A library of proton lineal energy spectra spanning the full range of clinically relevant energies.

Purpose: In locations where the proton energy spectrum is broad, lineal energy spectrum-based proton biological effects models may be more accurate than dose-averaged linear energy transfer (LET) based models. However, the development of microdosimetric spectrum-based biological effects models is hampered by the extreme computational difficulty of calculating microdosimetric spectra. Given a precomputed library of lineal energy spectra for monoenergetic protons, a weighted summation can be performed which yields the lineal energy spectrum of an arbitrary polyenergetic beam.

RapidBrachyIVBT: A dosimetry software for patient-specific intravascular brachytherapy dose calculations on optical coherence tomography images.

Background: Coronary artery disease is the most common form of cardiovascular disease. It is caused by excess plaque along the arterial wall, blocking blood flow to the heart (stenosis). A percutaneous coronary intervention widens the arterial wall with the inflation of a balloon inside the lesion area and leaves behind a metal stent to prevent re-narrowing of the artery (restenosis).

GPU-accelerated calculation of proton microdosimetric spectra as a function of target size, proton energy, and bounding volume size.

Shortcomings of dose-averaged linear energy transfer (LET), the quantity which is most commonly used to quantify proton relative biological effectiveness, have long been recognized. Microdosimetric spectra may overcome the limitations of LETbut 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.

Patient-specific microdosimetry: a proof of concept.

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.

Development of patient-specific 3D models from histopathological samples for applications in radiation therapy.

The biological effects of ionizing radiation depend on the tissue, tumor type, radiation quality, and patient-specific factors. Inter-patient variation in cell/nucleus size may influence patient-specific dose response. However, this variability in dose response is not well investigated due to lack of available cell/nucleus size data.