2003
New late-intensification schedules for cancer treatments
Jorge A. González, Harold P. de Vladar, Morella Rebolledo
Acta Científica Venezolana, 54: 263-273, 2003
The nonlinear Gompertz equation that describes the evolutions of tumors under different treatments was investigated. It was shown that late logarithmic intensification asymptotically reduces the tumor cell population to zero. Using the same total amount of therapy, the schedule following a logarithmic function produces a larger reduction of tumor cell population than the schedule using a therapy with constant intensity. On the other hand, a logarithmic intensification should be more tolerable by the patients than a faster temporal growth therapy. We have shown that during the treatment the dose intensity should not be decreased at any time while the therapy is applied, because this will allow the tumor to relapse. The therapy intensity should be continuously increased as possible. We have solved an optimization problem for several late intensification treatments by the constraints of the total dose and the maximum individual (or daily) dose. Based on our results, we have designed new chemotherapy and radiotherapy treatments.
A knee arthoplasty software tool for preoperative planning
Alejandro Madero, Ernesto Coto, Omaira Rodríguez
INTERNATIONAL CONGRESS ON COMPUTATIONAL BIOENGINEERING, M. Doblaré , M. Cerrolaza and H. Rodrigues (Eds.), Spain, 2003
This paper describes the software tool NSKS (Navigation and Simulation Knee System), which allows
the user to analyze, model and adjust the total knee arthoplastic procedure during a preoperative planning. This work incorporates contour detection and volume generation modules, eliminating the need of using expensive and sophisticated commercial software tools, which procedures are not familiar to the natural user, the surgeon. The new NSKS version reduces the workflow of the volume generation to only one phase. Using Computational Tomography (CT) data, the application shows a 3D reconstruction of the knee articulation, providing a precise spatial orientation and additional information about physical parameters e.g. load and tension that guarantees the correct insertion of the prosthetic component. The system is not invasive and does not require any mechanical devices that cause more traumas to the patient.
3D watershed transform combined with a probabilistic atlas for medical image segmentation
Matús Straka, Alexandra La Cruz, Arnold Köchl, Milos Srámek, Eduard Gröller, Dominik Fleischmann
Journal of Medical Informatics & Technologies, 6 (2003) IT69-78
Recent advances in medical imaging technology using multiple detector-row computed tomography (CT) provide volumetric datasets with unprecedented spatial resolution. This has allowed for CT to evolve into an excellent non-invasive vascular imaging technology, commonly referred to as CT-angiography. Visualisation of vascular structures from CT datasets is demanding, however, and identification of anatomic objects in CT-datasets is highly desirable. Density and/or gradient operators have been used most commonly to classify CT data. In CT angiography, simple density/gradient operators do not allow precise and reliable classification of tissues due to the fact that different tissues (e.g. bones and vessels) possess the same density range and may lie in close spatial vicinity. We think, that anatomic classification can be achieved more accurately, if both spatial location and density properties of volume data are taken into account. We present a combination of two well-known methods for volume data processing to obtain accurate tissue classification. 3D watershed transform is used to partition the volume data in morphologically consistent blocks and a probabilistic anatomic atlas is used to distinguish between different kinds of tissues based on their density.
Bone Segmentation in CT-Angiography Data Using a Probabilistic Atlas
Matús Straka, Alexandra La Cruz, Leonid I. Dimitrov, Milos Srámek, Dominik Fleischmann, Eduard Gröller
VMV (2003)
Automatic segmentation of bony structures in CT angiography datasets is an essential pre-processing step necessary for most visualization and analysis tasks. Since traditional density and gradient operators fail in non-trivial cases (or at last require extensive operator work), we propose a new method for segmentation of CTA data based on a probabilistic atlas. Storing densities and masks of previously manually segmented tissues to the atlas can constitute a statistical information base for latter accurate segmentation. In order to eliminate dimensional and anatomic variability of the atlas input datasets, these have to be spatially normalized (registered) rst by applying a non-rigid transformation. After this transformation, densities and tissue masks are statistically processed (e.g. averaged) within the atlas. Records in the atlas can be later evaluated for estimating the probability of bone tissue in a voxel of an unsegmented dataset.
