Ecocardiografia 3D real-time: principi fisici, tecnologia e applicazioni fetali per diagnosi prenatale

La diagnosi clinica, definita come un giudizio clinico espresso da un esperto sulla salute di un individuo, dopo aver effettuato degli esami obiettivi attraverso la strumentazione adeguata allo specifico caso clinico, rappresenta un elemento fondamentale nel paradigma Prevenzione – Diagnosi – Cura – Riabilitazione, che ha come fine ultimo la salute del paziente. In questo elaborato viene presentata una tecnica di imaging che permette di fare diagnosi in uno degli organi più importanti e delicati del corpo umano, cioè il cuore, sia degli adulti, sia dei feti: l’ecocardiografia 3D Real-Time. L’elaborato si sviluppa in tre capitoli, come di seguito presentato. - Capitolo 1: si descrive la tecnologia su cui si fonda l’ecocardiografia volumetrica Real-Time attraverso le varie fasi di realizzazione dello scanner, il quale consente sia l’acquisizione sia la visualizzazione dei volumi in tempo reale; - Capitolo 2: il sistema di imaging presentato nel capitolo precedente, viene contestualizzato in un organo specifico, ovvero il cuore, illustrandone le caratteristiche, le differenze rispetto a tecniche ritenute meno performanti nella valutazione di patologie cardiache, oltre che alcune particolari evoluzioni, quali Strain Rate Imaging e Tissue Doppler Imaging; - Capitolo 3: si descrive in cosa consiste l’ecocardiografia 3D Real-Time fetale, qual è la sua finalità e quali potrebbero essere alcune applicazioni cliniche tramite cui fare una diagnosi prenatale; inoltre, si evidenzia l’importanza dell’ecocardiografia per studiare le modifiche a cui è soggetto l’apparato cardiovascolare di una donna durante i mesi di gestazione e, quindi, sottoporla alle cure opportune.

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Image segmentation is an ubiquitous task in medical image analysis, which is required to estimate morphological or functional properties of given anatomical targets. While automatic processing is highly desirable, image segmentation remains to date a supervised process in daily clinical practice. Indeed, challenging data often requires user interaction to capture the required level of anatomical detail. To optimize the analysis of 3D images, the user should be able to efficiently interact with the result of any segmentation algorithm to correct any possible disagreement. Building on a previously developed real-time 3D segmentation algorithm, we propose in the present work an extension towards an interactive application where user information can be used online to steer the segmentation result. This enables a synergistic collaboration between the operator and the underlying segmentation algorithm, thus contributing to higher segmentation accuracy, while keeping total analysis time competitive. To this end, we formalize the user interaction paradigm using a geometrical approach, where the user input is mapped to a non-cartesian space while this information is used to drive the boundary towards the position provided by the user. Additionally, we propose a shape regularization term which improves the interaction with the segmented surface, thereby making the interactive segmentation process less cumbersome. The resulting algorithm offers competitive performance both in terms of segmentation accuracy, as well as in terms of total analysis time. This contributes to a more efficient use of the existing segmentation tools in daily clinical practice. Furthermore, it compares favorably to state-of-the-art interactive segmentation software based on a 3D livewire-based algorithm.

Real-time 3D semi-local surface patch extraction using GPGPU

Feature vectors can be anything from simple surface normals to more complex feature descriptors. Feature extraction is important to solve various computer vision problems: e.g. registration, object recognition and scene understanding. Most of these techniques cannot be computed online due to their complexity and the context where they are applied. Therefore, computing these features in real-time for many points in the scene is impossible. In this work, a hardware-based implementation of 3D feature extraction and 3D object recognition is proposed to accelerate these methods and therefore the entire pipeline of RGBD based computer vision systems where such features are typically used. The use of a GPU as a general purpose processor can achieve considerable speed-ups compared with a CPU implementation. In this work, advantageous results are obtained using the GPU to accelerate the computation of a 3D descriptor based on the calculation of 3D semi-local surface patches of partial views. This allows descriptor computation at several points of a scene in real-time. Benefits of the accelerated descriptor have been demonstrated in object recognition tasks. Source code will be made publicly available as contribution to the Open Source Point Cloud Library.

Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.

Are echo techniques comparable for measurement of left ventricular dysynchrony? Comparison of real time 3D and tissue Doppler echocardiography in patients with ischaemic cardiomyopathy

Tissue Doppler (TD) assessment of dysynchrony (DYS) is established in evaluation for bi-ventricular pacing. Time to regional minimal volume by real-time 3D echo (3D) has been applied to DYS. 3D offers simultaneous assessment of all segments and may limit errors in localization of maximum delay due to off-axis images.We compared TD and 3D for assessment of DYS. 27 patients with ischaemic cardiomyopathy (aged 60±11 years, 85% male) underwent TD with generation of regional velocity curves. The interval between QRS onset and maximal systolic velocity (TTV) was measured in 6 basal and 6 mid-cavity segments. Onthe same day,3Dwas performed and data analysed offline with Q-Lab software (Philips, Andover, MA). Using 12 analogous regional time-volume curves time to minimal volume (T3D)was calculated. The standard deviation (S.D.) between segments in TTV and T3D was calculated as a measure ofDYS. In 7 patients itwas not possible to measureT3D due to poor images. In the remaining 20, LV diastolic volume, systolic volume and EF were 128±35 ml, 68±23 ml and 46±13%, respectively. Mean TTV was less than mean T3D (150±33ms versus 348±54 ms; p < 0.01). The intrapatient range was 20–210ms for TTV and 0–410ms for T3D. Of 9 patients (45%) with significantDYS (S.D. TTV > 32 ms), S.D. T3D was 69±37ms compared to 48±34ms in those without DYS (p = ns). In DYS patients there was concordance of the most delayed segment in 4 (44%) cases.Therefore, different techniques for assessing DYS are not directly comparable. Specific cut-offs for DYS are needed for each technique.

Real-time 3D shape sensing and reconstruction scheme based upon fibre optic bragg gratings

An array of FBG curvature sensors are wavelength-interrogated and the recovered data combined with a three-dimensional algorithm to reconstruct in real time the enveloped object with a 1% to 9% volumetric error. © 2012 OSA.

ATD: a multiplatform for semiautomatic 3-D detection of kidneys and their pathology in real time

This research presents a novel multi-functional system for medical Imaging-enabled Assistive Diagnosis (IAD). Although the IAD demonstrator has focused on abdominal images and supports the clinical diagnosis of kidneys using CT/MRI imaging, it can be adapted to work on image delineation, annotation and 3D real-size volumetric modelling of other organ structures such as the brain, spine, etc. The IAD provides advanced real-time 3D visualisation and measurements with fully automated functionalities as developed in two stages. In the first stage, via the clinically driven user interface, specialist clinicians use CT/MRI imaging datasets to accurately delineate and annotate the kidneys and their possible abnormalities, thus creating “3D Golden Standard Models”. Based on these models, in the second stage, clinical support staff i.e. medical technicians interactively define model-based rules and parameters for the integrated “Automatic Recognition Framework” to achieve results which are closest to that of the clinicians. These specific rules and parameters are stored in “Templates” and can later be used by any clinician to automatically identify organ structures i.e. kidneys and their possible abnormalities. The system also supports the transmission of these “Templates” to another expert for a second opinion. A 3D model of the body, the organs and their possible pathology with real metrics is also integrated. The automatic functionality was tested on eleven MRI datasets (comprising of 286 images) and the 3D models were validated by comparing them with the metrics from the corresponding “3D Golden Standard Models”. The system provides metrics for the evaluation of the results, in terms of Accuracy, Precision, Sensitivity, Specificity and Dice Similarity Coefficient (DSC) so as to enable benchmarking of its performance. The first IAD prototype has produced promising results as its performance accuracy based on the most widely deployed evaluation metric, DSC, yields 97% for the recognition of kidneys and 96% for their abnormalities; whilst across all the above evaluation metrics its performance ranges between 96% and 100%. Further development of the IAD system is in progress to extend and evaluate its clinical diagnostic support capability through development and integration of additional algorithms to offer fully computer-aided identification of other organs and their abnormalities based on CT/MRI/Ultra-sound Imaging.