KEYWORDS: Data modeling, Digital breast tomosynthesis, Breast, Model-based design, Sensors, Optical spheres, 3D modeling, Medical imaging, 3D image processing, Breast imaging
Model-based iterative reconstruction (MBIR) is implemented to process full clinical data sets of dedicated breast tomosynthesis (DBT) in a low dose condition and achieves less spreading of anatomical structure between slices. MBIR is a statistical based reconstruction which can control the trade-off between data fitting and image regularization. In this study, regularization is formulated with anisotropic prior weighting that independently controls the image regularization between in-plane and out-of-plane voxel neighbors. Studies at complete and partial convergence show that the appropriate formulation of data-fit and regularization terms along with anisotropic prior weighting leads to a solution with improved localization of objects within a more narrow range of slices. This result is compared with the solutions using simultaneous iterative reconstruction technique (SIRT), which is one of the state of art reconstruction in DBT. MBIR yields higher contrast-to-noise for medium and large size microcalcifications and diagnostic structures in volumetric breast images and supports opportunity for dose reduction for 3D breast imaging.
In breast X-ray images, texture has been characterized by a noise power spectrum (NPS) that has an inverse power-law shape described by its slope β in the log-log domain. It has been suggested that the magnitude of the power-law spectrum coefficient β is related to mass lesion detection performance. We assessed β in reconstructed digital breast tomosynthesis (DBT) images to evaluate its sensitivity to different typical reconstruction algorithms including simple back projection (SBP), filtered back projection (FBP) and a simultaneous iterative reconstruction algorithm (SIRT 30 iterations). Results were further compared to the β coefficient estimated from 2D central DBT projections. The calculations were performed on 31 unilateral clinical DBT data sets and simulated DBT images from 31 anthropomorphic software breast phantoms. Our results show that β highly depends on the reconstruction algorithm; the highest β values were found for SBP, followed by reconstruction with FBP, while the lowest β values were found for SIRT. In contrast to previous studies, we found that β is not always lower in reconstructed DBT slices, compared to 2D projections and this depends on the reconstruction algorithm. All β values estimated in DBT slices reconstructed with SBP were larger than β values from 2D central projections. Our study also shows that the reconstruction algorithm affects the symmetry of the breast texture NPS; the NPS of clinical cases reconstructed with SBP exhibit the highest symmetry, while the NPS of cases reconstructed with SIRT exhibit the highest asymmetry.
KEYWORDS: Signal attenuation, Digital breast tomosynthesis, Sensors, Reconstruction algorithms, Optical spheres, Model-based design, Breast, Data modeling, Computed tomography, Tissues
Model-based iterative reconstruction (MBIR) is an emerging technique for several imaging modalities and appli-
cations including medical CT, security CT, PET, and microscopy. Its success derives from an ability to preserve
image resolution and perceived diagnostic quality under impressively reduced signal level. MBIR typically uses a
cost optimization framework that models system geometry, photon statistics, and prior knowledge of the recon-
structed volume. The challenge of tomosynthetic geometries is that the inverse problem becomes more ill-posed
due to the limited angles, meaning the volumetric image solution is not uniquely determined by the incom-
pletely sampled projection data. Furthermore, low signal level conditions introduce additional challenges due to
noise. A fundamental strength of MBIR for limited-views and limited-angle is that it provides a framework for
constraining the solution consistent with prior knowledge of expected image characteristics. In this study, we
analyze through simulation the capability of MBIR with respect to prior modeling components for limited-views,
limited-angle digital breast tomosynthesis (DBT) under low dose conditions. A comparison to ground truth
phantoms shows that MBIR with regularization achieves a higher level of fidelity and lower level of blurring
and streaking artifacts compared to other state of the art iterative reconstructions, especially for high contrast
objects. The benefit of contrast preservation along with less artifacts may lead to detectability improvement of
microcalcification for more accurate cancer diagnosis.
KEYWORDS: Nonlinear filtering, Image filtering, Digital filtering, Image processing, Image quality, Linear filtering, Denoising, Gaussian filters, Statistical modeling, Signal to noise ratio
Non-linear image processing and reconstruction algorithms that reduced noise while preserving edge detail are currently being evaluated in medical imaging research literature. We have implemented a robust statistics analysis of four widely utilized methods. This work demonstrates consistent trends in filter impact by which such non-linear algorithms can be evaluated. We calculate observer model test statistics and propose metrics based on measured non-Gaussian distributions that can serve as image quality measures analogous to SDNR and detectability. The filter algorithms that vary significantly in their approach to noise reduction include median (MD), bilateral (BL), anisotropic diffusion (AD) and total-variance regularization (TV). It is shown that the detectability of objects limited by Poisson noise is not significantly improved after filtration. There is no benefit to the fraction of correct responses in repeated n-alternate forced choice experiments, for n=2-25. Nonetheless, multi-pixel objects with contrast above the detectability threshold appear visually to benefit from non-linear processing algorithms. In such cases, calculations on highly repeated trials show increased separation of the object-level histogram from the background-level distribution. Increased conspicuity is objectively characterized by robust statistical measures of distribution separation.
KEYWORDS: Digital breast tomosynthesis, Breast, 3D modeling, Tissues, Binary data, 3D image processing, Biopsy, 3D acquisition, Reconstruction algorithms, X-rays
Needle insertion planning for digital breast tomosynthesis (DBT) guided biopsy has the potential to improve patient
comfort and intervention safety. However, a relevant planning should take into account breast tissue deformation and
lesion displacement during the procedure. Deformable models, like finite elements, use the elastic characteristics of the
breast to evaluate the deformation of tissue during needle insertion. This paper presents a novel approach to locally
estimate the Young's modulus of the breast tissue directly from the DBT data. The method consists in computing the
fibroglandular percentage in each of the acquired DBT projection images, then reconstructing the density volume.
Finally, this density information is used to compute the mechanical parameters for each finite element of the deformable
mesh, obtaining a heterogeneous DBT based breast model. Preliminary experiments were performed to evaluate the
relevance of this method for needle path planning in DBT guided biopsy. The results show that the heterogeneous DBT
based breast model improves needle insertion simulation accuracy in 71% of the cases, compared to a homogeneous
model or a binary fat/fibroglandular tissue model.
KEYWORDS: Digital breast tomosynthesis, Medical imaging, Computer aided diagnosis and therapy, Current controlled current source, Architectural distortion, Databases, Breast
We propose a new method to detect architectural distortions and spiculated masses in digital breast tomosynthesis volumes. To achieve this goal, an a contrario approach is used. In this approach, an event, corresponding to a minimal number of structures converging toward the same location, is defined such that its expectation of occurrence within a random image is very low. Occurrences of this event in real images are then detected and considered as possible lesion locations. During the last step, the number of false positives is reduced through classification using attributes computed on histograms of structure orientations.
The approach was tested using the leave-one-out method on a database composed of 38 breasts (10 containing a lesion and 28 containing no lesion). A sensitivity of 0.8 at 1.68 false positives/breast was achieved.
In this paper we propose a method to classify masses in digital breast tomosynthesis (DBT) datasets. First,
markers of potential lesions are extracted and matched over the different projections. Then two level-set models
are applied on each finding corresponding to spiculated and circumscribed mass assumptions respectively. The
formulation of the active contours within this framework leads to several candidate contours for each finding. In
addition, a membership value to the class contour is derived from the energy of the segmentation model, and
allows associating several fuzzy contours from different projections to each set of markers corresponding to a
lesion. Fuzzy attributes are computed for each fuzzy contour. Then the attributes corresponding to fuzzy contours
associated to each set of markers are aggregated. Finally, these cumulated fuzzy attributes are processed by two
distinct fuzzy decision trees in order to validate/invalidate the spiculated or circumscribed mass assumptions.
The classification has been validated on a database of 23 real lesions using the leave-one-out method. An error
classification rate of 9% was obtained with these data, which confirms the interest of the proposed approach.
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