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Microscopic energy deposition distributions from ionizing radiation vary depending on biological target size and are used to predict the biological effects of an irradiation. Ionizing radiation is thought to kill cells or inhibit the cell cycle mainly by damaging DNA in the cell nucleus. The size of cells and nuclei depends on tissue type, cell cycle, and malignancy, all of which vary between patients. The aim of this study was to develop methods to perform patient-specific microdosimetry, that being, determining microdosimetric quantities in volumes that correspond to the sizes of cells and nuclei observed in a patient’s tissue. A histopathological sample extracted from a stage I lung adenocarcinoma patient was analyzed. A pouring simulation was used to generate a three-dimensional tissue model from cell and nucleus size information determined from the histopathological sample. Microdosimetric distributions including and were determined for and in a patient-specific model containing a distribution of cell and nucleus sizes. Fixed radius models and a summation method were compared to the full patient-specific model to evaluate their suitability for fast determination of patient-specific microdosimetric parameters. In the summation method, f(y) from many fixed radii models are summed. Fixed radius models do not provide a close approximation of the full patient-specific model or for the lower energy sources investigated, and The higher energy sources investigated, and are less sensitive to target size variation than and The summation method yields the most accurate approximation of the full model for all radioisotopes investigated. The use of a summation method allows for the computation of patient-specific microdosimetric distributions with the computing power of a personal computer. With appropriate biological inputs the microdosimetric distributions computed using these methods can yield a patient-specific relative biological effectiveness as part of a multiscale treatment planning approach.

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Objective: Shortcomings of dose-averaged linear energy transfer (LET_D), the quantity which is most commonly used to quantify proton relative biological effectiveness (RBE), have long been recognized. Microdosimetric spectra may overcome the limitations of LET_D but are extremely computationally demanding to calculate. A systematic library of lineal energy spectra for monoenergetic protons could enable rapid determination of microdosimetric spectra in a clinical environment. The objective of this work was to calculate and validate such a library of lineal energy spectra. 

Approach: SuperTrack, a GPU-accelerated CUDA/C++ based application, was developed to superimpose tracks calculated using Geant4 onto targets of interest and to compute microdosimetric spectra. Lineal energy spectra of protons with energies from 0.1 to 100 MeV were determined in spherical targets of diameters from 1 nm to 10 μm and in bounding voxels with side lengths of 5 μm and 3 mm.

Main Results: Compared to an analogous Geant4-based application, SuperTrack is up to 3500 times more computationally efficient if each track is resampled 1000 times. Dose spectra of lineal energy and dose-mean lineal energy calculated with SuperTrack were consistent with values published in the literature and with comparison to a Geant4 simulation. Using SuperTrack, we developed the largest known library of proton microdosimetric spectra as a function of primary proton energy, target size, and bounding volume size. 

Significance: SuperTrack greatly increases the computational efficiency of the calculation of microdosimetric spectra. The elevated lineal energy observed in a 3 mm side length bounding volume suggests that lineal energy spectra determined experimentally or computed in small bounding volumes may not be representative of the lineal energy spectra in voxels of a dose calculation grid. The library of lineal energy spectra calculated in this work could be integrated with a treatment planning system for rapid determination of lineal energy spectra in patient geometries. 

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