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

PURPOSE: The aim of this study was to reduce scan time in 177 Lu planar scintigraphy through the use of convolutional neural network (CNN) to facilitate personalized dosimetry for 177 Lu-based peptide receptor radionuclide therapy. METHODS: The 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 177 Lu 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 ). RESULTS: No 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. CONCLUSION: Our 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 177 Lu-based peptide receptor radionuclide therapy in clinical practice.