Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Comparative validation of single-shot optical techniques for laparoscopic 3-D surface reconstruction

Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at www.open-CAS.com upon publication of the paper

Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review

International audienceThe study of mass and energy transfer across landscapes has recently evolved to comprehensive considerations acknowledging the role of biota and humans as geomorphic agents, as well as the importance of small-scale landscape features. A contributing and supporting factor to this evolution is the emergence over the last two decades of technologies able to acquire high resolution topography (HRT) (meter and sub-meter resolution) data. Landscape features can now be captured at an appropriately fine spatial resolution at which surface processes operate; this has revolutionized the way we study Earth-surface processes. The wealth of information contained in HRT also presents considerable challenges. For example, selection of the most appropriate type of HRT data for a given application is not trivial. No definitive approach exists for identifying and filtering erroneous or unwanted data, yet inappropriate filtering can create artifacts or eliminate/distort critical features. Estimates of errors and uncertainty are often poorly defined and typically fail to represent the spatial heterogeneity of the dataset, which may introduce bias or error for many analyses. For ease of use, gridded products are typically preferred rather than the more information-rich point cloud representations. Thus many users take advantage of only a fraction of the available data, which has furthermore been subjected to a series of operations often not known or investigated by the user. Lastly, standard HRT analysis work-flows are yet to be established for many popular HRT operations, which has contributed to the limited use of point cloud data.In this review, we identify key research questions relevant to the Earth-surface processes community within the theme of mass and energy transfer across landscapes and offer guidance on how to identify the most appropriate topographic data type for the analysis of interest. We describe the operations commonly performed from raw data to raster products and we identify key considerations and suggest appropriate work-flows for each, pointing to useful resources and available tools. Future research directions should stimulate further development of tools that take advantage of the wealth of information contained in the HRT data and address the present and upcoming research needs such as the ability to filter out unwanted data, compute spatially variable estimates of uncertainty and perform multi-scale analyses. While we focus primarily on HRT applications for mass and energy transfer, we envision this review to be relevant beyond the Earth-surface processes community for a much broader range of applications involving the analysis of HRT

Ricerche di Geomatica 2011

Questo volume raccoglie gli articoli che hanno partecipato al Premio AUTeC 2011. Il premio è stato istituito nel 2005. Viene conferito ogni anno ad una tesi di Dottorato giudicata particolarmente significativa sui temi di pertinenza del SSD ICAR/06 (Topografia e Cartografia) nei diversi Dottorati attivi in Italia

Time-of-flight resolved light field fluctuations reveal deep human tissue physiology.

Red blood cells (RBCs) transport oxygen to tissues and remove carbon dioxide. Diffuse optical flowmetry (DOF) assesses deep tissue RBC dynamics by measuring coherent fluctuations of multiply scattered near-infrared light intensity. While classical DOF measurements empirically correlate with blood flow, they remain far-removed from light scattering physics and difficult to interpret in layered media. To advance DOF measurements closer to the physics, here we introduce an interferometric technique, surmounting challenges of bulk motion to apply it in awake humans. We reveal two measurement dimensions: optical phase, and time-of-flight (TOF), the latter with 22 picosecond resolution. With this multidimensional data, we directly confirm the unordered, or Brownian, nature of optically probed RBC dynamics typically assumed in classical DOF. We illustrate how incorrect absorption assumptions, anisotropic RBC scattering, and layered tissues may confound classical DOF. By comparison, our direct method enables accurate and comprehensive assessment of blood flow dynamics in humans

СТРУКТУРНАЯ ДИНАМИКА СВОБОДНЫХ МОЛЕКУЛ И КОНДЕНСИРОВАННОГО СОСТОЯНИЯ ВЕЩЕСТВА. Часть I. ТЕОРИЯ И ЭКСПЕРИМЕНТАЛЬНЫЕ МЕТОДЫ

To understand the dynamic features of molecular systems with a complex landscape of potential energy surfaces, it is necessary to study them in the associated 4D space-time continuum. The introduction of time in the diffraction methods and the development of coherent principles of the research process opened up new approaches for the study of the dynamics of wave packets, intermediates and transient states of the chemical reactions, short-lived compounds in the gaseous and condensed media. Time-resolved electron diffraction, the new method for the structural dynamic studies of free molecules, clusters and condensed matter, differs from the traditional method of electron diffraction both in the experimental part and in the theoretical approaches used in the interpretation of diffraction data. Here there is particularly pronounced the need of a corresponding theoretical basis for the processing of the electron diffraction data and the results of spectral investigations of the coherent dynamics in the field of intense ultrashort laser radiation. Such unified and integrated approach can be formulated using the adiabatic potential energy surfaces of the ground and excited states of the systems under study. The combination of state-of-the-art optical techniques and electron diffraction methods based on different physical phenomena, but complementing each other, opens up new possibilities of the structural studies at time sequences of ultrashort duration. It provides the required integration of the triad, "structure - dynamics - functions" in chemistry, biology and materials science.Для понимания особенностей динамики молекулярных систем со сложным ландшафтом поверхности потенциальной энергии, необходимо исследовать их в четырехмерном пространственно-временном континууме. Введение времени в дифракционные методы и развитие когерентных принципов процесса исследования открывают новые подходы к изучению динамики волновых пакетов, промежуточных и переходных состояний химических реакций, короткоживущих соединений в газовой и конденсированной средах. Дифракция электронов с временным разрешением, новый метод структурных динамических исследований свободных молекул, кластеров и конденсированных сред, отличается от традиционного метода дифракции электронов как по экспериментальному оборудованию, так и теоретическими подходами, используемыми при интерпретации дифракционных данных. В методах с временным разрешением особенно выражена необходимость соответствующей теоретической основы для обработки данных дифракции электронов и результатов спектральных исследований когерентной динамики с использованием интенсивного ультракороткого лазерного излучения. Такой единый и комплексный подход можно сформулировать, используя понятие адиабатической поверхности потенциальной энергии основного и возбужденных состояний исследуемых систем. Сочетание самых современных оптических технологий и методов дифракции электронов, основанных на различных физических явлениях, дополняющих друг друга, открывает новые возможности структурных исследований с использованием импульсных последовательностей ультракороткой длительности. Такое сочетание обеспечивает необходимую интеграцию триады «структура - динамика - свойство» в химии, биологии и материаловедении

Landscape-scale prediction of forest productivity by hyperspectral remote sensing of canopy nitrogen

Foliar nitrogen concentration represents a direct and primary link between carbon and nitrogen cycling in terrestrial ecosystems. Although foliar N is used by many ecosystem models to predict leaf-level photosynthetic rates, it has rarely been examined as a direct scalar to stand-level carbon gain. Significant improvements in remote sensing detector technology in the list decade now allow for improved landscape-level estimation of the biochemical attributes of forest ecosystems. In this study, relationships among forest growth (aboveground net primary productivity (ANPP) and aboveground woody biomass production (AWBP)), canopy chemistry and structure, and high resolution imaging spectrometry were examined for 88 long-term forest growth inventory plots maintained by the USDA Forest Service within the 300,000 ha White Mountain National Forest, New Hampshire. Analysis of plot-level data demonstrates a highly predictive relationship between whole canopy nitrogen concentration (g/100 g) and aboveground forest productivity (ANPP: R2 = 0.81, p \u3c 0.000; AWBP: R 2 = 0.86, p \u3c 0.000) within and among forest types. Forest productivity was more strongly related to mass-based foliar nitrogen concentration than with either total canopy N or canopy leaf area. Empirical relationships were developed among spectral data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and field-measured canopy nitrogen concentration (mass basis). Results of this analysis suggest that hyperspectral remote sensing can be used to accurately predict foliar nitrogen concentration, by mean of a full-spectrum partial least squares calibration method, both within a single scene (R2 = 0.84, SECV = 0.23) and across a large number of contiguous images (R2 = 82, SECV = 0.25), as well as between image dates (R2 = 0.69, SECV = 0.25). Forest productivity coverages for the White Mountain National Forest were developed by estimating whole canopy foliar N concentration from AVIRIS spectral response. Image spatial patterns broadly reflect the distribution of functional types, while fine scale spatial variation results from a variety of natural and anthropogenic factors. This approach provides the potential to increase the accuracy of forest growth and carbon gain estimates at the landscape level by providing information at the fine spatial scale over which environmental characteristics and human land use vary

Mathematics and Algorithms in Tomography

This was the ninth Oberwolfach conference on the mathematics of tomography. Modalities represented at the workshop included X-ray tomography, radar, seismic imaging, ultrasound, electron microscopy, impedance imaging, photoacoustic tomography, elastography, emission tomography, X-ray CT, and vector tomography along with a wide range of mathematical analysis