A practical method for the reuse of nanoDot OSLDs and predicting sensitivities up to at least 7000 cGy.

Purpose: Optically stimulated luminescence dosimeter (OSLDs) are often used to make in vivo dose measurements. Most users calibrate the OSLDs using the software provided by the vendor which typically is intended for doses up to about 300 cGy with an uncertainty of ±5.5%.

An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images.

Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this problem, this paper proposes a computer-aided parameter decision system based on image texture features associated with neural networks.

Sensitivity study of an automated system for daily patient QA using EPID exit dose images.

The dosimetric consequences of errors in patient setup or beam delivery and anatomical changes are not readily known. A new product, PerFRACTION (Sun Nuclear Corporation), is designed to identify these errors by comparing the exit dose image measured on an electronic portal imaging device (EPID) from each field of each fraction to those from baseline fraction images. This work investigates the sensitivity of PerFRACTION to detect the deviation caused by these errors in a variety of realistic scenarios.

Validation of OSLD and a treatment planning system for surface dose determination in IMRT treatments.

Purpose: To evaluate the accuracy of skin dose determination for composite multibeam 3D conformal radiation therapy (3DCRT) and intensity modulated radiation therapy (IMRT) treatments using optically stimulated luminescent dosimeters (OSLDs) and Eclipse treatment planning system.

Methods: Surface doses measured by OSLDs in the buildup region for open field 6 MV beams, either perpendicular or oblique to the surface, were evaluated by comparing against dose measured by Markus Parallel Plate (PP) chamber, surface diodes, and calculated by Monte Carlo simulations. The accuracy of percent depth dose (PDD) calculation in the buildup region from the authors' Eclipse system (Version 10), which was precisely commissioned in the buildup region and was used with 1 mm calculation grid, was also evaluated by comparing to PP chamber measurements and Monte Carlo simulations.

Dosimetric study and verification of total body irradiation using helical tomotherapy and its comparison to extended SSD technique.

The American College of Radiology practice guideline for total body irradiation (TBI) requires a back-up treatment delivery system. This study investigates the development of helical tomotherapy (HT) for delivering TBI and compares it with conventional extended source-to-surface distance (X-SSD) technique. Four patients' head-to-thigh computed tomographic images were used in this study, with the target defined as the body volume without the left and right lungs.

Statistical validation of a new helical tomotherapy patient transfer station.

The purpose of this work is to evaluate statistically the accuracy of a patient transfer station (PTS, TomoTherapy Inc., Madison, WI) that automatically converts one planning-station-generated treatment plan to another one with a different beam model. In our department we have installed 2 HI*ART tomotherapy systems, and patients often need to be transferred from one tomotherapy unit to the other.

Performance measure characterization for evaluating neuroimage segmentation algorithms.

Characterizing the performance of segmentation algorithms in brain images has been a persistent challenge due to the complexity of neuroanatomical structures, the quality of imagery and the requirement of accurate segmentation. There has been much interest in using the Jaccard and Dice similarity coefficients associated with Sensitivity and Specificity for evaluating the performance of segmentation algorithms. This paper addresses the essential characteristics of the fundamental performance measure coefficients adopted in evaluation frameworks.

Skull-stripping magnetic resonance brain images using a model-based level set.

The segmentation of brain tissue from nonbrain tissue in magnetic resonance (MR) images, commonly referred to as skull stripping, is an important image processing step in many neuroimage studies. A new mathematical algorithm, a model-based level set (MLS), was developed for controlling the evolution of the zero level curve that is implicitly embedded in the level set function. The evolution of the curve was controlled using two terms in the level set equation, whose values represented the forces that determined the speed of the evolving curve.