Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms

Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had <1% errors across all SPR values. Relative to known physical molecular compositions, imaging-determined compositions differed by approximately ≤10%. In the brain case, the largest differences between SPRstoichand SPRMMwere in bone and high lipids/fat tissue. The magnitudes and trends of these differences matched phantom results.Significance. Our MR-based multimodal method determined molecular compositions and SPR in various tissue-mimicking phantoms with high accuracy, as confirmed with proton beam measurements. This method also revealed significant SPR differences compared to stoichiometric kVCT-only calculation in a clinical case, with the largest differences in bone. These findings support that including MRI in proton therapy treatment planning can improve the accuracy of calculated SPR values and reduce range uncertainties.

Validation of prompt gamma-ray spectroscopy for proton range verification in tissue-mimicking and porcine samples

Objective. Proton therapy of cancer improves dose conformality to the target and sparing of surrounding healthy tissues compared to conventional photon treatments. However, proton therapy's advantage could be even larger if proton range uncertainties were reduced. Sources of range uncertainties include computed tomography treatment planning images and variations in patient anatomy and setup. To reduce range uncertainties, we have developed a system for real-timein vivorange monitoring. The system is based on spectroscopy of prompt gamma-rays emitted through proton-nuclear interactions during irradiation. We validated the performance of our prompt gamma-ray spectroscopy detector prototype using tissue-mimicking and porcine samples.Approach. Measurements were performed in water, four tissue-mimicking samples (spongiosa, muscle, adipose tissue, and cortical bone), and two porcine samples (liver and brain). A dose of 0.9 Gy was delivered to a target at a depth of 12.5-17.5 cm. Multi-layer ionization chamber measurements were performed to determine stopping power ratios relative to water and ground truth proton ranges. Ground truth elemental compositions were determined using combustion analysis. Proton ranges and elemental compositions measured using prompt gamma-ray spectroscopy were compared to the ground truth.Main results. For all samples, the mean measured range over all pencil-beam spots differed from the ground truth by less than 1.2 mm. The mean standard deviation was 0.9 mm (range: 0.4-1.6 mm). The mean difference between ground truth and measured elemental compositions was 0.06gcm3(range: 0.00gcm3to 0.12gcm3).Significance. We verified the performance of our prompt gamma-ray spectroscopy detector prototype for proton range verification using tissue-mimicking and porcine samples. Measured proton ranges and elemental sample compositions were in good agreement with the ground truth. These measurements confirm the system's reliability for a variety of tissues and bridge the gap between previously-reported experiments and ongoingin vivopatient measurements.

Assessment of quantitative information for radiation therapy at a first-generation clinical photon-counting computed tomography scanner

As one of the latest developments in X-ray computed tomography (CT), photon-counting technology allows spectral detection, demonstrating considerable advantages as compared to conventional CT. In this study, we investigated the use of a first-generation clinical photon-counting computed tomography (PCCT) scanner and estimated proton relative (to water) stopping power (RSP) of tissue-equivalent materials from virtual monoenergetic reconstructions provided by the scanner. A set of calibration and evaluation tissue-equivalent inserts were scanned at 120 kVp. Maps of relative electron density (RED) and effective atomic number (EAN) were estimated from the reconstructed virtual monoenergetic images (VMI) using an approach previously applied to a spectral CT scanner with dual-layer detector technology, which allows direct calculation of RSP using the Bethe-Bloch formula. The accuracy of RED, EAN, and RSP was evaluated by root-mean-square errors (RMSE) averaged over the phantom inserts. The reference RSP values were obtained experimentally using a water column in an ion beam. For RED and EAN, the reference values were calculated based on the mass density and the chemical composition of the inserts. Different combinations of low- and high-energy VMIs were investigated in this study, ranging from 40 to 190 keV. The overall lowest error was achieved using VMIs at 60 and 180 keV, with an RSP accuracy of 1.27% and 0.71% for the calibration and the evaluation phantom, respectively.