NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS

Material property has great importance in surgical simulation and virtual reality. The mechanical properties of the human soft tissue are critical to characterize the tissue deformation of each patient. Studies have shown that the tissue stiffness described by the tissue properties may indicate abnormal pathological process. The (recovered) elasticity parameters can assist surgeons to perform better pre-op surgical planning and enable medical robots to carry out personalized surgical procedures. Traditional elasticity parameters estimation methods rely largely on known external forces measured by special devices and strain field estimated by landmarks on the deformable bodies. Or they are limited to mechanical property estimation for quasi-static deformation. For virtual reality applications such as virtual try-on, garment material capturing is of equal significance as the geometry reconstruction. In this thesis, I present novel approaches for automatically estimating the material properties of soft bodies from images or from a video capturing the motion of the deformable body. I use a coupled simulation-optimization-identification framework to deform one soft body at its original, non-deformed state to match the deformed geometry of the same object in its deformed state. The optimal set of material parameters is thereby determined by minimizing the error metric function. This method can simultaneously recover the elasticity parameters of multiple regions of soft bodies using Finite Element Method-based simulation (of either linear or nonlinear materials undergoing large deformation) and particle-swarm optimization methods. I demonstrate the effectiveness of this approach on real-time interaction with virtual organs in patient-specific surgical simulation, using parameters acquired from low-resolution medical images. With the recovered elasticity parameters and the age of the prostate cancer patients as features, I build a cancer grading and staging classifier. The classifier achieves up to 91% for predicting cancer T-Stage and 88% for predicting Gleason score. To recover the mechanical properties of soft bodies from a video, I propose a method which couples statistical graphical model with FEM simulation. Using this method, I can recover the material properties of a soft ball from a high-speed camera video that captures the motion of the ball. Furthermore, I extend the material recovery framework to fabric material identification. I propose a novel method for garment material extraction from a single-view image and a learning based cloth material recovery method from a video recording the motion of the cloth. Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, I propose a method that can compute a 3D model of a human body and its outfit from a single photograph with little human interaction. My proposed learning-based cloth material type recovery method exploits simulated data-set and deep neural network. I demonstrate the effectiveness of my algorithms by re-purposing the reconstructed garments for virtual try-on, garment transfer, and cloth animation on digital characters. With the recovered mechanical properties, one can construct a virtual world with soft objects exhibiting real-world behaviors.Doctor of Philosoph

The EFSUMB Guidelines and Recommendations for the Clinical Practice of Elastography in Non-Hepatic Applications: Update 2018

This manuscript describes the use of ultrasound elastography, with the exception of liver applications, and represents an update of the 2013 EFSUMB (European Federation of Societies for Ultrasound in Medicine and Biology) Guidelines and Recommendations on the clinical use of elastography

The EFSUMB Guidelines and Recommendations for the Clinical Practice of Elastography in Non-Hepatic Applications : Update 2018

Funding Information: Odd Helge Gilja: Advisory Board/Consultant fee from: AbbVie, Bracco, GE Healthcare, Samsung, and Takeda Paul S. Sidhu: Speaker honoraria, Bracco, Siemens, Samsung, Hiatchi, GE and Philips Christoph F. Dietrich: Speaker honoraria, Bracco, Hitachi, GE, Mindray, Supersonic, Pentax, Olympus, Fuji, Boston Scientific, AbbVie, Falk Foundation, Novartis, Roche; Advisory, Board Member, Hitachi, Mindray, Siemens; Research grant, GE, Mindray, SuperSonic Vito Cantisani: Speaker honoraria, Canon/Toshiba, Bracco, Samsung Dominique Amy: Speaker honoraria, Hitachi, Supersonic, EpiSonica Marco Brock: Speaker honoraria, Hitachi Fabrizio Calliada: Speaker honoraria, Bracco, Hitachi, Shenshen Mindray Dirk Andre Clevert: Speaker honoraria, Siemens, Samsung, GE, Bracco, Philips; Advisory Board, Siemens, Samsung, Bracco, Philips Jean-Michel Correas: Speaker honoraria, Hitachi-Aloka, Canon/Toshiba, Philips, Supersonic, Bracco, Guerbet; Research collaboration, Bracco Sonocap, Guerbet NsSafe and Secure protocols Mirko D’Onofrio: Speaker honoraria, Siemens, Bracco, Hitachi; Advisory Board Siemens, Bracco Andre Farrokh: Speaker honoraria, Hitachi Pietro Fusaroli: Speaker honoraria, Olympus Roald Flesland Havre: Speaker honoraria, GE Healthcare, Conference participation support from Pharmacosmos, Ultrasound equipment from Samsung Medison André Ignee: Speaker honoraria: Siemens, Canon/Toshiba, Hitachi, Boston Scientific, Bracco, Supersonic, Abbvie Christian Jenssen: Speaker honoraria, Bracco, Hitachi, Canon/Toshiba, Falk Foundation, Covidien; Research grant, Novartis Maija Radzina: Speaker honoraria, Bracco, Canon/Toshiba Luca Sconfienza: Travel grants from Bracco Imaging Italia Srl, Esaote SPA, Abiogen SPA, Fidia Middle East. Speaker honoraria from Fidia Middle East Ioan Sporea: Speaker honoraria, Philips, GE, Canon/Toshiba; Advisory Board Member, Siemens; Congress participation support, Siemens Mickael Tanter: Speaker honoraria, Supersonic; Co Founder and shareholder, Supersonic; Research collaboration, Supersonic Peter Vilmann: Speaker honoraria, Pentax, Norgine; Advisory Board, Boston Scientific; Consultancy MediGlobe The following members declared no conflicts of interest: Adrian Săftoiu, Michael Bachmann Nielsen, Flaviu Bob, Jörg Bojunga, Caroline Ewertsen, Michael Hocke, Andrea Klauser, Christian Kollmann, Kumar V Ramnarine, Carolina Solomon, Daniela Fodor, Horia Ștefănescu Publisher Copyright: © 2019 Georg Thieme Verlag KG Stuttgart New York.This manuscript describes the use of ultrasound elastography, with the exception of liver applications, and represents an update of the 2013 EFSUMB (European Federation of Societies for Ultrasound in Medicine and Biology) Guidelines and Recommendations on the clinical use of elastography.Peer reviewe

Why Are Viscosity and Nonlinearity Bound to Make an Impact in Clinical Elastographic Diagnosis?

The contributions by Antonio Gomez, Monica Contreras and Francisca S. Molina are gratefully acknowledged.The adoption of multiscale approaches by the biomechanical community has caused a major improvement in quality in the mechanical characterization of soft tissues. The recent developments in elastography techniques are enabling in vivo and non-invasive quantification of tissues’ mechanical properties. Elastic changes in a tissue are associated with a broad spectrum of pathologies, which stems from the tissue microstructure, histology and biochemistry. This knowledge is combined with research evidence to provide a powerful diagnostic range of highly prevalent pathologies, from birth and labor disorders (prematurity, induction failures, etc.), to solid tumors (e.g., prostate, cervix, breast, melanoma) and liver fibrosis, just to name a few. This review aims to elucidate the potential of viscous and nonlinear elastic parameters as conceivable diagnostic mechanical biomarkers. First, by providing an insight into the classic role of soft tissue microstructure in linear elasticity; secondly, by understanding how viscosity and nonlinearity could enhance the current diagnosis in elastography; and finally, by compounding preliminary investigations of those elastography parameters within different technologies. In conclusion, evidence of the diagnostic capability of elastic parameters beyond linear stiffness is gaining momentum as a result of the technological and imaging developments in the field of biomechanics.This research was funded by Ministerio de Educación, Cultura y Deporte grant numbers DPI2017-83859-R, DPI2014-51870-R, UNGR15-CE-3664 and EQC2018-004508-P; Ministerio de Sanidad, Servicios Sociales e Igualdad grant numbers DTS15/00093 and PI16/00339; Instituto de Salud Carlos III y Fondos Feder; Junta de Andalucía grant numbers PI-0107-2017, PIN-0030-2017 and IE2017-5537; Juan de la Cierva Incorporación IJC2018-037167-I, Ministerio de Ciencia, Innovación y Universidades grant number PRE2018-086085

Endoluminal Ultrasonography of the Rectum and the Anal Canal

Ultrasonography of the anal canal, the rectum and the surrounding tissues using intraluminal transducers with transanal/rectal imaging provides high-resolution imaging with clearly distinguishable tissue‐dependent echo signals. Endorectal ultrasonography depicts rectal wall layers and adjuvant structures with a high degree of precision. Anal endosonography is carried out to examine the sphincter muscles and the pelvic floor

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world