Giovanni Palma

Detection of masses and architectural distortions in digital breast tomosynthesis images using fuzzy and a contrario approaches

Digital breast tomosynthesis (DBT) is a new 3D imaging technique, which overcomes some limitations of traditional digital mammography. Its development induces an increased amount of data to be processed, thus calling for a computer aided detection system to help the radiologist. Towards this aim, this paper focuses on the detection of masses and architectural distortions in DBT images. A complete detection scheme is proposed, consisting of two parts, called channels, each dedicated to one type of lesions, which are then merged in a final decision step, thus handling correctly the potential overlap between the two types of lesions. The first detection channel exploits the dense kernel nature of masses and the intrinsic imprecision of their attributes in a fuzzy approach. The second detection channel models the convergence characteristics of architectural distortions in an a contrario approach. The experimental results on 101 breasts, including 53 lesions, demonstrate the usefulness of the proposed approach, which leads to a high sensitivity with a reduced number of false positives, and compares favorably to existing approaches.

Spiculated lesions and architectural distortions detection in digital breast tomosynthesis datasets

Digital Breast Tomosynthesis (DBT) is a new 3D imaging technique aiming at overcoming some limitations of mammography A computer aided detection system may help the radiologist to process the increased amount of data of this new modality In this paper we propose to address the detection of masses and architectural distortions in DBT datasets To achieve this task, we propose a detection scheme composed of two separate channels, each of them being dedicated to the detection of one of the target radiological signs. We propose a description of these channels as well as a validation on clinical data We also compare the performance with existing approaches.

Masses classification using fuzzy active contours and fuzzy decision trees

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.

Noise reduction in dual-energy contrast enhanced digital breast tomosynthesis using regularization

Dual-Energy Contrast Enhanced Digital breast tomosynthesis is an emerging technique for breast cancer detection, which combines the strengths of functional and morphological 3D imaging The projection images acquired with two energy spectra at several angulations are combined to obtain “iodine” projections These are then reconstructed to provide 3D iodine images The combination process significantly increases the noise in the images, which is further amplified by the 3D reconstruction This paper proposes a regularized reconstruction method based on the simultaneous algebraic reconstruction technique to be used for the reconstruction of the iodine volume The regularization represents a constraint for the reconstructed volume, which causes the reduction of the noise and preserves the structures of interest Preliminary results on clinical data demonstrate a significant increase of the visibility of iodine-enhanced regions without affecting their sharpness and morphology.

Fast detection of convergence areas in Digital Breast Tomosynthesis

In this paper we propose a fast method to detect spiculated lesions and architectural distortions in Digital Breast Tomosynthesis datasets. This approach relies on an a contrario modeling of the problem. First, an indicator corresponding to the convergence of structures is defined, then the a contrario framework is used to set a threshold on it in order to detect zones where its value is unlikely. We propose, as a main contribution of this paper, a fast algorithm to implement this method, which significantly reduces its computational cost. The method was evaluated on 38 breasts (10 containing a lesion), and a sensitivity of 0.8 at 1.68 false positive per breast was obtained.

Detectability and image quality metrics based on robust statistics: following non-linear, noise-reduction filters

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.

Toward a dose reduction strategy using model-based reconstruction with limited-angle tomosynthesis

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.

3D density estimation in digital breast tomosynthesis: application to needle path planning for breast biopsy

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 Youngs 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.

Clinical image benefits after model-based reconstruction for low dose dedicated breast tomosynthesis

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.

Influence of DBT reconstruction algorithm on power law spectrum coefficient

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.