Bogdan Mihai Maris

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.

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

A compact navigation system for free hand needle placement in percutaneos procedures

\mathbb {R}^d

Deformable surface registration for breast tumors tracking: A phantom study

Rd that define a shape regardless of translations, 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.

Virtual Reality for Neuroarchitecture: Cue Reactivity in Built Spaces

ObjectivesTo evaluate MRI derived whole-tumour histogram analysis parameters in predicting pancreatic neuroendocrine neoplasm (panNEN) grade and aggressiveness.MethodsPre-operative MR of 42 consecutive patients with panNEN >1 cm were retrospectively analysed. T1-/T2-weighted images and ADC maps were analysed. Histogram-derived parameters were compared to histopathological features using the Mann-Whitney U test. Diagnostic accuracy was assessed by ROC-AUC analysis; sensitivity and specificity were assessed for each histogram parameter.ResultsADCentropy was significantly higher in G2-3 tumours with ROC-AUC 0.757; sensitivity and specificity were 83.3 % (95 % CI: 61.2–94.5) and 61.1 % (95 % CI: 36.1–81.7). ADCkurtosis was higher in panNENs with vascular involvement, nodal and hepatic metastases (p= .008, .021 and .008; ROC-AUC= 0.820, 0.709 and 0.820); sensitivity and specificity were: 85.7/74.3 % (95 % CI: 42–99.2 /56.4–86.9), 36.8/96.5 % (95 % CI: 17.2–61.4 /76–99.8) and 100/62.8 % (95 % CI: 56.1–100/44.9–78.1). No significant differences between groups were found for other histogram-derived parameters (p >.05).ConclusionsWhole-tumour histogram analysis of ADC maps may be helpful in predicting tumour grade, vascular involvement, nodal and liver metastases in panNENs. ADCentropy and ADCkurtosis are the most accurate parameters for identification of panNENs with malignant behaviour.Key Points• Whole-tumour ADC histogram analysis can predict aggressiveness in pancreatic neuroendocrine neoplasms.• ADC entropy and kurtosis are higher in aggressive tumours.• ADC histogram analysis can quantify tumour diffusion heterogeneity.• Non-invasive quantification of tumour heterogeneity can provide adjunctive information for prognostication.

Retrospective Study on Phantom for the Application of Medical Image Registration in the Operating Room Scenario

In this work we have designed and developed a new navigation system for interventional radiology, implemented in a light and compact device. The system attached to the needle is composed by a small screen that gives hints about the position and the orientation, a controller that commands the screen and interfaces with the computer, and a marker that communicates with a tracking system. By using a real time software the user is guided to move the needle along the desired position and orientation. To the best of our knowledges, this is the first system to have the navigation display integrated directly on the tool. The in-vitro tests we have performed, show how such a system yields a higher precision in the execution of the task and a reduction of the time required to complete the procedure.

System and method for guiding the manual insertion of a needle into the body of a patient

PurposePatient-specific biomedical modeling of the breast is of interest for medical applications such as image registration, image guided procedures and the alignment for biopsy or surgery purposes. The computation of elastic properties is essential to simulate deformations in a realistic way. This study presents an innovative analytical method to compute the elastic modulus and evaluate the elasticity of a breast using magnetic resonance (MRI) images of breast phantoms.MethodsAn analytical method for elasticity computation was developed and subsequently validated on a series of geometric shapes, and on four physical breast phantoms that are supported by a planar frame. This method can compute the elasticity of a shape directly from a set of MRI scans. For comparison, elasticity values were also computed numerically using two different simulation software packages.ResultsApplication of the different methods on the geometric shapes shows that the analytically derived elongation differs from simulated elongation by less than 9% for cylindrical shapes, and up to 18% for other shapes that are also substantially vertically supported by a planar base. For the four physical breast phantoms, the analytically derived elasticity differs from numeric elasticity by 18% on average, which is in accordance with the difference in elongation estimation for the geometric shapes. The analytic method has shown to be multiple orders of magnitude faster than the numerical methods.ConclusionIt can be concluded that the analytical elasticity computation method has good potential to supplement or replace numerical elasticity simulations in gravity-induced deformations, for shapes that are substantially supported by a planar base perpendicular to the gravitational field. The error is manageable, while the calculation procedure takes less than one second as opposed to multiple minutes with numerical methods. The results will be used in the MRI and Ultrasound Robotic Assisted Biopsy (MURAB) project.

Multimodal Data Fusion and Registration for Needle Guidance in Percutaneous Procedures

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 pre-operative 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 multimodal 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.