A quantitative comparison of the performance of three deformable registration algorithms in radiotherapy

AbstractWe present an evaluation of various non-rigid registration algorithms for the purpose of compensating interfractional motion of the target volume and organs at risk areas when acquiring CBCT image data prior to irradiation. Three different deformable registration (DR) methods were used: the Demons algorithm implemented in the iPlan Software (BrainLAB AG, Feldkirchen, Germany) and two custom-developed piecewise methods using either a Normalized Correlation or a Mutual Information metric (featureletNC and featureletMI). These methods were tested on data acquired using a novel purpose-built phantom for deformable registration and clinical CT/CBCT data of prostate and lung cancer patients. The Dice similarity coefficient (DSC) between manually drawn contours and the contours generated by a derived deformation field of the structures in question was compared to the result obtained with rigid registration (RR). For the phantom, the piecewise methods were slightly superior, the featureletNC for the intramodality and the featureletMI for the intermodality registrations. For the prostate cases in less than 50% of the images studied the DSC was improved over RR. Deformable registration methods improved the outcome over a rigid registration for lung cases and in the phantom study, but not in a significant way for the prostate study. A significantly superior deformation method could not be identified

A quantitative comparison of the performance of three deformable registration algorithms in radiotherapy

AbstractWe present an evaluation of various non-rigid registration algorithms for the purpose of compensating interfractional motion of the target volume and organs at risk areas when acquiring CBCT image data prior to irradiation. Three different deformable registration (DR) methods were used: the Demons algorithm implemented in the iPlan Software (BrainLAB AG, Feldkirchen, Germany) and two custom-developed piecewise methods using either a Normalized Correlation or a Mutual Information metric (featureletNC and featureletMI). These methods were tested on data acquired using a novel purpose-built phantom for deformable registration and clinical CT/CBCT data of prostate and lung cancer patients. The Dice similarity coefficient (DSC) between manually drawn contours and the contours generated by a derived deformation field of the structures in question was compared to the result obtained with rigid registration (RR). For the phantom, the piecewise methods were slightly superior, the featureletNC for the intramodality and the featureletMI for the intermodality registrations. For the prostate cases in less than 50% of the images studied the DSC was improved over RR. Deformable registration methods improved the outcome over a rigid registration for lung cases and in the phantom study, but not in a significant way for the prostate study. A significantly superior deformation method could not be identified

Optimization of extracranial stereotactic radiation therapy of small lung lesions using accurate dose calculation algorithms

BACKGROUND: The aim of this study was to compare and to validate different dose calculation algorithms for the use in radiation therapy of small lung lesions and to optimize the treatment planning using accurate dose calculation algorithms. METHODS: A 9-field conformal treatment plan was generated on an inhomogeneous phantom with lung mimics and a soft tissue equivalent insert, mimicking a lung tumor. The dose distribution was calculated with the Pencil Beam and Collapsed Cone algorithms implemented in Masterplan (Nucletron) and the Monte Carlo system XVMC and validated using Gafchromic EBT films. Differences in dose distribution were evaluated. The plans were then optimized by adding segments to the outer shell of the target in order to increase the dose near the interface to the lung. RESULTS: The Pencil Beam algorithm overestimated the dose by up to 15% compared to the measurements. Collapsed Cone and Monte Carlo predicted the dose more accurately with a maximum difference of -8% and -3% respectively compared to the film. Plan optimization by adding small segments to the peripheral parts of the target, creating a 2-step fluence modulation, allowed to increase target coverage and homogeneity as compared to the uncorrected 9 field plan. CONCLUSION: The use of forward 2-step fluence modulation in radiotherapy of small lung lesions allows the improvement of tumor coverage and dose homogeneity as compared to non-modulated treatment plans and may thus help to increase the local tumor control probability. While the Collapsed Cone algorithm is closer to measurements than the Pencil Beam algorithm, both algorithms are limited at tissue/lung interfaces, leaving Monte-Carlo the most accurate algorithm for dose prediction

Non-rigid registration for radiotherapy applications

Inhalt der Arbeit ist die Erstellung eines Verformungsmodells aus den während einer strahlentherapeutischen Behandlung aufgenommenen Bilddatenstzen; hierfür ist es erforderlich, mit einem Minimum an manueller Interaktion und ohne weitgehendes Detailwissen ber Art und Lage der anatomischen Strukturen aus tomographischen Bilddatensätzen unterschiedlicher Qualität einen Algorithmus zur stückweisen nicht-rigiden Registrierung zu entwickeln. Der Algorithmus unterteilt die Bilder, um sie in Teilvolumina (sogenannten featurelets) zu registrieren und sie mittels eine räumlichen Transformation an die entsprechende Suchregion anzupassen. Das Modell wurde zunächst auf einem einfachen verformbaren Phantom mit Markern geprüft; das Ergebnis war als gut im Vergleich zu starrer Registrierung und verformbarer Registrierung (vermittels B-Splines) zu betrachten. Ein weiterer Vergleich wurde mit drei verschiedenen Methoden der deformierbaren Registrierung (DR) durchgeführt; zum Einsatz kamen der Demons-Algorithmus, der in der kommerziellen iPlan Software (BrainLAB AG, Feldkirchen, Deutschland) implementriert ist, und zwei Eigenentwicklungen. Hierbei handelte es sich um stückweise rigide Registrationsverfahren, die entweder auf einem normierten Korrelationskoeffizienten oder Mutual Information Metrik (featurelet_NC und featurelet_MI) basierten. Diese Methoden wurden mit Hilfe eines neuartigen Phantoms für DR und mit klinischen computertomographischen (CT) und Cone-Beam CT (CBCT) Daten von Prostata- und Lungenkrebspatienten validiert. Der Dice-Koeffizient (Dice Coefficient Similarity - DSC) zwischen manuell gezeichneten Konturen und den automatisch generierten Konturen aus einem abgeleiteten Deformationsfeld der Strukturen wurde mit dem Ergebnis einer starren Registrierung (rigid registration - RR) verglichen. Für das Phantom warendie stückweise Methoden leicht überlegen; im Detail zeigten featurelet_NC für die intramodale und featurelet_MI für die intermodale Registrierung die beste Performance. Für die Prostatafälle wurde in weniger als 50 % der untersuchten Bilder der DSC gegenber einer rein starren, globalen Registration verbessert. DR- methoden verbesserten das Ergebnis gegenüber eine starre Registrierung für die Lungenfälle und in der Phantomstudie, jedoch nicht in signifikanter Weise für die Prostatafälle. Eine deutlich überlegenes DR-Verfahren konnte nicht identifiziert werden.Obtaining a deformation model from the acquired images during treat- ment without much previous intervention or knowledge of the site and type of structures involved, able to deal with different qualities and types of images was the main motivation of creating a piecewise-non- rigid registration algorithm. The algorithm divide the images to be register in sub-volumes (fea- turelets) and rigidly register them to the corresponding search region. The model was first tested on a simple deformable phantom with fidu- cial markers, showing a good result in terms of the final distance of the markers obtain in comparison to rigid registration and B-spline deformable registration. Another comparison was done using three different deformable reg- istration (DR) methods: the Demons algorithm implemented in the iP lan Software (BrainLAB AG, Feldkirchen, Germany) and two custom- developed piecewise methods using either a Normalized Correlation or a Mutual Information metric (featurelet N C and featurelet M I ). These methods were tested on data acquired using a novel purpose-built phantom for deformable registration and clinical CT/CBCT data of prostate and lung cancer patients. The Dice similarity coefficient (DSC) between manually drawn contours and the contours gener- ated by a derived deformation field of the structures in question was compared to the result obtained with rigid registration (RR). For the phantom, the piecewise methods were slightly superior, the featurelet N C for the intramodality and the featurelet M I for the in- termodality registrations. For the prostate cases in less than 50% of the images studied the DSC was improved. Deformable registration methods improved the outcome over a rigid registration for lung cases and in the phantom study, but not in a significant way for the prostate study. A significantly superior deformation method could not be identified.Daniella FabriAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheWien, Med. Univ., Diss., 2013OeBB(VLID)171630