ESTRO 36 Abstract Book

S479 ESTRO 36 2017 _______________________________________________________________________________________________

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

Results Figure 2 illustrates that proxy-free and common external breathing signal-driven PB-reconstructed 4DCT data are comparable both in terms of image quality and represented motion amount. In detail, the considered proxy-free datasets contained approximately 5% more artifacts than the PB data sets. Differences of represented tumor mass center motion as well as the amount of e.g. diaphragm motion between end-inhalation and - exhalation were negligible (max. 1 voxel).

Conclusion We presented a novel approach for proxy-free slow-pitch helical 4DCT reconstruction and illustrated its feasibility. Although the proxy-free reconstructed images contain slightly more motion artifacts, we consider the approach to be helpful especially in the case of corrupted breathing signals recordings (no need for re-scanning the patient). PO-0883 Clinical Implementation Model-Based CT to Replace 4DCT for Lung Cancer Treatment Planning D. Low 1 , D. O'Connell 1 , L. Yang 1 , J. Lewis 1 , P. Lee 1 1 UCLA Medical Center, Department of Medical Physics, Los Angeles, USA Purpose or Objective To implement motion-model based CT into clinical practice, replacing 4DCT for breathing motion management treatment planning. Material and Methods A breathing motion model that employs a mathematical motion equation, two real-time breathing surrogates, breathing amplitude and breathing rate, and employing multiple fast helical, low-dose CT scanning has been introduced into clinical practice. The imaging process uses a bellows-based system to monitor the breathin g cycle, which is defined as the amplitude and rate of the bellows signal. The fast helical CT scans are reg istered to determine the lung tissue positions, correlate d to the breathing amplitude and rate on a slice-by-s lice basis. A published motion equation is employed to characterize the motion for each voxel. The motion model is employed to reconstruct the original fast helical CT scans and the original and reconstructed scans compared to determine the overall model motion prediction accuracy. Eight amplitude-based CT images are constructed and sent to