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3D Ultrasound Computed Tomography (USCT) is an imaging method for early breast cancer detection. The third generation 3 USCT device is developed at Karlsruhe Institute of Technology. The USCT III device has a hemispherical transducer distribution and emits and receives nearly spherical waves. This enables reflection and transmission imaging simultaneously and fully in 3D. The main challenges for the front-end design are to integrate a large number of transducers, to allow high voltage coded excitation, and to receive low amplitude signals with high quality. These challenges were solved using a smart sensor frontend design with a custom application specific integrated circuit (ASIC).

Breast cancer is the most common cancer type among women. Approximately 40,000 women are expected to die from breast cancer every year. While digital mammography has a central role in the early diagnosis of breast cancer, many cancers are not visible in mammography, for example in women with dense breast tissue. Contrast enhanced magnetic resonance imaging (CE-MRI) of the breast is often used to detect lesions not visible in mammography. Lesions with suspicious characteristics on CE-MRI need to be further assessed with MRI-guided biopsy. However, MRI-guided biopsy is expensive, time consuming, and not widely available. In this paper, a novel method for a matching tool between MRI and spot mammograms is proposed. Our aim is to transfer information that is only visible in MRI onto mammographic spot projections, to enable X-ray guided biopsy even if the lesion is only visible in MRI. Two methods of registration in combination are used; a biomechanical model based registration between MRI and full view X-ray mammograms and a subsequent image based registration between full mammograms and spot mammograms. Preliminary results assessed for one patient from the Medical University of Vienna are presented. The target registration error (TRE) of biomechanical model based registration is 2.4 mm and the TRE of the image based registration is 9.5 mm. The total TRE of the two steps is 7.3 mm.

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.

A promising candidate for sensitive imaging of breast cancer is 3D Ultrasound Computer Tomography (3D USCT). So far its clinical applicability for diagnosis has been limited by the duration of the demanding image reconstruction. In this paper we investigate how signal processing and image reconstruction can be accelerated for diagnosis by using heterogeneous hardware. Additionally, the time and costs for real-time system for a future diagnosis and therapy device is estimated. Reusing the device's built-in FPGA-based data acquisition system (DAQ) through reconfiguration results in a speed-up by a factor of 7 for signal processing and by a factor of 2 for image reconstruction. Applying cutting-edge single FPGAs and GPUs, speed-ups by a factor of 10 (FPGA) and 6 (GPU) for signal processing and 15 (FPGA) and 37 (GPU) for image reconstruction were achieved compared to a recent quad-core Intel Core-i7 CPU. Using quad-core CPUs and a cluster of eight GPUs allowed us for the first time to calculate volumes in less than 30 min with an overall speed-up by a factor of 47, enabling a first clinical study. Based on these results we extrapolated that real-time reconstruction for a therapeutic 3D USCT will be possible in the year 2020 if the trend in density follows the ITRS roadmap.

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.

Ultrasound Computer Tomography (USCT) is a promising breast imaging modality under development. Comparison to a standard method like mammography is essential for further development. Due to significant differences in image dimensionality and compression state of the breast, correlating USCT images and X-ray mammograms is challenging. In this paper we present a 2D/3D registration method to improve the spatial correspondence and allow direct comparison of the images. It is based on biomechanical modeling of the breast and simulation of the mammographic compression. We investigate the effect of including patient-specific material parameters estimated automatically from USCT images. The method was systematically evaluated using numerical phantoms and in-vivo data. The average registration accuracy using the automated registration was 11.9mm. Based on the registered images a method for analysis of the diagnostic value of the USCT images was developed and initially applied to analyze sound speed and attenuation images based on X-ray mammograms as ground truth. Combining sound speed and attenuation allows differentiating lesions from surrounding tissue. Overlaying this information on mammograms, combines quantitative and morphological information for multimodal diagnosis.

In heterogeneous computing, application developers have to identify the best-suited target platform from a variety of alternatives. In this work, we compare performance and architectural efficiency of Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs) for two algorithms taken from a novel medical imaging method named 3D ultrasound computer tomography. From the 40nm and 28nm generations, we use top-notch devices and those with similar power consumption values. For our two benchmark algorithms from the signal processing and imaging domain, the results show that if power consumption is not considered, the GPU and FPGA from the 40nm generation give both, a similar performance and efficiency per transistor. In the 28nm process, in contrast, the FPGA is superior to its GPU counterpart by 86% and 39%, depending on the algorithm. If power is limited, FPGAs outperform GPUs in each investigated case by at least a factor of four.

As today's standard screening methods frequently fail to detect breast cancer before metastases have developed, early diagnosis is still a major challenge. With the promise of high-quality volume images, three-dimensional ultrasound computer tomography is likely to improve this situation, but has high computational needs. In this work, we investigate the acceleration of the ray-based transmission reconstruction by a GPU-based implementation of the iterative numerical optimization algorithm TVAL3. We identified the regular and transposed sparse-matrix-vector multiply as the performance limiting operations. For accelerated reconstruction we propose two different concepts and devise a hybrid scheme as optimal configuration. In addition we investigate multi-GPU scalability and derive the optimal number of devices for our two primary use-cases: a fast preview mode and a high-resolution mode. In order to achieve a fair estimation of the speedup, we compare our implementation to an optimized CPU version of the algorithm. Using our accelerated implementation we reconstructed a preview 3D volume with 24,576 unknowns, a voxel size of (8?mm)3 and approximately 200,000 equations in 0.5?s. A high-resolution volume with 1,572,864 unknowns, a voxel size of (2mm)3 and approximately 1.6 million equations was reconstructed in 23?s. This constitutes an acceleration of over one order of magnitude in comparison to the optimized CPU version. We accelerate a ray-based 3D ultrasound CT reconstruction by GPU processing.By use-case optimized SpMV variants a speedup of one order of magnitude is obtained.We derive the optimal number of GPUs for reconstructions that do not fit on one GPU.A 3D-preview is reconstructed in 0.5 s, a high-resolution volume in 23 s.

Computer aided diagnosis (CAD) of breast cancer is mainly focused on monomodal applications. Here we present a fully automated multimodal CAD, which uses patient-specific image registration of MRI and two-view X-ray mammography. The image registration estimates the spatial correspondence between each voxel in the MRI and each pixel in cranio-caudal and mediolateral-oblique mammograms. Thereby we can combine features from both modalities. As a proof of concept we classify fixed regions of interest (ROI) into normal and suspect tissue. We investigate the classification performance of the multimodal classification in several setups against a classification with MRI features only. The average sensitivity of detecting suspect ROIs improves by approximately 2% when combining MRI with both mammographic views compared to MRI-only detection, while the specificity stays at a constant level. We conclude that automatically combining MRI and X-ray can enhance the result of a breast CAD system.

A promising candidate for breast cancer imaging is ultrasound computer tomography (USCT). At Karlsruhe Institute of Technology (KIT) a 3D USCT with a semiellipsoidal aperture consisting of several hundreds of US transducers was built. The utilized imaging methods require accurate and reliable temperature information over the measured space and acquisition time. Several methods were applied which improved the temperature accuracy and reliability beyond the hardware defaults by one order of magnitude from ±1°C to approx. 0.005°C beyond the requirement of 0.1°C.

As today's standard screening methods frequently fail to diagnose breast cancer before metastases have developed, earlier breast cancer diagnosis is still a major challenge. Three-dimensional ultrasound computer tomography promises high-quality images of the breast, but is currently limited by a time-consuming image reconstruction. In this work, we investigate the acceleration of the image reconstruction by GPUs and FPGAs. We compare the obtained performance results with a recent multi-core CPU. We show that both architectures are able to accelerate processing, whereas the GPU reaches the highest performance. Furthermore, we draw conclusions in terms of applicability of the accelerated reconstructions in future clinical application and highlight general principles for speed-up on GPUs and FPGAs.

Ultrasound is frequently used to evaluate suspicious masses in breasts. These evaluations could be improved by taking advantage of advanced imaging algorithms, which become feasible for low frequencies if accurate knowledge about the phase and amplitude of the wave field illuminating the volume of interest is available. In this study, we compare five imaging and inversion methods: time-of-flight tomography, synthetic aperture focusing technique, backpropagation, Born inversion, and contrast source inversion. All methods are tested on the same full-wave synthetic data representing a 2-D scan using a circular array enclosing a cancerous breast submerged in water. Of the tested methods, only contrast source inversion yielded an accurate reconstruction of the speed-ofsound profile of the tumor and its surroundings, because only this method takes effects such as multiple scattering, refraction, and diffraction into account.

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.

The paper is focused on sound-speed image reconstruction in 3-D ultrasound transmission tomography. Along with ultrasound reflectivity and the attenuation coefficient, sound speed is an important parameter which is related to the type and pathological state of the imaged tissue. This is important in the intended application, breast cancer diagnosis. In contrast to 2-D ultrasound transmission tomography systems, a 3-D system can provide an isotropic spatial resolution in the x-, y-, and z-directions in reconstructed 3-D images of ultrasound parameters. Several challenges must, however, be addressed for 3-D systems-namely, a sparse transducer distribution, low signal-to-noise ratio, and higher computational complexity. These issues are addressed in terms of sound-speed image reconstruction, using edge-preserving regularized algebraic reconstruction in combination with synthetic aperture focusing. The critical points of the implementation are also discussed, because they are crucial to enable a complete 3-D image reconstruction. The methods were tested on a synthetic data set and on data sets measured with the Karlsruhe 3-D ultrasound computer tomography (USCT) I prototype using phantoms. The sound-speed estimates in the reconstructed volumes agreed with the reference values. The breast-phantom outlines and the lesion-mimicking objects were also detectable in the resulting sound-speed volumes.

A promising candidate for breast cancer imaging is ultrasound computer tomography (USCT). At KIT a 3D USCT with a semi-ellipsoidal aperture consisting of several hundreds of US transducers was built. Spherical waves are sequentially emitted and received. 3D SAFT is applied to reconstruct reflectivity volumes. However, straight forward SAFT imaging leads to blurred images. A post-imaging de-blurring approach applying aperture characteristics is described here and analyzed with a simulation and a clinical data set. A increase of 26% in the mean resolution in imaging simulation with a point scatters was achieved. A increase in resolution with breast images was observed, too.

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.

3D ultrasound computer tomography (3D USCT) is a new imaging method aimed at early breast cancer detection. For synthetic aperture focusing the data is acquired with (nearly) unfocussed ultrasound emission and reception. Therefore the SNR of the data is low and needs to be optimized.

In seismic, radar, and sonar imaging the exact determination of the reflectivity distribution is usually intractable so that approximations have to be applied. A method called synthetic aperture focusing technique (SAFT) is typically used for such applications as it provides a fast and simple method to reconstruct (3D) images. Nevertheless, this approach has several drawbacks such as causing image artifacts as well as offering no possibility to model system-specific uncertainties. In this paper, a statistical approach is derived, which models the region of interest as a probability density function (PDF) representing spatial reflectivity occurrences. To process the nonlinear measurements, the exact PDF is approximated by well-placed Extended Kalman Filters allowing for efficient and robust data processing. The performance of the proposed method is demonstrated for a 3D ultrasound computer tomograph and comparisons are carried out with the SAFT image reconstruction.

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.

In previous work we optimized the aperture of our 3D Ultrasound Computer Tomography (USCT) system with emphasis on reflection tomography. Based on the promising clinical results with speed of sound and attenuation images, the next generation aperture will be upgraded to contain also optimization for transmission tomography. The main changes to be implemented in aperture, transducers, and transducer arrays are: (1) Overall 3D aperture: due to the shape of the buoyant breast a simpler hemispherical aperture can be applied. (2) The diameter of the aperture will be increased and the diameter of the transducers will be decreased for further homogenization of the illumination. (3) The disjunctive sampling of the transducers will be increased and the transducers will be distributed randomly to enhance the uniformity of transmission tomography. (4) Transducers will be connected both as emitters and receivers to decrease the need for mechanical movement of the aperture.

The KIT 3D USCT surrounds the breast with ultrasound transducers on a 3D aperture and emits and receives nearly spherical wave fronts for synthetic aperture focusing. This full 3D system achieves isotropic 3D resolution, has a nearly spatial invariant point spread function, and allows fast data acquisition. The 3D USCT device is equipped with 2041 ultrasound transducers. The acquisition is carried out by sequentially selecting a single emitter, sending a chirp at 2.5 MHz center frequency and recording the transmitted and reflected waves with all receivers. Rotational and translational movement of the aperture is applied to enhance the image contrast. Up to 40 GB of raw data is acquired with 480 parallel channels for digitization at 12 bit and 20 MHz sampling frequency. In a first pilot study, ten patients with different lesions were imaged. Speed of sound, attenuation, and reflection images of each patient were derived from the raw data. Overlaid volumes of the modalities show qualitative and quantitative information at a glance. The results are promising because the breasts' tissue structures and cancerous lesions could be identified in the USCT images.

Major problems in treating breast cancer are the early detection of tumors and accurate biopsy of small volumes of breast (mamma) tissue. This report presents an elastic registration algorithm of two x-ray mammograms and a corresponding magnetic resonance imaging (MRI) volume. To cope with the soft tissue deformation of the breast during mammography, a two-dimensional model of breast deformation behavior is used as an elastic transformation. Normalized mutual information is employed as a measure of similarity. Regions of interest in the uncompressed x-ray mammograms are projected into the MRI volume to determine their three-dimensional origin.