Quality Assurance and Clinical Integration of a Prototype for Intelligent 4DCT Sequence Scanning

Type: MA thesis

Status: finished

Date: August 1, 2020 - February 1, 2021

Supervisors: Andreas Maier

With 1.8 million deaths worldwide in 2018 (353.000 deaths in Europe in 2012 [1]) lung cancer is the most deadly cancer disease [2]. The prognosis for lung cancer are quite poor, only 15% of the men (21% of the women) survive 5 years [3] .
75% of these patients receive radiation therapy [4]. Nevertheless, it is challenged by breathing-related movements which lead to artifacts possibly causing both incorrect diagnosis and dosimetric errors of the therapy itself. As a result, the target volume might not be covered by the scheduled amount of radiation.
Computed tomography (CT) is an essential part of the treatment planning process. While 3D CT images can correctly display static anatomy, 4D imaging additionally records the breathing cycles and synchronizes it retrospectively with the acquired images. Thus the results of a 4D CT scan are time-resolved data of a 3D volume.
4D CT imaging with fixed beam on/ off slots and irregular breathing can lead to missing data coverage in desired breathing states, known as a violation of the data sufficiency condition (DSC). [5] The caused artifacts are expressed in the image as a strong blurring of anatomical structures and requires in the worst case a second treatment planning CT and as a consequence a delay of patient treatment as well as additional dose.
The idea of the intelligent 4D CT (i4DCT)-algorithm is to improve data coverage to reduce these artifacts. During the initial learning period the patient-specific respiratory cycle is analyzed. For every slice the scanner generates data for a whole respiratory cycle. Based on an online comparison of reference and current breathing curves during data acquisition, the selection of beam on/off periods is adjusted. If the data sufficiency condition is fulfilled the scan is stopped and the table moves to the next z-position. This process is repeated until the targeted scan area is covered. [5]
To ensure the effective, safe and reliable use of i4DCT-algorithm in everyday clinical practice quality assurance must be given.
The aim of this Master’s thesis is to develop and perform quality tests. Subsequently results are evaluated and interpreted to draw conclusions for clinical application.
Phantom measurements are performed with the CIRS Motion Thorax Phantom (CIRS, Norfolk, USA). This is a lung-equivalent solid epoxy rod containing a soft tissue target (symbolizing the tumor). In order to get close to realistic circumstances, the target can be moved by CIRS Motion Software according to an artificially created, irregular breathing pattern in three dimensions. The breathing curve is tracked by the Varian ‘respiratory gating for scanners’ system (RGSC, Varian Medical Systems, Inc. Palo Alto, CA). It consists of two main parts. All measurements are performed on SOMATOM go Open Pro CT scanner (Siemens Healthcare, Forchheim, Germany).
The tests include different reconstruction methods (Maximum Intensity Projection and amplitude/ phase based reconstruction), investigating the dimensions of the artificial tumor in every body axis, verifying the match of recorded breathing pattern in RGSC and CT as well as testing the limits of RGSC/ i4DCT algorithm.



[1] J. Ferlay, E. Steliarova-Foucher, J. Lortet-Tieulent, S. Rosso, J. W. W. Coebergh, H. Comber, D. Forman und F. I. Bray, „Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012,“ European Journal of Cancer, Bd. 49, Nr. 6, pp. 1374-1403, 2013.
[2] World Health Organisation (WHO), „Cancer,“ 2018. [Online]. Available:
https://www.who.int/news-room/fact-sheets/detail/cancer. [Zugriff am 10 09 2020].
[3] Zentrum für Krebsregisterdaten, „Lungenkrebs (Bronchialkarzinom),“ 17 12 2019. [Online]. Available:
https://www.krebsdaten.de/Krebs/DE/Content/Krebsarten/Lungenkrebs/lungenkrebs_node.html. [Zugriff am 10 09 2020].
[4] R. Werner, „Strahlentherapie atmungsbewegter Tumoren: Bewegungsfeldschätzung und Dosisakkumulation anhand von 4D-Bilddaten,“ Springer Vieweg, 2013, p. 1.
[5] R. Werner, T. Sentker, F. Madesta, T. Gauer und C. Hofman, „Intelligent 4D CT sequence scanning (i4DCT): Concept and performance,“ Medical Physics, Nr. 46, pp. 3462-3474, 22 May 2019.