Presentation of the 3D ultrafast ultrasound scanners and probes

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Visualisation of Doppler Modes

Until now, we described how to estimate motion without discussing how to visualise it. Data visualisation is crucial for the effectiveness of a technique, and depends on the information to be extracted from the data, and from the nature of the data itself. The first method to visualize motion was used on CW and pulsed Doppler and consisted of displaying a spectrogram of the Doppler signal received (Figure 1.10a). In the case of pulsed Doppler, measurements of motion are made in a few pixels wide region of interest (ROI) manually selected on the B-Mode image by the operator, and the spectrogram is calculated in these pixels. The small size of the ROI allows to repeat a high number of focalized transmissions along its scan line, improving the sampling of the Doppler signal and thus the quality of the spectrogram, but is preventing to form an image of motion. The spectrogram is the method of choice to measure blood flow velocities, as well as cardiac synchronisation (e.g. delay between peak velocities across the different valves). Note that this technique is often referred as “pulsed- Doppler” itself; to avoid this ambiguity, we will refer to it as “spectral” Doppler in this document.
Colour Doppler and Power Doppler aim to image motion in 2D (or 3D). Therefore, pulsed Doppler measurements are repeated along several scan lines which implies a limitation: the bigger the region of interest selected, the less focalised transmission can be repeated for each scan lines, to maintain a sufficient PRF. Hence, the measurements cannot be as precise as in the case of spectral Doppler. Moreover, as the PRF is lower, so is the maximum velocity measurable. Each pixel in the specified region of interest has a specific colour-assigned value overlaid onto B-Mode images depending on Doppler data. In the case of colour Doppler, the central frequency of the Doppler signal of each pixel is calculated and color-coded on the image. By convention, the colour axis goes from blue to red to represent motion away and toward the probe, respectively (Figure 1.10b). The estimation of velocity and axial direction is of great interest to understand the blood circulation organisation (for example in the brain), the displacement of tissues (free walls of the heart) or turbulences associated to high speed flows (carotid arteries, cardiac valves …).
Power Doppler is another methodology to visualize motion which associates at each pixel the energy of its Doppler signal. By doing so, both directional and quantitative information about flow velocity are lost, however the sensitivity of the resulting image is enhanced. Therefore this mode is appreciated to visualize vessels architecture. Power Doppler is usually encoded in a red to white colour axis (Figure 1.10c).

Plane waves, diverging waves and coherent compounding

In the following paragraphs, we will present ultrafast ultrasound imaging, to be opposed to conventional ultrasound. In the latter, we saw that an image is formed line by line, so the maximum framerate is limited by the necessary time to form one line (i.e., the depth of the image) multiplied by number of desired lines. Ultrafast imaging relies on the insonification of the whole region to be scanned in one ultrasound transmission. Therefore, the time to form an image is only limited by the time-of-flight necessary for the wave to travel back and forth the depth of the image. If the desired image has N=100 lines, ultrafast imaging allows to multiply the framerate by a factor N=100, allowing to reach up to 10.000 frames/seconds. For linear array probes, a plane wave is transmitted to scan the medium directly below the probe, whereas diverging waves can be sent by phased-array probes to image a wider sector2 (Figure 1.13). To form the image, the echoes are processed by a conventional delay-and-sum beamformer. The drawback of ultrafast imaging is that as the transmitted beam is not focalized, the contrast and the resolution of the resulting image is lower than what is obtained with conventional ultrasound imaging.

Elastography

Elastography is a technique consisting of measuring the stiffness of tissues. One of the primary application of ultrafast ultrasound was the imaging of the propagation of shear waves within the body (Shear Wave Elastography, SWE). Indeed, the shear wave speed is a good way to assess the rigidity of the tissues in which they travel, which is a relevant marker to detect pathologies. In the human body, shear waves travel at speeds in the range 1 to 10m/s: thus they propagate through the field of view of ultrasound within a few milliseconds. Hence, ultrafast framerate (>1000frames/second) are required to correctly image their motion and estimate their speed in a 2D region of interest. The proof of concept of imaging shear waves propagation (Sandrin et al., 1999), local shear modulus calculation (Sandrin et al., 2002), and medical application on breast cancer detection (Bercoff et al., 2003) were developed in our group in parallel to the emergence of 2D ultrafast ultrasound imaging. For a more detailed introduction to elastography, please refer to the chapter 4 of this thesis.

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Ultrafast Doppler

A few years after the developments of elastography, ultrafast ultrasound was also applied to Doppler imaging (Bercoff et al., 2011). Indeed, we saw that pulsed Doppler modes were limited by the size of the region of interest in which Doppler measurements are made. Ultrafast imaging allows to overcome this limit as it provides a continuous sampling at a high PRF for each pixel of the image, which makes possible the computing of all Doppler modalities in each point: spectral, colour and power Doppler (Figure 1.15). Therefore, although it may seem surprising at the first time, the high framerate brought by ultrafast ultrasound improves the imaging of slow motions as well. Applied to brain imaging, it led to high sensitivity mapping of cerebral blood volume and functional ultrasound (Macé et al., 2011; Mace et al., 2013; Demené et al., 2014).

Imaging the propagation of natural waves

Ultrafast ultrasound also allows for the imaging of shear waves propagating naturally in the body. For instance, the propagation of the pulse wave in the blood vessels has been successfully imaged (Figure 1.16) and linked to the biomechanical properties of the arterial wall (Fujikura et al., 2007; Couade et al., 2010; Konofagou et al., 2011). The electromechanical wave in the heart has also been imaged, and showed to be a promising tool for the detection of arrhythmias (Provost et al., 2011). This topic will be further discussed in chapter 5.

3D ultrafast ultrasound imaging

The developments of the technologies brought by 2D ultrafast imaging can be extended to 3D and significantly broaden their scope of application. For instance, both natural and shear waves propagation occur in 3D, but their quantitative assessment is based on 2D observations at the cost of simplifying assumptions. This is of crucial importance to detect mechanical anisotropy (Lee et al., 2012) or analysing cardiac contraction activation (Provost et al., 2013). Furthermore, extending ultrafast Doppler to 3D would allow for the complete and qualitative angiography of complex networks of blood vessels, such as brain or tumours. Unfortunately, as discussed previously, 3D conventional imaging is limited by its low framerate. 3D ultrafast ultrasound was recently developed based on the use of 2D matrix arrays (Jensen et al., 2013; Provost et al., 2014) and promising applications have been investigated. For instance, the 3D ultrafast ultrasound scanner developed in our group was used to perform 3D ultrafast Doppler imaging of the carotid bifurcation (Figure 1.17), 3D shear wave imaging, and 3D vector flow imaging (Provost et al., 2014; Correia et al., 2016, 2018). These studies used heavy equipment, as the matrix array had 1024 channels to be sampled. Thus, promising researches are made to reduce the number of channels of 3D probes, either by using sparsely populated (Roux et al., 2017) or row-column arrays (Rasmussen et al., 2015; Flesch et al., 2017).

Table of contents :

Table of contents
chapter 1: Introduction Chapter
1.1. Introduction
1.2. Anatomy of the human heart
1.3. Clinical tools for cardiologists
1.3.1. Electrocardiogram
1.3.2. Magnetic Resonance Imaging
1.3.3. X-rays
1.3.4. Nuclear medicine
1.3.5. Ultrasounds
1.3.6. Current limits of clinical tools
1.4. Ultrafast ultrasound imaging
1.4.1. Plane waves, diverging waves and coherent compounding
1.4.2. Elastography
1.4.3. Ultrafast Doppler
1.4.4. Imaging the propagation of natural waves
1.4.5. 3D ultrafast ultrasound imaging
1.5. Thesis objectives
1.6. Chapter bibliography
chapter 2: 3D Ultrafast Ultrasound Imaging
2.1. Introduction
2.2. Presentation of the 3D ultrafast ultrasound scanners and probes
2.3. Development of specific tools
2.3.1. Real-time imaging
2.3.2. Visualisation tools
2.4. Conclusion
2.5. Chapter bibliography
chapter 3: 3D Backscatter Tensor Imaging
3.1. Introduction
3.1.1. Motivations
3.1.2. State of the art
3.1.3. Objectives
3.2. 3D Mapping of cardiac fibres orientation
3.2.1. Material and methods
3.2.2. Results
3.2.3. Discussion
3.3. Correction of aberrations due to movement
3.3.1. Material and methods
3.3.2. Results
3.3.3. Discussion
3.4. Conclusion
3.5. Chapter bibliography
chapter 4: 3D passive elastography of the left ventricle
4.1. Introduction
4.1.1. Motivations: why myocardial stiffness in interesting
4.1.2. State of the art: how to measure (myocardial) stiffness
4.1.3. Objectives: non-invasive passive elastography
4.2. Material and methods
4.2.1. Simulation
4.2.2. Ultrafast Ultrasound Imaging in-vivo
4.2.3. Post processing computation
4.3. Results
4.3.1. Simulations
4.3.2. In-vivo experiments
4.4. Discussion
4.5. Conclusion
4.6. Chapter Bibliography
chapter 5: 3D Ultrafast Imaging of Myocardial Contraction Activation
5.1. Introduction
5.2. State of the art, motivation, objectives
5.2.1. Electrophysiology of the heart and electrocardiogram
5.2.2. Imaging the cardiac electrophysiology
5.2.3. Imaging the cardiac contraction
5.2.4. Objectives
5.3. 3D mapping
5.3.1. Material and methods
5.3.2. Results
5.3.3. Discussion
5.4. Toward clinical applications
5.4.1. Material and methods
5.4.2. Results
5.4.3. Discussion
5.5. Conclusion
5.6. Chapter Bibliography
chapter 6: Conclusion

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