MRI-based quantification of carbon and oxygen concentrations in human soft tissues for range verification in proton therapy

Technical note: Implementing MRI/CT-based elemental concentration data to Monte Carlo simulation for yielding positron emitters in proton therapy

BACKGROUND

The elemental concentration (especially oxygen and carbon) and mass density must be accurately assigned to perform Monte Carlo (MC) simulations for predicting proton-induced nuclear reactions in the human body. We recently proposed an approach to quantify elemental concentrations and mass densities of human soft tissues from water content (WC) data obtained by quantitative magnetic resonance (MR) imaging (which we called "MRWC").

PURPOSE

This study presents the first implementation of MRWC-derived elemental concentrations and mass densities as complementary inputs into MC simulations on a virtual head phantom, and demonstrates the simulation of positron emitter production yields in proton therapy.

METHODS

An MC code, PHITS, was used to simulate proton therapy with a monoenergetic 140 MeV beam for a digital head phantom provided by BrainWeb. Three different head images were synthesized as inputs: a conventional CT image, an ideal CT image as a reference, and a WC image coupled with the bone-only CT image for a hybrid approach (MRWC/CT). Thereafter, the performance of the MRWC/CT method was evaluated by comparing its accuracy in predicting the production yields of positron emitters (11C and 15O) with the gold-standard CT-only method.

RESULTS

The MRWC/CT method could predict 11C and 15O production yield maps that closely resembled the corresponding reference maps, while the CT-only method failed. The structural similarity index measures between the reference and CT- or MRWC/CT-derived maps were improved from 0.67 (CT-only) to 0.87 (MRWC/CT) for 11C and 0.76 (CT-only) to 0.93 (MRWC/CT) for 15O. Furthermore, applying post-processing normalizations to account for elemental density variations in the production yields of positron emitters facilitated the determination of distal fall-off positions in depth activity profiles.

CONCLUSION

At least in the head area, the MRWC/CT method demonstrated potential for more precise predictions of proton-induced positron emitter distributions via MC simulations than that of the CT-only method.

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.