Conventional reflectivity imaging with Ultrasound Tomography (USCT) reconstructs qualitative images proportional to the magnitude of the impedance gradient. We propose a method to additionally recover the scatter characteristics for each reconstructed voxel, i.e. whether a reflection is diffuse or specular. This is achieved using a modification of 3D Synthetic Aperture Focusing Technique (SAFT). Our novel approach separates the incoming and outgoing ultrasound intensity in each voxel according to the incident direction and the direction to the receiver. To reduce memory requirements, we propose several strategies for selecting a subset of data or aggregating data to predefined directions. The reconstruction leads to five-dimensional data, from which for each voxel in 3D space, a 2D scatter map can be derived. It can be interpreted as the distribution of energy which has been introduced from a certain direction and which has been reflected into a particular direction. We validated our approach by a simulation based on the Phong reflection model and perform first reconstructions of experimental data. Using the 2D scatter maps it is possible to distinguish specular from diffuse reflections visually. Extracting first-order statistics from the 2D scatter maps for each voxel can be a means to break down the 5D information to 3D for traditional slice-based visualization. The multidimensional data provided by our method may be used in future as a biomarker for diagnosis as e.g. a plain surface of a cyst may reflect the ultrasound differently than a rough surface of a spiculated mass.
Ultrasound Tomography (USCT) is an emerging technology for early breast cancer detection. At Karlsruhe Institute of Technology we recently realized a new generation of full 3D USCT device with a pseudo-randomly sampled hemispherical aperture. In this paper we summarize first imaging results with phantoms and first volunteer images. Using a gelatin phantom with PVC inclusions we evaluated transmission imaging, which showed a deviation from the ground truth of less than 5 m/s in the sound speed and 0:2 dB/cm in the attenuation for the phantom body and less than 15 m/s and 0:2 dB/cm for an inclusion with a diameter of 2:2 cm. Geometric errors are in average in the range of 0:2 cm. For reflectivity imaging we showed that the point spread function is nearly isotropic and with an average of 0:26 mm close to the theoretical predictions for the current system. While the system is still in final commissioning, the results of the phantom and volunteer imaging are very promising: after further calibration and deeper analysis with phantoms we aim at starting a clinical study.
The most typical type of cancer among women is breast cancer. Despite the crucial role that digital mammography plays in the early identification of breast cancer, many tumors could not be discriminated on mammography, especially in women with dense breast tissue. Contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast is routinely used to find lesions that are invisible on mammography. MRI-guided biopsies must be used to further analyze these lesions. But MRI-guided biopsy is highly priced, time-consuming, and not frequently accessible. In our earlier work, we introduced a novel method using two methods of registration: biomechanical and image-based registration to transfer lesions from MRI to spot mammograms to allow x-ray guided biopsy. In this paper, we focus on enhancing and developing the image-based registration between full and spot mammograms and analyzing a correlation between the accuracy of our method and features such as views, location of lesion, breast area, size of lesion in each modality, and age. Results for 48 patients from the Medical University of Vienna are provided. The median target registration error is 20.9 mm and the standard deviation is 23.9 mm.
Compared to traditional magnetic resonance imaging, which provides anatomical information with high contrast, diffusion weighted imaging (DWI) can add functional information for a more precise detection and localization of breast cancer. However, DWI may suffer from artifacts due to off-resonance effects, including geometric distortions. This hinders combined view, e.g. by image fusion. In this work, we investigate a distortion correction of DWI based on a nonlinear image registration with a T2 weighted image. Our method consists of three steps: a data cleaning step in which differences in image sections and resolution are compensated, an edge detection step which extracts the outline and inner structures of the breast in both DWI and T2 weight image, and finally a non-rigid registration step using the demons algorithm. We use two clinical datasets with a total of seven patients for evaluation. Manual annotations of landmarks in 227 slices serve as basis to calculate the registration error. Our method reduces the target registration error based on the center of gravity of annotations from in average 5.5 mm to 3.1 mm and is most effective in cases with large initial deformation. Compared to the other methods tested in this study the proposed method shows the lowest error. The method may contribute to a better combined diagnosis and e.g. facilitate computer aided detection and diagnosis by enabling combination of spatially well-aligned information.
In multimodal diagnosis for early breast cancer detection, spatial alignment by means of image registration is an important task. We develop patient-specific biomechanical models of the breast, for which one of the challenges is automatic segmentation for magnetic resonance imaging (MRI) of the breast. In this paper, we propose a novel method using unsupervised neural networks with pre-processing and post-processing to enable automatic breast MRI segmentation for three tissue types simultaneously: fatty, glandular, and muscular tissue. Pre-processing aims at facilitating training of the network. The architecture of neural network is a Kanezaki-net extended to 3D and consists of two sub-networks. Post-processing is enhancing the obtained segmentations by removing common errors. 25 datasets of T2 weighted MRI from the Medical University of Vienna have been evaluated qualitatively by two observers while eight datasets have been evaluated quantitatively based on a ground truth annotated by a medical practitioner. As a result of the qualitative evaluation, 22 out of 25 are usable for biomechanical models. Quantitatively, we achieved an average dice coefficient of 0.88 for fatty tissue, 0.5 for glandular tissue, and 0.86 for muscular tissue. The proposed method can serve as a robust method for automatic generation of biomechanical models.
A promising candidate for improved imaging of breast cancer is ultrasound tomography (USCT). To make full use of the 3D interaction of the ultrasound fields with the breast, we are focusing our research on full 3D USCT systems. While our previous 3DUSCTII device allowed nearly unfocussed emission and reception with approx. 600 emitters and 1400 receivers, the spatial sampling of the object is very sparse. In order to improve contrast in a sparse system, we realized an optimized pseudo-randomly sampled USCT device (3DUSCTIII) with approx. 2300 transducers. Additionally, the opening angle, the bandwidth and the active area of the transducers were improved. New front-end electronics with custom ASICs allow bidirectional operation of the transducers to acquire approx. six times more A-scans at one data acquisition step. This paper presents the setup of the new system and initial results acquired during the ongoing commissioning.
Breast cancer is the most common cancer for women worldwide. 3D Ultrasound Computed Tomography (3D USCT) is a novel imaging method for early breast cancer diagnosis, which allows reconstruction of quantitative tissue parameters like speed of sound and attenuation. For reconstruction we use the paraxial approximation of the Helmholtz equation as forward model. We have realized the forward solution, backprojection and reconstruction for a ring transducer arrangement. The reconstruction software was evaluated with data simulated with k-Wave, resulting in the mean error for the speed of sound map of 12.6 m/s for a pixel size of 0.3 mm. Spatial resolution was estimated with a resolution phantom containing circular inclusions with realistic speed of sound values for breast tissues, allowing maximum resolution of 2 mm. In this paper we show that our method has accurate forward solution, we present the new backprojection technique and initial results of reconstructing simulated data.
KEYWORDS: Skull, Image segmentation, Brain, Magnetic resonance imaging, Acoustics, Ultrasonography, Neuroimaging, In vivo imaging, 3D modeling, Image resolution
For opening the blood brain barrier using focused ultrasound (FUS) to treat neurodegenerative diseases, mouse- specific therapy planning is an essential step. For our therapy planning approach based on acoustic simulations we here propose to automatically segment the mouse skull and brain from magnetic resonance imaging, which is usually used in combination with FUS for monitoring purposes. The proposed method consists of (1) pre- processing to enhance the image contrast and remove noise, (2) a rough skull segmentation using morphological operations and adaptive binarization, (3) segmentation of the brain using the established 3D-PCNN method, (4) correction of the skull segmentation using the anatomical information about the brain location and (5) a post-processing to remove obvious errors from the final skull segmentation. The method is evaluated with four in-vivo datasets obtained with different parameters. The median Matthews Correlation Coefficient (MCC) on all slices of four datasets was 0.85 for the brain segmentation, 0.69 for the overall skull segmentation and 0.78 for the skull cap. Finally for showcasing the application an acoustic simulation based on the segmentation is presented, which results in a comparable prediction of the pressure field prediction as our earlier method based on micro-CT, and lines up well with literature estimations of the ultrasound attenuation.
Breast cancer is the most dominant cancer type among women. Although digital mammography plays an important role in early breast cancer detection, many cancers cannot be distinguished on mammography only, particularly in individuals with dense breast tissue. Lesions not recognizable on mammography are frequently detected by contrast enhanced magnetic resonance imaging (CE-MRI) of the breast. Based on the suspicious characteristics, these lesions need to be further evaluated with MRI-guided biopsy. However, MRI-guided biopsy is costly, time consuming, and not commonly available. In our earlier work, we proposed a novel method for a matching tool between MRI and spot mammograms using a biomechanical model based registration to match MRI and full X-ray mammograms and an image based registration to align full X-ray mammograms and spot mammograms. In this paper, we focus on developing and evaluating methods for image based registration between full X-ray mammograms and spot mammograms. Results assessed for thirteen patients from the Medical University of Vienna are presented. The median target registration error (TRE) of the image based registration is 21.7 mm and the standard deviation is 9.3 mm.
Ultrasound transmission tomography promises a high potential and novel imaging method for early breast cancer diagnosis; it can quantitatively characterize tissues or materials by the attenuation and speed of sound (SoS). Reconstruction of ultrasound transmission tomography is an inverse problem that can be solved iteratively based on a paraxial approximation of the Helmholtz equation as forward model, which is highly non-linear and time-consuming. In order to address these problems and reconstruct desired images, we design a dual domain network architecture for ultrasound transmission tomography reconstruction. It can enhance the information of measurement domain and directly reconstruct from pressure field measurements without using any initialization of reconstruction and fully connected layer. We train the network on simulated ImageNet data and transfer it for ultrasound transmission tomography images to avoid overfitting when the amount of ultrasound transmission tomography images is limited. Our experimental results demonstrate that a dual domain network produces significant improvements over state-of-the-art methods. It improves the measured structural similarity measure (SSIM) from 0.54 to 0.90 and normalized root mean squared error (nRMSE) from 0.49 to 0.01 on average concerning the SoS reconstruction, and from 0.46 to 0.98 for SSIM, from 353 to 0.03 for nRMSE on average concerning the attenuation reconstruction.
Breast cancer is the most common cancer type among women. Approximately 40,000 women are expected to die from breast cancer every year. While digital mammography has a central role in the early diagnosis of breast cancer, many cancers are not visible in mammography, for example in women with dense breast tissue. Contrast enhanced magnetic resonance imaging (CE-MRI) of the breast is often used to detect lesions not visible in mammography. Lesions with suspicious characteristics on CE-MRI need to be further assessed with MRI-guided biopsy. However, MRI-guided biopsy is expensive, time consuming, and not widely available. In this paper, a novel method for a matching tool between MRI and spot mammograms is proposed. Our aim is to transfer information that is only visible in MRI onto mammographic spot projections, to enable X-ray guided biopsy even if the lesion is only visible in MRI. Two methods of registration in combination are used; a biomechanical model based registration between MRI and full view X-ray mammograms and a subsequent image based registration between full mammograms and spot mammograms. Preliminary results assessed for one patient from the Medical University of Vienna are presented. The target registration error (TRE) of biomechanical model based registration is 2.4 mm and the TRE of the image based registration is 9.5 mm. The total TRE of the two steps is 7.3 mm.
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.
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.
In ultrasound transmission tomography, image reconstruction is an inverse problem which is solved iteratively based on a forward model that simulates the wave propagation of ultrasound. A commonly used forward model is paraxial approximation of the Helmholtz equation, which is time-consuming. Hence developing optimizers that minimize the number of forward solutions is crucial to achieve clinically acceptable reconstruction time, while the state-of-the-art methods in this field such as Gauss-Newton conjugate gradient (CG) and nonlinear CG are not capable of reaching this goal. To that end, we focus on Jacobian-free optimizers or accelerators in this paper, since the computation of the Jacobian is expensive. We investigate the limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm as a preconditioning technique due to its ability to efficiently approximate inverse Hessian without performing forward model or its adjoint. We show L-BFGS can reach a speedup of more than one order of magnitude for the noise-free case, while the method still halves the reconstruction time in presence of noise in the data. The performance drop is explained by perturbed gradients due to noise in the data. We also show when used alone as a quasi-Newton method, L-BFGS is competitive with the accelerated CG based methods regarding the number of iterations, and outperforms them regarding reconstruction time.
For Ultrasound Tomography reflectivity imaging Synthetic Aperture Focusing Technique (SAFT) is often applied. Phase aberration correction is required to achieve images with high resolution and high contrast, for which a sound speed map is required. For USCT systems these sound speed maps are usually reconstructed using the transmission data from the raw data set, which is also used for reflection tomography. We compare straight and bent ray phase aberration correction SAFT algorithms with respect to different reconstruction algorithms to derive the sound speed map. Evaluations are carried out based on a simulated phantom and measured data from the Multimodal Ultrasound Breast Imaging System (MUBI). Phase aberration correction enables recovering the contrast of the image, while without SAFT results in considerably unfocused inner structures. By applying a reconstructed sound speed map however the local contrast cannot be fully recovered compared to the ideal case. Introducing bent ray transmission reconstruction approaches based on the Fast Marching or B´ezier curve method in all cases improves the results over the straight ray transmission tomography.
KEYWORDS: Magnetic resonance imaging, Image registration, Breast, 3D image processing, X-rays, 3D modeling, X-ray imaging, Tissues, Computer simulations, Digital breast tomosynthesis
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 is an exciting new technology mostly aimed at breast cancer imaging. Due to the complex interaction of ultrasound with human tissue, the large amount of raw data, and the large volumes of interest, both image acquisition and image reconstruction are challenging. Following the idea of open science, the long term goal of the USCT reference database is establishing open and easy to use data and code interfaces and stimulating the exchange of available reconstruction algorithms and raw data sets of different USCT devices. The database was established with freely available and open licensed USCT data for comparison of reconstruction algorithms, and will be maintained and updated. Additionally, the feedback about data and system architecture of the scientists working on reconstruction methods will be published to help to drive further development of the various measurement setups.
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.
In the past years we have perceived within the USCT research community a demand for freely available USCT data sets.
Inspired by the idea of Open Science, this collection of data sets could stimulate the collaboration and the exchange of
ideas and experiences between USCT researchers. In addition, it may lead to comprehensive comparison of different
reconstruction algorithms and their results. Finally, by collecting feedback from the users about data and system
architecture, valuable information is gathered for further development of measurement setups. For the above reasons, we
have initiated a digital portal with several reference data sets and access scripts under free licenses. To kick off this
initiative, we organized a USCT data challenge event at SPIE Medical Imaging 2017.
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.
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.
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.
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.
KEYWORDS: Signal attenuation, Breast, Image quality, 3D image processing, In vivo imaging, Ultrasonography, Receivers, Reconstruction algorithms, 3D metrology, Computed tomography
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.
A promising candidate for improved imaging of breast cancer is ultrasound computer tomography (USCT). The aim of this work was to design a new aperture for our full 3D USCT which extends the properties of the current aperture to a larger ROI fitting the buoyant breast in water and decreasing artifacts in transmission tomography. The optimization resulted in a larger opening angle of the transducers, a larger diameter of the aperture and an approximately homogeneous distribution of the transducers, with locally random distances. The developed optimization methods allow us to automatically generate an optimized aperture for given diameters of apertures and transducer arrays, as well as quantitative comparison to other arbitrary apertures. Thus, during the design phase of the next generation KIT 3D USCT, the image quality can be balanced against the specification parameters and given hardware and cost limitations. The methods can be applied for general aperture optimization, only limited by the assumptions of a hemispherical aperture and circular transducer arrays.
KEYWORDS: Image segmentation, Breast, Edge detection, 3D image processing, Computed tomography, Reflection, In vivo imaging, Ultrasonography, Tomography, Signal attenuation
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.
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 (USCT) requires a large number of transducers approx. two orders of magnitude larger than in a 2D system. Technical feasibility limits the number of transducer positions to a much smaller number resulting in a sparse aperture and causing artifacts due to grating lobe effects in the images. Usually, grating lobes are suppressed by using a non-sparse geometry. Thus, there is no quantitative estimation method available how much the image contrast is degraded when a sparse aperture is applied and how much the contrast is improved when adding more transducers, changing the overall aperture or the object. In this paper the effect of the grating lobes on the image quality was analyzed for a spherical, a hemispherical and the semi-ellipsoidal USCT aperture: The background noise due to grating lobes is very similar for the three apertures and mainly influenced by the sparseness and the imaged object. A model for noise reduction was fitted to simulated and experimental data, and can be used to predict the peak-signal-to-noise- ratio for a given object and number of aperture positions.
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.
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. 3DUSCT emitting and receiving spherical wave fronts overcomes these limitations. We built an
optimized 3DUSCT with nearly isotropic 3DPSF, realizing for the first time the full benefits of a 3Dsystem.
In this paper results of the 3D point spread function measured with a dedicated phantom and images acquired
with a clinical breast phantom are presented. 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 3TeslaMRI volume of the breast phantom. Image quality and
resolution is isotropic in all three dimensions, confirming the successful optimization experimentally.
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.
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.
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