ESTRO 36 Abstract Book

S879

ESTRO 36 2017

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This can potentially minimize operator dependence or

even remove the need of the skilled operator.

Material and Methods

In this study, co-registered CT and 3D transperineal US

(TPUS) volumes with corresponding anatomical structure

delineations of a prostate cancer patient were available.

After preprocessing, a thresholding-based segmentation

algorithm was used to extract the bones from the CT scan

(Fig. 1A). The retrieved bone mask and the internal

perineum boundaries enabled the identification of the

patients’ perineal area on the skin (Fig. 1A-B-C).

Subsequently, the scrotum was localized in order to

identify the underlying perineal skin. Finally, the areas on

which the US probe could not be positioned in clinical

practice (e.g. the region around the anus) were removed.

In this way, a skin area accessible for TPUS volume

acquisition was automatically identified (Fig. 1D).

All probe setups proposed by the algorithm should allow

visualization of the whole prostate and seminal vesicles,

as well as the adjacent edges of bladder and rectum, as

clinically required. To determine these setups, the

accessible skin area, in combination with the structure

delineations and an estimation of the potential probe

pressure, was used. The setup that also allowed

visualization of most of the remaining anatomical

structures was considered the best option.

Results

By positioning a virtual probe on the patients’ virtual skin

and subsequently translating it along the Y axis (1 mm

step), rotating around Y or Z axes (up to ±3 degrees with

1 degree step) or rotating around the X axis (up to ±15

degrees with 3 degree step), 12,936 possible probe setups

were identified. In total 108 of these setups allowed

visualization of all clinically required structures without

the occurrence of blockage by the patients’ bones.

In Fig. 2 the sector of the best probe setup is superimposed

in yellow on the center slices of the patients’ CT volume.

If the physician had been provided with this setup prior to

the US scan, potentially 96% of the delineated anatomical

structures could have been visualized, in comparison with

87% using the current setup (cyan sector in Fig. 2), which

was manually determined by trial-and-error.

Conclusion

The best probe setup out of 108 possible setups could

potentially increase the visualization of the anatomical

structures from 87% to 96%, while still fulfilling the clinical

requirements. Future steps should focus on enabling

skilled and unskilled workers to position the US probe

according to the calculated setup.

EP-1643 Simulate baseline shift uncertainties to

improve robustness of proton therapy treatments

K. Souris

, A. Barragan

, D. Di Perri

, X. Geets

, E.

Sterpin

, J.A. Lee

UCL - IREC Molecular Imaging Radiology and Oncology

MIRO, Molecular Imaging Radiology and Oncology MIRO,

Brussels, Belgium

Purpose or Objective

Several studies reported both systematic and random

variations of the mean position of mobile tumors from

fraction to fraction. This so-called baseline shift is a major

source of uncertainties for mobile targets and can

jeopardize treatment quality. Unlike conventional photon

therapy, the inclusion of this error in a PTV margin is

inadequate in proton therapy because of the range

uncertainties. Accounting for this uncertainty in a robust

optimizer is much more appropriate, using for instance

population-based estimations of the shifts. We developed

a baseline-shift model able to automatically generate

modified 4D-CT series used as uncertainty scenarios in the

TPS.

Material and Methods

An average CT scan and a Mid-Position CT scan (MidPCT)

of the patient at planning time are generated from a 4D-

CT data. The GTV contour in the MidPCT represents the

mean position of the tumor along the breathing cycle. Our

model can simulate a baseline shift by generating a local

deformation field that moves the tumor on all phases of

the 4D-CT, without creating any non-physical artifact. The

deformation field is comprised of normal and tangential

components with respect to the lung wall, in order to

allow the tumor to slide within the lung instead of

deforming the lung surface. The deformation field is

eventually smoothed in order to enforce continuity. Two

4D-CT series acquired at 1 week of interval were used to

validate the model.

Results

After rigid registration, a baseline shift of 9.5 mm is

measured between the first- and second-week 4D-CT sets

(W1-CT and W2-CT). In order to validate our model, a third

4D-CT series (BS-W1-CT) was generated from W1-CT to

reproduce the measured shift (Figure 1). Water equivalent

thickness (WET) has been computed for each voxel of the

3 MidPCTs and revealed that the baseline shift between

W1-CT and W2-CT led to a root mean square error (RMSE)

of 0.52 mm in the GTV. This WET RMSE was reduced to

0.18 mm between W2-CT and the simulated BS-W1-CT. In

addition, a proton therapy plan was optimized on the

average W1-CT scan and recomputed on the average W2-

CT and BS-W1-CT scans. Figure 2 compares the resulting

DVH for all dose distributions. The dose distribution

computed on BS-W1-CT reproduces the dose degradation