Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction. Issue 11 (1st October 2018)

• Record Type: Journal Article
• Authors: Taasti, Vicki T.; Hansen, David C.; Michalak, Gregory J.; Deisher, Amanda J.; Kruse, Jon J.; Muren, Ludvig P.; Petersen, Jørgen B. B.; McCollough, Cynthia H.
• Is Part Of: Medical physics. Volume 45:Issue 11(2018)
• Page Start: 5186
• Page End: 5196
• Publication Date: 2018-10-01
• Subjects: CT noise -- experimental verification -- noise robustness -- photon counting detector CT -- proton stopping power ratio; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13173
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Photon counting detectors (PCDs) are being introduced in advanced x‐ray computed tomography (CT) scanners. From a single PCD‐CT acquisition, multiple images can be reconstructed, each based on only a part of the original x‐ray spectrum. In this study, we investigated whether PCD‐CT can be used to estimate stopping power ratios (SPRs) for proton therapy treatment planning, both by comparing to other SPR methods proposed for single energy CT (SECT) and dual energy CT (DECT) as well as to experimental measurements. Methods: A previously developed DECT‐based SPR estimation method was adapted to PCD‐CT data, by adjusting the estimation equations to allow for more energy spectra. The method was calibrated directly on noisy data to increase the robustness toward image noise. The new PCD SPR estimation method was tested in theoretical calculations as well as in an experimental setup, using both four and two energy bin PCD‐CT images, and through comparison to two other SPR methods proposed for SECT and DECT. These two methods were also evaluated on PCD‐CT images, full spectrum (one‐bin) or two‐bin images, respectively. In a theoretical framework, we evaluated the effect of patient‐specific tissue variations (density and elemental composition) and image noise on the SPR accuracy; the latter effect was assessed by applying three different noise levels (low, medium, and high noise). SPR estimates derived using real PCD‐CT images were compared to experimentally measured SPRs in nine organic tissue samples, including fat, muscle, and bone tissues. Results: For the theoretical calculations, the root‐mean‐square error (RMSE) of the SPR estimation was 0.1% for the new PCD method using both two and four energy bins, compared to 0.2% and 0.7% for the DECT‐ and SECT‐based method, respectively. The PCD method was found to be very robust toward CT image noise, with a RMSE of 2.7% when high noise was added to the CT numbers. Introducing tissue variations, the RMSE only increased to 0.5%; even when adding high image noise to the changed tissues, the RMSE stayed within 3.1%. In the experimental measurements, the RMSE over the nine tissue samples was 0.8% when using two energy bins, and 1.0% for the four‐bin images. Conclusions: In all tested cases, the new PCD method produced similar or better results than the SECT‐ and DECT‐based methods, showing an overall improvement of the SPR accuracy. This study thus demonstrated that PCD‐CT scans will be a qualified candidate for SPR estimations.