Showing papers by "Miguel Ángel González Ballester published in 2010"
TL;DR: A framework is developed that can virtually fit a proposed implant design to samples drawn from the statistical model, and assess which range of the population is suitable for the implant, and highlights which patterns of bone variability are more important for implant fitting.
88 citations
TL;DR: The patients undergoing rhinoplasties preferred new technologies in the preoperative period due to the higher number of reintervention requests, and the advantages of using the 3D radiologic viewer included improved preoperative planning, reduction in intraoperative stress, and reduced costs.
Abstract: This report describes preoperative digital planning for rhinoplasty using a new three-dimensional (3D) radiologic viewer that allows both patients and surgeons to visualize on a common monitor the 3D real aspect of the nose in its inner and outer sides. In the period 2002 to 2008, 210 patients underwent rhinoplasty procedures in the authors’ clinic. The patients were randomly divided into three groups according to the type of preoperative planning used: photos only, a simulated result by Adobe Photoshop, or the 3D radiologic viewer. The parameters evaluated included the number of patients that underwent surgery after the first consultation, the number of patients who asked for a reintervention, patient satisfaction (according to a test given to the patients 12 months postoperatively), the surgical time required for a functional intervention, and the improvement in nasal function by postoperative rhinomanometry and subjective evaluation. Computer-aided technologies led to a higher number of patients deciding to undergo a rhinoplasty. Simulation of the postoperative results was not as useful in the postoperative period due to the higher number of reintervention requests. The patients undergoing rhinoplasties preferred new technologies in the preoperative period. The advantages of using the 3D radiologic viewer included improved preoperative planning, reduction in intraoperative stress, a higher number of patients undergoing surgery, reduction in postoperative surgical corrections, reduction in surgical time for the functional intervention, a higher rate of improvement in nasal function, a higher percentage of postoperative satisfaction, and reduced costs.
41 citations
TL;DR: The method presents the automated hierarchical partitioning of organs into anatomically significant components that represent potentially important constraints for abdominal diagnosis and modeling, and that may be used as a complement to multi-level statistical shape models.
Abstract: We present a framework for multi-level statistical shape analysis, applied to the study of anatomical variability of abdominal organs. Statistical models were built hierarchically, allowing the representation of different levels of detail. Principal factor analysis was used for decomposition of deformation fields obtained from non-rigid registration at different levels, and provided a compact model to study shape variability within the abdomen. To assess and ease the interpretability of the resulting deformation modes, a clustering technique of the deformation vectors was proposed. The analysis of deformation fields showed a strong correlation with anatomical landmarks and known mechanical deformations in the abdomen. Clusters of modes of deformation from fine-to-coarse levels explain tissue properties, and inter-organ relationships. Our method further presents the automated hierarchical partitioning of organs into anatomically significant components that represent potentially important constraints for abdominal diagnosis and modeling, and that may be used as a complement to multi-level statistical shape models.
12 citations
01 Jan 2010
TL;DR: An integrated approach using a multi-resolution point distribution model (MR-PDM) to reconstruct a patient-specific surface model of the proximal femur from sparse input data, which may consist of sparse point data or a limited number of calibrated X-ray images.
Abstract: Accurate reconstruction of a patient-specific surface model of the proximal femur from preoperatively or intraoperatively available sparse data plays an important role in planning and supporting various computer-assisted surgical procedures. In this paper, we present an integrated approach using a multi-resolution point distribution model (MR-PDM) to reconstruct a patient-specific surface model of the proximal femur from sparse input data, which may consist of sparse point data or a limited number of calibrated X-ray images. Depending on the modality of the input data, our approach chooses different PDMs. When 3D sparse points are used, which may be obtained intraoperatively via a pointer-based digitization or from a calibrated ultrasound, a fine level point distribution model (FL-PDM) is used in the reconstruction process. In contrast, when calibrated X-ray images are used, which may be obtained preoperatively or intraoperatively, a coarse level point distribution model (CL-PDM) will be used. The present approach was verified on 31 femurs. Three different types of input data, i.e., sparse points, calibrated fluoroscopic images, and calibrated X-ray radiographs, were used in our experiments to reconstruct a surface model of the associated bone. Our experimental results demonstrate promising accuracy of the present approach. A multi-resolution point distribution model facilitate the reconstruction of a patient-specific surface model of the proximal femur from sparse input data.
11 citations
TL;DR: It is shown that by using active contours based on level sets the shape of the injected cement can be accurately detected and the model has been improved for segmentation as proposed in the previous work by including a term that restricts the level set function to the vertebral body.
Abstract: Vertebroplasty is a minimally invasive procedure with many benefits; however, the procedure is not without risks and potential complications, of which leakage of the cement out of the vertebral body and into the surrounding tissues is one of the most serious. Cement can leak into the spinal canal, venous system, soft tissues, lungs and intradiscal space, causing serious neurological complications, tissue necrosis or pulmonary embolism. We present a method for automatic segmentation and tracking of bone cement during vertebroplasty procedures, as a first step towards developing a warning system to avoid cement leakage outside the vertebral body. We show that by using active contours based on level sets the shape of the injected cement can be accurately detected. The model has been improved for segmentation as proposed in our previous work by including a term that restricts the level set function to the vertebral body. The method has been applied to a set of real intra-operative X-ray images and the results show that the algorithm can successfully detect different shapes with blurred and not well-defined boundaries, where the classical active contours segmentation is not applicable. The method has been positively evaluated by physicians.