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The time response of the ultrasound transducers used in our 3D ultrasound tomography device shows a slight reverberation. This may causes artifacts in the reconstructed images. Loss properties of materials used in the array fabrication have a big impact on their complex vibration behavior. Unfortunately, material parameters for accurate modeling are often not available in literature. Here, we present a method to derive loss properties of polymers and composites and how to include them in a finite element analysis (FEA). The method has three steps: First, an experiment to measure the frequency and thickness dependent sound attenuation. Second, a brute-force fit to a frequency-power law expression to obtain an analytic formulation. Third, a conversion of the sound attenuation to an equivalent structural loss factor. The last step is necessary as acoustic attenuation can not directly be implemented in structural mechanics FEA. We applied the method to derive loss properties of the filler and backing material which we use for our ultrasound transducer arrays. When including the loss factor in the simulation a reverberation is predicted, which matches the measurement well. Hence, considering loss properties allows more accurate modeling of complex vibration behavior. This aids in optimizing our ultrasound transducer array design towards better 3D ultrasound imaging.

Ultrasound computer tomography (USCT) is a promising modality for breast cancer diagnosis which images the reflectivity, sound speed and attenuation of tissue. Elastic properties of breast tissue, however, cannot directly be imaged although they have shown to be applicable as a discriminator between different tissue types. In this work we propose a novel approach combining USCT with the principles of strain elastography. Socalled USCT-SE makes use of imaging the breast in two deformation states, estimating the deformation field based on reconstructed images and thereby allows localizing and distinguishing soft and hard masses. We use a biomechanical model of the breast to realistically simulate both deformation states of the breast. The analysis of the strain is performed by estimating the deformation field from the deformed to the undeformed image by a non-rigid registration. In two experiments the non-rigid registration is applied to ground truth sound speed images and simulated SAFT images. Results of the strain analysis show that for both cases soft and hard lesions can be distinguished visually in the elastograms. This paper provides a first approach to obtain mechanical information based on external mechanical excitation of breast tissue in a USCT system.

Focused ultrasound (FUS) is a promising method to open the blood brain barrier (BBB) for treatment of neurodegenerative diseases. Accurate targeting is essential for a successful BBB opening (BBBo). We aim to develop a robust therapy planning for BBBo in mice, which is challenging due to the size of the brain and the influence of the skull on the ultrasound pressure distribution. For enabling mouse individual therapy planning, a simulation tool is proposed, developed and validated. We used the k-Wave toolbox to enable 3D acoustic simulations of the commercial FUS system from Image Guided Therapy (IGT). Micro-CT scans were used to model the geometry of skulls. Simulations using a mouse skull showed an attenuation of approx. 20–24% depending on the position of penetration, which was validated by hydrophone measurements in the same range. Based on these validations we planned BBBo in m ice by placing the transducer at different positions over the mouse brain and varying the excitation amplitude. With different transducer positions, the peak pressure in the brain varied between 0.54 MPa and 0.62 MPa at 11% output level, which is expected to enable safe BBBo. Subsequently, in vivo experiments were conducted using the aforementioned simulation parameters. BBBo was confirmed by contrast enhanced T1 weighted magnetic resonance images immediately after sonication.

For image registration of breast MRI and X-ray mammography we apply detailed biomechanical models. Synthesizing X-ray mammograms from these models is an important processing step for optimizing registration parameters and deriving images for multi-modal diagnosis. A fast computation time for creating synthetic images is essential to enable a clinically relevant application. In this paper we present a method to create synthetic X-ray attenuation images with an hardware-optimized ray tracing algorithm on recent graphics processing units’ (GPU) ray tracing (RT) cores. The ray tracing algorithm is able to calculate the attenuation of the X-rays by tracing through a triangular polygon-mesh. We use the Vulkan API, which enables access to RT cores. One frame for a triangle mesh with over 5 million triangles in the mesh and a detector resolution of \(1080\times 1080\) can be calculated and transferred to and from the GPU in about 0.76 s on NVidia RTX 2070 Super GPU. Calculation duration of an interactive application without the transfer overhead allows real time application with more than 30 frames per second (fps) even for very large polygon models. The presented method is able to calculate synthetic X-ray images in a short time and has the potential for real-time applications. Also it is the very first implementation using RT cores for this purpose. The toolbox will be available as an open source.

Synthetic-aperture (SA) imaging is a popular method to visualize the reflectivity of an object from ultrasonic reflections. The method yields an image of the (volume) contrast in acoustic impedance with respect to the embedding. Typically, constant mass density is assumed in the underlying derivation. Due to the band-limited nature of the recorded data, the image is blurred in space, which is quantified by the associated point spread function. SA volume imaging is valid under the Born approximation, where it is assumed that the contrast is weak. When objects are large with respect to the wavelength, it is questionable whether SA volume imaging should be the method-of-choice. Herein, we propose an alternative solution that we refer to as SA interface imaging. This approach yields a vector image of the discontinuities of acoustic impedance at the tissue interfaces. Constant wave speed is assumed in the underlying derivation. The image is blurred in space by a tensor, which we refer to as the interface spread function. SA interface imaging is valid under the Kirchhoff approximation, where it is assumed that the wavelength is small compared to the spatial dimensions of the interfaces. We compare the performance of volume and interface imaging on synthetic data and on experimental data of a gelatin cylinder with a radius of 75 wavelengths, submerged in water. As expected, the interface image peaks at the gelatin-water interface, while the volume image exposes a peak and trough on opposing sides of the interface.

3-D Ultrasound Computer Tomography (USCT) is aimed at early detection of breast cancer. The system uses Synthetic Aperture Focusing Technique (SAFT) for reconstruction of images. 2041 unfocused ultrasound transducers with approx. 2.5MHz center frequency, 1MHz bandwidth and 36° opening angle at −3dB. The system was optimized for highest image quality within a Region Of Interest (ROI). The analysis shows that to increase the size of ROI for the next generation USCT, the opening angle of transducers needs to be increased to 60° while the other characteristics should be preserved or even improved. The approach presented in this paper for fabricating the transducers fulfilled the expectation of reproducible transducer arrays regarding disc thickness and fibre position and size. The mean electrical impedance and phase of the fibres were approx. 10 kΩ and −30° respectively. Though the −3dB opening angle is successfully increased to 61.6°, unexpectedly the characteristics of all the fibres possess great variability and the absolute sound pressure is down by approx. 80%.

3D Ultrasound Computer Tomography (3D USCT) promises reproducible high-resolution images for early detection of breast tumors. For reflectivity reconstruction a 3D synthetic aperture focusing technique (SAFT) is used which calculates one reflectivity value for each voxel from about 10 million A-scans. In this work the standard SAFT algorithm is extended to calculate reflectivity characteristics for each voxel in the volume using a directional vector derived from the transducer positions. The reflectivity characteristic is evaluated with in-vivo data and enables new information about the reflecting tissues, e.g. local normals. The contrast of SAFT images of an example breast could be increased by 32% and the data shows potential for tissue classification by comparing reflectivity characteristics. Regardless of calculating twelve-times more data for the simplest case, a performance of 46% of the standard SAFT algorithm on GPU could be reached.

3D USCT II is a new imaging method aimed at early detection of breast cancer. Synthetic aperture focusing technique is used for reconstructing images which requires unfocused ultrasound emission and reception. Thus the SNR received is low. Coded excitation (CE) and match filtering techniques are used to increase the SNR. The Currently applied CE is a linear chirp.

As today's standard screening methods often fail to diagnose breast cancer before metastases have developed, an earlier breast cancer diagnosis is still a major challenge. To improve this situation, we are currently developing a fully three-dimensional ultrasound computer tomography (3D USCT) system, promising high-quality volume images of the breast. For obtaining these images, a time-consuming reconstruction has to be performed. As this is currently done on a PC, parallel processing in reconfigurable hardware could accelerate both signal and image processing. In this work, we investigated the suitability of an existing data acquisition (DAQ) system for further computation tasks. The reconfiguration features of the embedded FPGAs have been exploited to enhance the systems functionality. We have adapted the DAQ system to allow for bidirectional communication and to provide an overall process control. Our results show that the studied system can be applied for data processing.

The paper is focused on ultrasonic transmission tomography as a potential medical imaging modality for breast cancer diagnosis. Ultrasound speed is one of the tissue parameters which are related to the pathological tissue state. An alternative to the commonly used filtered backprojection is presented, which can solve the problem of sparse transducer distribution. It is based on regularization of the image reconstruction problem which imposes smoothness in the resulting images while preserving edges. The approach is analyzed on synthetic data sets and illustrated on data acquired on a tissue phantom.

At Forschungszentrum Karlsruhe an ultrasound computer tomograph for breast cancer imaging in 3D is under development. The aim of this project is the support of early diagnosis of breast cancer. The process of reconstructing the 20 GB of measurement data into a visual 3D volume is very compute intensive. The reconstruction algorithms are written in Matlab. As a calculation platform we are using grid technologies based on gLite, which provides a powerful computing infrastructure.

One of the most powerful approach in ultrasound tomography (UT) is making use of distorted Born iterative (DBI) method to reconstruct high quality image in order to help locate and identify tumors more precisely. Due to its iterative nature, it begins with Born approximation as the initial guess. Then, it makes use of the inhomogeneous Greens function, as the kernel function, to alternatively calculate the total field for the forward problem and the scattering function for the inverse problem. One principal computational problem involved is that inverse problem is ill-posed, which will result in divergence of the DBI method if inappropriate regularization is used. This paper presents the regularization with truncated total least square (TTLS) where the adaptive algorithm is used to choose the regularization parameter in each iteration of DBI instead of using a fixed truncated value in all the iterations. In order to prevent the solution from being contaminated by noise, adaptive algorithm truncates the smallest singular values while minimizing the loss of signal obtained from transducers. Numerical simulations demonstrate that the proposed adaptive algorithm in conjunction with TTLS outperform TTLS with fixed truncation parameter by effectively reducing the noise and minimizing the relative error.