ESTRO 36 Abstract Book

S230 ESTRO 36 2017 _______________________________________________________________________________________________

The phantom was scanned at different tube potentials (80 kV, 120 kV and 140 kV) with a novel SOMATOM Confidence® RT Pro scanner (Siemens Healthcare GmbH, Germany). Images were reconstructed both into HU and ED for each tube potential. Next, the usability of the reconstruction algorithm was evaluated in a clinical workflow. Five patients with an abdominal lesion (e.g. rectal or prostate cancer) were scanned using the clinically used tube potential of 120 kV and an additional dual-spiral dual-energy CT acquisition was made at 80 kV and 140 kV. Dose distributions (Eclipse TM , Varian, USA) of the ED images of the 80 kV, 120 kV, 140 kV acquisitions using the novel reconstruction algorithm were then compared with the clinical plan based on the 120 kV acquisition using the clinical CT to ED curve with the standard HU image of the 120 kV scan. The difference in mean doses delivered to the planning target volume were quantified (i.e. relative difference ± 1 SD). Results The CT to ED conversion curve for the HU images depended on the tube potential of the CT scanner. The novel reconstruction algorithm produced ED values that had a residual from the identity line of -0.1% ± 2.2% for all inserts and energies and is shown in Figure 1. The dose distributions between the standard and the novel reconstruction algorithm were compared for different energies. The relative differences in target dose ranges were small and ranged from -0.2% to 0.7% for 80 kV, -0.1% to 1.1% for 120 kV, and 0.1 to 1.0% for 140 kV.

response can be quantified and predicted using Tumor Metabolic Ratio (TMR) matrix obtained during the early treatment weeks from multiple FDG-PET imaging. Material and Methods FDG-PET/CT images of 15 HN cancer patients obtained pre- and weekly during the treatment were used. TMR was constructed following voxel-by-voxel deformable image registration. TMR of each tumor voxel, v , was a function of the pre-treatment SUV and the delivered dose, d , such as TMR( v , d ) = SUV( v , d )/SUV( v , 0). Utilizing all voxel values of TMR in the controlled tumor group at the last treatment week, a bounding function between the pre- treatment SUV and TMR was formed, and applied in early treatment days for all tumor voxels to model a tumor voxel control probability (TVCP). At the treatment week k , TVCP of each tumor voxel was constructed based on its pre- treatment SUV and TMR obtained at the week k using the maximum likelihood estimation on the Poisson TCP model for all dose levels. The DPF at the week k was created selecting the maximum TVCP at each level of the pre- treatment SUV and TMR measured at the week k . In addition, 150Gy was used as an upper limit for the target dose. Results TVCP estimated in the early treatment week, i.e. week 2, had their D 50 =13~65Gy; g 50 = 0.56~1.6 respectively with respect to TMR = 0.4~1.2; Pre-treatment SUV = 3.5~16. Figure 1 shows the TVCP estimated using the TMR measured at the week 2 with different levels of pre- treatment SUV, as well as TVCP at different weeks, the week 2 ~ week 4. Large dose will be required to achieve the same level of tumor control for the same level of TMR appeared in the later week of treatment. Figure 2 shows the corresponding DPF for the week 3 TMR, as well as the prescribed tumor dose distribution for the 3 failures.

Figure 1: The linear conversion curve (fitted) of the novel reconstruction algorithm. Conclusion A novel reconstruction algorithm to derive directly relative electron density irrespective of the tube potential of the CT scanner was evaluated. A single identity curve for the CT to ED could be used in the treatment planning system. This reconstruction algorithm may enhance the clinical workflow by selecting an optimal tube potential for the individual patient examination that is not restricted to the commonly used 120 kV tube potential. OC-0441 Dose Prescription Function from Tumor Voxel Dose Response for Adaptive Dose Painting by Number D. Yan 1 , S. Chen 2 , G. Wilson 1 , P. Chen 1 , D. Krauss 1 1 Beaumont Health System, Radiation Oncology, Royal Oak MI, USA 2 Beaumont Health System, Radiation Oncology, Royal Oak, USA Purpose or Objective Dose-painting-by-number (DPbN) needs a novel Dose Prescription Function (DPF) which provides the optimal clinical dose to each tumor voxel based on its own dose response. To obtain the DPF for adaptive DPbN, a voxel- by-voxel tumor dose response matrix needs to be constructed during the early treatment course. The study demonstrated that the voxel-by-voxel tumor dose

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