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Biomedical subjects

S Cosby

Publications and source records attributed to S Cosby.

8 recordsLinked to original sources
PubMed2001

Technical note: a transparent template system for positioning independent radiation-therapy shielding blocks.

To assist the radiation therapist/technologist with setting up patients for radiation therapy treatments, a set of accessories has been developed for placing a transparent template of the treatment-field border in the light-field of a radiation teletherapy unit. These accessories permit the efficient, accurate, and reproducible manual placement of independent lead-shielding blocks. Software has been developed to print templates on standard transparency sheets using a laser printer. A transparent template holder slides into the wedge slot of the teletherapy unit. The template holder features 2 alignment pegs to assure rapid, accurate, and reproducible placement of the template. A specially-developed hole-punch is used to cut, in the template, alignment holes that fit snugly over the pegs of the template holder. This system currently supports Siemens MX and KD units and the Theratronics Theratron 780C Co unit, as well as the Helax TMS block file format.

Equipment Design↗
PubMed1999

Computer-assisted decision making in portal verification--optimization of the neural network approach.

PURPOSE: Conventional portal verification requires that a qualified radiation oncologist make decisions as to the set-up acceptability. This scheme is no longer sustainable with the large numbers of images available on-line and stringent time constraints. Therefore the objective of this study was to develop, optimize, and evaluate on clinical data an artificial intelligence decision-making tool for portal verification. The tool, based on the artificial neural network (ANN) approach, should approximate, as closely as possible, portal verification assessments made by a radiation oncologist expert. METHODS AND MATERIALS: A total of 328 electronic portal images of tangential breast irradiations were included in the study. A radiation oncologist expert evaluated these images and rated the treatment set-up acceptability on a scale from 0 to 10. Translational and rotational errors in the placement of the radiation field boundaries formed seven-dimensional feature vectors that represented each of the 328 portal images/treatments. The feature vectors were used as inputs to a three-layer, feedforward ANN. The neural network was trained on the oncologist's ratings. RESULTS: The rms discrepancy between the ANN and the expert's ratings was 1.05 rating points. Using the decision threshold equal to 5 for both sets of ratings, the ANN classifier was capable of detecting 100% of the portals classified as "unacceptable" by the expert. Only 6.5% of portals acceptable to the oncologist were misclassified as "unacceptable" by the ANN. CONCLUSION: The results of this study indicate the feasibility of using the ANN portal image classifier as an automated assistant to the radiation oncologist. Its role would be to recommend an appropriate decision as to the acceptability or otherwise of a given treatment set-up depicted in a portal image.

Breast Neoplasms↗
PubMed1999

Application of a fuzzy pattern classifier to decision making in portal verification of radiotherapy.

With the large volume of electronic portal images acquired and stringent time constraints, it is no longer feasible to follow the convention whereby the radiation oncologist reviews and approves or rejects all portals. For that purpose we have developed a portal image classifier based on the fuzzy k-nearest neighbour (k-NN) algorithm. Each portal image is represented by a feature vector that consists of translational and rotational errors in the placement of radiation field borders that were measured in the portal image. Memberships in the acceptable portal class for the reference portal images within a training dataset were defined by a radiation oncologist expert. The fuzzy k-NN portal image classifier was trained and tested on a dataset of 328 portal images acquired during tangential irradiations of the breast. The memberships in the acceptable portal class produced by the fuzzy k-NN algorithm agreed very well with those defined by the expert. The linear correlation coefficient was equal to 0.89. Performance of the fuzzy k-NN classifier was also evaluated from the portal decision-making point of view using the measures of accuracy, sensitivity and specificity. The fuzzy k-NN portal classifier was capable of identifying almost all the truly unacceptable portals with an acceptably low false alarm rate.

Artificial Intelligence↗
PubMed1995

Radiotherapy portal verification: an observer study.

In many radiotherapy facilities radiotherapy portal verification is currently a subjective process based on the visual comparison of a treatment or portal image with a prescription or simulation image. The reliability of this process is unknown. We describe here a study in which 16 observers (oncologists, physicists and therapists) independently evaluated the geometric accuracy of 530 treatment fields on 45 patients. The treatment images were acquired by the BEAMVIEW on-line portal imaging system (Siemens Medical Laboratories, Concord, CA, USA). Illustrative examples of the large variation in observers' assessments of the same field are given. The kappa statistic is used to evaluate the degree of agreement between observers and between on-line (at the treatment unit) and off-line (in a quiet viewing room) assessments. The best interobserver agreement was between the four oncologists contributing to the study although this level of agreement was rated only as "fair". Comparison of on-line and off-line decisions made by therapists exhibited "poor" agreement. This study has provided statistical confirmation of the suspicions of many workers in the field of radiotherapy portal verification, viz that the subjective evaluation of field accuracy is unreliable. However, the degree of unreliability is surprisingly large. The inconsistencies between observers documented in this study need to be clearly acknowledged in the development of protocols for the clinical use of on-line portal imaging systems. Acceptable reliability in radiotherapy portal verification will only be achieved when subjective decision making is eliminated.

Humans↗
PubMed1994

Technical note: an aid to radiation therapy simulation.

We have mounted a transmission liquid crystal display unit on the head of a radiotherapy simulator and projected, using the field light, a digitized fluoroscopic image with treatment prescription overlay. It is suggested that this approach (i) can aid visualization on the patient of complex field borders including those defined by multileaf collimators; (ii) could play a role in computed tomography or magnetic resonance based treatment planning and (iii) contributes to configuring non-coplanar beams.

Computer Simulation↗
PubMedDate not supplied

Video techniques for on-line portal imaging.

The application of on-line portal imaging techniques to the verification of treatment precision is reviewed. The design parameters for a video portal imaging system are described, and the optimization of image quality is discussed with particular emphasis on photon noise. On-line images are presented for a head phantom imaged on a 4 MV linac, and compared with a conventional portal film. The relative advantages of an on-line system are compared with conventional portal film analysis.

Humans↗
PubMedDate not supplied

Automatic analysis of gated heart studies using a Galois lattice.

Gated heart studies are currently assessed using the parametric images of first harmonic phase and amplitude in addition to the original images. More information can be obtained by multi-harmonic analysis, but the quantity of information is too great to be assessed by visual methods alone. We propose an automatic diagnosis support system which will use all the additional information, compress and analyse the data, and present the results in a single image to assist the consultant in his or her diagnosis. Multi-harmonic analysis produces many parameters to describe features of the images. A set of parameters will be selected using guidelines that link parameters and intervals of their values to clinical conditions. Images are processed using these parameters and the results stored in a binary matrix. A Galois lattice structure is then derived from the matrix where a vertex of the lattice represents a 'significant' region of the heart together with a 'logic description' of this region. Using this, areas of clinical interest can be found and displayed on a diastolic image of the heart.

Data Interpretation, Statistical↗