ESTRO 36 Abstract Book

S483 ESTRO 36 _______________________________________________________________________________________________

Subsequently, the 2D slices were binned in 10 equidistant bins according to the 1D diaphragm position (amplitude binning). To account for outliers, we developed a strategy that sets the inclusion range such that 95% of the diaphragm positions are included, while the peak-to-peak range is minimized (denoted Min95). We compared this with two frequently used strategies (Fig.1): one that selects the maximum inhale and exhale position as range (MaxIE), not discarding outliers, and one that selects the mean inhale and exhale position as inclusion range (MeanIE). The strategies were evaluated based on the following parameters: • * Data included (DI); the fraction of data used for reconstruction after exclusion of outliers.

Conclusion Our novel binnin g strategy for 4DMRI outperformed the classical strategies, resulting in a 4DMRI with h igh precision and fewer artefacts in the presence of irregular breathing. PO-0882 Proxy-free slow-pitch helical 4DCT reconstruction R. Werner 1 , C. Hofmann 2 , T. Gauer 3 1 University Medical Center Hamburg-Eppendorf, Department of Computational Neuroscience, Hamburg, Germany 2 Siemens Healthcare, Imaging & Therapy Systems, Forchheim, Germany 3 University Medical Center Hamburg-Eppendorf, Department of Radiotherapy and Radio-Oncology, Hamburg, Germany Purpose or Objective Standard 4DCT protocols correlate external breathing signals (exploiting e.g. surface tracking devices or abdominal belts) to raw or reconstructed image data to allow for reconstruction of a series of CT volumes at different breathing phases. From a radiotherapy (RT) workflow perspective, dealing with external devices for breathing signal recording is cumbersome. Moreover, if the respiratory signal is corrupted, 4DCT reconstruction is not possible at all. At this, proxy-free reconstruction – i.e. 4DCT reconstruction without using an external breathing signal – could improve RT workflows. We present a novel approach for slow-pitch helical 4DCT reconstruction and Similar to standard external breathing signal-driven slow- pitch helical CT we assume a sufficiently low pitch and gantry rotation time to be given to ensure existence of appropriate raw data for reconstruction of image data at each z position and desired breathing phase. We then pursue a three-step process: (1) image-based derivation of a differential breathing signal; (2) correlation of the extracted breathing signal to raw data; and (3) integration and minimization of an artifact-metric into the final (here: phase-based) reconstruction process. For the crucial step (1), we initially reconstruct slices at a series of z-positions and points in time and determine (slice wise, averaged over a specific region of interest) the change Δ torso /Δt in chest wall height. As Δ torso /Δt can be considered as derivation of the desired breathing signal (figure 1); its zero-crossings represent the end-inspiration (and the end- expiration) breathing phases to be correlated to the raw data. Feasibility of the afore-mentioned approach is investigated using routinely acquired 4DCT lung and liver data sets. A detailed analysis of motion dynamics and image artifacts is performed in proxy-free reconstructed 4DCT data sets of three patients and resulting numbers are compared to corresponding standard external breathing curve-driven phase-based (PB) reconstructions based on the same 4DCT raw data plus RPM breathing signal. illustrate its feasibility. Material and Methods

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* Reconstruction completeness (RC); the fraction of the 110 (11 slices x 10 bins) bin/slice combinations in the 4D data set that are filled. * Intra-bin variation (IBV); the standard error of the mean diaphragm position inside a bin/slice combination. * Image smoothness (S); assessed by quantifying how well a parabola fits the diaphragm shape in a sagittal plane of the reconstructed 4DMRI, per bin (S = R 2 adj averaged over all bins). S ranges from 0 (discontinuous diaphragm shape; artefacts) to 1 (smooth shape; no artefacts). * Peak-to-peak range (PP);

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A low DI indicates underestimation of motion amplitude. A low IBV indicates high binning precision. Low RC, low S and high IBV result in image artefacts, e.g. discontinuities between reconstructed slices. A paired Wilcoxon’s signed rank test was used to test differences in parameters between binning strategies.

Results Excluding only 5% of images during amplitude binning, the developed Min95 strategy outperformed the MaxIE strategy with a 9.5% higher mean RC, 5.6 mm lower mean PP and virtually the same mean IBV and S (all significant, Table 1). The MeanIE strategy with a mean DI of 76.4%, severely underestimated the motion amplitude even though it had a higher S, higher RC and lower IBV compared to MaxIE. The Min95 strategy outperformed the MeanIE strategy with an 18.6% higher mean DI.