Article

Simulation study on potential accuracy gains from dual energy CT tissue segmentation for low-energy brachytherapy Monte Carlo dose calculations.

Department of Radiation Oncology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht 6201 BN, The Netherlands.
Physics in Medicine and Biology (Impact Factor: 2.92). 09/2011; 56(19):6257-78. DOI: 10.1088/0031-9155/56/19/007
Source: PubMed

ABSTRACT This work compares Monte Carlo (MC) dose calculations for (125)I and (103)Pd low-dose rate (LDR) brachytherapy sources performed in virtual phantoms containing a series of human soft tissues of interest for brachytherapy. The geometries are segmented (tissue type and density assignment) based on simulated single energy computed tomography (SECT) and dual energy (DECT) images, as well as the all-water TG-43 approach. Accuracy is evaluated by comparison to a reference MC dose calculation performed in the same phantoms, where each voxel's material properties are assigned with exactly known values. The objective is to assess potential dose calculation accuracy gains from DECT. A CT imaging simulation package, ImaSim, is used to generate CT images of calibration and dose calculation phantoms at 80, 120, and 140 kVp. From the high and low energy images electron density ρ(e) and atomic number Z are obtained using a DECT algorithm. Following a correction derived from scans of the calibration phantom, accuracy on Z and ρ(e) of ±1% is obtained for all soft tissues with atomic number Z ∊ [6,8] except lung. GEANT4 MC dose calculations based on DECT segmentation agreed with the reference within ±4% for (103)Pd, the most sensitive source to tissue misassignments. SECT segmentation with three tissue bins as well as the TG-43 approach showed inferior accuracy with errors of up to 20%. Using seven tissue bins in our SECT segmentation brought errors within ±10% for (103)Pd. In general (125)I dose calculations showed higher accuracy than (103)Pd. Simulated image noise was found to decrease DECT accuracy by 3-4%. Our findings suggest that DECT-based segmentation yields improved accuracy when compared to SECT segmentation with seven tissue bins in LDR brachytherapy dose calculation for the specific case of our non-anthropomorphic phantom. The validity of our conclusions for clinical geometry as well as the importance of image noise in the tissue segmentation procedure deserves further experimental investigation.

0 Bookmarks
 · 
106 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Dual-energy computed tomography (DECT) images can undergo a two-material decomposition process which results in two images containing material density information. Material density images obtained by that process result in images with increased pixel noise. Noise reduction in those images is desirable in order to improve image quality. A noise reduction algorithm for material density images was developed and tested. A three-level wavelet approach combined with the application of an anisotropic diffusion filter was used. During each level, the resulting noise maps are further processed, until the original resolution is reached and the final noise maps obtained. Our method works in image space and, therefore, can be applied to any type of material density images obtained from any DECT vendor. A quantitative evaluation of the noise-reduced images using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and 2D noise power spectrum was done to quantify the improvements. The noise reduction algorithm was applied to a set of images resulting in images with higher SNR and CNR than the raw density images obtained by the decomposition process. The average improvement in terms of SNR gain was about 49 % while CNR gain was about 52 %. The difference between the raw and filtered regions of interest mean values was far from reaching statistical significance (minimum [Formula: see text], average [Formula: see text]). We have demonstrated through a series of quantitative analyses that our novel noise reduction algorithm improves the image quality of DECT material density images.
    International Journal of Computer Assisted Radiology and Surgery 05/2014; · 1.36 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Seit kurzem stehen Präzisions-Bestrahlungsgeräte mit einer integrierten, hoch auflösenden Röntgen-CT-Bildgebung für präklinische Studien zur Verfügung. Diese Forschungsplattformen bieten erhebliche Vorteile gegenüber Tier-Bestrahlungsgeräten der älteren Generationen hinsichtlich der Genauigkeit der bildgeführten, gezielten Strahlentherapie. Diese Plattformen werden wahrscheinlich eine entscheidende Rolle bei der Entwicklung von Experimenten spielen, welche die Übertragung von Forschungsergebnissen in klinische Situationen zum Ziel haben. Innerhalb des Fachgebietes Strahlentherapie, aber auch in anderen Bereichen wie zum Beispiel der Neurologie, bieten diese Geräte einzigartige Möglichkeiten, unter anderen Substanzen die Synergie zwischen Bestrahlung und Medikamenten oder anderen Agentien zu erforschen. Um die Vorteile dieser neuen Technologie voll aus-schöpfen zu können, sind genaue Methoden notwendig, um die Bestrahlung planen und die dreidimensionale Dosisverteilung im Organismus berechnen zu können. Spezielle, hierfür entworfene Bestrahlungsplanungssysteme sind hierbei essentiell. In dieser Übersichtsarbeit erörtern wir die spezielle Situation der Präzisionsbestrahlung von Kleintieren, wir beschreiben die Arbeitsweise der Bestrahlungsplanung bei Tieren, und wir untersuchen verschiedene Algorithmen zur Dosisberechnung (Ray Tracing, Superposition-Konvolution, Monte-Carlo-Simulation), die für die Tierbestrahlung mittels Kilovolt-Photonen verwendet werden. Des Weiteren werden Punke, wie zum Beispiel Methoden der Dosismeldung, Photonenstreuung, Gewebesegmentation und Bewegung kurz angerissen.
    Zeitschrift für Medizinische Physik 12/2014; · 1.81 Impact Factor
  • Source
    Frontiers in Physics 01/2013; 1.

Full-text

Download
7 Downloads
Available from
Nov 25, 2014