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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.

Conventional iterative methods for image reconstruction in ultrasound transmission tomography need to perform many iterations where at each iteration one has to compute the complex forward model of ultrasound wave propagation, and hence they are time-consuming. We use a U-net neural network to accelerate the reconstruction, by training the network to map from an initial reconstruction obtained via a few iterations of L-BFGS method to the target ground truth image. Since the computation of a forward pass of the neural network is very fast, we can expect a significant acceleration using the trained network for image reconstruction. Experiments show that our trained network can replace 40 L-BFGS iterations to generate equivalent reconstructions with slightly better quantitative quality in terms of normalized root mean square error and better visual quality due to the network's denoising effect. It can achieve up to 283× speedup compared with L-BFGS method for reconstructing small-size sound speed images with 80×80 pixels. This implies that we can expect even greater acceleration effects when applying such approach to reconstruct large-size 3D images.

A semi-automated packaging process for transducer array manufacturing is presented. As an major innovation, a transducer disc is integrated in a sandwich structure between a printed circuit board (PCB) and an acoustic matching layer. Each of the transducer discs contains 18 lead-zirconium-titanate (PZT) fibres embedded in epoxy. To interconnect the transducer array components, adhesive bonding and automatic pick-and-place processes were used. A pre-series was evaluated by measuring the electro-mechanical impedance (EMI) before and after the assembly. Statistical analysis showed consistent behaviour of the series resonance f s and the electro-mechanical coupling k eff before and after the packaging. This encouraged the manufacturing of 256 arrays. These arrays will now be integrated in an ultrasound computer tomography (USCT) system with 3D scanning aperture for breast cancer imaging. With this system, we intend to bridge the gap towards clinical use of full 3D USCT.

The requirements of our third generation 3D ultrasound computer tomography system (USCT) led to a transducer array design based on single-fibre piezocomposites. The used piezoelectric ceramic fibres exhibit a thickness to diameter ratio of 4/3. This ratio results in a proximity of the dominant axial and lateral vibration resonances. The standard Krimholtz-Leedom-Matthaei (KLM) model considers only one spatial dimension. This limits the validity of the modelled predictions for our application. By adding a coupled transmission line in series to the axial resonator, the standard model can be extended to take additional spatial dimensions into account. Using model parameters from literature resulted in a low model-to-measurement fit. To enhance the prediction accuracy, a brute-force optimisation over four model parameters was conducted. With the optimal parameter set, a fit of 90.3% could finally be achieved. The necessary model parameter changes were found reasonable for the piezocomposite design. With the presented model enhancement it is now possible to consider both dominant vibration modes of our transducers.

Ultrasound transmission tomography is a promising modality for breast cancer diagnosis. For image reconstruc- tion approximations to the acoustic wave equation such as straight or bent rays are commonly used due to their low computational complexity. For sparse apertures the coverage of the volume by rays is very limited, thereby requiring strong regularization in the inversion process. The concept of fat rays reduces the sparseness and includes the contributions to the measured signal originating from the first Fresnel zone. In this work we investi- gate the application of the fat ray concept to ultrasound transmission tomography. We implement a straight ray, bent ray and fat ray forward model. For the inversion process a least squares solver (LSQR), a simultaneous al- gebraic reconstruction technique (SART) and a compressive sensing based total variation minimization (TVAL3) is applied. The combination of forward models and inversion processes has been evaluated by synthetic data. TVAL3 outperforms SART and LSQR, especially for sparse apertures. The fat ray concept is able to decrease the error with respect to the ground truth compared to the bent ray method especially for SART and LSQR inversion, and especially for very sparse apertures.

A 3D ultrasound computer tomography (USCT) device with a nearly isotropic and spatially invariant 3D point spread function has been constructed at Institute for Data Processing and Electronic (IPE), Karlsruhe Institute of Technology (KIT). This device is currently applied in clinical studies for breast cancer screening. In this paper, a new method to develop an automated segmentation algorithm for USCT acquired images is proposed. The method employs distance regularized level set evolutionary (DRLSE) active contours along with surface fitting extrapolation and 3D binary mask generation for fully automatic segmentation outcome. In the first stage of the proposed algorithm, DRLSE is applied to those 3D USCT slice images which contain breast and are less affected by noise and ring artifacts named as Cat2. The DRLSE segmentation results are employed to extrapolate the rest of slice images known as Cat1. To overcome defectively segmented slice images, a 3D binary mask is generated out of USCT attenuation images. The 3D binary mask is multiplied by the DRLSE-based segmentation results to form finally segmented 3D USCT images. The method was tested on 12 clinical dataset images. According to F-measure criterion, the proposed method shows higher performance than the previously proposed semiautomatic segmentation one.

A promising candidate for improved imaging of breast cancer is ultrasound computer tomography (USCT). Current experimental USCT systems are still focused in elevation dimension resulting in a large slice thickness, limited depth of field, loss of out-of-plane reflections, and a large number of movement steps to acquire a stack of images. 3D USCT emitting and receiving spherical wave fronts overcomes these limitations. We built an optimized 3D USCT, realizing for the first time the full benefits of a 3D system. The point spread function could be shown to be nearly isotropic in 3D, to have very low spatial variability and fit the predicted values. The contrast of the phantom images is very satisfactory in spite of imaging with a sparse aperture. The resolution and imaged details of the reflectivity reconstruction are comparable to a 3 T MRI volume. Important for the obtained resolution are the simultaneously obtained results of the transmission tomography. The KIT 3D USCT was then tested in a pilot study on ten patients. The primary goals of the pilot study were to test the USCT device, the data acquisition protocols, the image reconstruction methods and the image fusion techniques in a clinical environment. The study was conducted successfully; the data acquisition could be carried out for all patients with an average imaging time of six minutes per breast. The reconstructions provide promising images. Overlaid volumes of the modalities show qualitative and quantitative information at a glance. This paper gives a summary of the involved techniques, methods, and first results.

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.

Increasing interest in multimodal breast cancer diagnosis has led to the development of methods for MRI to X-ray mammography registration. The severe breast deformation in X-ray mammography is often tackled by biomechanical models, yet there is no common consensus in literature about the required complexity of the deformation model and the simulation strategy. We present for the first time an automated patient-specific biomechanical model based image registration of MRI to digital breast tomosynthesis (DBT). DBT provides three-dimensional information of the compressed breast and as such drives the registration by a volume similarity metric. We compare different simulation strategies and propose a patient-specific optimization of simulation and model parameters. The average three-dimensional breast overlap measured by Dice coefficient of DBT and registered MRI improves for four analyzed subjects by including the estimation of unloaded state, simulation of gravity, and a concentrated pull force that mimics manual positioning of the breast on the plates from 88.1% for a mere compression simulation to 93.1% when including all our proposed simulation steps, whereas additional parameter optimization further increased the value to 94.4%.

Increasing interest in multimodal breast cancer diagnosis has led to the development of methods for MRI to X-ray mammography registration to provide direct correlation of modalities. The severe breast deformation in X-ray mammography is often tackled by biomechanical models, which however have not yet brought the registration accuracy to a clinically applicable level. We present a novel registration approach of MRI to X-ray tomosynthesis. Tomosynthesis provides three-dimensional information of the compressed breast and as such has the ability to open new possibilities in the registration of MRI and X-ray data. By bundling the 3D information from the tomosynthesis volume with the 2D projection images acquired at different measuring angles, we provide a correlation between the registration error in 3D and 2D and evaluate different 3D- and 2D-based similarity metrics to drive the optimization of the automated patient-specific registration approach. From the preliminary study of four analysed patients we found that the projected registration error is in general larger than the 3D error in case of small registration errors in the cranio-caudal direction. Although both image shape and intensitybased 2D similarity metrics showed a clear correlation with the 2D registration error at different projection angles, metrics that relied on the combined 2D and 3D information yielded in most of the cases the minimal registration error and as such had better performance than similarity metrics that rely only on the shape similarity of volumes.

In Ultrasound computer tomography (USCT) Synthetic aperture focusing technique (SAFT) is often applied for reflectivity image reconstruction. Phase aberration correction is essential to cope with the large sound speed differences in water and the different human tissues. In this paper we compare two approaches for phase aberration correction: a straight ray approximation using the Bresenham algorithm (B-SAFT) and a bent ray approximating using a multi-stencil Fast Marching Method (FMM-SAFT). The analysis is carried out with simulated point scatterers and simulated phantoms to measure the effect on the image resolution and contrast. The method is additionally applied to experimental data. B-SAFT degrades the image resolution and contrast in cases of large sound speed differences of objects and if the reconstructed point is close to a boundary where a change in impedance is present. FMM-SAFT is able to recover the image quality in these cases if the sound speed distribution is known accurately and with high resolution. If these requirements cannot be met, B-SAFT proved to be more robust.

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.

An airborne ultrasound imaging system was developed for reflection tomography. The ultrasound transducers surround the region of interest (ROI) in an arrangement optimized for maximum coverage and homogeneous distributed image quality. In this work, we developed a workflow for automatic segmentation and classification of objects in the reconstructed images. Our workflow can be applied for varying intensities of object edges with a local maxima based segmentation and a multi-parameter image reconstruction. The segmented regions are classified with a neural network, and the object localization was implemented with Generalized Hough Transform using a custom template for each classified object in the data set. A classification accuracy of 95% for six trained test objects and a localization accuracy of 5 mm were achieved.

Ultrasound Computer Tomography (USCT) is a promising new imaging system for breast cancer diagnosis. An essential step before further processing is to remove the water background from the reconstructed images. In this paper we present a fully-automated image segmentation method based on three-dimensional active contours. The active contour method is extended by applying gradient vector flow and encoding the USCT aperture characteristics as additional weighting terms. A surface detection algorithm based on a ray model is developed to initialize the active contour, which is iteratively deformed to capture the breast outline in USCT reflection images. The evaluation with synthetic data showed that the method is able to cope with noisy images, and is not influenced by the position of the breast and the presence of scattering objects within the breast. The proposed method was applied to 14 in-vivo images resulting in an average surface deviation from a manual segmentation of 2.7 mm. We conclude that automated segmentation of USCT reflection images is feasible and produces results comparable to a manual segmentation. By applying the proposed method, reproducible segmentation results can be obtained without manual interaction by an expert.

Synthetic Aperture Focusing Technique (SAFT) allows fast data acquisition and optimally focused images. The computational burden for 3D imaging is large as for each voxel the delay for each acquired A-scan has to be calculated, e.g. O(N5) for N3 voxels and N2 A-scans. For 3D reconstruction of objects which are large in terms of the wavelength, e.g. ≥ (100 λ)3, the computation of one volume takes several days on a current multicore PC. If the 3D distribution of the speed of sound is applied to correct the delays, the computation time increases further. In this work a time of flight interpolation based GPU implementation (TOFI-SAFT) is presented which accelerates our previous GPU implementation of speed of sound corrected SAFT by a factor of 7 to 16 min. with only minor reduction of image quality.

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.

Due to their different physical origin, X-ray mammography and Magnetic Resonance Imaging (MRI) provide complementary diagnostic information. However, the correlation of their images is challenging due to differences in dimensionality, patient positioning and compression state of the breast. Our automated registration takes over part of the correlation task. The registration method is based on a biomechanical finite element model, which is used to simulate mammographic compression. The deformed MRI volume can be compared directly with the corresponding mammogram. The registration accuracy is determined by a number of patient-specific parameters. We optimize these parameters--e.g. breast rotation--using image similarity measures. The method was evaluated on 79 datasets from clinical routine. The mean target registration error was 13.2mm in a fully automated setting. On basis of our results, we conclude that a completely automated registration of volume images with 2D mammograms is feasible. The registration accuracy is within the clinically relevant range and thus beneficial for multimodal diagnosis.

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.

Airborne ultrasonic sensors are widely used in industry for detecting movement of targets and measuring their distances to the sensor. More complex object localization is usually done with optical systems, yet their application is limited. In this work an airborne ultrasound imaging system is developed, which reconstructs an image of the region-of-interest (ROI) with multiple objects. With further image processing, this system allows more sophisticated object localization. The proposed optimization method results in a sensor system with minimum number of sensors and improved sensor distribution for maximum coverage of the ROI and homogeneous performance.

For iterative image reconstruction of transmission tomography we apply the paraxial approximation of the Helmholtz equation for a spherical transducer arrangement. We choose this approach due to its three order of magnitude lower complexity than full wave solutions with the same precision for transmission tomography. In homogeneous media we prove that our forward solution is exact. With the help of this forward solution 2D and 3D ultrasound measurements could be simulated for transmission tomography. 2D reconstructions of a breast-like numerical phantom had a deviation in sound speed of 0.14 m/s and a deviation in attenuation of 6.5% from the ground truth. Applications up to now are breast cancer diagnostics and non-destructive testing.

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.

In our first clinical study with a full 3D Ultrasound Computer Tomography (USCT) system patient data was acquired in eight minutes for one breast. In this paper the patient movement during the acquisition was analyzed quantitatively and as far as possible corrected in the resulting images. The movement was tracked in ten successive reflectivity reconstructions of full breast volumes acquired during 10 s intervals at different aperture positions, which were separated by 41 s intervals. The mean distance between initial and final position was 2.2 mm (standard deviation (STD) ± 0.9 mm, max. 4.1 mm, min. 0.8 mm) and the average sum of all moved distances was 4.9 mm (STD ± 1.9 mm, max. 8.8 mm, min. 2.7 mm). The tracked movement was corrected by summing successive images, which were transformed according to the detected movement. The contrast of these images increased and additional image content became visible.

Ultrasound Computer Tomography (USCT) is a promising new imaging method for breast cancer diagnosis. We developed a 3D USCT system and tested it in a pilot study with encouraging results: 3D USCT was able to depict two carcinomas, which were present in contrast enhanced MRI volumes serving as ground truth. To overcome severe differences in the breast shape, an image registration was applied. We analyzed the correlation between average sound speed in the breast and the breast density estimated from segmented MRIs and found a positive correlation with R=0.70. Based on the results of the pilot study we now carry out a successive clinical study with 200 patients. For this we integrated our reconstruction methods and image post-processing into a comprehensive workflow. It includes a dedicated DICOM viewer for interactive assessment of fused USCT images. A new preview mode now allows intuitive and faster patient positioning. We updated the USCT system to decrease the data acquisition time by approximately factor two and to increase the penetration depth of the breast into the USCT aperture by 1 cm . Furthermore the compute-intensive reflectivity reconstruction was considerably accelerated, now allowing a sub-millimeter volume reconstruction in approximately 16 minutes. The updates made it possible to successfully image first patients in our ongoing clinical study.

Ultrasound Computer Tomography (USCT), developed at KIT, is a promising new imaging system for breast cancer diagnosis, and was successfully tested in a pilot study. The 3D USCT II prototype consists of several hundreds of ultrasound (US) transducers on a semi-ellipsoidal aperture. Spherical waves are sequentially emitted by individual transducers and received in parallel by many transducers. Reflectivity volumes are reconstructed by synthetic aperture focusing (SAFT). However, straight forward SAFT imaging leads to blurred images due to system imperfections. We present an extension of a previously proposed approach to enhance the images. This approach includes additional a priori information and system characteristics. Now spatial phase compensation was included. The approach was evaluated with a simulation and clinical data sets. An increase in the image quality was observed and quantitatively measured by SNR and other metrics.

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.

Rationale and objectives To evaluate the semi-automatic image registration accuracy of X-ray-mammography (XR-M) with high-resolution high-field (3.0 T) MR-mammography (MR-M) in an initial pilot study. Material and methods MR-M was acquired on a high-field clinical scanner at 3.0 T (T1-weighted 3D VIBE ± Gd). XR-M was obtained with state-of-the-art full-field digital systems. Seven patients with clearly delineable mass lesions >10 mm both in XR-M and MR-M were enrolled (exclusion criteria: previous breast surgery; surgical intervention between XR-M and MR-M). XR-M and MR-M were matched using a dedicated image-registration algorithm allowing semi-automatic non-linear deformation of MR-M based on finite-element modeling. To identify registration errors (RE) a virtual craniocaudal 2D mammogram was calculated by the software from MR-M (with and w/o Gadodiamide/Gd) and matched with corresponding XR-M. To quantify REs the geometric center of the lesions in the virtual vs. conventional mammogram were subtracted. The robustness of registration was quantified by registration of X-MRs to both MR-Ms with and w/o Gadodiamide. Results Image registration was performed successfully for all patients. Overall RE was 8.2 mm (1 min after Gd; confidence interval/CI: 2.0–14.4 mm, standard deviation/SD: 6.7 mm) vs. 8.9 mm (no Gd; CI: 4.0–13.9 mm, SD: 5.4 mm). The mean difference between pre- vs. post-contrast was 0.7 mm (SD: 1.9 mm). Conclusion Image registration of high-field 3.0 T MR-mammography with X-ray-mammography is feasible. For this study applying a high-resolution protocol at 3.0 T, the registration was robust and the overall registration error was sufficient for clinical application.

A three-dimensional ultrasound computer tomography (3-D USCT) system for early breast cancer diagnosis is currently being developed at Institute for Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology, Karlsruhe, Germany. A field-programmable gate array (FPGA)-based data acquisition (DAQ) system is used to acquire the measurement data. Over 3.5 million data sets are recorded for each breast, resulting in 20 GB of raw data. The data is subsequently transferred to a PC for time-consuming image reconstruction, which is in the order of days. A promising approach in reducing reconstruction time is mapping further computation to the FPGAs in the DAQ system. This paper covers the investigation of FPGA-based processing for a set of signal processing algorithms. In the best suited approach, processing time per data set is approximately 50 μs, occupying less than 30% of device resources.

Breast cancer is the most common cancer among women. The established screening method to detect breast cancer is X-ray mammography. Additionally, MRI is used for diagnosis in clinical routine. Due to complementary diagnostic information, both modalities are often read in combination. Yet, the correlation is challenging due to different dimensionality of images and different patient positioning. In this paper, we describe a method to fuse X-ray mammograms with DCE-MRI. The present study was conducted to evaluate the feasibility of the approach.For the combination of information from both modalities, the images have to be registered using a compression simulation based on a patient-specific biomechanical model. The registered images can be compared directly. The contrast enhancement in the DCE-MRI volume is evaluated using parametric enhancement maps. A projection image of the contrast enhancement is created. The image fusion combines it with X-ray mammograms for intuitive multimodal diagnosis.The image fusion was evaluated using 11 clinical datasets. For 10 of 11 datasets, a good accuracy of the image registration was achieved. The overlap of contrast-enhanced regions with marked lesions in the mammogram is 61%. Lesions are clearly differentiable from surrounding tissue by the DCE-MRI projection in 10 of 11 cases.The described preliminary results are promising, thus we expect the visualization of quantitative information from dynamic MRI together with mammograms to be beneficial for multimodal diagnosis. Because of the use of clinical standard modalities, no additional image acquisition is needed.

Ultrasound Computer Tomography (USCT) medical imaging is a promising approach for early detection of breast cancer. At Karlsruhe Institute of Technology (KIT) a 3D USCT system is developed. The system operates 2041 ultrasound transducers in a semi-ellipsoidal aperture surrounding a region of interest (ROI) of 10 cm x 10 cm x 10 cm. Results from a first patient study reveal the requirement of a significantly increased ROI to cover bodily variations. Design considerations and simulations show a demand for circular transducers of approximately 500 mm diameter, increasing the opening angle of the transducers to approximately 60°. Piezofiber composite technology is predestinated to simply provide circular transducers of the required dimensions. Moreover, piezocomposites based on single PZT (lead zirconate titanate Pb[ZrxTi1-x]O3) fibers enable a cost-effective and series-production alternative to currently used dice-and-fill composites. A transducer design is presented which utilizes individually arranged single piezoceramic fibers with 460 µm in diameter within piezocomposite discs. As result fibers are independently addressable as single transducer elements allowing for the desired transducer arrangement. The electrical performance of each piezoceramic fiber is determined proofing a strong dependence of coupling coefficient and resonance frequency from transducer thickness. In further processing, the piezocomposite discs are connected to printed circuits, integrated into a cylindrical housing and backfilled with polyurethane. Ultrasound characteristics such as sound pressure and opening angle are evaluated quantitatively. The transducer opening angles are in the expected range, desired center frequency is achieved and bandwidth preserved compared to former dice-and-fill transducers.

Computer aided detection CADe of breast cancer is mainly focused on monomodal applications. We propose an automated multimodal CADe approach, which uses patient-specific image registration of MRI and X-ray mammography to estimate the spatial correspondence of tissue structures. Then, based on the spatial correspondence, features are extracted from both MRI and X-ray mammography. As proof of principle, distinct regions of interest ROI were classified into normal and suspect tissue. We investigated the performance of different classifiers, compare our combined approach against a classification with MRI features only and evaluate the influence of the registration error. Using the multimodal information, the sensitivity for detecting suspect ROIs improved by 7i¾?% compared to MRI-only detection. The registration error influences the results: using only datasets with a registration error below $$10\,mm$$, the sensitivity for the multimodal detection increases by 10i¾?% to a maximum of 88i¾?%, while the specificity remains constant. We conclude that automatically combining MRI and X-ray can enhance the result of a CADe system.

A device for 3D ultrasound computer tomography system is currently under development at KIT with the goal of high-resolution images for early breast cancer detection. With its semi-ellipsoidal positioning of 2041 ultrasound transducers around the breast in space, full 3D images can be reconstructed. Calibration process of such a complex system is very time-consuming and difficult. This paper proposes a Newton's method based self calibration using time-of-flight measurements between each emitter and receiver. One unique feature of this method is the separation of each potential error sources in the system and sequential calibration according to their magnitudes. This enables the analysis of each error component in the system. Simulation and application on real data have both shown sub-wavelength accuracy in the calibration results.

3D Ultrasound Computer Tomography (USCT) aims at early detection of breast cancer. The system was optimized for Synthetic Aperture Focusing Technique (SAFT) using unfocused ultrasound transducers. Due to low SNR of the signals, Coded Excitation (CE) and Matched Filter (MF) are applied. For complex objects the received signals (A-scans) consist of multiple overlaid reflections. The overlaid reflections are difficult to separate due to the system's limited bandwidth which results in side lobes. Non-separable reflections limit the resolution and contrast of the resulting images. Using Huffman Sequences (HS) as CE, a trade-off can be set between the side lobe suppression and loss of main lobe energy. Higher suppression of side lobes results in reduced energy in the CE, i.e. smaller SNR. So an approach has been implemented which suppresses the side lobes, but preserves the energy of CE by designing a Huffman sequences based mismatched filter. Combination of a CE and different filters were evaluated for single reflection and multiple overlaid reflections. The results show high side lobe suppression within limited bandwidth and preserve the SNR.

The quality of ultrasound computed tomography imaging is primarily determined by the accuracy of ultrasound transit time measurement. A major problem in analysis is the overlap of signals making it difficult to detect the correct transit time. The current standard is to apply a matched-filtering approach to the input and output signals. This study compares the matched-filtering technique with active set deconvolution to derive a transit time spectrum from a coded excitation chirp signal and the measured output signal. The ultrasound wave travels in a direct and a reflected path to the receiver, resulting in an overlap in the recorded output signal. The matched-filtering and deconvolution techniques were applied to determine the transit times associated with the two signal paths. Both techniques were able to detect the two different transit times; while matched-filtering has a better accuracy (0.13 μs versus 0.18 μs standard deviations), deconvolution has a 3.5 times improved side-lobe to main-lobe ratio. A higher side-lobe suppression is important to further improve image fidelity. These results suggest that a future combination of both techniques would provide improved signal detection and hence improved image fidelity.

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.

3D Ultrasound Computer Tomography (USCT) is a new imaging method for breast cancer diagnosis. In the current state of development it is essential to correlate USCT with a known imaging modality like MRI to evaluate how different tissue types are depicted. Due to different imaging conditions, e.g. with the breast subject to buoyancy in USCT, a direct correlation is demanding. We present a 3D image registration method to reduce positioning differences and allow direct side-by-side comparison of USCT and MRI volumes. It is based on a two-step approach including a buoyancy simulation with a biomechanical model and free form deformations using cubic B-Splines for a surface refinement. Simulation parameters are optimized patient-specifically in a simulated annealing scheme. The method was evaluated with in-vivo datasets resulting in an average registration error below 5mm. Correlating tissue structures can thereby be located in the same or nearby slices in both modalities and three-dimensional non-linear deformations due to the buoyancy are reduced. Image fusion of MRI volumes and USCT sound speed volumes was performed for intuitive display. By applying the registration to data of our first in-vivo study with the KIT 3D USCT, we could correlate several tissue structures in MRI and USCT images and learn how connective tissue, carcinomas and breast implants observed in the MRI are depicted in the USCT imaging modes.

3D Ultrasound Computer Tomography (3D USCT) promises reproducible high-resolution images for early detection of breast tumors. The KIT prototype provides three different modalities: reflectivity, speed of sound, and attenuation. The reflectivity images are reconstructed using a Synthetic Aperture Focusing Technique (SAFT) algorithm. For high-resolution re ectivity images, with spatially homogeneous reflectivity, attenuation correction is necessary. In this paper we present a GPU accelerated attenuation correction for 3D USCT and evaluate the method by means of image quality metrics; i.e. absolute error, contrast and spatially homogeneous reflectivity. A threshold for attenuation correction was introduced to preserve a high contrast. Simulated and in-vivo data were used for analysis of the image quality. Attenuation correction increases the image quality by improving spatially homogeneous reflectivity by 25 %. This leads to a factor 2.8 higher contrast for in-vivo data.

We present a new approach for automatic registration of X-ray mammograms and MR images. Multimodal breast cancer diagnosis is supported by automatic localization of small lesions, which are only visible in the mammograms or in the MR image. To cope with the huge deformation of the breast during mammography, a finite element model of the deformable behavior of the breast is applied during the registration. An evaluation of the registration with six clinical data sets resulted in an accurate localization with a mean displacement of 4.3 mm (/spl plusmn/1 mm) and 3.9 mm (/spl plusmn/1.7 mm) for predicting the lesion position in mammograms and in the MR images, respectively.

aDepartment of Neuroradiology, University of Erlangen-Nurnberg, Schwabachanlage 6, D-91054, Germany bDepartment of Radiology, Division of Molecular and Gender Imaging, Medical University Vienna, Wahringer Gurtel 18–20, A-1090, Vienna, Austria c Karlsruhe Institute of Technology (KIT), Institute for Data Processing and Electronics, Postfach 3640, D-76021 Karlsruhe, Germany d Institute of Diagnostic and Interventional Radiology I, Friedrich-Schiller-University Jena, Erlanger Allee 101, D-07740 Jena, Germany

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 3D USCT II system needs a method to localize and reposition a non-centrally positioned breast. A novel imaging technique (IIT) was introduced to retrieve intersection images of breasts. By detecting the breast deviation direction from these images, the breast position can be estimated. This method limits the amount of data and speeds up the data transmission by producing only 2D images and generalizing the breast as convex. It achieves an mean accuracy of 0.73° for the estimation of the breast deviation direction. IIT achieves a data reduction rate of 99.99%, which speeds up the data transfer to 0.1 s. Therefore, IIT achieves a fast and sufficiently accurate detection of the breast position for USCT II system.

In a first pilot study with 3D Ultrasound Computer Tomography especially the speed of sound images of the breast showed promising results for breast cancer detection. Yet, the resolution of transmission tomography in our system is limited in comparison to the reflectivity volumes. In this paper we describe a setup for an improved device overcoming this limitation. An optimized geometry of the transducers in a sparse distribution improves both the contrast in reflection and contrast and resolution in transmission tomography by nearly an order of magnitude. Furthermore data acquisition is accelerated from 10 to less than 2 minutes. The derived methods, simulation, and measuring results are described.