Biomedical subjects

S Boyko

Publications and source records attributed to S Boyko.

6 recordsLinked to original sources

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↗

PubMed1998

An image registration scheme applied to verification of radiation therapy.

The introduction of modern conformal radiation therapy techniques requires high geometric precision in treatment delivery which must be verified. For that purpose we have developed an automated system based on registration of portal and simulation (or planning) image pairs. The image registration is performed on anatomical features which are automatically extracted from the portal image. The portal image is then registered with a planning or simulation radiographic image which represents the geometric prescription for the treatment, using an optimized version of the chamfer matching algorithm. Subsequently, the magnitude of the radiation field displacement during treatment is measured by registering the prescribed and treated field boundaries. Algorithms based on chamfer matching and polygon matching have been used for the field boundary registration. Performance of the entire scheme was evaluated on a series of 15 portal images of a pelvic phantom representing various known degrees of the radiation field displacement. The measurements of the radiation field displacements performed by the automated system proved very reliable and after correction for systematic bias agreed to within 1.5 mm or 1 degree with the displacements applied. Second test series involved comparisons between the automated registrations and those performed manually/visually by an experienced human observer, on 31 portal images acquired during treatments of 18 pelvic patients. These tests showed close agreement (in 80% of cases discrepancies were smaller than 1.5 mm or 1.5 degrees) between the automated scheme and the human observer. It is concluded that the developed scheme would be suitable for online geometric verification of radiation therapy treatments.

Algorithms↗

PubMed1997

On the controversies surrounding the origins of radiation therapy.

In this study the authors analyze the documentation regarding the earliest known X-ray treatments with the objective of identifying the true origin of radiation therapy. The four most often quoted events, including X-ray treatments allegedly performed in 1896 are analyzed in the light of available published reports. From this it is concluded that Despeignes of Lyon, who in July of 1896 irradiated a patient with cancer of the stomach, is in all likelihood the first person to perform documented radiation therapy treatments with a scientific and logical basis.

History, 19th Century↗

PubMed1996

Quantitative vs. subjective portal verification using digital portal images.

PURPOSE: Off-line, computer-aided prescription (simulator) and treatment (portal) image registration using chamfer matching has been implemented on PC based viewing station. The purposes of this study were (a) to evaluate the performance of interactive anatomy and field edge extraction and subsequent registration, and (b) to compare observer's perceptions of field accuracy with measured discrepancies following anatomical registration. METHODS AND MATERIALS: Prescription-treatment image pairs for 48 different patients were examined in this study. Digital prescription images were produced with the aid of a television camera and a digital frame grabber, while the treatment images were obtained directly from an on-line portal imaging system. To facilitate perception of low contrast anatomical detail, on-line portal images were enhanced with selective adaptive histogram equalization prior to extraction of anatomical edges. Following interactive extraction of anatomical and field border information by an experienced observer, the identified anatomy was registered using chamfer matching. The degree of conformity between the prescription and treatment fields was quantified using several parameters, which included relative prescription field coverage and overcoverage, as well as the translational and rotational displacements as measured by chamfer matching applied to the boundaries of the two fields. These quantitative measures were compared with subjective evaluations made by four radiation oncologists. RESULTS: All the images in this series that included a range of the most commonly seen treatment sites were registered and the conformity parameters were found. The mean treatment/prescription field coverage and overcoverage were approximately 95 and 7%, respectively before registration. The mean translational displacement in the transverse and cranio-caudal directions were 2.9 and 3.4 mm, respectively. The mean rotational displacement was approximately 2 degrees. For all four oncologists, the portals classified as unacceptable, in terms of the field placement, exhibited significantly higher (p < 0.03) translational errors in the transverse direction. The field coverages were significantly lower (p < 0.05) and the translational errors in the cranio-caudal direction were significantly higher (p < 0.05) for the portals rated as unacceptable by two of the oncologists. CONCLUSIONS: From the parameters that were used to quantify the degree of conformity between the prescription and treatment fields, the translational error in the transverse direction correlated best with the oncologists' assessments on the field placement. Field coverage and translational error in the cranio-caudal direction correlated well with assessments of only two out of the four participating oncologists. This can be explained by the fact that for the majority of treatment sites included in the study the positioning of field borders was more critical for the transverse direction. A conclusion for the design of future quantitative and automated on-line portal verification systems is that they will have to model different perceived significances of different types of localization errors intrinsic to oncologist evaluation of portal images.

Algorithms↗

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↗