Métodos de reconstrucción en dominio temporal para tomografía por transmisión de ultrasonidos

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Física Atómica, Molecular y Nuclear, leída el 06-06-2017Breast cancer (BC) is the leading cause of cancer-related death for women in Europe, and the second one after lung cancer in the US [World Cancer Report, 2008]. Early detection is very important for the survival rate of BC, because the smaller the local extension of the neoplasia, the better the output of the surgical treatments employed. Besides, early detection increases the possibility of preserving the breast and decreases the probability of needing more invasive treatments [Secretaría de Salud, 2007, Alteri et al., 2011]. Mammography is currently the standard procedure employed for breast screening programs around the world. Nevertheless, its efficiency has been questioned lately because: (i) it generates many abnormal findings not related to cancer, (ii) it requires irradiating the patient and (iii) it has low specificity with dense breasts [Santen and Mansel, 2005]. Consequently, complementary techniques to mammography are being proposed to improve the detection and characterization of BC. Among these techniques, is the Ultrasound Computed Tomography (USCT), in reflection mode (which provides qualitative maps with the concentration of scatterers in the tissue), and transmission mode (which provides quantitative maps of the sound speed (SS) and the acoustic attenuation (AA) of the tissues). The images provided by the transmission modality have been proposed for BC detection as they can improve the detectability of malignancies in the breast [Mast, 2000, Duric et al., 2009]...El cáncer de mama (CM) es el cáncer más mortal entre las mujeres europeas, y el segundo más común en Estados Unidos [World Cancer Report, 2008]. La detección temprana es un factor que condiciona en gran medida la tasa de supervivencia a esta enfermedad, ya que a menor tamaño de la neoplasia detectada, mejores resultados pueden esperarse para los tratamientos quirúrgicos que se realicen. Además, la detección temprana aumenta la posibilidad de conservar la mama después de la cirugía y disminuye la necesidad de emplear otros tratamientos más invasivos[Secretaría de Salud, 2007, Alteri et al., 2011]. La mamografía es actualmente el procedimiento estándar que se emplea para el cribado del CM. Sin embargo, en los últimas años su eficiencia está siendo muy cuestionada por varios factores: (i) alta tasa de falsos positivos, (ii) requiere la irradiación del paciente y (iii) baja especificidad en mamas densas 2. Debido a lo anterior, para mejorar la detección y caracterización del CM se han propuesto varias técnicas complementarias. Entre ellas está la tomografía ultrasónica (TU), que es una técnica en desarrollo que presenta dos modalidades principales: la reflexión (proporciona mapas cualitativos de la concentración de dispersores en el tejido) y la transmisión (proporciona mapas cuantitativos de la velocidad y atenuación del sonido en el tejido). Los mapas del modo transmisión han sido propuestos como una eficiente alternativa, libre de radiación, para la detección del CM, ya que proporcionan alto contraste y especificidad [Mast, 2000, Duric et al., 2009]...Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEunpu

High Resolution 3D Ultrasonic Breast Imaging by Time-Domain Full Waveform Inversion

Ultrasound tomography (UST) scanners allow quantitative images of the human breast's acoustic properties to be derived with potential applications in screening, diagnosis and therapy planning. Time domain full waveform inversion (TD-FWI) is a promising UST image formation technique that fits the parameter fields of a wave physics model by gradient-based optimization. For high resolution 3D UST, it holds three key challenges: Firstly, its central building block, the computation of the gradient for a single US measurement, has a restrictively large memory footprint. Secondly, this building block needs to be computed for each of the $10^3-10^4$ measurements, resulting in a massive parallel computation usually performed on large computational clusters for days. Lastly, the structure of the underlying optimization problem may result in slow progression of the solver and convergence to a local minimum. In this work, we design and evaluate a comprehensive computational strategy to overcome these challenges: Firstly, we introduce a novel gradient computation based on time reversal that dramatically reduces the memory footprint at the expense of one additional wave simulation per source. Secondly, we break the dependence on the number of measurements by using source encoding (SE) to compute stochastic gradient estimates. Also we describe a more accurate, TD-specific SE technique with a finer variance control and use a state-of-the-art stochastic LBFGS method. Lastly, we design an efficient TD multi-grid scheme together with preconditioning to speed up the convergence while avoiding local minima. All components are evaluated in extensive numerical proof-of-concept studies simulating a bowl-shaped 3D UST breast scanner prototype. Finally, we demonstrate that their combination allows us to obtain an accurate 442x442x222 voxel image with a resolution of 0.5mm using Matlab on a single GPU within 24h

Improved misfit function for attenuation and speed reconstruction in ultrasound computed tomography

The reconstruction of acoustic attenuation maps for transmission Ultrasound Computed Tomography (USCT) based on the standard least-squares full wave inversion method requires the accurate knowledge of the sound speed map in the region under study. Any deviation in the reconstructed speed maps creates a very significant bias in the attenuation map, as the standard least-squares misfit function is more sensitive to time misalignments than to amplitude differences of the signals. In this work, we propose a generalized misfit function which includes an additional term that accounts for the amplitude differences between the measured and the estimated signals. The functional gradients used to minimize the proposed misfit function were obtained using an adjoint field formulation and the fractional Laplacian wave equation. The forward and backward wave propagation was obtained with the parallelized GPU version of the software k-Wave and the optimization was performed with a line search method. A numerical phantom simulating breast tissue and synthetic noisy data were used to test the performance of the proposed misfit function. The attenuation was reconstructed based on a converged speed map. An edge-preserving regularization method based on total variation was also implemented. To quantify the quality of the results, the mean values and their standard deviations in several regions of interest were analyzed and compared to the reference values. The proposed generalized misfit function decreases considerably the bias in the attenuation map caused by the deviations in the speed map in all the regions of interest analyzed

High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion

Ultrasound tomography (UST) scanners allow quantitative images of the human breast's acoustic properties to be derived with potential applications in screening, diagnosis and therapy planning. Time domain full waveform inversion (TD-FWI) is a promising UST image formation technique that fits the parameter fields of a wave physics model by gradient-based optimization. For high resolution 3D UST, it holds three key challenges: firstly, its central building block, the computation of the gradient for a single US measurement, has a restrictively large memory footprint. Secondly, this building block needs to be computed for each of the 1000 to 10000 measurements, resulting in a massive parallel computation usually performed on large computational clusters for days. Lastly, the structure of the underlying optimization problem may result in slow progression of the solver and convergence to a local minimum. In this work, we design and evaluate a comprehensive computational strategy to overcome these challenges: firstly, we exploit a gradient computation based on time reversal that dramatically reduces the memory footprint at the expense of one additional wave simulation per source. Secondly, we break the dependence on the number of measurements by using source encoding (SE) to compute stochastic gradient estimates. Also we describe a more accurate, TD-specific SE technique with a finer variance control and use a state-of-the-art stochastic LBFGS method. Lastly, we design an efficient TD multi-grid scheme together with preconditioning to speed up the convergence while avoiding local minima. All components are evaluated in extensive numerical proof-of-concept studies simulating a bowl-shaped 3D UST breast scanner prototype. Finally, we demonstrate that their combination allows us to obtain an accurate 442 × 442 × 222 voxel image with a resolution of 0.5 mm using Matlab on a single GPU within 24 h

Multi-modal ultrasound imaging for breast cancer detection

This work describes preliminary results of a two-modality imaging system aimed at the early detection of breast cancer. The first technique is based on compounding conventional echographic images taken at regular angular intervals around the imaged breast. The other modality obtains tomographic images of propagation velocity using the same circular geometry. For this study, a low-cost prototype has been built. It is based on a pair of opposed 128-element, 3.2 MHz array transducers that are mechanically moved around tissue mimicking phantoms. Compounded images around 360 degrees provide improved resolution, clutter reduction, artifact suppression and reinforce the visualization of internal structures. However, refraction at the skin interface must be corrected for an accurate image compounding process. This is achieved by estimation of the interface geometry followed by computing the internal ray paths. On the other hand, sound velocity tomographic images from time of flight projections have been also obtained. Two reconstruction methods, Filtered Back Projection (FBP) and 2D Ordered Subset Expectation Maximization (2D OSEM), were used as a first attempt towards tomographic reconstruction. These methods yield useable images in short computational times that can be considered as initial estimates in subsequent more complex methods of ultrasound image reconstruction. These images may be effective to differentiate malignant and benign masses and are very promising for breast cancer screening. (C) 2015 The Authors. Published by Elsevier B.V

Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment

"Funding: This work received funding from the Cancer Research for Personalized Medicine—CARPEM project (Site de Recherche Intégré sur le Cancer SIRIC), from the Plan Cancer Physicancer (grant C16025KS), and from the Région Ile-de-France. In vivo imaging was performed at the Life Imaging Facility of Université Paris Cité (Plateforme Imageries du Vivant - PIV), supported by France Life Imaging (grant ANR-11-INBS-0006) and Infrastructures Biologie-Santé (IBiSa). Nesrin Mansouri received a scholarship from the Ministère de l’Enseignement Supérieur et de la Recherche. This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement no. 101030046 of M. P.-L." "Acknowledgments: The authors thank Laure Fournier, Judith Favier, Charlotte Lussey-Lepoutre,Irène Buvat, Béatrice Berthon and J.M. Udías for rich scientific advice and discussions"The standard assessment of response to cancer treatments is based on gross tumor characteristics, such as tumor size or glycolysis, which provide very indirect information about the effect of precision treatments on the pharmacological targets of tumors. Several advanced imaging modalities allow for the visualization of targeted tumor hallmarks. Descriptors extracted from these images can help establishing new classifications of precision treatment response. We propose a machine learning (ML) framework to analyze metabolic–anatomical–vascular imaging features from positron emission tomography, ultrafast Doppler, and computed tomography in a mouse model of paraganglioma undergoing anti-angiogenic treatment with sunitinib. Imaging features from the follow-up of sunitinib-treated (n = 8, imaged once-per-week/6-weeks) and sham-treated (n = 8, imaged once-per-week/3-weeks) mice groups were dimensionally reduced and analyzed with hierarchical clustering Analysis (HCA). The classes extracted from HCA were used with 10 ML classifiers to find a generalized tumor stage prediction model, which was validated with an independent dataset of sunitinib-treated mice. HCA provided three stages of treatment response that were validated using the best-performing ML classifier. The Gaussian naive Bayes classifier showed the best performance, with a training accuracy of 98.7 and an average area under curve of 100. Our results show that metabolic–anatomical–vascular markers allow defining treatment response trajectories that reflect the efficacy of an anti-angiogenic drug on the tumor target hallmark.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUECancer Research for Personalized Medicine—CARPEM project (Site de Recherche Intégré sur le Cancer SIRIC)Plan Cancer PhysicancerRégion Ile-de-Francee Life Imaging Facility of Université Paris Cité (Plateforme Imageries du Vivant - PIV)France Life ImagingInfrastructures Biologie-Santé (IBiSa)Ministère de l’Enseignement Supérieur et de la RechercheEuropean Union’s Horizon 2020 research and innovation programpu

Iterative determination of clinical beam phase space from dose measurements

Monte Carlo (MC) method can accurately compute the dose produced by medical linear accelerators. However, these calculations require a reliable description of the electron and/or photon beams delivering the dose, the phase space (PHSP), which is not usually available. A method to derive a phase space model from reference measurements that does not heavily rely on a detailed model of the accelerator head is presented. The iterative optimization process extracts the characteristics of the particle beams which best explains the reference dose measurements in water and air, given a set of constrain

High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion

Ultrasound tomography (UST) scanners allow quantitative images of the human breast's acoustic properties to be derived with potential applications in screening, diagnosis and therapy planning. Time domain full waveform inversion (TD-FWI) is a promising UST image formation technique that fits the parameter fields of a wave physics model by gradient-based optimization. For high resolution 3D UST, it holds three key challenges: firstly, its central building block, the computation of the gradient for a single US measurement, has a restrictively large memory footprint. Secondly, this building block needs to be computed for each of the 1000 to 10000 measurements, resulting in a massive parallel computation usually performed on large computational clusters for days. Lastly, the structure of the underlying optimization problem may result in slow progression of the solver and convergence to a local minimum. In this work, we design and evaluate a comprehensive computational strategy to overcome these challenges: firstly, we exploit a gradient computation based on time reversal that dramatically reduces the memory footprint at the expense of one additional wave simulation per source. Secondly, we break the dependence on the number of measurements by using source encoding (SE) to compute stochastic gradient estimates. Also we describe a more accurate, TD-specific SE technique with a finer variance control and use a state-of-the-art stochastic LBFGS method. Lastly, we design an efficient TD multi-grid scheme together with preconditioning to speed up the convergence while avoiding local minima. All components are evaluated in extensive numerical proof-of-concept studies simulating a bowl-shaped 3D UST breast scanner prototype. Finally, we demonstrate that their combination allows us to obtain an accurate 442 × 442 × 222 voxel image with a resolution of 0.5 mm using Matlab on a single GPU within 24 h