Adaptive matched filters for contrast imaging

Sébastien Ménigot, Iulian Voicu, Anthony Novell, Mélouka Elkateb Hachemi

Amar, Jean-Marc Girault

► To cite this version:

Sébastien Ménigot, Iulian Voicu, Anthony Novell, Mélouka Elkateb Hachemi Amar, Jean-Marc Girault. Adaptive matched filters for contrast imaging. IEEE. Ultrasonics Symposium (IUS), 2010 IEEE International, Oct 2010, San Diego, United States. IEEE, pp.1728 - 1731, 2010, 10.1109/ULT-SYM.2010.5935573. [hal-01075515](https://hal.science/hal-01075515)

HAL Id: hal-01075515

https://hal.science/hal-01075515

Submitted on 17 Oct 2014

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<figure><img src="image_1.png" /></figure>
## 1. Introduction

Conventional ultrasound contrast imaging systems use a fixed transmit frequency. However it is known that the insonified medium (microbubbles) is time-varying and therefore an adapted time-varying excitation is expected. We suggest an adaptive imaging technique which selects the optimal transmit frequency that maximizes the contrast tissue ratio (CTR). This ratio can be maximized if the microbubbles backscattered power is maximized.

$$CTR = \frac{E_{\text{bubbles}}}{E_{\text{tissue}}}, \text{ with } \begin{cases} E_{\text{bubbles}} & \text{the microbubbles backscattered power} \\ E_{\text{tissue}} & \text{the tissue backscattered power} \end{cases}$$

Two matched filters (MF) techniques are used to improve the image contrast. The first technique is an adaptive MF technique and the second is a RLS technique derived from identification theory.

## 2. Materials

### Microbubbles

* Microbubbles SonoVue™: mean diameter of 4.5 µm with shell thickness of 1 nm; Resonance frequency: $f_R = 2.1$ MHz

* Concentration: 1/2000 diluted solution of Sonovue™

* Immersed in a blood mimicking fluid (BMF)

### Acoustical Measurements

* Arbitrary function generator piloted by Matlab®

* 2 perpendicular transducers:
  1. Emission: 2.25 MHz - BW 74%
  2. Reception: 3.5 MHz - BW 63%

<figure><img src="image_2.png" /></figure>

## 3. Methods

1. Switch in position 1

1.1 Sending a pulse on the bubbles

1.2 Receiving the backscattered signal

1.3 Repeating (1.??) and (1.??) 20 times

1.4 Linear combination(ACP) from this 20 signals

1.5 Identification system with an adaptive filter

2. Switch in position 2

2.1 Amplification of the signal such as $E_5 = E_2$ and reversal

2.2 Pulse is the signal

2.3 return to ??

## 4. Matched Filter

### First Matched Filter

<figure><img src="image_3.png" /></figure>

### Recursive Least Squares (RLS) filter

Identification by RLS, i.e. minimizing the squared error such as

$$e_n = y_n - x_n^T \theta_{n-1}$$

<figure><img src="image_4.png" /></figure>

## 5.1 Results & Discussion : first simulation

<figure><img src="image_5.png" /></figure>

Gain between signal in (fig 1) and signal in (fig 4) : 17.68 dB

## 5.2 Results & Discussion : second simulation

<figure><img src="image_6.png" /></figure>

SNR = 4.74 dB

<figure><img src="image_7.png" /></figure>

Gain with RLS : 24.72 dB
Gain without RLS : 10.85 dB

A Filter RLS of fourth order is sufficient to modelize the bubble system. The RLS filter allows us to extract the information of the bubble system better than the simple match filter.

## 5.3 Results & Discussion : experiment

<figure><img src="image_8.png" /></figure>

Compared to the non-optimized imaging technique, the both proposed methods give a gain superior to around 5 dB.

* Advantage: gain improvement ;

* Drawback: increasing of the system complexity.

## 6. Conclusion & future prospects

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

* Implementation in an open echograph.