Deformable surface registration for breast tumors tracking: A phantom study

A phantom study for breast tumor registration based on the deformation of the external surface is proposed. This study aims at the integration into an image guided system for breast cancer biopsy or ablation. To compensate potentially large breast displacements, due to different positions of the breast during biopsy or ablation compared with preoperative data, where the diagnosis was made, an initial linear alignment using visible landmarks is involved, followed by thin-plate spline (TPS) registration of the linearly aligned surfaces. Subsequently, the TPS deformation will be applied to the tumors. The results were validated using a multi modal phantom of the breast, while the tumors and the surface were segmented on four different positions of the phantom: prone, supine, vertical and on a side. The use of computed tomography (CT) dataset allowed us to obtain a very precise segmentation of the external surface, of the tumors and the landmarks. Despite large variation among the different positions of the phantom due to the gravitational force, the accuracy of the method at the target point was under 5 millimeters. These results allow us to conclude that, using our prototype image registration system, we are able to align acquisition of the breast in different positions with clinically relevant accuracy

Retrospective study on phantom for the application of medical image registration in the operating room scenario

This paper presents a phantom study to asses the feasibility of the medical image registration algorithms in the operating room (OR) scenario. The main issues of the registration algorithms in an OR application are, on one hand, the lack of the initial guess of the registration transformation - the images to be registered may be completely independentand, on the other hand, the multimodality of the data. Other requirements to be addressed by the OR registration algorithms are: real-time execution and the necessity of the validation of the results. This work analyzes how, under these requirements, the current state of the art algorithms in medical image registration may be used and shows which direction should be taken when designing a OR navigation system that includes registration as a component

Trajectory planning with task constraints in densely filled environments

In this paper the problem of computing a rigid object trajectory in an environment populated with deformable objects is addressed. The problem arises in Minimally Invasive Robotic Surgery (MIRS) from the needs of reaching a point of interest inside the anatomy with rigid laparoscopic instruments. We address the case of abdominal surgery. The abdomen is a densely populated soft environment and it is not possible to apply classical techniques for obstacle avoidance because a collision free solution is, most of the time, not feasible. In order to have a convergent algorithm with, at least, one possible solution we have to relax the constraints and allow collision under a specific contact threshold to avoid tissue damaging. In this work a new approach for trajectory planning under these peculiar conditions is implemented. The method computes offline the path which is then tested in a surgical simulator as part of a pre-operative surgical plan

Force control of lightweight series elastic systems using enhanced disturbance observers

This paper analyzes the control challenges associated to lightweight series elastic systems in force control applications, showing that a low end-point inertia can lead to high sensitivity to environment uncertainties. Where mainstream force control methods fail, this paper proposes a control methodology to enhance the performance robustness of existing disturbance observers (DOBs). The approach is validated experimentally and successfully compared to basic control solutions and state of the art DOB approaches

Registration of medical images for applications in minimally invasive procedures

Il punto di partenza di questa tesi \ue8 l'analisi dei metodi allo stato dell'arte di registrazione delle immagini mediche per verificare se sono adatti ad essere utilizzati per assistere il medico durante una procedura minimamente invasiva , ad esempio una procedura percutanea eseguita manualmente o un intervento teleoperato eseguito per mezzo di un robot . La prima conclusione \ue8 che, anche se ci sono tanti lavori dedicati allo sviluppo di algoritmi di registrazione da applicare nel contesto medico, la maggior parte di essi non sono stati progettati per essere utilizzati nello scenario della sala operatoria (OR) anche perch\ue9, rispetto ad altre applicazioni , OR richiede anche la validazione, prestazioni in tempo reale e la presenza di altri strumenti . Gli algoritmi allo stato dell'arte sono basati su un iterazione in tre fasi : ottimizzazione - trasformazione - valutazione della somiglianza delle immagini registrate. In questa tesi, studiamo la fattibilit\ue0 dell'approccio in tre fasi per applicazioni OR, mostrando i limiti che tale approccio incontra nelle applicazioni che stiamo considerando. Verr\ue0 dimostrato come un metodo semplice si potrebbe utilizzare nella OR. Abbiamo poi sviluppato una teoria che \ue8 adatta a registrare grandi insiemi di dati non strutturati estratti da immagini mediche, tenendo conto dei vincoli della OR . Vista l'impossibilit\ue0 di lavorare con dati medici di tipo DICOM, verr\ue0 impiegato un metodo per registrare dataset composti da insiemi di punti non strutturati. Gli algoritmi proposti sono progettati per trovare la corrispondenza spaziale in forma chiusa tenendo conto del tipo di dati, il vincolo del tempo e la presenza di rumore e /o piccole deformazioni. La teoria e gli algoritmi che abbiamo sviluppato sono derivati dalla teoria delle forme proposta da Kendall (Kendall's shapes) e utilizza un descrittore globale della forma per calcolare le corrispondenze e la distanza tra le strutture coinvolte . Poich\ue9 la registrazione \ue8 solo una componente nelle applicazioni mediche, l' ultima parte della tesi \ue8 dedicata ad alcune applicazioni pratiche in OR che possono beneficiare della procedura di registrazione .The registration of medical images is necessary to establish spatial correspondences across two or more images. Registration is rarely the end-goal, but instead, the results of image registration are used in other tasks. The starting point of this thesis is to analyze which methods at the state of the art of image registration are suitable to be used in assisting a physician during a minimally invasive procedure, such as a percutaneous procedure performed manually or a teleoperated intervention performed by the means of a robot. The first conclusion is that, even if much previous work has been devoted to develop registration algorithms to be applied in the medical context, most of them are not designed to be used in the operating room scenario (OR) because, compared to other applications, the OR requires also a strong validation, real-time performance and the presence of other instruments. Almost all of these algorithms are based on a three phase iteration: optimize-transform-evaluate similarity. In this thesis, we study the feasibility of this three steps approach in the OR, showing the limits that such approach encounter in the applications we are considering. We investigate how could a simple method be realizable and what are the assumptions for such a method to work. We then develop a theory that is suitable to register large sets of unstructured data extracted from medical images keeping into account the constraints of the OR. The use of the whole radiologic information is not feasible in the OR context, therefore the method we are introducing registers processed dataset extracted from the original medical images. The framework we propose is designed to find the spatial correspondence in closed form keeping into account the type of the data, the real-time constraint and the presence of noise and/or small deformations. The theory and algorithms we have developed are in the framework of the shape theory proposed by Kendall (Kendall's shapes) and uses a global descriptor of the shape to compute the correspondences and the distance between shapes. Since the registration is only a component of a medical application, the last part of the thesis is dedicated to some practical applications in the OR that can benefit from the registration procedure

Autonomous Robotic System for Breast Biopsy With Deformation Compensation

Image-guided biopsy is the most common technique for breast cancer diagnosis. Although magnetic resonance imaging (MRI) has the highest sensitivity in breast lesion detection, ultrasound (US) biopsy guidance is generally preferred due to its non-invasiveness and real-time image feedback during the insertion. In this work, we propose an autonomous robotic system for US-guided biopsy of breast lesions identified on pre-operative MRI. After initial MRI to breast registration, the US probe attached to the robotic manipulator compresses the breast tissues until a pre-determined force level is reached. This technique, known as preloading, will allow to minimize lesion displacement during the needle insertion. Our workflow integrates a deformation compensation strategy based on patient-specific biomechanical model to update the US probe orientation keeping the target lesion on the US image plane during compression. By relying on a deformation model, the proposed system does not require lesion visibility on US. Experimental evaluation is performed to assess the performance of the system on a realistic breast phantom with 15 internal lesions, considering different preloading forces. The deformation compensation strategy allows to improve localization accuracy, and as a consequence final probe positioning, for all considered lesions. Median lesion localization error is 3.3 mm, which is lower than the median lesion radius, when applying a preloading of 2 N, which guarantees both minimal needle insertion error and tissue stress

Generalized Shapes and Point Sets Correspondence and Registration

The theory of shapes, as proposed by David Kendall, is concerned with sets of labeled points in the Euclidean space Rd that define a shape regardless of trans- lations, rotations and dilatations. We present here a method that extends the theory of shapes, where, in this case, we use the term generalized shape for structures of unlabeled points. By using the distribution of distances between the points in a set we are able to define the existence of generalized shapes and to infer the computation of the correspondences and the orthogonal transformation between two elements of the same generalized shape equivalence class. This study is oriented to solve the registration of large set of landmarks or point sets derived from medical images but may be employed in other fields such as computer vision or biological morphometry

Needle and Biopsy Robots: a Review

Purpose of the review Robotics is a rapidly advancing field, and its introduction in healthcare can have a multitude of benefits for clinical practice. Especially, applications depending on the radiologist\u2019s accuracy and precision, such as percutaneous interventions, may profit. This paper provides an overview of recent robot-assisted percutaneous solutions. Recent findings Percutaneous interventions are relatively simple and the quality of the procedure increases a lot by introducing robotics due to the improved accuracy and precision. The success of the procedure is heavily dependent on the ability to merge pre- and intraoperative images, as an accurate estimation of the current target location allows to exploit the robot\u2019s capabilities. Summary Despite much research, the application of robotics in some branches of healthcare is not commonplace yet. Recent advances in percutaneous robotic solutions and imaging are highlighted, as they will pave the way to more widespread implementation of robotics in clinical practic

Increasing the precision of the biopsy with robots: two case studies

Robotics is a rapidly advancing field and its introduction in healthcare can have a multitude of benefits for clinical practice. Especially applications depending on the radiologist’s accuracy and precision, such as percutaneous interventions, may profit. Percutaneous interventions are relatively simple and the quality of the procedure increases a lot by introducing robotics due to the improved accuracy and precision. This paper provides the description of two robotic systems for percutaneous interventions: breast biopsy and prostate biopsy. The systems presented here are complete prototypes in an advanced state ready to be tested in clinical practice.https://youtu.be/KZxfRtg0afg https://www.youtube.com/watch?v=AB3Qa6LyHP

Virtual Reality for Neuroarchitecture: Cue Reactivity in Built Spaces

Domestic and urban environments are associated to our life experiences and behaviors. These environments may acquire an emotional and motivational value and, in turn, shape our behaviors. Although there is a well-established knowledge of the effects of built space features on perception, feelings, and affective responses (Ulrich, 1991), only a limited attention has been however paid to physical space-induced motivated behaviors. There is still a strong attitude to consider the control of motivated behaviors as a matter of individual desires, free will, moral choices, executive control, etc.—and not as the interaction between environment and personality, genetics, and brain mechanisms. Recently, there has been a convergent agreement from architects, designers, psychologists, and neuroscientists about the multifactorial nature of the reciprocal interaction between humans and built space, and how it could impact on well-being psychological distress and risky behaviors (Sternberg, 2009). The emerging interdisciplinary field of “neuroarchitecture” developed conceptual paradigms and empirical frameworks based on the interaction between brain and built spaces (see Academy of Neuroscience for Architecture; www.anfarch.org). Within this framework, we would like to propose the “Cue Reactivity” phenomenon as a paradigmatic example of such as interaction. Cue reactivity (C-R) is the adaptive response to salient information in the environment (Niaura et al., 1988). Salient information is that associated to drugs, sex, palatable food, and to a variety of natural and non-natural rewards (such as gambling, shopping, etc.). Drug C-R manifests itself as an array of responses to stimuli previously associated to drug effect. The detrimental consequence of C-R is relapse to drug-seeking and drug-taking (Rohsenow et al., 1991). On the other hand, C-R is an evolutionary phenotype of the interaction with the environment: in fact, spatial context rich of reward-related cues may stimulate both positive and risky motivated behaviors. In this Opinion paper, we will show that identification and design of specific physical space features may affect mental health, and that indoor and furniture of drinking venues are associated to alcohol use. Based on what we know about C-R, and on the effects of built spaces on psychological and behavioral processes, we think that more research is now possible to plan and design research-based “C-R-free situations.” For instance, investigations on outdoor and indoor features associated to C-R may help to develop “motivational safer built environments.” The complexity of real world investigations is not however easily modeled in the laboratory, but technologies like virtual reality may offer the possibility to increase subject's presence in a spatial context simulation and, in the meantime, the control of the experimental parameters (García-Rodríguez et al., 2012). For these reasons, we propose virtual reality as a methodological approach in-between naturalistic and experimental lab setting for a better understanding of built space features affecting C-R.The “5per mille 2012” research grant by the Italian Cancer League (Lega Italiana Lotta per i Tumori, LILT) supported the study (PI: CC) and research grant for GB. LILT also supported CC and SF with educational grants