Further investigation of 3D dose verification in proton therapy utilizing acoustic signal, wavelet decomposition and machine learning.

Online dose verification in proton therapy is a critical task for quality assurance. We further studied the feasibility of using a wavelet-based machine learning framework to accomplishing that goal in three dimensions, built upon our previous work in 1D. The wavelet decomposition was utilized to extract features of acoustic signals and a bidirectional long-short-term memory (Bi-LSTM) recurrent neural network (RNN) was used. The 3D dose distributions of mono-energetic proton beams (multiple beam energies) inside a 3D CT phantom, were generated using Monte-Carlo simulation. The 3D propagation of acoustic signal was modeled using the k-Wave toolbox. Three different beamlets (i.e. acoustic pathways) were tested, one with its own model. The performance was quantitatively evaluated in terms of mean relative error (MRE) of dose distribution and positioning error of Bragg peak (ΔBP), for two signal-to-noise ratios (SNRs). Due to the lack of experimental data for the time being, two SNR conditions were modeled (SNR = 1 and 5). The model is found to yield good accuracy and noise immunity for all three beamlets. The results exhibit an MRE below 0.6% (without noise) and 1.2% (SNR = 5), andΔBPbelow 1.2 mm (without noise) and 1.3 mm (SNR = 5). For the worst-case scenario (SNR = 1), the MRE andΔBPare below 2.3% and 1.9 mm, respectively. It is encouraging to find out that our model is able to identify the correlation between acoustic waveforms and dose distributions in 3D heterogeneous tissues, as in the 1D case. The work lays a good foundation for us to advance the study and fully validate the feasibility with experimental results.

Correction: The potential role of borophene as a radiosensitizer in boron neutron capture therapy (BNCT) and particle therapy (PT).

Correction for 'The potential role of borophene as a radiosensitizer in boron neutron capture therapy (BNCT) and particle therapy (PT)' by Pengyuan Qi et al., Biomater. Sci., 2020, 8, 2778-2785, DOI: .

Feasibility study of range verification based on proton-induced acoustic signals and recurrent neural network.

Range verification in proton therapy is a critical quality assurance task. We studied the feasibility of online range verification based on proton-induced acoustic signals, using a bidirectional long-short-term-memory recurrent neural network and various signal processing techniques. Dose distribution of 1D pencil proton beams inside a CT image-based phantom was analytically calculated. The propagation of acoustic signal inside the phantom was modeled using the k-Wave toolbox. For signal processing, five methods were investigated: down-sampling (DS), DS + HT (Hilbert transform), Wavelet decomposition (Wavedec db1, db4 and db20). The performances were quantitatively evaluated in terms of mean absolute error, mean relative error (MRE) and the Bragg peak localization error ([Formula: see text]). In addition, the study analyzed the impact of noise levels, the number of sensors, as well as the location of sensors. For the noiseless case (32 sensors), the Wavedec db1 method demonstrates the best performance: [Formula: see text] is less than one pixel and the dose accuracy over the region adjacent to the Bragg peak (MRE50) is ∼3.04%. With the presence of noise, the Wavedec db1 method demonstrates the best noise immunity, achieving [Formula: see text] less than 1 mm and an MRE50 of ∼12%. The proposed machine learning framework may become a useful tool allowing for online range verification in proton therapy.

The potential role of borophene as a radiosensitizer in boron neutron capture therapy (BNCT) and particle therapy (PT).

The potential role of borophene as a radiosensitizer in PT and BNCT was investigated. Our study focused on two aspects: (1) the synthesis and characterization of borophene nanomaterials; and (2) biocompatibility and dose enhancement. To overcome the limitation of vapor-based technology, we successfully deployed the liquid-phase exfoliation (LPE) method to produce borophene targeting for biomedical applications. Bringing together spatial distribution and dose deposition, the in vitro microdosimetry study was carried out in the presence of borophene. A quantitative study of the dose enhancement ratio (DER) was performed with Monte-Carlo simulation. The synthesized borophene showed good biocompatibility with less than 10% cell death at a concentration of up to 0.2 mg ml-1. The uptake of borophene within individual cells penetrated through cell membranes but outside the nucleus. For proton PT, no significant change in the DER is found. For carbon PT, the DER increases by about 5% as the concentration of 10B reaches 1 mg g-1. For BNCT, a DER of more than 2 can be obtained for a concentration as low as 100 µg g-1. This study lays a foundation for utilizing novel borophene-based nanomaterials as radiosensitizers as well as imaging probes in cancer treatment.