Detection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume Rendering

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 also crucial, as their diagnosis is associated with their 3D morphology. In this work, DBT data from a public database were used to train a faster region-based convolutional neural network (R-CNN) to locate MCs in entire DBT. Additionally, the detected MCs were further analyzed through standard 2D visualization and 3D volume rendering (VR) specifically developed for DBT data. For MCs detection, the sensitivity of our Faster R-CNN was 60% with 4 false positives. These preliminary results are very promising and can be further improved. On the other hand, the 3D VR visualization provided important information, with higher quality and discernment of the detected MCs. The developed pipeline may help radiologists since (1) it indicates specific breast regions with possible lesions that deserve additional attention and (2) as the rendering of the MCs is similar to a segmentation, a detailed complementary analysis of their 3D morphology is possible

Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs

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. Only about 25% of the studies are specific to MCs, and all of them are based on the classification of small preselected regions. Classifying the whole image according to the presence or absence of MCs is a difficult task due to the size of MCs and all the information present in an entire image. A completely automatic and direct classification, which receives the entire image, without prior identification of any regions, is crucial for the usefulness of these techniques in a real clinical and screening environment. The main purpose of this work is to implement and evaluate the performance of convolutional neural networks (CNNs) regarding an automatic classification of a complete DBT image for the presence or absence of MCs (without any prior identification of regions). In this work, four popular deep CNNs are trained and compared with a new architecture proposed by us. The main task of these trainings was the classification of DBT cases by absence or presence of MCs. A public database of realistic simulated data was used, and the whole DBT image was taken into account as input. DBT data were considered without and with preprocessing (to study the impact of noise reduction and contrast enhancement methods on the evaluation of MCs with CNNs). The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. Very promising results were achieved with a maximum AUC of 94.19% for the GoogLeNet. The second-best AUC value was obtained with a new implemented network, CNN-a, with 91.17%. This CNN had the particularity of also being the fastest, thus becoming a very interesting model to be considered in other studies. With this work, encouraging outcomes were achieved in this regard, obtaining similar results to other studies for the detection of larger lesions such as masses. Moreover, given the difficulty of visualizing the MCs, which are often spread over several slices, this work may have an important impact on the clinical analysis of DBT images

Dose calculation in Abdominal-Pelvic CT examinations from two hospitals in the Lisbon area

This work aims to evaluate whether the dose values received by patients submitted to Abdominal-pelvic Computed Tomography (CT) in two hospitals in the Lisbon area do agree with the Diagnostic Reference Levels. The dosimetric units of examinations performed with modulated and continuous current in both hospitals were also compared. The study consisted in collecting data from 200 abdominal - pelvic CT's, obtained in two hospitals (100 per hospital). In Hospital A, the mean DLP was 562.34 mGy.cm and the average values CTDIvol was 12.06 mGy. In Hospital B, the mean DLPand CTDIvol was 767.14 mGy.cm and 15.02 mGy, respectively. We have concluded that, from this sample, none of the dosimetric units values exceeded the Diagnostic Reference Levels values. It was also noted that tests performed in B involved higher doses of patient exposure to ionizing radiation

2D Iterative Image Reconstruction for a Dual Planar Detector for Positron Emission Mammography

Tese de doutoramento em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008The Clear-PEM system is a prototype scanner for Positron Emission Mammography (PEM), currently under development. In the scope of this project, we present the implementation of three iterative algorithms for 2D image reconstruction, using linograms: the Algebraic Reconstruction Technique (ART), the Maximum Likelihood Expectation Maximization (ML-EM) and the Ordered Subsets Expectation Maximization (OS-EM) algorithms. To calculate the system matrix required by all algorithms, we have developed three different methods (the pixel-driven, the ray-driven and the tube-driven method). Additionally, we have implemented a method to correct the sensitivity differences between different regions of the field-of-view and to compensate for gaps between the detector elements. The comparison of the algorithms' performance was done with the help of Monte Carlo simulated data, which allowed evaluating image spatial resolution, convergence speed, image uniformity, contrast between lesions and background, signalto- noise ratio and computational speed. The results showed that the OS-EM algorithm produced the best results regarding all evaluated parameters, except the computation time by iteration, in which ML-EM and ART presented better results. Compared to OS-EM, ML-EM presented similar results but at the expense of additional iterations, while ART converged after a similar number of iterations, but produced worse results. Regarding the system matrix, the best results were obtained using the tube-driven method. The results pointed to the need of a precise rebinning method and the importance of the system sensitivity correction. The results indicate that, with these algorithms, we can achieve a spatial resolution of 1,5 mm and detect lesions having 3 mm in diameter. The results also lead to think that 2 mm lesions may be detected with acquisition times higher than the ones simulated for this work. These results compare well with results from other PEM scanners, being better in some of the evaluated parameters. However, these results must be validated with real data during the clinical trials that will be carried on in a near future.Fundação para a Ciência e a Tecnologia (FCT), (SFRH/BD/6187/2001); Fundação da Faculdade de Ciência

A tomographic simulator for differential optical absorption spectroscopy

PDE/BDE/ 114549/2016 UIDB/00645/2020TomoSim comes as part of project ATMOS, a miniaturised Differential Optical Absorption Spectroscopy (DOAS) tomographic atmospheric evaluation device, designed to fit a small drone. During the development of the project, it became necessary to write a simulation tool for system validation. TomoSim is the answer to this problem. The software has two main goals: to mathemati-cally validate the tomographic acquisition method; and to allow some adjustments to the system before reaching final product stages. This measurement strategy was based on a drone performing a sequential trajectory and gathering projections arranged in fan beams, before using some classical tomographic methods to reconstruct a spectral image. The team tested three different reconstruction algorithms, all of which were able to produce an image, validating the team’s initial assumptions regarding the trajectory and acquisition strategy. All algorithms were assessed on their computational performance and their ability for reconstructing spectral “images”, using two phantoms, one of which custom made for this purpose. In the end, the team was also able to uncover certain limitations of the TomoSim approach that should be addressed before the final stages of the system.publishersversionpublishe

Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches

Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice

AI in Breast Cancer Imaging: A Survey of Different Applications

Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. This paper surveys different applications of AI in Breast Imaging. First, traditional Machine Learning and Deep Learning methods that can detect the presence of a lesion and classify it into benign/malignant—which could be important to diminish reading time and improve accuracy—are analyzed. Following that, researches in the field of breast cancer risk prediction using mammograms—which may be able to allow screening programs customization both on periodicity and modality—are reviewed. The subsequent section analyzes different applications of augmentation techniques that allow to surpass the lack of labeled data. Finally, still concerning the absence of big datasets with labeled data, the last section studies Self-Supervised learning, where AI models are able to learn a representation of the input by themselves. This review gives a general view of what AI can give in the field of Breast Imaging, discussing not only its potential but also the challenges that still have to be overcome

Digital Breast Tomosynthesis: Towards Dose Reduction through Image Quality Improvement

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 patient. Here, a method based on 2D Total Variation (2D TV) minimization to improve image quality without the need to increase the dose was proposed. Two phantoms were used to acquire data at different dose ranges (0.88–2.19 mGy for Gammex 156 and 0.65–1.71 mGy for our phantom). A 2D TV minimization filter was applied to the data, and the image quality was assessed through contrast-to-noise ratio (CNR) and the detectability index of lesions before and after filtering. The results showed a decrease in 2D TV values after filtering, with variations of up to 31%, increasing image quality. The increase in CNR values after filtering showed that it is possible to use lower doses (−26%, on average) without compromising on image quality. The detectability index had substantial increases (up to 14%), especially in smaller lesions. So, not only did the proposed approach allow for the enhancement of image quality without increasing the dose, but it also improved the chances of detecting small lesions that could be overlooked

Evaluation of the Reference Dose Levels in the Chest CT scan

This study was performed in a Hospital in Lisbon, where 30 CT chest scans were evaluated. With this work, we sought to quantify the dose reference level of CTequipment in an hospital focusing the study on the thorax evaluation. It was also intended to quantify an average value for the CT dosimetric quantities, including CTDIw, DLP and effective dose, and then, to compare obtained values with diagnostic reference levels regulated by the ICRP. The dose reference level obtained in the Hospital, for the studied exams was 13, 67 mGy. The average CTDIw was 12, 18 mGy, the mean DLPcorresponded to 291, 49mGy.cm and the average effective dose found was 4, 95 mSv. This study also allowed to conclude, as predicted, that there is a low risk of cancer occurrence or severe congenital effects in all individuals of the studied sample, and that the effective dose received in each test corresponds approximately to a radiation exposure equivalent to 2,06 years of natural radiation. The average value of all dosimetric quantities did not exceed the limits set by the ICRP

Optimization of Breast Tomosynthesis Visualization through 3D Volume Rendering

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 dimension in z direction and sampling distance) on DBT rendered data. Both parameters were studied with a real phantom and one clinical DBT data set. The voxel size was changed from 0.085 × 0.085 × 1.0 mm3 to 0.085 × 0.085 × 0.085 mm3 using ten interpolation functions available in the Visualization Toolkit library (VTK) and several sampling distance values were evaluated. The results were investigated at 90º using volume rendering visualization with composite technique. For phantom quantitative analysis, degree of smoothness, contrast-to-noise ratio, and full width at half maximum of a Gaussian curve fitted to the profile of one disk were used. Additionally, the time required for each visualization was also recorded. Hamming interpolation function presented the best compromise in image quality. The sampling distance values that showed a better balance between time and image quality were 0.025 mm and 0.05 mm. With the appropriate rendering parameters, a significant improvement in rendered images was achieved