Reducing scan time in 177Lu planar scintigraphy using convolutional neural network: A Monte Carlo simulation study

Abstract

AbstractPurposeThe aim of this study was to reduce scan time in 177Lu planar scintigraphy through the use of convolutional neural network (CNN) to facilitate personalized dosimetry for 177Lu‐based peptide receptor radionuclide therapy.MethodsThe CNN model used in this work was based on DenseNet, and the training and testing datasets were generated from Monte Carlo simulation. The CNN input images (IMGinput) consisted of 177Lu planar scintigraphy that contained 10–90% of the total photon counts, while the corresponding full‐count images (IMG100%) were used as the CNN label images. Two‐sample t‐test was conducted to compare the difference in pixel intensities within region of interest between IMG100% and CNN output images (IMGoutput).ResultsNo difference was found in IMGoutput for rods with diameters ranging from 13 to 33 mm in the Derenzo phantom with a target‐to‐background ratio of 20:1, while statistically significant differences were found in IMGoutput for the 10‐mm diameter rods when IMGinput containing 10% to 60% of the total photon counts were denoised. Statistically significant differences were found in IMGoutput for both right and left kidneys in the NCAT phantom when IMGinput containing 10% of the total photon counts were denoised. No statistically significant differences were found in IMGoutput for any other source organs in the NCAT phantom.ConclusionOur results showed that the proposed method can reduce scan time by up to 70% for objects larger than 13 mm, making it a useful tool for personalized dosimetry in 177Lu‐based peptide receptor radionuclide therapy in clinical practice.