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Purpose The aim of this work was to investigate the lesion detectability of Tc‐99m planar scintigraphy acquired with a low‐energy high‐resolution and sensitivity (LEHRS) collimator and processed by Clarity 2D for patients with different body sizes through phantom study. Methods A NEMA IEC body phantom set was covered by two layers of 25‐mm‐thick bolus to construct phantom in three different sizes. All image data were performed on a Discovery NM/CT 870 DR with an LEHRS collimator and processed by Clarity 2D with blend ratio a of 0%, 20%, 40%, 60%, 80%, and 100%. The lesion detectability in gamma scintigraphy was evaluated by calculating the contrast‐to‐noise ratio (CNR). Multiple linear regression methods were used to analyze the impact of body size, target size, and Clarity 2D blending weight on the lesion detectability of Tc‐99m planar scintigraphy. Results It was found that changing the blend ratio could improve CNR, and this phenomenon was more significant in anterior view than in posterior view. Our results also suggested that the blend ratio should be selected according to patient body size in order to maintain consistent CNR. Hence, when a blend ratio of 60% was used for a patient before cancer treatment, a lower blend ratio should be used for the same patient experiencing treatment‐related weight loss to achieve consistent lesion detectability in Tc‐99m planar scintigraphy acquired with LEHRS and processed by Clarity 2D. Conclusion The magnitude of photon attenuation and scattering is higher in patients with larger body size, so Tc‐99m planar scintigraphy usually has lower lesion detectability in obese patients. Although photon attenuation and scattering are inevitable during image formation, their impacts on image quality can be eased by employing appropriate image protocol parameters.

PurposeThe 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.