Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Automatic Breast Density Classification on Tomosynthesis Images

Breast cancer (BC) is the type of cancer that most greatly affects women globally hence its early detection is essential to guarantee an effective treatment. Although digital mammography (DM) is the main method of BC detection, it has low sensitivity with about 30% of positive cases undetected due to the superimposition of breast tissue when crossed by the X-ray beam. Digital breast tomosynthesis (DBT) does not share this limi tation, allowing the visualization of individual breast slices due to its image acquisition system. Consecutively, DBT was the object of this study as a means of determining one of the main risk factors for BC: breast density (BD). This thesis was aimed at developing an algorithm that, taking advantage of the 3D nature of DBT images, automatically clas sifies them in terms of BD. Thus, a quantitative, objective and reproducible classification was obtained, which will contribute to ascertain the risk of BC. The algorithm was developed in MATLAB and later transferred to a user interface that was compiled into an executable application. Using 350 images from the VICTRE database for the first classification phase – group 1 (ACR1+ACR2) versus group 2 (ACR3+ACR4), the highest AUC value of 0,9797 was obtained. In the classification within groups 1 and 2, the AUC obtained was 0,7461 and 0,6736, respectively. The algorithm attained an accuracy of 82% for these images. Sixteen exams provided by Hospital da Luz were also evaluated, with an overall accuracy of 62,5%. Therefore, a user-friendly and intuitive application was created that prioritizes the use of DBT as a diagnostic method and allows an objective classification of BD. This study is a first step towards preparing medical institutions for the compulsoriness of assessing BD, at a time when BC is still a very present pathology that shortens the lives of thousands of people

History of mammography: analysis of breast imaging diagnostic achievements over the last century

Breast cancer is the most common forms of cancer and a leading cause of mortality in women. Early and correct diagnosis is, therefore, essential to save lives. The development of diagnostic imaging applied to the breast has been impressive in recent years and the most used diagnostic test in the world is mammography, a low-dose X-ray technique used for imaging the breast. In the first half of the 20th century, the diagnosis was in practice only clinical, with consequent diagnostic delay and an unfavorable prognosis in the short term. The rise of organized mammography screening has led to a remarkable reduction in mortality through the early detection of breast malignancies. This historical review aims to offer a complete panorama of the development of mammography and breast imaging during the last century. Through this study, we want to understand the foundations of the pillar of radiology applied to the breast through to the most modern applications such as contrast-enhanced mammography (CEM), artificial intelligence, and radiomics. Understanding the history of the development of diagnostic imaging applied to the breast can help us understand how to better direct our efforts toward an increasingly personalized and effective diagnostic approach. The ultimate goal of imaging applied to the detection of breast malignancies should be to reduce mortality from this type of disease as much as possible. With this paper, we want to provide detailed documentation of the main steps in the evolution of breast imaging for the diagnosis of breast neoplasms; we also want to open up new scenarios where the possible current and future applications of imaging are aimed at being more precise and personalized

A new approach for the in-vivo characterization of the biomechanical behavior of the breast and the cornea

The characterization of the mechanical behavior of soft living tissues is a big challenge in Biomechanics. The difficulty arises from both the access to the tissues and the manipulation in order to know their physical properties. Currently, the biomechanical characterization of the organs is mainly performed by testing ex-vivo samples or by means of indentation tests. In the first case, the obtained behavior does not represent the real behavior of the organ. In the second case, it is only a representation of the mechanical response of the indented areas. The purpose of the research reported in this thesis is the development of a methodology to in-vivo characterize the biomechanical behavior of two different organs: the breast and the cornea. The proposed methodology avoids invasive measurements to obtain the mechanical response of the organs and is able to completely characterize of the biomechanical behavior of them. The research reported in this thesis describes a methodology to in-vivo characterize the biomechanical behavior of the breast and the cornea. The estimation of the elastic constants of the constitutive equations that define the mechanical behavior of these organs is performed using an iterative search algorithm which optimizes these parameters. The search is based on the iterative variation of the elastic constants of the model in order to increase the similarity between a simulated deformation of the organ and the real one. The similarity is measured by means of a volumetric similarity function which combines overlap-based coefficients and distance-based coefficients. Due to the number of parameters to be characterized as well as the non-convergences that the solution may present in some regions, genetic heuristics were chosen to drive the search algorithm. In the case of the breast, the elastic constants of an anisotropic hyperelastic neo-Hookean model proposed to simulate the compression of the breast during an MRI-guided biopsy were estimated. Results from this analysis showed that the proposed algorithm accurately found the elastic constants of the proposed model, providing an average relative error below 10%. The methodology was validated using breast software phantoms. Nevertheless, this methodology can be easily transferred into its use with real breasts. In the case of the cornea, the elastic constants of a hyperelastic second-order Ogden model were estimated for 24 corneas corresponding to 12 patients. The finite element method was applied in order to simulate the deformation of the human corneas due to non-contact tonometry. The iterative search was applied in order to estimate the elastic constants of the model which approximates the most the simulated deformation to the real one. Results showed that these constants can be estimated with an error of about 5%. After the results obtained for both organs, it can be concluded that the iterative search methodology presented in this thesis allows the \textit{in-vivo} estimation the patient-specific elastic constants of the constitutive biomechanical models that govern the biomechanical behavior of these two organs.Lago Ángel, MÁ. (2014). A new approach for the in-vivo characterization of the biomechanical behavior of the breast and the cornea [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/44116TESI

GPCALMA, a mammographic CAD in a GRID connection

6 pages, 4 figures, to appear in CARS 2003 Proceedings, Computer Assisted Radiology and Surgery 17th International Congress and Exhibition, London, June 25-28, 2003Purpose of this work is the development of an automatic system which could be useful for radiologists in the investigation of breast cancer. A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. GPCALMA (Grid Platform Computer Assisted Library for MAmmography), a collaboration among italian physicists and radiologists, has built a large distributed database of digitized mammographic images (at this moment about 5500 images corresponding to 1650 patients). This collaboration has developed a CAD (Computer Aided Detection) system which, installed in an integrated station, can also be used for digitization, as archive and to perform statistical analysis. With a GRID configuration it would be possible for the clinicians tele- and co-working in new and innovative groupings ('virtual organisations') and, using the whole database, by the GPCALMA tools several analysis can be performed. Furthermore the GPCALMA system allows to be abreast of the CAD technical progressing into several hospital locations always with remote working by GRID connection. We report in this work the results obtained by the GPCALMA CAD software implemented with a GRID connection

Focal Spot, Summer 2003

https://digitalcommons.wustl.edu/focal_spot_archives/1094/thumbnail.jp