抑制信号中的伪影信号，保留真实信号；将抑制信号中的真实信号值作为图像中该像素的灰度值，保存在图片中，最终完成图像的重建。

Description

一种抑制伪影的光声图像重建方法 技术领域
本发明属于光学、声学、生物医学、信号处理及图像处理领域，尤其涉及一种抑制伪影的光声图像重建方法。
背景技术
在医学、军事、物质监测等多种领域需要进行非破坏性的图像重建方式。其中光声成像作为一种新型的成像方式，反映出了成像区域内物质的光吸收分布。目前主流的实时光声成像方法是一种基本的延时求和方法，具有简单、快速的特点。但是延时求和方法会在图片中带来明显的伪影，影响图片的质量，因而在保持方法实时性的同时，减少重建方法中的伪影至关重要。
发明内容
发明目的：本发明所要解决的技术问题是通过基于时序的多重延时求和的方法来去除光声图像中的伪影。
为了解决上述技术问题，本发明公开了一种抑制伪影的光声图像重建方法，包括如下步骤：
步骤1，获取系统的相关参数，对成像区域采集光声信号；
步骤2，选取合适的像素尺寸，将成像区域分割成多个像素；
步骤3，对像素逐个按照时序顺序计算多重的延时求和，恢复出该像素信号的波形，并计算信号包络；
步骤4，对每个像素点恢复出的信号包络，进行信号抑制，抑制信号中的伪影信号，保留真实信号；
步骤5，将抑制信号中的真实信号值作为图像中该像素的灰度值，保存在图片中，最终完成图像的重建。
本发明中，步骤1中，获取系统的相关参数包括传感器的采样频率f s，传感器单元的个数N，每个传感器单元的实际物理位置为
Figure PCTCN2019106051-appb-000001
获取传感器参数后，控制激光照射成像区域，并利用传感器，采集每个传感器单元的信号
Figure PCTCN2019106051-appb-000002
本发明中，步骤2中，选取的像素尺寸为Δr，按照选取的像素尺寸将成像区域分割成相应的多个像素块，共计M个像素，每个像素的实际物理位置为
Figure PCTCN2019106051-appb-000003
本发明中，步骤3中，对每个像素，恢复出该像素的波束信号：对第m,m＝1,2,3,...,M个 像素，首先计算该像素点与每个传感器单元的距离
Figure PCTCN2019106051-appb-000004
根据距离计算出声音从该像素的位置传播到每个传感器单元的延时
Figure PCTCN2019106051-appb-000005
其中c 0是声速；利用算得的延时找到在传感器单元对应的信号，并恢复出该像素点的波束形成信号
Figure PCTCN2019106051-appb-000006
恢复出信号后，利用希尔伯特变换提取信号包络
Figure PCTCN2019106051-appb-000007
本发明中，步骤4中，抑制信号中的伪影成分：首先定位出该像素点对应的信号包络的最大值
Figure PCTCN2019106051-appb-000008
选取合适的抑制系数k，抑制系数k用来控制信号抑制的强度，计算抑制后的信号
Figure PCTCN2019106051-appb-000009
本发明中，步骤5中，选取零时刻的抑制信号的真实信号值
Figure PCTCN2019106051-appb-000010
作为图像中该像素的灰度值，存入图像中；对每个像素点计算抑制后的信号
Figure PCTCN2019106051-appb-000011
最终完成图像的重建。
本发明中，无需多余的信号采集，仅需激光照射成像区域，同时传感器采集产生的光声信号，即可进行重建图像。
本发明中，延时的计算仅需要传感器单元的位置，对传感器的形状和单元的分布无特殊要求。
本发明中，需要选取合适的抑制系数k来抑制噪声和伪影信号，抑制系数k用来控制抑制程度，k越大，对噪声和伪影的抑制效果越强。
本发明中，计算仅涉及基本运算和快速傅里叶变换，同时各像素计算相互独立，具有极高的可并行化，适用于实时成像。
本发明中，重建的方法同时适用于二维和三维重建。
附图说明
下面结合附图和具体实施方式对本发明做更进一步的说明，上述和/或其他方面的优点将会变得更加清楚。
图1是信号采集的示意图。
图2是线性传感器的示意图。
图3是信号提取包络和抑制的示意图。
具体实施方式
发明目的：本发明所要解决的技术问题是通过基于时序的多重延时求和的方法来去除光声图像中的伪影。
为了解决上述技术问题，本发明公开了一种抑制伪影的光声图像重建方法，包括如下步骤：
步骤1，获取系统的相关参数，对成像区域采集光声信号；
步骤2，选取合适的像素尺寸，将成像区域分割成多个像素；
步骤3，对像素逐个按照时序顺序计算多重的延时求和，恢复出该像素信号的波形，并计算信号包络；
步骤4，对每个像素点恢复出的信号包络，进行信号抑制，抑制信号中的伪影信号，保留真实信号；
步骤5，将抑制信号中的真实信号值作为图像中该像素的灰度值，保存在图片中，最终完成图像的重建。
本实施例中，步骤1中，获取系统的相关参数包括传感器的采样频率f s，传感器单元的个数N，每个传感器单元的实际物理位置为
Figure PCTCN2019106051-appb-000012
在本实施例中，选用N＝128通道(单元)的线性传感器探头，相邻传感器单元间隔0.298mm,采样频率f s为28.98MHz，则传感器单元的实际物理位置为
Figure PCTCN2019106051-appb-000013
获取传感器和系统参数后，这些参数作为不变量存储在静态空间。成像区域为线性传感器下方25mm×25mm的方形二维区域，并利用传感器，控制激光照射成像区域的中间部分，采集每个传感器单元的信号
Figure PCTCN2019106051-appb-000014
本实施例中，步骤2中，选取的像素尺寸为Δr＝(0.1mm,0.1mm)，即每个像素对应的物理尺寸为0.1mm×0.1mm，按照选取的像素尺寸将成像区域分割成相应的250×250个像素块，共计M＝62500个像素，第m个像素的像素坐标为
Figure PCTCN2019106051-appb-000015
则每个像素的实际物理位置为
Figure PCTCN2019106051-appb-000016
Figure PCTCN2019106051-appb-000017
本实施例中，步骤3中，对每个像素，恢复出该像素的波束信号：对第m,m＝1,2,3,...,62500个像素，首先计算该像素点与每个传感器单元的距离
Figure PCTCN2019106051-appb-000018
其中
Figure PCTCN2019106051-appb-000019
根据距离计算出声音从该像素的位置传播到每个传感器单元的延时
Figure PCTCN2019106051-appb-000020
其中c 0是声速，与当前的介质和温度有关，在本实施例中，成像区域位于水中，温度为20℃，此时的声速为1.482mm/us；利用算得的延时找到在传感器单元对应的信号，并恢复出该像素点的波束形成信号
Figure PCTCN2019106051-appb-000021
恢复出信号后，利用希尔伯特变换提取信号包络
Figure PCTCN2019106051-appb-000022
希尔伯特变换可以通过傅里叶变换实现。此处因为信号为离散的采样点，因而可以通过快速傅里叶变换实现，处理时间完全可以满足实时成像；为了减少计算时间，在恢复信号
Figure PCTCN2019106051-appb-000023
时，仅截取
Figure PCTCN2019106051-appb-000024
信号从τ(m,n)开始往前和往后各128个点，针对这256个点的信号计算包络。
本实施例中，步骤4中，抑制信号中的伪影成分：首先定位出该像素点对应的信号包络的最大值
Figure PCTCN2019106051-appb-000025
在本实施例中，默认最大值出现在0时刻附近，即在256个信号点中的中心位置128点附近，因而从128点开始向两边搜寻最大值，第一次出现峰值时认为该峰值为最大值；此时选取合适的抑制系数k，该抑制系数k用来控制信号抑制的强度，将k定义为整数值可以减小计算的复杂度，本实施例中选取k＝3，计算抑制后的信号
Figure PCTCN2019106051-appb-000026
本实施例中，步骤5中，选取零时刻的抑制信号的真实信号值
Figure PCTCN2019106051-appb-000027
作为图像中该像素的灰度值，存入图像中；对总共62500个像素中的每个像素点计算抑制后的信号
Figure PCTCN2019106051-appb-000028
对于伪影和噪声所在的像素点，信号最大值偏离零时刻，因而抑制后的信号在零时刻处将会被抑制，而非伪影和噪声的像素点的信号在零时刻处不会被抑制，从而最终完成图像的重建，以及对噪声和伪影的抑制。
本实施例中，无需多余的信号采集，仅需激光照射成像区域，同时传感器采集产生的光声信号，即可进行重建图像。
本发明中，延时的计算仅需要传感器单元的位置，对传感器的形状和单元的分布无特殊要求，因而除却本实施例中的线性传感器，其余如环形传感器、三维传感器等均可适用。
本发明中，需要选取合适的抑制系数k来抑制噪声和伪影信号，抑制系数k用来控制抑 制程度，k越大，对噪声和伪影的抑制效果越强。
上述所涉及的多重延时求和方法，可以加入传感器方向性系数、距离系数进行精确化计算。同时该方法中每个像素的计算并不相互依赖，从而可以实现高度的并行化，重建图片的时间可以大大减小，理论的最小计算时间在并行化各个像素后不依赖像素数目，因而可以进行实时、快速的成像。在本方法中计算仅涉及基本运算和快速傅里叶变换，同时各像素计算相互独立，具有极高的可并行化，适用于实时成像。
本发明提出了一种抑制伪影的光声图像重建方法，应当指出，所需的激光器和超声装置的形式不对本专利构成限制；所使用的超声传感器单元的个数，每个单元所在的位置不对本专利构成限制；传感器和激光器以及成像区域的相对位置不对本专利构成限制；所述的方法的软件、硬件实现方式不对本专利构成限制。应当指出，对于本技术领域的普通人员来说，在不脱离发明原理的前提下还可以做出若干改进和润饰，这些也应视为本发明的保护范围。另外，本实施例中未明确的各组成部分均可用现有技术加以实现。

Claims (6)

  1. 一种抑制伪影的光声图像重建方法，其特征在于，包括如下步骤：
    步骤1，获取系统的相关参数，对成像区域采集光声信号；
    步骤2，选取合适的像素尺寸，将成像区域分割成多个像素；
    步骤3，对像素逐个按照时序顺序计算多重的延时求和，恢复出该像素信号的波形，并计算信号包络；
    步骤4，对每个像素点恢复出的信号包络，进行信号抑制，抑制信号中的伪影信号，保留真实信号；
    步骤5，将抑制信号中的真实信号值作为图像中该像素的灰度值，保存在图片中，最终完成图像的重建。
  2. 根据权利要求书1所述的一种抑制伪影的光声图像重建方法，其特征在于，步骤1中，获取系统的相关参数包括传感器的采样频率f s，传感器单元的个数N，每个传感器单元的实际物理位置为
    Figure PCTCN2019106051-appb-100001
    获取传感器参数后，控制激光照射成像区域，并利用传感器，采集每个传感器单元的信号
    Figure PCTCN2019106051-appb-100002
  3. 根据权利要求书1所述的一种抑制伪影的光声图像重建方法，其特征在于，步骤2中，选取的像素尺寸为Δr，按照选取的像素尺寸将成像区域分割成相应的多个像素块，共计M个像素，每个像素的实际物理位置为
    Figure PCTCN2019106051-appb-100003
  4. 根据权利要求书1所述的一种抑制伪影的光声图像重建方法，其特征在于，步骤3中，对每个像素，恢复出该像素的波束信号：对第m,m＝1,2,3,...,M个像素，首先计算该像素点与每个传感器单元的距离
    Figure PCTCN2019106051-appb-100004
    根据距离计算出声音从该像素的位置传播到每个传感器单元的延时
    Figure PCTCN2019106051-appb-100005
    其中c 0是声速；利用算得的延时找到在传感器单元对应的信号，并恢复出该像素点的波束形成信号
    Figure PCTCN2019106051-appb-100006
    恢复出信号后，利用希尔伯特变换提取信号包络
    Figure PCTCN2019106051-appb-100007
  5. 根据权利要求书1所述的一种抑制伪影的光声图像重建方法，其特征在于，步骤4中，抑制信号中的伪影成分：首先定位出该像素点对应的信号包络的最大值
    Figure PCTCN2019106051-appb-100008
    选取合适的抑制系数k，抑制系数k用来控制信号抑制的强度，计算 抑制后的信号
    Figure PCTCN2019106051-appb-100009
  6. 根据权利要求书1所述的一种抑制伪影的光声图像重建方法，其特征在于，步骤5中，选取零时刻的抑制信号的真实信号值
    Figure PCTCN2019106051-appb-100010
    作为图像中该像素的灰度值，存入图像中；对每个像素点计算抑制后的信号
    Figure PCTCN2019106051-appb-100011
    最终完成图像的重建。
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