Biomedical subjects

Lennert S Ploeger

Publications and source records attributed to Lennert S Ploeger.

5 recordsLinked to original sources

PubMed2003

Feasibility of geometrical verification of patient set-up using body contours and computed tomography data.

BACKGROUND AND PURPOSE: Body contours can potentially be used for patient set-up verification in external-beam radiotherapy and might enable more accurate set-up of patients prior to irradiation. The aim of this study is to test the feasibility of patient set-up verification using a body contour scanner. MATERIAL AND METHODS: Body contour scans of 33 lung cancer and 21 head-and-neck cancer patients were acquired on a simulator. We assume that this dataset is representative for the patient set-up on an accelerator. Shortly before acquisition of the body contour scan, a pair of orthogonal simulator images was taken as a reference. Both the body contour scan and the simulator images were matched in 3D to the planning computed tomography scan. Movement of skin with respect to bone was quantified based on an analysis of variance method. RESULTS: Set-up errors determined with body-contours agreed reasonably well with those determined with simulator images. For the lung cancer patients, the average set-up errors (mm)+/-1 standard deviation (SD) for the left-right, cranio-caudal and anterior-posterior directions were 1.2+/-2.9, -0.8+/-5.0 and -2.3+/-3.1 using body contours, compared to -0.8+/-3.2, -1.0+/-4.1 and -1.2+/-2.4 using simulator images. For the head-and-neck cancer patients, the set-up errors were 0.5+/-1.8, 0.5+/-2.7 and -2.2+/-1.8 using body contours compared to -0.4+/-1.2, 0.1+/-2.1, -0.1+/-1.8 using simulator images. The SD of the set-up errors obtained from analysis of the body contours were not significantly different from those obtained from analysis of the simulator images. Movement of the skin with respect to bone (1 SD) was estimated at 2.3 mm for lung cancer patients and 1.7 mm for head-and-neck cancer patients. CONCLUSION: Measurement of patient set-up using a body-contouring device is possible. The accuracy, however, is limited by the movement of the skin with respect to the bone. In situations where the error in the patient set-up is relatively large, it is possible to reduce these errors using a computer-aided set-up technique based on contour information.

Computer Simulation↗

PubMed2003

Application of video imaging for improvement of patient set-up.

BACKGROUND AND PURPOSE: For radiotherapy of prostate cancer, the patient is usually positioned in the left-right (LR) direction by aligning a single marker on the skin with the projection of a room laser. The aim of this study is to investigate the feasibility of a room-mounted video camera in combination with previously acquired CT data to improve patient set-up along the LR axis. MATERIAL AND METHODS: The camera was mounted in the treatment room at the caudal side of the patient. For 22 patients with prostate cancer 127 video and portal images were acquired. The set-up error determined by video imaging was found by matching video images with rendered CT images using various techniques. This set-up error was retrospectively compared with the set-up error derived from portal images. It was investigated whether the number of corrections based on portal imaging would decrease if the information obtained from the video images had been used prior to irradiation. Movement of the skin with respect to bone was quantified using an analysis of variance method. RESULTS: The measurement of the set-up error was most accurate for a technique where outlines and groins on the left and right side of the patient were delineated and aligned individually to the corresponding features extracted from the rendered CT image. The standard deviations (SD) of the systematic and random components of the set-up errors derived from the portal images in the LR direction were 1.5 and 2.1 mm, respectively. When the set-up of the patients was retrospectively adjusted based on the video images, the SD of the systematic and random errors decreased to 1.1 and 1.3 mm, respectively. From retrospective analysis, a reduction of the number of set-up corrections (from nine to six corrections) is expected when the set-up would have been adjusted using the video images. The SD of the magnitude of motion of the skin of the patient with respect to the bony anatomy was estimated to be 1.1 mm. CONCLUSION: Video imaging is an accurate technique for measuring the set-up of prostate cancer patients in the LR direction. The outline of the patient is a more accurate estimate of the set-up of the bony anatomy than the marker on the patient's abdomen.

Analysis of Variance↗

PubMed2003

Evaluation of cost functions for gray value matching of two-dimensional images in radiotherapy.

In external beam radiotherapy, portal imaging is applied for verification of the patient setup. Current automatic methods for portal image registration, which are often based on segmentation of anatomical structures, are especially successful for images of the pelvic region. For portal images of more complicated anatomical structures, e.g., lung, these techniques are less successful. It is desirable to have a method for image registration that is applicable for a wide range of treatment sites. In this study, a registration method for two-dimensional (2D) registration of portal and reference images based on intensity values was tested on portal images of various anatomical sites. Tests were performed with and without preprocessing (unsharp mask filtering followed by histogram equalization) for 96 image pairs and six cost functions. The images were obtained from treatments of the rectum, salivary gland, brain, prostate, and lung. To get insight into the behavior of the various cost functions, cost function values were computed for each portal image for 20,000 transformations of the corresponding reference image, translating the reference image in a range of +/- 1 cm and rotating +/- 10 degrees with respect to the clinical match. The automatic match was defined as the transformation associated with the global minimum (found by an exhaustive search). Without preprocessing, the registration reliability was low (less than 27%). With preprocessing, about 90% of the matches were successful, with a difference with our gold standard (manual registration) of about 1 mm and 1 degree SD. All tested cost functions performed similarly. However, the number of local minima using mutual information was larger than for the other tested cost functions. A cost function based on the mean product of the corresponding pixel values had the least number of local minima. In conclusion, gray value based registration of portal images is applicable for a wide range of treatment sites. However, pre-processing of the images is essential.

Algorithms↗

PubMed2002

Confocal DNA cytometry: a contour-based segmentation algorithm for automated three-dimensional image segmentation.

BACKGROUND: Confocal laser scanning microscopy (CLSM) presents the opportunity to perform three-dimensional (3D) DNA content measurements on intact cells in thick histological sections. So far, these measurements have been performed manually, which is quite time-consuming. METHODS: In this study, an intuitive contour-based segmentation algorithm for automatic 3D CLSM image cytometry of nuclei in thick histological sections is presented. To evaluate the segmentation algorithm, we measured the DNA content and volume of human liver and breast cancer nuclei in 3D CLSM images. RESULTS: A high percentage of nuclei could be segmented fully automatically (e.g., human liver, 92%). Comparison with (time-consuming) interactive measurements on the same CLSM images showed that the results were well correlated (liver, r = 1.00; breast, r = 0.92). CONCLUSIONS: Automatic 3D CLSM image cytometry enables measurement of volume and DNA content of large numbers of nuclei in thick histological sections within an acceptable time. This makes large-scale studies feasible, whereby the advantages of CLSM can be exploited fully. The intuitive modular segmentation algorithm presented in this study detects and separates overlapping objects, also in two-dimensional (2D) space. Therefore, this algorithm may also be suitable for other applications.

Algorithms↗

PubMed2002

A method for geometrical verification of dynamic intensity modulated radiotherapy using a scanning electronic portal imaging device.

In order to guarantee the safe delivery of dynamic intensity modulated radiotherapy (IMRT), verification of the leaf trajectories during the treatment is necessary. Our aim in this study is to develop a method for on-line verification of leaf trajectories using an electronic portal imaging device with scanning read-out, independent of the multileaf collimator. Examples of such scanning imagers are electronic portal imaging devices (EPIDs) based on liquid-filled ionization chambers and those based on amorphous silicon. Portal images were acquired continuously with a liquid-filled ionization chamber EPID during the delivery, together with the signal of treatment progress that is generated by the accelerator. For each portal image, the prescribed leaf and diaphragm positions were computed from the dynamic prescription and the progress information. Motion distortion effects of the leaves are corrected based on the treatment progress that is recorded for each image row. The aperture formed by the prescribed leaves and diaphragms is used as the reference field edge, while the actual field edge is found using a maximum-gradient edge detector. The errors in leaf and diaphragm position are found from the deviations between the reference field edge and the detected field edge. Earlier measurements of the dynamic EPID response show that the accuracy of the detected field edge is better than 1 mm. To ensure that the verification is independent of inaccuracies in the acquired progress signal, the signal was checked with diode measurements beforehand. The method was tested on three different dynamic prescriptions. Using the described method, we correctly reproduced the distorted field edges. Verifying a single portal image took 0.1 s on an 866 MHz personal computer. Two flaws in the control system of our experimental dynamic multileaf collimator were correctly revealed with our method. First, the errors in leaf position increase with leaf speed, indicating a delay of approximately 0.8 s in the control system. Second, the accuracy of the leaves and diaphragms depends on the direction of motion. In conclusion, the described verification method is suitable for detailed verification of leaf trajectories during dynamic IMRT.

Algorithms↗