Visualization Of Dose Distribution Research Articles

Visualization of dose distribution and basic study of dose estimation using plastic scintillator and digital camera

Radiation can be visualized using a scintillator and a digital camera. If the amount of light emitted by the scintillator increases with dose, the dose estimation can be obtained from the amount of light emitted. In this study, the basic performance of the scintillator and digital camera system was evaluated by measuring computed tomography dose index (CTDI). A circular plastic scintillator plate was sandwiched between polymethyl methacrylate (PMMA) phantoms, and x-rays were irradiated to them while rotating the x-ray tube to confirm changes in light emission. In addition, CTDI was estimated from the amount of light emitted by the scintillator during the helical scan and compared with the value measured from dosimeter. The scintillator emitted light while changing its distribution according to the movement of the x-ray tube. The measured CTDIvol was 33.20 mGy, the CTDIvol estimated from the scintillation light was approximately 46 mGy, which was 40% larger. In particular, when the scintillator was directly irradiated, the dose was overestimated compared with the value measured from the dosimeter. This overestimation can be because of the reproducibility of the position and the difference between the sensitivity of the scintillator to detect light emission and the sensitivity of the dosimeter, and the non-uniformity of position sensitivity due to the wide-angle lens.

Immersive Radiation Experience for Interventional Radiology with Virtual Reality Radiation Dose Visualization Using Fast Monte Carlo Dose Estimation.

For interventional radiology (IR), understanding the precise dose distribution is crucial to reduce the risks of radiation dermatitis to patients and staff. Visualization of dose distribution is expected to support radiation safety efforts immensely. This report presents techniques for perceiving the dose distribution using virtual reality (VR) technology and for estimating the air dose distribution accurately using Monte Carlo simulation for VR dose visualization. We adopted an earlier reported Monte-Carlo-based estimation system for IR and simulated the dose in a geometrical area resembling an IR room with fluoroscopic conditions. Users of our VR system experienced a simulated air dose distribution in the IR room while the irradiation angle, irradiation timing, and lead shielding were controlled. The estimated air dose was evaluated through comparison with measurements taken using a radiophotoluminescence glass dosimeter. Our dose estimation results were consistent with dosimeter readings, showing a 13.5% average mutual difference. The estimated air dose was visualized in VR: users could view a virtual IR room and walk around in it. Using our VR system, users experienced dose distribution changes dynamically with C-arm rotation. Qualitative tests were conducted to evaluate the workload and usability of our VR system. The perceived overall workload score (18.00) was lower than the scores reported in the literature for medical tasks (50.60) and computer activities (54.00). This VR visualization is expected to open new horizons for understanding dose distributions intuitively, thereby aiding the avoidance of radiation injury.

Cancer therapy — Computation and visualization of dose distribution in external beam therapy

The development of general field equations that calculate percentage depth doses resulting from external sources of ionizing radiation have made possible the computation of many points in the treatment volume and the visualization of the dose distribution through on-line printing or graphing devices. This, in turn, has led therapists to study the best conditions for treatment and seek improved methods by which the shape and pattern of the treated body volume may be manipulated. The results have been felt already in a decrease in undesirable treatment side-effects and there is presumptive proof that optimization of planning will result in increased survival.

Automation of radiation treatment planning. II. Calculation of non-convergent field dose distributions.

Automation of Radiation Treatment Planning. II. Calculation of Non-convergent Field Dose DistributionsTheodor D. Sterling, Harold Perry and James J. WeinkamTheodor D. SterlingMedical Computing Center and the Department of Radiology of the College of Medicine, University of CincinnatiSearch for more papers by this author, Harold PerryMedical Computing Center and the Department of Radiology of the College of Medicine, University of CincinnatiSearch for more papers by this author and James J. WeinkamMedical Computing Center and the Department of Radiology of the College of Medicine, University of CincinnatiSearch for more papers by this authorPublished Online:28 Jan 2014https://doi.org/10.1259/0007-1285-36-421-63SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail AboutAbstractA simple method for the automated computation of dose distributions for non-convergent fixed-field radiation therapy is described. Necessary principles of computation and programming are given. Previous article Next article FiguresReferencesRelatedDetailsCited byThe objective evaluation of alternative treatment plans: I. images of regretInternational Journal of Radiation Oncology*Biology*Physics, Vol. 15, No. 3Computational methods in beam therapy planningComputer Programs in Biomedicine, Vol. 2, No. 3Treatment planningTreatment planningThe Study of the Distribution of Dose in Radiotherapy with the Aid of Electronic ComputersAustralasian Radiology, Vol. 10, No. 2Cancer therapy — Computation and visualization of dose distribution in external beam therapyJournal of Chronic Diseases, Vol. 19, No. 4The Organization of Clinical Dosimetry. II Some special topics in treatment planning8 July 2009 | Acta Radiologica: Therapy, Physics, Biology, Vol. 4, No. 4PLANNING RADIATION TREATMENT ON THE COMPUTERS*†1 July 1964 | Annals of the New York Academy of Sciences, Vol. 115, No. 2PLANNING RADIATION TREATMENT ON THE COMPUTER*,†16 December 2006 | Annals of the New York Academy of Sciences, Vol. 115, No. 2PATIENT SIMULATION IN X-RAY THERAPY*16 December 2006 | Annals of the New York Academy of Sciences, Vol. 115, No. 2AUTOMATION OF A HOSPITAL RADIOLOGY DEPARTMENTAutomation of radiation treatment planning. III. A simplified system of digitising isodoses and direct print-out of dose distributionTheodor D. Sterling, Harold Perry and James J. Weinkam29 May 2014 | The British Journal of Radiology, Vol. 36, No. 427Reviews La Télécobalthérapie en Cancérologie. By PourquierH., pp. 240, 131 illus., 1962 (Paris, Masson et Cie), 60 NF.G. Picciotto29 May 2014 | The British Journal of Radiology, Vol. 36, No. 427Reviews Reports on Progress in Physics. Vol. XXV, pp. 529, 1962 (London, The Institute of Physics and The Physical Society).S. J. Wyard29 May 2014 | The British Journal of Radiology, Vol. 36, No. 427 Volume 36, Issue 421January 1963Pages: 1-76 © The British Institute of Radiology History ReceivedJune 01,1962Published onlineJanuary 28,2014 Metrics Download PDF