Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks

Article Article scientifique Article Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks eng 2022-06-18 2022-10 author Zaker Neda 900398 author Haddad Kamal author Faghihi Reza author Arabi Hossein 793659 author Zaidi Habib 0000-0001-7559-5297 98447 4048-4063 10.1007/s00259-022-05867-w 35716176 1619-7070 PMC9525418 https://link.springer.com/10.1007/s00259-022-05867-w 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. Dynamic PET imaging Clinical oncology Deep learning Patlak analysis Lesion detectability Fluorodeoxyglucose F18 Humans Image Processing, Computer-Assisted / methods Neural Networks, Computer Positron Emission Tomography Computed Tomography / methods Positron-Emission Tomography / methods Whole Body Imaging / method Dewey 616.8 Dewey 616.0757 Swiss National Science Foundation Towards patient-specific hybrid whole-body PET parametric imaging 320030_176052 CH Project funding Private Foundation of Geneva University Hospitals RC-06–01 62 Département de radiologie et informatique médicale 227 16 Centre interfacultaire de neurosciences 0D9 false 287 Imagerie Médicale (TEP et TEMP) 542 European journal of nuclear medicine and molecular imaging 49 12 1745687b-cb62-4476-936a-2c5afb69f32d Article (Published version) Public s00259-022-05867-w.pdf 4002115 application/pdf CC BY-4.0 61438add-97a1-4d58-90ae-ac0bc8b6c3c8 Supplemental data Public 259_2022_5867_MOESM1_ESM.pdf 612100 application/pdf openAccess Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale Centres et instituts / Centre interfacultaire de neurosciences

Title	Published in	Year	Views	Downloads

Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks	European journal of nuclear medicine and molecular imaging	2022	138	159
Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping	Clinical Nuclear Medicine	2020	334	5

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