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.

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.

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 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.