Accurate Proton Stopping Power Images Reconstructed using Joint Statistical Dual Energy CT: Experimental Verification and Impact of Fan-Beam CT Scatter

利用联合统计双能CT重建精确的质子阻止本领图像:实验验证及扇形束CT散射的影响

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Abstract

Proton radiotherapy has the potential to provide clinically effective treatment and highly conformal dose delivery when the rapid dose falloff at the end of its proton-beam range is correctly aligned to the distal margin of the clinical target volume. However, in current clinical practice an additional 2-3.5% safety margin must be added to the proton range to account for uncertainties in the estimation of proton-beam range when using stopping-power ratios (SPRs) derived from single-energy CT scans. Several approaches have been proposed to estimate stopping power by using dual-energy CT (DECT) and have been shown through theoretical analysis to outperform single-energy CT (SECT) under the presence of tissue composition and density variations. Our lab previously proposed a joint statistical image reconstruction (JSIR) method built on a basis-vector model (BVM) tissue parameterization for SPR estimation, which was shown to perform comparatively better than other DECT image- and sinogram-domain decomposition approaches on simulated as well as experimental data. This comparison, however, assumed theoretical SPR values calculated from the samples' known compositions and densities as ground truth and used the mean excitation energy and effective electron density from ICRU reports along with a simplified version of the Bethe-Bloch equation to determine SPR reference values. Furthermore, CT scans were acquired with an assumed ideal point source at a narrow beam collimation; thus, the signal formation assumed by our JSIR process neglected scatter and off-focal radiation. In this paper, we verify the accuracy of our method by comparing the SPR values derived from JSIR-BVM to direct measurements of relative SPR, as well as present a preliminary study on the impact of fan-beam scatter radiation on JSIR-BVM SPR prediction accuracy.

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