Biomedical subjects

Y Censor

Publications and source records attributed to Y Censor.

4 recordsLinked to original sources

PubMed2001

BICAV: a block-iterative parallel algorithm for sparse systems with pixel-related weighting.

Component averaging (CAV) was recently introduced by Censor, Gordon, and Gordon as a new iterative parallel technique suitable for large and sparse unstructured systems of linear equations. Based on earlier work of Byrne and Censor, it uses diagonal weighting matrices, with pixel-related weights determined by the sparsity of the system matrix. CAV is inherently parallel (similar to the very slowly converging Cimmino method) but its practical convergence on problems of image reconstruction from projections is similar to that of the algebraic reconstruction technique (ART). Parallel techniques are becoming more important for practical image reconstruction since they are relevant not only for supercomputers but also for the increasingly prevalent multiprocessor workstations. This paper reports on experimental results with a block-iterative version of component averraging (BICAV). When BICAV is optimized for block size and relaxation parameters, its very first iterates are far superior to those of and more or less on a par with ART. Similar to CAV, BICAV is also inherently parallel. The fast convergence is demonstrated on problems of image reconstruction from projections, using the SNARK93 image reconstruction software package. Detailed plots of various measures of convergence, and reconstructed images are presented.

Algorithms↗

PubMed1989

An iterative approach to plan combination in radiotherapy.

Radiation therapy concerns the delivery of a proper dose of radiation to a tumor volume without causing irreparable damage to surrounding healthy tissue and critical organs. The problem of plan combination in radiation therapy treatment planning (RTTP) proposed, formulated and studied here, addresses a situation when for a specific clinical case, a set of several treatment plans is proposed, but each one of them violates the prescribed dose in at least one significant region of the volume that has to be treated. We represent treatment plans as vectors in the Euclidean space, and define their equivalence, acceptability and realizability. A simple linear algebraic model for combining them is then used in order to derive, from the given set of approximate plans, a combined treatment plan, which will be both acceptable, and technically realizable. In the event that such a combined plan dose not exist, the alternatives for relaxing the treatment requirements can be systematically considered.

Algorithms↗

PubMed1989

Semi-automated radiotherapy treatment planning with a mathematical model to satisfy treatment goals.

Iterative algorithms can provide a feasible solution, if any exists, to specified treatment goals. Our model subdivides both the patient's cross section into a fine grid of points and the radiation beam into a set of "pencil" rays. The anatomy, treatment machine parameters, dose limits and homogeneity, are all defined. This process of subdivision leads to a large system of linear inequalities with a solution that provides a radiation intensity distribution that will deliver a prescribed dose distribution. The clinical results from two different algorithms will be presented and contrasted. Once the anatomy, treatment, and machine parameters have been entered, the computerized algorithms yield an answer in several minutes. The Cimmino algorithm also allows "weights" or priority assignments of the treatment goals. The resulting solution is biased towards fulfilling the specified doses for the anatomic regions which were given greater weight. It is desirable to have a systematic search of possible treatment alternatives in complex clinical situations, including 3-dimensional radiation therapy treatment planning (RTTP). Our method has been applied to 2-D RTTP, but is equally applicable to 3-D RTTP with minor modifications.

Algorithms↗

PubMed1980

Demonstration of a software package for the reconstruction of the dynamically changing structure of the human heart from cone beam x-ray projections.

The Dynamic Spatial Reconstructor (DSR) is a device constructed at the Biodynamics Research Unit of the Mayo Clinic for (among other things) the visualization of the beating heart inside the intact thorax. The device consists of 28 rotating X-ray sources arranged on a circular arc at 6 degrees intervals (total span 162 degrees) and a matching set of 28 imaging systems. The whole thorax of the patient is projected onto the two-dimensional screen of the imaging systems by cone beams of X rays from the sources. All of the X-ray sources are switched on and off within a total period of 10 milliseconds. The Medical Image Processing Group at the State University of New York at Buffalo has developed a software package for the design and evaluation of algorithms to be used by the DSR. In this paper we illustrate the operation of the package and a particular algorithm for the reconstruction of the dynamically changing structure of the heart from data collected by the DSR.

Computers↗