Clinical use of dual-energy CT for proton treatment planning to reduce CT-based range uncertainties

Purpose/Objective:

To improve CT-based particle treatment planning the additional tissue information provided by dual-energy CT (DECT) compared to single-energy CT (SECT) can be clinically used to reduce CT-based range uncertainties and to analyze intra- and interpatient tissue variations. First, a DECT scan protocol was optimized and clinically introduced. Second, in a first analysis patient DECT scans were evaluated concerning CT number variability.

Material and Methods:

After an experimental analysis of several CT scan settings concerning beam hardening, image quality and planned dose distribution using tissue surrogates, head and body phantoms and real tissues, an optimized and standardized DECT protocol (voltages: 80/140 kVp, kernel: D34) is clinically applied for patients treated with protons. 45 planning and 360 control DECT scans of overall 70 patients were acquired with a single-source DECT scanner (Siemens SOMATOM Definition AS) until October 2015. Contouring and treatment planning are performed on pseudo-monoenergetic CT scans (MonoCT) derived by a weighted sum of both CT datasets. 25 patients with different tumor sites (head, head & neck, prostate, pelvis) and overall 200 DECT scans were initially investigated to evaluate intra- and interpatient tissue variabilities. Based on the frequency distribution of voxelwise 80/140kVp CT number pairs, a linear correlation of low-density, soft and bony tissues can be determined, respectively.

Results:

A DECT-based MonoCT of 79 keV is found optimal for proton treatment planning. Assuming identical CT dose to a SECT scan, the MonoCT shows a signal-to-noise ratio increased by 8% and a CT number constancy raised by 23% on average and up to 69% for bones. Consequently, the current uncertainties of a heuristic conversion of CT numbers into stopping power ratios (SPR) using a look-up table are reduced.
Evaluation of patient variability revealed that 80/140kVp CT number pairs of human tissues are on average well described by linear correlations with a slope (± σ) of (1.023 ± 0.006) for low-density, (0.825 ± 0.008) for soft and (0.696 ± 0.006) for bony tissues. The slope variation between different patients, independent from tumor site and patient size, is comparable to the variability between different control DECT scans of one patient (σ of about 1-3%). However, a band of CT number pairs deviating from the mean linear correlation, e.g. caused by image noise and partial volume effects, reveals potential insuperable uncertainties of a voxel-based heuristic CT number-to-SPR conversion.

Conclusions:
The clinical application of DECT-based MonoCT can contribute to a more precise range prediction. Further improvements are expected from a direct, non-heuristic SPR calculation, which is not yet clinically available. The further growing DECT patient database enables not only a detailed analysis of intra- and interpatient variations, but also a robustness analysis for different direct SPR prediction approaches.