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Comparison of x ray computed tomography number to proton relative linear stopping power conversion functions using a standard phantom.

ABSTRACT: Adequate evaluation of the results from multi-institutional trials involving light ion beam treatments requires consideration of the planning margins applied to both targets and organs at risk. A major uncertainty that affects the size of these margins is the conversion of x ray computed tomography numbers (XCTNs) to relative linear stopping powers (RLSPs). Various facilities engaged in multi-institutional clinical trials involving proton beams have been applying significantly different margins in their patient planning. This study was performed to determine the variance in the conversion functions used at proton facilities in the U.S.A. wishing to participate in National Cancer Institute sponsored clinical trials.A simplified method of determining the conversion function was developed using a standard phantom containing only water and aluminum. The new method was based on the premise that all scanners have their XCTNs for air and water calibrated daily to constant values but that the XCTNs for high density/high atomic number materials are variable with different scanning conditions. The standard phantom was taken to 10 different proton facilities and scanned with the local protocols resulting in 14 derived conversion functions which were compared to the conversion functions used at the local facilities.For tissues within ±300 XCTN of water, all facility functions produced converted RLSP values within ±6% of the values produced by the standard function and within 8% of the values from any other facility's function. For XCTNs corresponding to lung tissue, converted RLSP values differed by as great as ±8% from the standard and up to 16% from the values of other facilities. For XCTNs corresponding to low-density immobilization foam, the maximum to minimum values differed by as much as 40%.The new method greatly simplifies determination of the conversion function, reduces ambiguity, and in the future could promote standardization between facilities. Although it was not possible from these experiments to determine which conversion function is most appropriate, the variation between facilities suggests that the margins used in some facilities to account for the uncertainty in converting XCTNs to RLSPs may be too small.

SUBMITTER: Moyers MF

PROVIDER: S-EPMC4032432 | biostudies-other | 2014 Jun

REPOSITORIES: biostudies-other

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Project description:PurposePhoton 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.MethodsA 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.ResultsFor 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.ConclusionsIn 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.

Project description:PurposeTo experimentally commission a dual-energy CT (DECT) joint statistical image reconstruction (JSIR) method, which is built on a linear basis vector model (BVM) of material characterization, for proton stopping power ratio (SPR) estimation.MethodsThe JSIR-BVM method builds on the relationship between the energy-dependent photon attenuation coefficients and the proton stopping power via a pair of BVM component weights. The two BVM component images are simultaneously reconstructed from the acquired DECT sinograms and then used to predict the electron density and mean excitation energy (I-value), which are required by the Bethe equation for SPR computation. A post-reconstruction image-based DECT method, which utilizes the two separate CT images reconstructed via the scanner's software, was implemented for comparison. The DECT measurement data were acquired on a Philips Brilliance scanner at 90 and 140 kVp for two phantoms of different sizes. Each phantom contains 12 different soft and bony tissue surrogates with known compositions. The SPR estimation results were compared to the reference values computed from the known compositions. The difference of the computed water equivalent path lengths (WEPL) across the phantoms between the two methods was also compared.ResultsThe overall root-mean-square (RMS) of SPR estimation error of the JSIR-BVM method are 0.33% and 0.37% for the head- and body-sized phantoms, respectively, and all SPR estimates of the test samples are within 0.7% of the reference ground truth. The image-based method achieves overall RMS errors of 2.35% and 2.50% for the head- and body-sized phantoms, respectively. The JSIR-BVM method also reduces the pixel-wise random variation by 4-fold to 6-fold within homogeneous regions compared to the image-based method. The average differences between the JSIR-BVM method and the image-based method are 0.54% and 1.02% for the head- and body-sized phantoms, respectively.ConclusionBy taking advantage of an accurate polychromatic CT data model and a model-based DECT statistical reconstruction algorithm, the JSIR-BVM method accounts for both systematic bias and random noise in the acquired DECT measurement data. Therefore, the JSIR-BVM method achieves good accuracy and precision on proton SPR estimation for various tissue surrogates and object sizes. In contrast, the experimentally achievable accuracy of the image-based method may be limited by the uncertainties in the image formation process. The result suggests that the JSIR-BVM method has the potential for more accurate SPR prediction compared to post-reconstruction image-based methods in clinical settings.

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Comparison of x ray computed tomography number to proton relative linear stopping power conversion functions using a standard phantom.

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