ESTRO 36 Abstract Book

S7 ESTRO 36 _______________________________________________________________________________________________

SP-0023 CT image quality: Using a model observer for clinically relevant optimisation N. Ryckx 1 , D. Racine 1 , A. Ba 1 , A. Viry 1 , F. Bochud 1 , F.R. Verdun 1 1 CHUV - Institute of Radiation Physics IRA, Department of Radiology, Lausanne, Switzerland Introduction The number of computed tomography (CT) examinations has been increasing steadily in the last twenty years, and this trend does not seem to slow down. For example, the number of diagnostic CT examinations has increased by 17% between 2008 and 2013 in Switzerland. Furthermore, the purpose of a CT has been extended to further diagnostic and/or therapeutic procedures: Attenuation correction for molecular imaging in nuclear medicine, interventional CT fluoroscopy procedures and radiation therapy treatment planning. Recently, the introduction of iterative reconstruction algorithms allows for a potential dose reduction by artificially removing image noise. However, the risk of reducing radiation dose too low is to potentially remove vital diagnostic information while conserving the subjective visual aspect (especially in terms of image noise) of images acquired at higher dose but without iterative reconstruction. Finally, the usual image quality metrics (CNR, MTF, NPS) are less pertinent within the iterative reconstruction paradigm, because these reconstructions are highly non-linear and non- stationary. Materials and methods We seek to adapt image quality using clinically relevant metrics. For this purpose, we use model observers, that are in-silico image observers based on psychophysics and statistical decision theory. The four cornerstones of a model observer are the following: -A clinically relevant task (lesion detection, lesion localisation, etc.) -An observer (human or in-silico) -An adequate set of images, with a representative statistical fluctuation -A figure of merit (FOM) to quantify the observer performance In our approach, we evaluate CT image quality using a CHO (channelized Hotelling observer) with dense difference of Gaussian (DDoG) channels for low-contrast spherical lesion detection in the abdominal region (e.g. focal liver lesions) or a NPWE (non-pre-whitening matched filter with eye filter) observer for high contrast lesion (e.g. renal stone) detection. The images used for quality assessment are CT axial slices performed on either an anatomical abdomen phantom (QRM, Moehrendorf, Germany) with low-contrast spherical targets of known size (5 and 8 mm diameter) and contrast (-10 or -20 HU) or a home-made high-contrast phantom, consisting of three cylinders (diam. 10 cm) of different materials – Teflon for bone, polyethylene for fat and PMMA for soft tissue – immersed in a cylindrical water tank. The FOM used for quality assessment is either the area under the ROC (receiver operating characteristic) curve (AUC) or a detectability index d’. These assessments were performed on 70 CT units in Switzerland. Results and discussion For abdominal low contrast targets, no significant differences in AUC were noted between images reconstructed by standard filtered back-projection (FBP) and iterative reconstruction, but the dose-slice thickness product (DSP, used as a normalised dose metric) varied from 2.6 to 61 mGy mm, with reconstructed slice thicknesses ranging from 2 to 5 mm. For the 5mm/20HU target, 49% of the CT units gathered around an AUC range of 0.86-0.98 for a DSP range of 5-20 mGy mm. Nevertheless, 10% of the CTs were outliers because of relatively high dose levels and limited AUC scores, for which a more thorough investigation is probably needed. For the high contrast detection task, the FOM used for this task (d’ of a NPWE model) yields results that are way too

concept). The method that currently receives most interest is the worst-case scenario, which aims to ensure that the target volume receives a minimum dose under a reasonable number of displacements and range uncertainties. Although this approach is greatly superior to the PTV concept for particle beams, it shares the similarity that it makes no assumptions about the frequencies with which uncertainties occur. In other words, all scenarios are equally likely, which is contrary to intuition (and good practice!) and increases the price for target dose robustness in a population. Probabilistic planning adds the quantification of risks to robust optimization and therefore requires assumptions about the frequencies of uncertainties as input. A number of probabilistic planning concepts have been proposed for photon therapy, which all rely in some way or other on approximations and assumptions that will not hold for particle beams, primarily because the geometric changes disturb the dose distributions too much. In order to keep the problems manageable and the number of scenarios realistically high, new methods for dose evaluation in terms of loss-risk distributions need to be deployed. New formulations of the optimization problem can be devised that take advantage of these distributions. Numerical complexity is only a secondary issue on current computer hardware. A primary problem is the dependence on assumptions about the frequency of uncertainties, and the patient-individual component of these frequency distributions. Hence, it is to be expected that reality demands a restricted use of such approaches. SP-0022 Current status and potential of dual energy and spectral CT J. Andersson 1 1 Norrlands Universitetssjukhus, Department of Radiation Sciences- Radiation Physics, Umea, Sweden Conventionally, Computed Tomography (CT) images are reconstructed with data that has been collected using a single X-ray tube potential during a scan. CT images are presented in the Hounsfield scale, which is based on linear attenuation coefficients that have intrinsic energy dependence. A method for Dual Energy Computed Tomography (DECT) imaging was first proposed in the 1970’s, where two sets of data are collected with different radiation qualities. These two data sets are used to draw conclusions about the material that has been scanned by analysing the energy dependence of the linear attenuation coefficients. In recent years, the major CT manufacturers have adopted DECT and several technological solutions are available on the market, including CT scanners with two X-ray tubes, X-ray tubes with rapid tube voltage switching and detectors with two layers that provide an energy separation of the incoming photon spectrum. One of the major benefits of using DECT is that materials can be viewed in terms of composition and mass density. Further benefits of DECT include mitigation of specific artefacts in the CT images using virtual monochromatic reconstructions. Challenges for the application of DECT include temporal resolution of the data collection, image noise, scattered radiation in wide volume scanning and patient dose considerations. From an engineering viewpoint these challenges translate to X-ray tube and detector technology, as well as computer processing speed and optimization. In radiology DECT has become routine tool for certain imaging tasks where material characterization and quantification is important for diagnostics. Symposium: CT imaging, new developments