Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement.

Monte Carlo calculated calibration settings for commercially available nuclear medicine ionization chambers.

Purpose: A method for calculating nuclear medicine ionization chamber (NMIC) calibration settings with a Monte Carlo model is presented and validated against physical measurements. This work provides Monte Carlo-calculated calibration settings for select isotopes with no current manufacturer recommendations and a method by which NMIC manufacturers or standards laboratories can utilize highly detailed specifications to calculate comprehensive lists of calibration settings for general isotopes.

Methods: A Monte Carlo model of a Capintec PET series NMIC was developed and used to calculate the chamber response to relevant radioactive decay products over an energy range relevant to nuclear medicine.