Purpose This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (Ki) from standardized uptake value (SUV) and/or dynamic PET image series. Methods Whole-body 18F-FDG dynamic PET images of 19 subjects consisting of 13 frames or passes were employed for training a residual deep learning model with SUV and/or dynamic series as input and Ki-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme. Owing to the availability of SUV images acquired 60 min post-injection (20 min total acquisition time), the data sets used for the training of the models were split into two groups: “With SUV” and “Without SUV.” For “With SUV” group, the model was first trained using only SUV images and then the passes (starting from pass 13, the last pass, to pass 9) were added to the training of the model (one pass each time). For this group, 6 models were developed with input data consisting of SUV, SUV plus pass 13, SUV plus passes 13 and 12, SUV plus passes 13 to 11, SUV plus passes 13 to 10, and SUV plus passes 13 to 9. For the “Without SUV” group, the same trend was followed, but without using the SUV images (5 models were developed with input data of passes 13 to 9). For model performance evaluation, the mean absolute error (MAE), mean error (ME), mean relative absolute error (MRAE%), relative error (RE%), mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between the predicted Ki-Patlak images by the two groups and the reference Ki-Patlak images generated through Patlak analysis using the whole acquired data sets. For specific evaluation of the method, regions of interest (ROIs) were drawn on representative organs, including the lung, liver, brain, and heart and around the identified malignant lesions. Results The MRAE%, RE%, PSNR, and SSIM indices across all patients were estimated as 7.45 ± 0.94%, 4.54 ± 2.93%, 46.89 ± 2.93, and 1.00 ± 6.7 × 10−7, respectively, for models predicted using SUV plus passes 13 to 9 as input. The predicted parameters using passes 13 to 11 as input exhibited almost similar results compared to the predicted models using SUV plus passes 13 to 9 as input. Yet, the bias was continuously reduced by adding passes until pass 11, after which the magnitude of error reduction was negligible. Hence, the predicted model with SUV plus passes 13 to 9 had the lowest quantification bias. Lesions invisible in one or both of SUV and Ki-Patlak images appeared similarly through visual inspection in the predicted images with tolerable bias. Conclusion This study concluded the feasibility of direct deep learning-based approach to estimate Ki-Patlak parametric maps without requiring the input function and with a fewer number of passes. This would lead to shorter acquisition times for WB dynamic imaging with acceptable bias and comparable lesion detectability performance.

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Purpose: Recently, the multileaf collimators (MLC) have become an important part of any LINAC collimation systems because they reduce the treatment planning time and improves the conformity. Important factors that affects the MLCs collimation performance are leaves material composition and their thickness. In this study, we investigate the main dosimetric parameters of 120‐leaf Millennium MLC including dose in the buildup point, physical penumbra as well as average and end leaf leakages. Effects of the leaves geometry and density on these parameters are evaluated Methods: From EGSnrc Monte Carlo code, BEAMnrc and DOSXYZnrc modules are used to evaluate the dosimetric parameters of a water phantom exposed to a Varian xi for 100cm SSD. Using IAEA phasespace data just above MLC (Z=46cm) and BEAMnrc, for the modified 120‐leaf Millennium MLC a new phase space data at Z=52cm is produces. The MLC is modified both in leaf thickness and material composition. EGSgui code generates 521ICRU library for tungsten alloys. DOSXYZnrc with the new phase space evaluates the dose distribution in a water phantom of 60×60×20 cm3 with voxel size of 4×4×2 mm3. Using DOSXYZnrc dose distributions for open beam and closed beam as well as the leakages definition, end leakage, average leakage and physical penumbra are evaluated. Results: A new MLC with improved dosimetric parameters is proposed. The physical penumbra for proposed MLC is 4.7mm compared to 5.16 mm for Millennium. Average leakage in our design is reduced to 1.16% compared to 1.73% for Millennium, the end leaf leakage suggested design is also reduced to 4.86% compared to 7.26% of Millennium. Conclusion: The results show that the proposed MLC with enhanced dosimetric parameters could improve the conformity of treatment planning.