Utilizzo di sistemi bioreattori per l’ingegnerizzazione di sostituti del tessuto cartilagineo

La mancanza di vascolarizzazione dei costrutti tissutali cartilaginei ingegnerizzati rende opportuno e necessario l’impiego di bioreattori che permettano di indurre un flusso nel terreno di coltura favorendo il trasporto di massa e quindi lo sviluppo del metabolismo e del differenziamento cellulare. I bioreattori intendono replicare gli stimoli fisici fisiologici, nello specifico condrogenici, e a regolare la formazione della matrice extracellulare tramite meccanotrasduzione, il fenomeno biologico che traduce le sollecitazioni meccaniche applicate alle cellule in segnali biochimici che suscitano risposte adattative. In questo elaborato sono riportati i risultati di un recente lavoro - pubblicato da J. Hallas, A. J. Janvier, K. F. Hoettges & J. R. Henstock e intitolato “Pneumatic piston hydrostatic bioreactor for cartilage tissue engineering – che propone la realizzazione di un bioreattore idrostatico a pistone pneumatico, realizzato con componenti facilmente acquisibili a basso costo in commercio. Il bioreattore è collegato a una camera di coltura tramite un connettore pneumatico e un tubo per l’aria in polipropilene. La camera di coltura è realizzata in acido polilattico (PLA) tramite stampante 3D. Il dispositivo è in grado di applicare a una coltura cellulare tridimensionale una pressione idrostatica intermittente con ampiezza compresa tra 0 e 400 kPa e frequenza massima di 3,5 Hz. Condrociti provenienti dalla cartilagine di un’articolazione di ginocchio sono stati coltivati all’interno della camera di coltura del bioreattore dove sono stati sottoposti a una pressione di picco di 300 kPa per 3 ore al giorno per un totale di 5 giorni. Al termine della coltura si è ottenuto un aumento dell’attività metabolica cellulare del 21% e un aumento significativo del contenuto di glicosamminoglicani nell’ECM.

Voxel-based registration of simulated and real patient CBCT data for accurate dental implant pose estimation

The success of dental implant-supported prosthesis is directly linked to the accuracy obtained during implant’s pose estimation (position and orientation). Although traditional impression techniques and recent digital acquisition methods are acceptably accurate, a simultaneously fast, accurate and operator-independent methodology is still lacking. Hereto, an image-based framework is proposed to estimate the patient-specific implant’s pose using cone-beam computed tomography (CBCT) and prior knowledge of implanted model. The pose estimation is accomplished in a threestep approach: (1) a region-of-interest is extracted from the CBCT data using 2 operator-defined points at the implant’s main axis; (2) a simulated CBCT volume of the known implanted model is generated through Feldkamp-Davis-Kress reconstruction and coarsely aligned to the defined axis; and (3) a voxel-based rigid registration is performed to optimally align both patient and simulated CBCT data, extracting the implant’s pose from the optimal transformation. Three experiments were performed to evaluate the framework: (1) an in silico study using 48 implants distributed through 12 tridimensional synthetic mandibular models; (2) an in vitro study using an artificial mandible with 2 dental implants acquired with an i-CAT system; and (3) two clinical case studies. The results shown positional errors of 67±34μm and 108μm, and angular misfits of 0.15±0.08º and 1.4º, for experiment 1 and 2, respectively. Moreover, in experiment 3, visual assessment of clinical data results shown a coherent alignment of the reference implant. Overall, a novel image-based framework for implants’ pose estimation from CBCT data was proposed, showing accurate results in agreement with dental prosthesis modelling requirements.

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the headand neck lymph node regions using a new active contourbased atlas registration model. We propose to segment thelymph node regions without directly including them in theatlas registration process; instead, they are segmentedusing the dense deformation field computed from theregistration of the atlas structures with distinctboundaries. This approach results in robust and accuratesegmentation of the lymph node regions even in thepresence of significant anatomical variations between theatlas-image and the patient's image to be segmented. Wealso present a quantitative evaluation of lymph noderegions segmentation using various statistical as well asgeometrical metrics: sensitivity, specificity, dicesimilarity coefficient and Hausdorff distance. Acomparison of the proposed method with two other state ofthe art methods is presented. The robustness of theproposed method to the atlas selection, in segmenting thelymph node regions, is also evaluated.

Automated Segmentation and Non-Rigid Registration for the Quantitative Evaluation of Myocardial Perfusion from Magnetic Resonance Images

Myocardial perfusion quantification by means of Contrast-Enhanced Cardiac Magnetic Resonance images relies on time consuming frame-by-frame manual tracing of regions of interest. In this Thesis, a novel automated technique for myocardial segmentation and non-rigid registration as a basis for perfusion quantification is presented. The proposed technique is based on three steps: reference frame selection, myocardial segmentation and non-rigid registration. In the first step, the reference frame in which both endo- and epicardial segmentation will be performed is chosen. Endocardial segmentation is achieved by means of a statistical region-based level-set technique followed by a curvature-based regularization motion. Epicardial segmentation is achieved by means of an edge-based level-set technique followed again by a regularization motion. To take into account the changes in position, size and shape of myocardium throughout the sequence due to out of plane respiratory motion, a non-rigid registration algorithm is required. The proposed non-rigid registration scheme consists in a novel multiscale extension of the normalized cross-correlation algorithm in combination with level-set methods. The myocardium is then divided into standard segments. Contrast enhancement curves are computed measuring the mean pixel intensity of each segment over time, and perfusion indices are extracted from each curve. The overall approach has been tested on synthetic and real datasets. For validation purposes, the sequences have been manually traced by an experienced interpreter, and contrast enhancement curves as well as perfusion indices have been computed. Comparisons between automatically extracted and manually obtained contours and enhancement curves showed high inter-technique agreement. Comparisons of perfusion indices computed using both approaches against quantitative coronary angiography and visual interpretation demonstrated that the two technique have similar diagnostic accuracy. In conclusion, the proposed technique allows fast, automated and accurate measurement of intra-myocardial contrast dynamics, and may thus address the strong clinical need for quantitative evaluation of myocardial perfusion.

A Hybrid Multimodal Non-Rigid Registration of MR Images Based on Diffeomorphic Demons

n this paper we present a novel hybrid approach for multimodal medical image registration based on diffeomorphic demons. Diffeomorphic demons have proven to be a robust and efficient way for intensity-based image registration. A very recent extension even allows to use mutual information (MI) as a similarity measure to registration multimodal images. However, due to the intensity correspondence uncertainty existing in some anatomical parts, it is difficult for a purely intensity-based algorithm to solve the registration problem. Therefore, we propose to combine the resulting transformations from both intensity-based and landmark-based methods for multimodal non-rigid registration based on diffeomorphic demons. Several experiments on different types of MR images were conducted, for which we show that a better anatomical correspondence between the images can be obtained using the hybrid approach than using either intensity information or landmarks alone.

Information Filtering for Ultrasound-based Real-time Registration

This paper presents methods based on Information Filters for solving matching problems with emphasis on real-time, or effectively real-time applications. Both applications discussed in this work deal with ultrasound-based rigid registration in computer-assisted orthopedic surgery. In the first application, the usual workflow of rigid registration is reformulated such that registration algorithms would iterate while the surgeon is acquiring ultrasound images of the anatomy to be operated. Using this effectively real-time approach to registration, the surgeon would then receive feedback in order to better gauge the quality of the final registration outcome. The second application considered in this paper circumvents the need to attach physical markers to bones for anatomical referencing. Experiments using anatomical objects immersed in water are performed in order to evaluate and compare the different methods presented herein, using both 2D as well as real-time 3D ultrasound.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

Graphical Model-Based Vertebra Identification from X-Ray Image(s)

Automated identification of vertebrae from X-ray image(s) is an important step for various medical image computing tasks such as 2D/3D rigid and non-rigid registration. In this chapter we present a graphical model-based solution for automated vertebra identification from X-ray image(s). Our solution does not ask for a training process using training data and has the capability to automatically determine the number of vertebrae visible in the image(s). This is achieved by combining a graphical model-based maximum a posterior probability (MAP) estimate with a mean-shift based clustering. Experiments conducted on simulated X-ray images as well as on a low-dose low quality X-ray spinal image of a scoliotic patient verified its performance.

Convergence analysis and validation of low cost distance metrics for computational cost reduction of the Iterative Closest Point algorithm

The Iterative Closest Point algorithm (ICP) is commonly used in engineering applications to solve the rigid registration problem of partially overlapped point sets which are pre-aligned with a coarse estimate of their relative positions. This iterative algorithm is applied in many areas such as the medicine for volumetric reconstruction of tomography data, in robotics to reconstruct surfaces or scenes using range sensor information, in industrial systems for quality control of manufactured objects or even in biology to study the structure and folding of proteins. One of the algorithm’s main problems is its high computational complexity (quadratic in the number of points with the non-optimized original variant) in a context where high density point sets, acquired by high resolution scanners, are processed. Many variants have been proposed in the literature whose goal is the performance improvement either by reducing the number of points or the required iterations or even enhancing the complexity of the most expensive phase: the closest neighbor search. In spite of decreasing its complexity, some of the variants tend to have a negative impact on the final registration precision or the convergence domain thus limiting the possible application scenarios. The goal of this work is the improvement of the algorithm’s computational cost so that a wider range of computationally demanding problems from among the ones described before can be addressed. For that purpose, an experimental and mathematical convergence analysis and validation of point-to-point distance metrics has been performed taking into account those distances with lower computational cost than the Euclidean one, which is used as the de facto standard for the algorithm’s implementations in the literature. In that analysis, the functioning of the algorithm in diverse topological spaces, characterized by different metrics, has been studied to check the convergence, efficacy and cost of the method in order to determine the one which offers the best results. Given that the distance calculation represents a significant part of the whole set of computations performed by the algorithm, it is expected that any reduction of that operation affects significantly and positively the overall performance of the method. As a result, a performance improvement has been achieved by the application of those reduced cost metrics whose quality in terms of convergence and error has been analyzed and validated experimentally as comparable with respect to the Euclidean distance using a heterogeneous set of objects, scenarios and initial situations.

Rigid and non-rigid point-based medical image registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Chitosan-based scaffolds for tissue regeneration: preparation and microstructure characterization

Scaffolds are porous three-dimensional supports, designed to mimic the extracellular environment and remain temporarily integrated into the host tissue while stimulating, at the molecular level, specific cellular responses to each type of body tissues. The major goal of the research work entertained herein was to study the microstructure of scaffolds made from chitosan (Ch), blends of chitosan and sodium alginate (Ch/NaAlg), blends of chitosan, sodium alginate and calcium chloride (Ch/NaAlg/CaCl2) and blends of chitosan, sodium alginate and hydroxyapatite (Ch/NaAlg/HA). Scaffolds possessing ideal physicochemical properties facilitate cell proliferation and greatly increase the rate of recovery of a damaged organ tissue. Using CT three-dimensional images of the scaffolds, it was observed that all scaffolds had a porosity in the range 64%-92%, a radius of maximum pore occurrence in the range 95m-260m and a permeability in the range 1×10-10-18×10-10 m2. From the results obtained, the scaffolds based on Ch, Ch/NaAlg and Ch/NaAlg/CaCl2 would be most appropriate both for the growth of osteoid and for bone tissue regeneration, while the scaffold made with a blend of Ch/NaAlg/HA, by possessing larger pores size, might be used as a support for fibrovascular tissue.

The physicochemical characterization and in vivo response of micro/nanoporous bioactive ceramic particulate bone graft materials

In this study, the physicochemical characteristics of calcium phosphate based bioactive ceramics of different compositions and blends presenting similar micro/nanoporosity and micrometer scale surface texture were characterized and evaluated in an in vivo model. Prior to the animal experiment, the porosity, surface area, particle size distribution, phase quantification, and dissolution of the materials tested were evaluated. The bone regenerative properties of the materials were evaluated using a rabbit calvaria model. After 2, 4, and 8 weeks, the animals were sacrificed and all samples were subjected to histologic observation and histomorphometric analysis. The material characterization showed that all materials tested presented variation in particle size, porosity and composition with different degrees of HA/TCP/lower stoichiometry phase ratios. Histologically, the calvarial defects presented temporal bone filling suggesting that all material groups were biocompatible and osteoconductive. Among the different materials tested, there were significant differences found in the amount of bone formation as a function of time. At 8 weeks, the micro/nanoporous material presenting similar to 55,TCP:45%,HA composition ratio presented higher amounts of new bone regeneration relative to other blends and a decrease in the amount of soft tissue infiltration. (C) 2014 Elsevier B.V. All rights reserved.

Micro-FEM models based on micro-CT reconstructions for the in vitro characterization of the elastic properties of trabecular bone tissue.

This master’s thesis describes the research done at the Medical Technology Laboratory (LTM) of the Rizzoli Orthopedic Institute (IOR, Bologna, Italy), which focused on the characterization of the elastic properties of the trabecular bone tissue, starting from october 2012 to present. The approach uses computed microtomography to characterize the architecture of trabecular bone specimens. With the information obtained from the scanner, specimen-specific models of trabecular bone are generated for the solution with the Finite Element Method (FEM). Along with the FEM modelling, mechanical tests are performed over the same reconstructed bone portions. From the linear-elastic stage of mechanical tests presented by experimental results, it is possible to estimate the mechanical properties of the trabecular bone tissue. After a brief introduction on the biomechanics of the trabecular bone (chapter 1) and on the characterization of the mechanics of its tissue using FEM models (chapter 2), the reliability analysis of an experimental procedure is explained (chapter 3), based on the high-scalable numerical solver ParFE. In chapter 4, the sensitivity analyses on two different parameters for micro-FEM model’s reconstruction are presented. Once the reliability of the modeling strategy has been shown, a recent layout for experimental test, developed in LTM, is presented (chapter 5). Moreover, the results of the application of the new layout are discussed, with a stress on the difficulties connected to it and observed during the tests. Finally, a prototype experimental layout for the measure of deformations in trabecular bone specimens is presented (chapter 6). This procedure is based on the Digital Image Correlation method and is currently under development in LTM.

Analysis of the scale effect in different computed tomography systems on the evaluation of bone tissue parameters

Tra le patologie ossee attualmente riconosciute, l’osteoporosi ricopre il ruolo di protagonista data le sua diffusione globale e la multifattorialità delle cause che ne provocano la comparsa. Essa è caratterizzata da una diminuzione quantitativa della massa ossea e da alterazioni qualitative della micro-architettura del tessuto osseo con conseguente aumento della fragilità di quest’ultimo e relativo rischio di frattura. In campo medico-scientifico l’imaging con raggi X, in particolare quello tomografico, da decenni offre un ottimo supporto per la caratterizzazione ossea; nello specifico la microtomografia, definita attualmente come “gold-standard” data la sua elevata risoluzione spaziale, fornisce preziose indicazioni sulla struttura trabecolare e corticale del tessuto. Tuttavia la micro-CT è applicabile solo in-vitro, per cui l’obiettivo di questo lavoro di tesi è quello di verificare se e in che modo una diversa metodica di imaging, quale la cone-beam CT (applicabile invece in-vivo), possa fornire analoghi risultati, pur essendo caratterizzata da risoluzioni spaziali più basse. L’elaborazione delle immagini tomografiche, finalizzata all’analisi dei più importanti parametri morfostrutturali del tessuto osseo, prevede la segmentazione delle stesse con la definizione di una soglia ad hoc. I risultati ottenuti nel corso della tesi, svolta presso il Laboratorio di Tecnologia Medica dell’Istituto Ortopedico Rizzoli di Bologna, mostrano una buona correlazione tra le due metodiche quando si analizzano campioni definiti “ideali”, poiché caratterizzati da piccole porzioni di tessuto osseo di un solo tipo (trabecolare o corticale), incluso in PMMA, e si utilizza una soglia fissa per la segmentazione delle immagini. Diversamente, in casi “reali” (vertebre umane scansionate in aria) la stessa correlazione non è definita e in particolare è da escludere l’utilizzo di una soglia fissa per la segmentazione delle immagini.