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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.

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.

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.

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.

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.

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.

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.

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.

The paper introduces a way to improve treatment planning of single-dose radiation therapy of liver tumors. Therefore, a new algorithm for the registration of diagnostic magnetic resonance images (MRI) and the computed tomography (CT) data used for treatment planning was developed. The described algorithm is divided into two stages. First, the two data sets are aligned by optimization of mutual information using rigid transformations. After this initial registration step, a nonrigid transformation based on thin-plate splines is performed, modeling the deformations of the soft tissue. An algorithm for automatic control point computation was developed, which is supposed to simplify the time-consuming task of defining the control points necessary for a thin-plate spline interpolation. The obtained results show that the implemented algorithm can successfully solve the registration problem.

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.

An essential processing step for comparison of Ultrasound Computer Tomography images to other modalities, as well as for the use in further image processing, is to segment the breast from the background. In this work we present a (semi-) automated 3D segmentation method which is based on the detection of the breast boundary in coronal slice images and a subsequent surface fitting. The method was evaluated using a software phantom and in-vivo data. The fully automatically processed phantom results showed that a segmentation of approx. 10% of the slices of a dataset is sufficient to recover the overall breast shape. Application to 16 in-vivo datasets was performed successfully using semi-automated processing, i.e. using a graphical user interface for manual corrections of the automated breast boundary detection. The processing time for the segmentation of an in-vivo dataset could be significantly reduced by a factor of four compared to a fully manual segmentation. Comparison to manually segmented images identified a smoother surface for the semi-automated segmentation with an average of 11% of differing voxels and an average surface deviation of 2mm. Limitations of the edge detection may be overcome by future updates of the KIT USCT system, allowing a fully-automated usage of our segmentation approach.

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 (SOS), and attenuation). For high resolution reflectivity images phase aberration correction using the SOS images is necessary. The synthetic aperture focusing technique (SAFT) used for reflectivity image reconstruction is highly compute-intensive but suitable for an accelerated execution on GPUs. In this paper we investigate how the calculation of the phase aberration correction can be optimized and integrated into the SAFT algorithm. We analysed different strategies to optimize the trade off between memory requirement and image quality. For 64 slices with 1024 2 pixels a reconstruction can be done in 34 min on eight GPUs with a performance of 58.4 GV/s in comparison to the GPU reconstruction without phase aberration correction which needs 23 min. The average error made by the optimized SOS calculation is negligible.

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.

Ultrasound Computer Tomography (USCT) is a promising modality for breast imaging. We developed and tested the first full 3D USCT system aimed at in-vivo imaging. It is based on approx. 2000 ultrasound transducers surrounding the breast within a water bath. From the acquired signal data, reflectivity, attenuation and sound speed images are reconstructed. In a first in-vivo study we imaged ten patients and compared them to MRI images. To overcome the considerably different breast positioning in both imaging methods, an image registration and image fusion based on biomechanical modeling of the buoyancy effect and surface-based refinement was applied. The resulting images are promising: compared with the MRI ground truth, similar tissue structures can be identified. While reflection images seem to image even small structures, sound speed imaging seems to be the best modality for detecting cancer. The registration of both imaging methods allows browsing the volume images side by side and enables recognition of correlating tissue structures. The first in-vivo study was successfully completed and encourages for a second in-vivo study with a considerably larger number of patients, which is currently ongoing.

The KIT 3D USCT was 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. First reconstructions provide promising images. Overlaid volumes of the modalities show qualitative and quantitative information at a glance. The results led to further optimization of the system and the data acquisition protocol.

Ultrasound Computer Tomography is an upcoming imaging modality for early breast cancer detection. For evaluation of the method, comparison with the standard method X-ray mammography is of strongest interest. To overcome the significant differences in dimensionality and compression state of the breast, in earlier work a registration method based on biomechanical modeling of the breast was proposed. However only homogeneous models could be applied, i.e. inner structures of the breast were neglected. In this work we extend the biomechanical modeling of the breast by estimating patient-specific tissue parameters automatically from the speed of sound volume. Two heterogeneous models are proposed modeling a quadratic and an exponential relationship between speed of sound and tissue stiffness. The models were evaluated using phantom images and clinical data. The size of all lesions is better preserved using heterogeneous models, especially using an exponential relationship. The presented approach yields promising results and gives a physical justification to our registration method. It can be considered as a first step towards a realistic modeling of the breast.

3D ultrasound computer tomography (3D USCT) promises reproducible high-resolution images for early detection of breast tumors. The synthetic aperture focusing technique (SAFT) used for image reconstruction is highly computeintensive but suitable for an accelerated execution on GPUs. In this paper we investigate how a previous implementation of the SAFT algorithm in CUDA C can be further accelerated and integrated into the existing MATLAB signal and image processing chain for 3D USCT. The focus is on an efficient preprocessing and preparation of data blocks in MATLAB as well as an improved utilisation of special hardware like the texture fetching units on GPUs. For 64 slices with 1024×1024 pixels each the overall runtime of the reconstruction including data loading and preprocessing could be decreased from 35 hours with CPU to 2.4 hours with eight GPUs.

We designed and built a 3D ultrasound computer tomography (USCT) device with a nearly isotropic and spatially invariant 3D point spread function, to be tested in a clinical study. The objective of this work was to image two healthy volunteers and to evaluate the USCT volumes in comparison to corresponding Magnetic Resonance Images (MRI). The here presented volumes are reflectivity images generated with 3D synthetic aperture focusing technique. The volunteers were imaged with different parameterizations of the data acquisition. The data acquisition time was between four and twelve minutes. For both volunteers we found that the breast surface and inner structures are clearly shown in the USCT volume and fit the structures given by the MRI.

Breast cancer is the most common cancer among women. The established screening method to detect breast cancer in an early state is X-ray mammography. However, X-ray frequently provides limited contrast of tumors located within glandular tissue. A new imaging approach is Ultrasound Computer Tomography generating threedimensional volumes of the breast. Three different images are available: reflectivity, attenuation and speed of sound. The correlation of USCT volumes with X-ray mammograms is of interest for evaluation of the new imaging modality as well as for a multimodal diagnosis. Yet, both modalities differ in image dimensionality, patient positioning and deformation state of the breast. In earlier work we proposed a methodology based on Finite Element Method to register speed of sound images with the according mammogram. In this work, we enhanced the methodology to register all three image types provided by USCT. Furthermore, the methodology is now completely automated using image similarity measures to estimate rotations in datasets. A fusion methodology is proposed which combines the information of the three USCT image types with the X-ray mammogram via semitransparent overlay images. The evaluation was done using 13 datasets from a clinical study. The registration accuracy was measured by the displacement of the center of a lesion marked in both modalities. Using the automated rotation estimation, a mean displacement of 10.4 mm was achieved. Due to the clinically relevant registration accuracy, the methodology provides a basis for evaluation of the new imaging device USCT as well as for multimodal diagnosis.

At Karlsruhe Institute of Technology a 3D Ultrasound Computer Tomography (USCT) system is under development for early breast cancer detection. With 3.5 million of acquired raw data and up to one billion voxels for one image, the reconstruction of breast volumes may last for weeks in highest possible resolution. The currently applied backprojection algorithm, based on the synthetic aperture focusing technique (SAFT), offers only limited potential for further decrease of the reconstruction time. An alternative reconstruction method could apply signal detected data and rasterizes the backprojected ellipsoids directly. A well-known rasterization algorithm is the Bresenham algorithm, which was originally designed to rasterize lines. In this work an existing Bresenham concept to rasterize circles is extended to comply with the requirements of image reconstruction in USCT: the circle rasterization was adapted to rasterize spheres and extended to floating point parameterization. The evaluation of the algorithm showed that the quality of the rasterization is comparable to the original algorithm. The achieved performance of the circle and sphere rasterization algorithm was 12MVoxel/s and 3.5MVoxel/s. When taking the performance increase due to the reduced A-Scan data into account, an acceleration of factor 28 in comparison to the currently applied algorithm could be reached. For future work the presented rasterization algorithm offers additional potential for further speed up.

Breast cancer is the most common cancer among women. The established screening method to detect breast cancer is X-ray mammography. However, X-ray frequently provides poor contrast of tumors located within glandular tissue. In this case, additional modalities like MRI are used for diagnosis in clinical routine. A new imaging approach is Ultrasound Computer Tomography, generating three-dimensional speed of sound images. High speed of sound values are expected to be an indicator of cancerous structures. Therefore, the combination of speed of sound images and X-ray mammograms may benefit early breast cancer diagnosis. In previous work, we proposed a method based on Finite Elements to automatically register speed of sound images with the according mammograms. The FEM simulation overcomes the challenge that X-ray mammograms show two-dimensional projections of a deformed breast whereas speed of sound images render a three-dimensional undeformed breast in prone position. In this work, 15 datasets from a clinical study were used for further evaluation of the registration quality. The quality of the registration was measured by the displacement of the center of a lesion marked in both modalities. We found a mean displacement of 7.1 mm. For visualization, an overlay technique was developed, which displays speed of sound information directly on the mammogram. Hence, the methodology provides a good basis for multimodal diagnosis using mammograms and speed of sound images. It proposes a guidance tool for radiologists who may benefit from the combined information.

At the Karlsruhe Institute of Technology (KIT) a three-dimensional ultrasound computer tomography (3D USCT) system for early breast cancer diagnosis is being developed. This method promises reproducible volume images of the female breast in 3D. Initial measurements and a simulation based optimization method, which took several physical properties into account, led to a new aperture setup. Yet this simulation is computational too demanding to systematically evaluate the different 'virtual' apertures which can be achieved by rotation and lifting of the system. In optics a Fourier based approach is available to simulate imaging systems as linear systems. For the two apertures used in our project and one hypothetical linear array aperture this concept was evaluated and compared to a reference simulation. An acceptable conformity between the new approach and the reference simulation could be shown. With this approach a fast evaluation of optimal 'virtual' apertures for specific measurement objects and imaging constraints can be carried out within an acceptable time constraint.

Breast cancer is the most common type of cancer among women in Europe and North America. The established screening method to detect breast cancer is X-ray mammography, although X-ray frequently provides poor contrast for tumors located within glandular tissue. A new imaging approach is Ultrasound Tomography generating three-dimensional speed of sound images. This paper describes a method to evaluate the clinical applicability of three-dimensional speed of sound images by automatically registering the images with the corresponding X-ray mammograms. The challenge is that X-ray mammograms show two-dimensional projections of a deformed breast whereas speed of sound images render a three-dimensional undeformed breast in prone position. This conflict requires estimating the relation between deformed and undeformed breast and applying the deformation to the three-dimensional speed of sound image. The deformation is simulated based on a biomechanical model using the finite element method. After simulation of the compression, the contours of the X-ray mammogram and the projected speed of sound image overlap congruently. The quality of the matching process was evaluated by measuring the overlap of a lesion marked in both modalities. Using four test datasets, the evaluation of the registration resulted in an average tumor overlap of 97%. The developed registration provides a basis for systematic evaluation of the new modality of three-dimensional speed of sound images, e.g. allows a greater understanding of tumor depiction in these new images.

In the previous paper [11], a method for geometrical and transducer-time-delay auto-calibration of an ultrasonic computed tomography (USCT) system has been described, aiming at calibration of individual ultrasonic (US) transducer positions. The present contribution describes a novel modification of the method utilizing the particular USCT system concept: the exactly known spatial relations among transducers grouped in each of the transducer array systems (TASes). The algorithms used for the calibration remain based on the principles similar to the global positioning system (GPS) navigation, however, the positions and orientations of complete TASes are calibrated, rather than individual positions of transducers. This way, the number of unknowns is substantially reduced while the number of available equations remains unchanged. Consequently, a solution substantially more robust with respect to measurement noise can be obtained based on this highly overdetermined equation system. The method is capable of calibrating the individual positions of all ultrasonic transducers via their positions in TASes as well as their individual time delays at once during sc. empty measurement, without a need for any particular arrangements, e.g. calibration phantoms.

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.

Ultrasound Computer Tomography (USCT) records the interaction of ultrasonic waves with an imaged object from many different angles. The main advantage of such a system is simultaneous recording of reflection, absorption and speed of sound images, and higher image quality with fast data acquisition. Until now, it was not feasible to build a 3D USCT due to the high demands on the required number of transducers, high data rate, and time consuming post-processing. The aim of this first experimental setup for 3D USCT was to analyze the feasibility for breast cancer diagnosis of such a system built with today’s technology and draw conclusions for future setups. The results of this system are presented and discussed in regard of data acquisition, image quality, and duration of image reconstruction. The main conclusion is that 3D USCT is feasible with today’s technology, if a sparse aperture or long DAQ times can be accepted. A setup for a second-generation 3D USCT with the main aim to image volunteers and carry out a small preclinical study is proposed.

Mammographic texture features have been shown to correlate with the risk of developing breast cancer. Digital breast tomosynthesis (DBT) is an emerging 3D x-ray breast imaging modality with superior tissue visualization compared to mammography, having the potential to provide more accurate estimation of parenchymal texture features. In this paper, we investigate the effect of DBT acquisition parameters on computer-extracted texture features. DBT images were simulated using an anthropomorphic breast tissue software model allowing for variations in DBT acquisition geometry. Our results show that DBT acquisition geometry appears to have an impact on the computed texture features; angular range appears to have a greater effect than the selected number of source projections. We attribute this effect to the differences in image quality resulting from the different reconstruction geometries. Our ultimate goal is to determine the DBT acquisition geometry that provides the optimal image quality to estimate parenchymal texture.

The aim of this work is to provide a simulation framework for generation of synthetic tomosynthesis images to be used for evaluation of future developments in the field of tomosynthesis. An anthropomorphic software tissue phantom was previously used in a number of applications for evaluation of acquisition modalities and image post-processing algorithms for mammograms. This software phantom has been extended for similar use with tomosynthesis. The new features of the simulation framework include a finite element deformation model to obtain realistic mammographic deformation and projection simulation for a variety of tomosynthesis geometries. The resulting projections are provided in DICOM format to be applicable for clinically applied reconstruction algorithms. Examples of simulations using parameters of a currently applied clinical setup are presented. The overall simulation model is generic, allowing multiple degrees of freedom to cover anatomical variety in the amount of glandular tissue, degrees of compression, material models for breast tissues, and tomosynthesis geometries.

This paper presents a method for geometrical and time-delay auto-calibration of an ultrasonic computed tomography (USCT) system. The algorithms used for the calibration are based on the principles similar to the global positioning system (GPS) navigation. Ultrasonic transmitters and receivers in USCT can be viewed like satellite transmitters and mobile receiver units in GPS. However, unlike in GPS, none of the positions of the transmitters or receivers in USCT are assumed to be known and all are the to-be-calibrated unknowns. The presented method is capable of calibrating the positions of all ultrasonic transducers and their individual time delays at once. No calibration phantoms are necessary.

At Forschungszentrum Karlsruhe a 3D ultrasound computer tomograph (USCT) for breast cancer diagnosis is currently under development. For many applications, i.e. reconstruction of speed of sound maps, signal denoising,signal compression and calibration, it is necessary to detect pulses and their parameters (center frequency, bandwidth factor, time of arrival, phase and amplitude) accurately and robustly. The pulse detection is demanding due to low signal-to-noise ratios (SNR) caused by unfocused pulses from single emitters. Besides that angle dependent pulse shapes result in ultrasonic echoes which are similar in their center frequency but vary strongly in their bandwidth factor. Additionally, in sum 3.5 million A-scans (20 GB) are acquired, so that the pulse detection has to be fast and efficient. This is achieved by using a fast pre-classifler in order to separate the ultrasonic echoes from the non-white system noise of our system. The detected echoes are then passed individually to a parameter estimation method to determine pulse parameters accurately. An analysis of several classifiers resulted in an alternating decision tree which is both fast and accurate. Classification performance of 95% could be achieved as well as robust parameter estimation with non-white system noise if the SNR is larger than 3 dB. A comparative image reconstruction resulted in significantly sharper images.

For our current 3D demonstrator system for ultrasound computer tomography (USCT) a synthetic aperture based image reconstruction is implemented whose calculation time is in the order of days. Faster approximations might lead to large errors for our USCT setup which uses many multistatic emitter-receiver combinations. The aim of this work is the analysis of non-linear signal to image mapping for a cylindrical aperture and quantify the errors of computational less complex approximations. Four approaches with increasing computational complexity were implemented, tested with the cylindrical setup and compared to a commonly used linear transducer setup. For the linear aperture the second approximation leads to satisfying results. For the more diverging USCT setup a more accurate approximation is needed: the third approximation, which is still less computational expensive as the ground-truth implementation, gives satisfying results.

Ultrasound computer tomography (USCT) is an ultrasound imaging method capable of producing volume images with high spatial resolution and image quality. The long term goal of the system under construction is high quality imaging in 3D for early breast cancer diagnosis. In this paper the first images of an experimental 3D-prototype are presented and discussed in respect to further modifications of our system.

At Forschungszentrum Karlsruhe a 3D ultrasound computer tomograph (USCT) is currently under construction aimed at early breast cancer diagnosis. Ellipsoidal backprojection is applied to reconstruct 3D images. As USCT is a system used for imaging, there is a profound interest in analyzing the image resolution and image quality of this system, especially with respect to its system parameters such as number of the sensors, the geometry of the aperture, applied signal processing, etc. In this paper the results of first assessments of the resolution are presented. The resolution is evaluated in terms of the 2D full width at half maximum (2D FWHM) of the system's 3D point spread function (PSF). Slice images of 3D PSFs have been calculated to assess the spatial variability of the PSF within the current setup of the system. However, calculating these results is very time-consuming, thus an approximative approach for the analysis of the 3D PSF in XY and Z has been developed. This approach allows the analysis of the PSF while changing a variety of system parameters in real time. The long term goal of this project is the optimization of the system parameters with respect to the image quality

At Forschungszentrum Karlsruhe the demonstrator of a new medical imaging system based on 3D ultrasound computer tomography will be available at the beginning of 2005. This system requires not only the recording but also the processing of very large datasets of about 3.5 GBytes. On a single PC the reconstruction of a high resolution volume will take up to several weeks. However the reconstruction process can be parallelized. Our long term goal is to accelerate the reconstruction process significantly by the use of GRID technologies. In this paper we present our approach to parallelize the reconstruction by A-scan-decomposition using Java RMI and Matlab.

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 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.