Ana Mota

University of Lisbon | UL · Instituto de Biofísica e Engenharia Biomédica (IBEB)

Breast cancer is the most commonly diagnosed cancer worldwide. The therapy used and its success depend highly on the histology of the tumor. This study aimed to explore the potential of predicting the molecular subtype of breast cancer using radiomic features extracted from screening digital mammography (DM) images. A retrospective study was perfor...

Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy—a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsi...

Currently, breast cancer is the most commonly diagnosed type of cancer worldwide. Digital Breast Tomosynthesis (DBT) has been widely accepted as a stand-alone modality to replace Digital Mammography, particularly in denser breasts. However, the image quality improvement provided by DBT is accompanied by an increase in the radiation dose for the pat...

Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions of interest previously selected....

Microcalcification clusters (MCs) are one of the most important biomarkers for breast cancer and Digital Breast Tomosynthesis (DBT) has consolidated its role in breast cancer imaging. As there are mixed observations about MCs detection using DBT, it is important to develop tools that improve this task. Furthermore, the visualization mode of MCs is...

Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a correct PSF is difficult to achieve and these methods amplify noise. When no information is availab...

3D volume rendering may represent a complementary option in the visualization of Digital Breast Tomosynthesis (DBT) examinations by providing an understanding of the underlying data at once. Rendering parameters directly influence the quality of rendered images. The purpose of this work is to study the influence of two of these parameters (voxel di...

Slice by slice visualization of Digital Breast Tomosynthesis (DBT) data is time consuming and can hamper the interpretation of lesions such as clusters of microcalcifications. With a visualization of the object through multiple angles, 3D volume rendering (VR) provides an intuitive understanding of the underlying data at once. 3D VR may play an imp...

Breast tissue superposition or parenchymal density have been known as Digital Mammography's (DM) main limitations. More expensive and case-specific tools such as MRI and ultrasound imaging may be used to address this problem, but 2D DM remains the most practical and cost-effective approach. Digital Breast Tomo-synthesis (DBT) has the ability to ove...

Digital Breast Tomosynthesis (DBT) is a developing imaging modality which produces 3D images of a breast. Iterative image reconstruction techniques, such as Algebraic reconstruction technique (ART), have been proposed to help increasing success in detecting masses and micro-calcifications. To enhance the quality of reconstructed image, total variat...

The purpose of this work was to implement and evaluate the performance of a 3D Total Variation (TV) minimization filter for Poisson noise and apply it to 3D digital breast tomosynthesis (DBT) data. The value of Lagrange multiplier (λ) to be used in filter equation has a direct relationship with the results obtained. Some preliminary studies about λ...

Digital Mammography is a well-established imaging modality for early detection and diagnosis of breast cancer. However its 2D nature results in tissue superposition, which can decrease sensitivity and specificity, leading to an unnecessary recall for additional examinations. Digital Breast Tomosynthesis (DBT) is a new technique that has the ability...

Purpose: Compressed sensing (CS) is a new approach in medical imaging which allows a sparse image to be reconstructed from undersampled data. Total variation (TV) based minimization algorithms are the one CS technique that has achieved great success due to its virtue of preserving edges while reducing image noise. The purpose of this work is to im...

The Partial Volume (PV) effect in Positron Emission Tomography (PET) imaging leads to loss in quantification accuracy, which manifests in PV effects (small objects occupy partially the sensitive volume of the imaging instrument, resulting in blurred images). Simultaneous acquisition of PET and Magnetic Resonance Imaging (MRI) produces concurrent me...