Experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual‐energy CT data

Abstract

Purpose: To 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. Methods: The 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. Results: The 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. Conclusion: By 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.

Initial Validation of Proton Dose Calculations on SPR Images from DECT in Treatment Planning System

“…However, with DECT, the relative electron density (ρ e ) and effective atomic number ( Z eff ) values are calculated directly from the pixel values of the DECT images. Using these values, several researchers have developed models for direct determination [ 13 , 15 , 18 ] and statistical or iterative determination [ 19 , 20 ] of the proton stopping power. Comparison studies of the SPR values calculated with these direct-calculation models against measured values for aluminum and titanium plugs, tissue-equivalent plastics, and animal tissues have recently been performed [ 13 , 14 , 18 , 21 – 23 ].…”

Refinement of the Hounsfield look‐up table by retrospective application of patient‐specific direct proton stopping‐power prediction from dual‐energy CT

“…31 An iterative method for the prediction of material properties based on x-ray projections of both CT acquisitions might even further reduce systematic effects of beam hardening and image noise on SPR estimation in future. 37,38 Since the overall dose distribution of each treatment field was analyzed, the results obtained for double scattering in this study can be also translated to treatment planning with pencil beam scanning. The impact of beam model and dose calculation algorithm (pencil beam vs Monte Carlo approach) on relative range shifts is expected to be minor and would not change the conclusions drawn here.…”

“…Hence, beam hardening can only be corrected for predefined materials, such as water and bone in this case, meaning that an uncertainty in CT number stability still remains . An iterative method for the prediction of material properties based on x‐ray projections of both CT acquisitions might even further reduce systematic effects of beam hardening and image noise on SPR estimation in future …”

Determination of proton stopping power ratio with dual‐energy CT in 3D‐printed tissue/air cavity surrogates

“…The ρ e of a given material was determined as ρ × wZ / A . Using these values for PLA, the ρ e and Z eff for water from ICRU Report 49, and a mean ionization energy for water ( I w ) of 76 eV, the theoretical SPR for PLA could be calculated using the Bethe–Bloch equation:where m e is the electron rest mass, c is the speed of light, and β = v/c with v is the speed of the incident proton (at either 120 MeV for this study).…”

“…In particular, uncertainties in the process of converting CT numbers in SECT images from units of HU to SPR have led many researchers to study the use of dual‐energy CT (DECT) scans for proton dose calculations . By scanning an object (e.g., a patient for proton radiotherapy applications) at two different x‐ray energies (tube voltages), the energy dependence of x‐ray interactions with atoms can be exploited to directly determine the SPR of a given material (or tissue).…”