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Biomedical subjects

K G Gilhuijs

Publications and source records attributed to K G Gilhuijs.

13 recordsLinked to original sources
PubMed2001

Displacement of breast tissue and needle deviations during stereotactic procedures.

RATIONALE AND OBJECTIVES: The aim of this study was to quantify the displacement of breast tissue and the inaccuracy of needle positioning for biopsy (14-gauge) and localization (19.5-gauge) needles. METHODS: For displacement of breast tissue, differences between the coordinates of identifiable microcalcifications in the images before (baseline) and after needle positioning were analyzed (n = 52). For accuracy of needle positioning, differences between the coordinates of the needle tip and the target were analyzed in breast tissue (n = 97) and in air (n = 246). RESULTS: Average target displacement was 2.1 mm for biopsy needles (95% prediction interval [PI] 0.6-7.8) and 1.0 mm (95% PI 0.3-3.9) for localization needles. Mean inaccuracy of needle positioning in breast tissue was 1.1 mm (95% PI 0.4-3.0) and 1.8 mm (95% PI 0.7-4.6) for biopsy and localization needles, respectively. CONCLUSIONS: Tissue and needle displacements cause a total positioning error of 2.4 mm in stereotactic core biopsy, which will limit the attainable diagnostic accuracy.

Biopsy, Needle↗
PubMed2001

Accurate measurement of the dynamic response of a scanning electronic portal imaging device.

An important condition for the safe introduction of dynamic intensity modulated radiotherapy (IMRT) using a multileaf collimator (MLC) is the ability to verify the leaf trajectories. In order to verify IMRT using an electronic portal imaging device (EPID), the EPID response should be accurate and fast. Noninstantaneous dynamic response causes motion blurring. The aim of this study is to develop a measurement method to determine the magnitude of the geometrical error as a result of motion blurring for imagers with scanning readout. The response of a liquid-filled ionization chamber EPID, as an example of a scanning imager, on a moving beam is compared with the response of a diode placed at the surface of the EPID. The signals are compared under the assumption that all EPID rows measure the same dose rate when a straight moving field edge is imaged. The measurements are performed at several levels of attenuation to investigate the influence of dose rate on the response of the detector. The accuracy of the measurement method is better than 0.25 mm. We found that the liquid-filled ionization chamber EPID does not suffer from significant motion blurring under clinical circumstances. Using a maximum gradient edge detector to determine the field edge in an image obtained by a liquid-filled ionization chamber EPID, errors smaller than 1 mm are found at a dose rate of 105 MU/min and a field edge speed of 1.1 cm/s. The errors reduce at higher dose rates. The presented method is capable of quantifying the geometrical errors in determining the position of the edge of a moving field with subpixel accuracy. The errors in field edge position determined by a liquid-filled ionization chamber EPID are negligible in clinical practice. Consequently, these EPIDs are suitable for geometric IMRT verification, as far as dynamic response is concerned.

Image Processing, Computer-Assisted↗
PubMed1998

Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging.

Contrast-enhanced magnetic resonance imaging (MRI) of the breast is known to reveal breast cancer with higher sensitivity than mammography alone. The specificity is, however, compromised by the observation that several benign masses take up contrast agent in addition to malignant lesions. The aim of this study is to increase the objectivity of breast cancer diagnosis in contrast-enhanced MRI by developing automated methods for computer-aided diagnosis. Our database consists of 27 MR studies from 27 patients. In each study, at least four MR series of both breasts are obtained using FLASH three-dimensional (3D) acquisition at 90 s time intervals after injection of Gadopentetate dimeglumine (Gd-DTPA) contrast agent. Each series consists of 64 coronal slices with a typical thickness of 2 mm, and a pixel size of 1.25 mm. The study contains 13 benign and 15 malignant lesions from which features are automatically extracted in 3D. These features include margin descriptors and radial gradient analysis as a function of time and space. Stepwise multiple regression is employed to obtain an effective subset of combined features. A final estimate of likelihood of malignancy is determined by linear discriminant analysis, and the performance of classification by round-robin testing and receiver operating characteristics (ROC) analysis. To assess the efficacy of 3D analysis, the study is repeated in two-dimensions (2D) using a representative slice through the middle of the lesion. In 2D and in 3D, radial gradient analysis and analysis of margin sharpness were found to be an effective combination to distinguish between benign and malignant masses (resulting area under the ROC curve: 0.96). Feature analysis in 3D was found to result in higher performance of lesion characterization than 2D feature analysis for the majority of single and combined features. In conclusion, automated feature extraction and classification has the potential to complement the interpretation of radiologists in an objective, consistent, and accurate way.

Biophysical Phenomena↗
PubMed1997

Effect of image artifacts, organ motion, and poor segmentation on the reliability and accuracy of three-dimensional chamfer matching.

Our objective was to investigate the influence of various image artifacts on three-dimensional chamfer matching. A number of artificial and natural artifacts (for instance, as a model for CT-MR matching) were introduced or suppressed in pairs of pelvic CT scans, and a perturbation study was used to determine reliability and accuracy in a well known ground truth situation. In general, chamfer matching is extremely robust against missing data, low resolution, and poor segmentation of the images. In the presence of artifacts, minimization of the average distance outperformed minimization of the root-mean-square distance. Outliers in the scan from which the point list is obtained must be avoided. For example, rotation of the femurs reduces CT-CT registration accuracy by 1-2 mm. The robustness of chamfer matching is confirmed by a limited perturbation study of CT-MR registration for the pelvic region. In conclusion, chamfer matching is extremely accurate and reliable if outliers are avoided in the scan from which the point list is derived, and the average distance is used as a cost function.

Artifacts↗
PubMed1996

Interactive three dimensional inspection of patient setup in radiation therapy using digital portal images and computed tomography data.

PURPOSE: Presently, the majority of clinical tools to quantify deviations in patient setup during external beam radiotherapy is based on two-dimensional (2D) analysis of portal images. The purpose of this study is to develop a tool for the inspection of the patient setup in three dimensions (3D) and to validate its clinical advantage over methods based on 2D analysis in the presence of out-of-plane rotations. METHODS AND MATERIALS: We developed an interactive procedure to quantify the setup deviation of the patient in 3D. The procedure is based on fast computation of digitally reconstructed radiographs (DRRs) in two beam directions and comparison of these DRRs with corresponding portal images. The potential of the tool is demonstrated on three selected cases of prostate and parotid gland treatment where conventional 2D analysis produced inconsistent results. The measurements from 3D analysis are compared with those obtained from the 2D analysis. RESULTS: Despite application of an immobilization cast, two investigated parotid gland setups showed rotational deviations in 3D up to 3 degrees. Two-dimensional analysis of these deviations produced inconsistent results. Analysis of the selected prostate setup in 3D showed a rotational deviation of 7 degrees around the left-right axis, possibly causing displacement of the seminal vesicles toward the borders of the conformal boost fields. Using 2D analysis, this out-of-plane rotation was misinterpreted as a translation resulting in the failure to trigger the decision protocol to correct the setup after the first fraction. Using the 3D patient setup analysis procedure, an accuracy of the order of 1 mm and 1 degree (SD) could be obtained. The computation time of the interactive DRRs is of the order of 1 s on a 60 MHz PC. The complete interactive 3D analysis requires about 10 min. CONCLUSIONS: Quantification of the patient setup in 3D provides essential additional information in cases where conventional 2D analysis is inconsistent, e.g., in the presence of out-of-plane rotations or geometrical degeneracies. The speed and accuracy of the interactive 3D patient setup inspection are acceptable for use in offline clinical studies and analysis of problem cases.

Humans↗
PubMed1996

Automatic three-dimensional inspection of patient setup in radiation therapy using portal images, simulator images, and computed tomography data.

In external beam radiotherapy, conventional analysis of portal images in two dimensions (2D) is limited to verification of in-plane rotations and translations of the patient. We developed and clinically tested a new method for automatic quantification of the patient setup in three dimensions (3D) using one set of computed tomography (CT) data and two transmission images. These transmission images can be either a pair of simulator images or a pair of portal images. Our procedure adjusts the position and orientation of the CT data in order to maximize the distance through bone in the CT data along lines between the focus of the irradiation unit and bony structures in the transmission images. For this purpose, bony features are either automatically detected or manually delineated in the transmission images. The performance of the method was quantified by aligning randomly displaced CT data with transmission images simulated from digitally reconstructed radiographs. In addition, the clinical performance were assessed in a limited number of images of prostate cancer and parotid gland tumor treatments. The complete procedure takes less than 2 min on a 90-MHz Pentium PC. The alignment time is 50 s for portal images and 80 s for simulator images. The accuracy is about 1 mm and 1 degrees. Application to clinical cases demonstrated that the procedure provides essential information for the correction of setup errors in case of large rotations (typically larger than 2 degrees) in the setup. The 3D procedure was found to be robust for imperfections in the delineation of bony structures in the transmission images. Visual verification of the results remains, however, necessary. It can be concluded that our strategy for automatic analysis of patient setup in 3D is accurate and robust. The procedure is relatively fast and reduces the human workload compared with existing techniques for the quantification of patient setup in 3D. In addition, the procedure improves the accuracy of treatment verification in 2D in some cases where rotational deviations in the setup occur.

Biophysical Phenomena↗
PubMed1995

Optimization of automatic portal image analysis.

The purpose of this study is to quantify and optimize the performance of an automatic portal image analysis procedure under clinical conditions and to compare the performance with that of human operators. A new method, based on analysis of variance, is introduced to quantify the clinical performance of portal image analysis tools in terms of systematic and random variations. The automatic portal image analysis procedure is based on chamfer matching. Two image enhancement techniques have been investigated in the automatic procedure: morphological top-hat (MTH) transformation and multiscale medial axis (MMA) transformation. The performance of these enhancements was quantified and optimized as a function of filter size using images obtained from clinical treatment. All images used for this study were obtained from pelvic treatment fields by means of an electronic portal imaging device. The random variations in the alignment of AP fields are typically 0.5 mm and 0.5 degrees (1 SD) for both the human operators and the optimized automatic analysis procedure. Random variations in the alignment of lateral pelvic fields are typically twice as large for all operators. MMA enhancement yields smaller random variations than MTH enhancement for lateral fields, but the differences are marginal for AP fields. The optimized automatic analysis procedure has a success rate ranging from 99% for AP large fields to 96% for lateral fields and 85% for AP boost fields. The accuracy of the method is comparable with the accuracy of the human operators for most investigated fields. For lateral boost fields and simultaneous boost fields, the random variations of the automatic analysis are typically two times larger than the variations of the human operators. Automatic analysis is 4 to 20 times faster than human operators yielding a large reduction in work load.

Analysis of Variance↗
PubMed1995

Electronic portal imaging.

In our institute, we have developed an electronic portal imaging system based on a matrix of 256 x 256 ionisation chambers. By improvements to the electronics, the system produces images with the same quality as the original system but 3-10 times faster. Software for automatic image analysis has been applied to more than 10,000 images over the last two years. Using an off-line correction strategy, the systematic patient set-up error has been limited to 5 mm or less for 98% of the patients treated for prostate cancer.

Equipment Design↗
PubMed1993

A comprehensive system for the analysis of portal images.

In recent years, several techniques for the processing and analysis of portal images have been developed. It is the aim of this study to integrate some of these techniques into one comprehensive system. An advantage of this approach is that clinical experience can be obtained with more than one technique and a comparison of the techniques becomes possible. The portal image analysis procedure is implemented in the following steps: preparation of the reference image, portal image field edge detection, field edge match, anatomy match and the presentation of the results. For most of these steps, several alternative methods (e.g., interactive and automatic) are implemented. In addition, two new visualisation techniques have been incorporated. The first is a method for combining the results of the analysis of multiple fields in two dimensions, e.g., large and boost fields. The second is a method for three-dimensional reconstruction of beam setup data, as derived from portal image analysis, on arbitrary reconstructed slices of a CT scan. With the latter method, the effect of setup errors on complex treatments (e.g., matching fields) can be studied. The new system has been in clinical use in our institution for two years and has been used to analyse about 5000 clinical portal images. The operators could choose freely from several matching methods. For 83% of the images our automatic matching algorithm was used. When required, the result of this method was corrected using the interactive drawing on image match. Significant corrections (more than 1 mm translation or 1 degree rotation) were applied to 27% of the automatically analysed images.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1993

An algorithm for automatic analysis of portal images: clinical evaluation for prostate treatments.

The aim of this study is to assess the clinical value of an algorithm for automatic analysis of portal images by measuring the method's performance in a clinical study of treatment of prostate cancer. The algorithm is based on chamfer matching and measures displacements of patients relative to prescribed radiation beam positions. In this paper we propose a method to quantify the mean standard deviation (MSD) of the performance of automatic analysis relative to the MSD of the performance of trained radiographers using the clinical data set only, i.e. without using additional phantoms or simulations. The clinical data set in this study consists of 99 regional AP prostate images of 15 different patients. To assess the performance the automatic analysis in relation to that of the human observers, we studied the results of the unsupervised automatic analysis, as well as the results of a less-trained human observer and a well-trained human observer assisted by the automatic analysis (in this combination, automatic analysis is done first and the result is modified by the well-trained observer if the observer does not agree). First, the intra-observer variations of the well-trained observer are measured by repetitive analysis of a small subset of the clinical data. The distribution of differences in analysis between two arbitrary observers is described by the chi 2 distribution, and is tabulated in literature. We define the agreement histogram of an observer O as an estimator for the chi 2 distribution between O and the well-trained human observer, parameterized by the ratio of the intra-observer variations of O and the well-trained observer.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMedDate not supplied

Automatic verification of radiation field shape using digital portal images.

Two computer methods for matching digital line drawings have been tested for automatic on-line verification of the radiation field shape during radiotherapy. This work is part of a research program aiming at automated inspection of on-line acquired digital portal images. Both methods, moment normalization and point distance minimization, compare the field edge detected in the portal image with the intended field edge and the beam shaping devices marked in the simulator image. Tests showed that the methods should be used together. First, shape deviations in the detected field edge are classified quickly, in less than a second (25 MHz 386 + 387 PC), as large (e.g., missing blocks) or small (e.g., shifts of a few mm) by moment normalization. Then the portal image is mapped to the simulator image by field edge alignment with a translation and magnification obtained from moment normalization and a rotation from point distance minimization. The mapped portal image and the simulator image juxtaposed on a monitor screen for visual inspection. Finally, the small field shape deviations are detected by an analysis of the relationship between the radiation field shape and the positioning of field shaping devices using point distance minimization.

Humans↗
PubMedDate not supplied

Automatic on-line inspection of patient setup in radiation therapy using digital portal images.

A new method is presented for inspection of patient setup in radiation therapy by automatic comparison of the patient position relative to the beam position in portal and simulator images. Quantification of patient-setup errors in terms of translation, rotation, and magnification is achieved by chamfer matching, a robust technique to match drawings and images, which is applied to both anatomy outlines and field edges. Applied to field edges, chamfer matching detects and visualizes deviations in field shape. Applied to anatomy outlines, the matching procedure quantifies and visualizes deviations in patient position relative to the radiation field. To test the method and to judge its feasibility, its behavior for four hundred different patient-setup deviations, which were simulated in four clinical images, was examined. These images show a top view of the pelvic region. The performance was measured in terms of accuracy and success rate for numerous cost functions and distance codings associated with the chamfer matching procedure. An average accuracy of 1.8 mm was found, a success rate of 90%, and an average overall computation time of 3 s on a 486 microcomputer. The whole analysis procedure is fast enough to allow on-line application.

Humans↗