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DOI: 10.1055/s-0031-1280785
Überblick über verschiedene Bildfusionstechniken und Empfehlungen für deren Verwendung bei der Integration von PET-Bilddaten in die Bestrahlungsplanung
Use and Recommendations of Image Fusion Methods for the Integration of PET-Image Data into Radiotherapy PlanningPublication History
Publication Date:
03 August 2011 (online)
Zusammenfassung
Die Verwendung von PET-Daten im radioonkologischen Planungsprozess war in den letzten Jahren Fokus intensiver Forschung. Hierbei spielte das Aufkommen multimodaler Bildgebungstechniken wie v. a. PET/CT eine wesentliche Rolle für das wachsende Interesse an den Ergebnissen dieser Forschungsrichtung. Es existieren jedoch noch eine Reihe offener Fragestellungen, wobei sich dieser Artikel im wesentlichen mit dem Problem der Bildfusion von PET-Daten mit dem Planungs-CT beschäftigt. Dies umfasst insbesondere die Herausforderungen, die sich aus der Atembewegung ergeben, sowie spezifische Probleme von Bildgebungsprotokollen, u. a. der klinisch weit verbreiteten Bildgebungspraxis, bei der sich die Patientenlagerung (Tisch, Masken/Lagerungshilfen) im PET/CT und Planungs-CT unterscheidet. In atemgetriggerten PET/CT-Aufnahmen können einerseits durch Atembewegung bedingte Artefakte minimiert werden, und andererseits die räumliche Fusion von PET und CT Daten durch Verwendung integrierter, multimodaler Bildgebungssysteme verbessert werden. Deformierbare („elastische“) Bildregistrierungsalgorithmen erlauben genaue Modellierung der Atembeweglichkeit in „4D“ PET- und CT-Datensätzen. Außerdem kann durch Anwendung solcher Algorithmen das 3D-Deformationsvektorfeld zwischen dem CT eines multimodal akquirierten PET/CT-Datensatzes und dem Planungs-CT bestimmt werden, mittels dessen dann die Bildregistrierung zwischen PET und Planungs-CT realisiert werden kann. Deformierbare Bildregistrierung ist unerlässlich für Anwendungen im Thorax-, Abdomen- und Kopf-Hals-Bereich, während rigide Bildregistrierung im Kopf-Hirn Bereich mit hinreichender Genauigkeit eingesetzt werden kann. Obwohl deformierbare Bildregistrierungsalgorithmen im Bereich klinischer Forschung etabliert und verbreitet sind, ist dies in der klinischen Routine leider nicht der Fall, da die Implementierung derartiger Algorithmen in üblicherweise verwendeter Software zur multimodalen Bildanalyse bzw. Bestrahlungsplanung noch aussteht. Weitere klinische Validierung sowie Algorithmen mit kurzen Rechenzeiten sind notwendig, um einem breiten Einsatz deformierbarer Bildregistrierung in der klinischen Routine von PET/CT Bildgebung und deren Anwendungen in der Bestrahlungsplanung zum Durchbruch zu verhelfen.
Abstract
The introduction of PET data into the treatment planning process has been the focus of intense development over the past few years. The availability of multi-modality devices such as PET/CT has clearly played a major role in intensifying the interest in this application. However, many issues remain and this article deals in particular with those concerning the accurate spatial fusion of PET and planning CT images. Associated issues include the effects of respiratory motion and the acquisition protocol used, particularly if the PET/CT acquisition does not correspond to a truly treatment planning acquisition, which is most frequently the case in clinical practice today. Respiratory synchronized PET/CT acquisitions can on the one hand minimize respiratory motion effects in PET datasets while on the other hand improve the spatial fusion of PET and CT images acquired in a multi-modality device. Deformable („elastic“) registration algorithms have been shown to allow accurate respiratory motion modeling from „4D“ PET or CT images. In addition, spatial matching between PET and planning CT images can be achieved through the use of 3D displacement fields obtained using such image registration algorithms to align the CT image obtained from a multi-modality PET/CT acquisition to the planning CT image. Deformable models are clearly necessary for thoracic, abdominal as well as head and neck regions, while rigid body registration may be used in brain imaging applications. Unfortunately, although well established in clinical research studies deformable image registration algorithms are not widely implemented in multi-modality image analysis or treatment planning software platforms, therefore minimizing their use in routine clinical practice. More clinical validation as well as high throughput software solutions are necessary in order to envisage the use of deformable image registration algorithms in clinical PET/CT imaging and associated applications in radiotherapy treatment planning.
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