Catálogo de publicaciones - libros

This paper investigates fundamental design, modeling and control issues related to untethered biomedical microrobots guided inside the human body through external magnetic fields. Immediate application areas for these microrobots include cardiovascular, intraocular and inner-ear diagnosis and surgery. A prototype microrobot and steering system are introduced. Experimental results on fluid drag and magnetization properties of the robots are presented along with an analysis of required magnetic fields for application inside blood vessels and vitreous humor.

This paper presents a needle-tissue interaction model that is a 3D extension of a prior work based on the finite element method. The model is also adapted to accommodate arbitrary meshes so that the anatomy can effectively be meshed using third-party algorithms. Using this model a prostate brachytherapy simulator is designed to help medical residents acquire needle steering skills. This simulation uses a prostate mesh generated from clinical data segmented as contours on parallel slices. Node repositioning and addition, which are methods for achieving needle-tissue coupling, are discussed. In order to achieve real-time haptic rates, computational approaches to these methods are compared. Specifically, the benefit of using the Woodbury formula (matrix inversion lemma) is studied. Our simulation of needle insertion into a prostate is shown to run faster than 1 kHz.

This work presents the development and application of a visualization and navigation system for planning deep-brain neurosurgeries. This system, which incorporates a digitized and segmented brain atlas, an electrophysiological database, and collections of final surgical targets of previous patients, provides assistance for non-rigid registration, navigation, and reconstruction of clinical image data. The fusion of standardized anatomical and functional data, once registered to individual patient images, facilitates the delineation of surgical targets. Our preliminary studies compared the target locations identified by a non-expert using this system with those located by an experienced neurosurgeon using regular technique on 8 patients who had undergone subthalamic nucleus (STN) deep-brain stimulations (DBS). The average displacement between the surgical target locations in both groups was 0.58mm ± 0.49mm, 0.70mm ± 0.37mm, and 0.69mm ± 0.34mm in x, y, and z directions respectively, indicating the capability of accurate surgical target initiation of our system, which has also shown promise in planning and guidance for other stereotactic deep-brain neurosurgical procedures.

The introduction of surgical robots in Minimally Invasive Surgery (MIS) has allowed enhanced manual dexterity through the use of microprocessor controlled mechanical wrists. Although fully autonomous robots are attractive, both ethical and legal barriers can prohibit their practical use in surgery. The purpose of this paper is to demonstrate that it is possible to use real-time binocular eye tracking for empowering robots with human vision by using knowledge acquired . By utilizing the close relationship between the horizontal disparity and the depth perception varying with the viewing distance, it is possible to use ocular vergence for recovering 3D motion and deformation of the soft tissue during MIS procedures. Both phantom and in vivo experiments were carried out to assess the potential frequency limit of the system and its intrinsic depth recovery accuracy. The potential applications of the technique include motion stabilization and intra-operative planning in the presence of large tissue deformation.

We investigated a method, motion compensated integration (MCI), for enhancing stent Contrast-to-Noise Ratio (CNR) such that stent deployment may be more easily assessed. MCI registers fluoroscopic frames on the basis of stent motion and performs pixel-wise integration to reduce noise. Registration is based on marker balls, high contrast interventional devices which guide the clinician in stent placement. It is assumed that stent motion is identical to that of the marker balls. Detecting marker balls and identifying their centroids with a high degree of accuracy is a non-trivial task. To address the required registration accuracy, in this work we examine MCI’s visualization benefit as a function of registration error. We employ adaptive forced choice experiments to quantify human discrimination fidelity. Perception results are contrasted with CNR measurements. For each level of registration inaccuracy investigated, MCI conferred a benefit (<0.05) on stent deployment assessment suggesting the technique is tolerant of modest registration error. We also consider the blurring effect of cardiac motion during the x-ray pulse and select frames for integration as a function of cardiac phase imaged.

The paper describes a method for automatic detection of colonic polyps, robust enough to be directly applied to low-dose CT colonographic datasets. Polyps are modeled using gray level intensity profiles and extended Gaussian images. Spherical harmonic decompositions ensure an easy comparison between a polyp candidate and a set of polypoid models, found in a previously built database. The detection sensitivity and specificity values are evaluated at different dose levels. Starting from the original raw-data (acquired at 55mAs), 5 patient datasets (prone and supine scans) are reconstructed at different dose levels (down to 5mAs), using different kernel filters and slice increments. Although the image quality decreases when lowering the acquisition mAs, all polyps above 6mm are successfully detected even at 15mAs. Accordingly the effective dose can be reduced from 4.93mSv to 1.61mSv, without affecting detection capabilities, particularly important when thinking of population screening.

Computer-based simulation is an important tool for surgical skills training and assessment. In general, the degree of realism experienced by the trainees is determined by the visual and biomechanical fidelity of the simulator. In minimally invasive surgery, specular reflections provide an important visual cue for tissue deformation, depth and orientation. This paper describes a novel image-based lighting technique that is particularly suitable for modeling mucous-covered tissue surfaces. We describe how noise functions can be used to control the shape of the specular highlights, and how texture noise is generated and encoded in image-based structure at a pre-processing stage. The proposed technique can be implemented at run-time by using the graphics processor to efficiently attain pixel-level control and photo-realism. The practical value of the technique is assessed with detailed visual scoring and cross comparison experiments by two groups of observers.

Current techniques for microscopic imaging do not provide necessary spatial and temporal resolutions for real time visualization of the nucleus. Images can only be acquired in time lapse mode, leading to significant loss of information between image frames. Such data, if available, can be extremely helpful in the study of nuclear organization and function. In this paper, we present a gamut of geometric-technique-based approaches for solving the problem. Our techniques, working together, can effectively recover complicated motion and deformation as well as the change of intensity surfaces from pairs of images in a microscopic image sequence, and has low time complexity, particularly desirable by many biological applications where large amount of DNA need to be processed. These techniques are also readily applicable to other types of images for reconstructing motion and intensity surfaces of deformable objects.

The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.

We present a spatio-temporal filtering method for significantly increasing the signal-to-noise ratio (SNR) in noisy fluorescence microscopic image sequences where small particles have to be tracked from frame to frame. Image sequence restoration is achieved using a statistical approach involving an appropriate on-line window geometry specification. We have applied this method to noisy synthetic and real microscopic image sequences where a large number of small fluorescently labeled vesicles are moving in regions close to the Golgi apparatus. The SNR is shown to be drastically improved and the enhanced vesicles can be segmented. This novel approach can be further exploited for biological studies where the dynamics of small objects of interest have to be analyzed in molecular and sub-cellular bio-imaging.