Verification of image quality and quantification in whole-body positron emission tomography with continuous bed motion

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

Purpose

This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (K_i) 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 K_i-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 K_i-Patlak images by the two groups and the reference K_i-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 K_i-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 K_i-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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05867-w.

Continuous bed motion in a silicon photomultiplier-based scanner provides equivalent spatial resolution and image quality in whole body PET images at similar acquisition times using the step-and-shoot method

This study investigated the spatial resolution and image quality of the continuous bed motion (CBM) method in a sensitive silicon photomultiplier (SiPM)-based positron emission tomography (PET)/computed tomography (CT) system compared with the traditional step-and-shoot (SS) method. Methods: Siemens Biograph Vision was used in this study. Data acquisition using the SS method was performed for 3 min per bed. In the CBM method, the bed speed ranged from 0.5 to 3.3 mm/s. The acquisition time equivalent to the SS method was 1.1 mm/s for 2-bed ranges and 0.8 mm/s for seven-bed ranges. The spatial resolution was investigated using 18F point sources and evaluated using the full width at half maximum. Image quality was investigated using a National Electrical Manufacturers Association International Electrotechnical Commission body phantom with six spheres 10-, 13-, 17-, 22-, 28-, and 37-mm inner diameters. The radioactivity concentration ratio of the 18F solution in all spheres and the background was approximately 4:1. The detectability of each sphere was visually evaluated on a five-step score. Image quality was physically evaluated using the noise equivalent count rate (NECphantom), contrast percentage of the 10-mm hot sphere (QH,10mm), background variability percentage (N10mm), and contrast-noise ratio (QH,10mm/N10mm). Results: The spatial resolution was not affected by the difference of acquisition methods and bed speeds. The detectability of the 10-mm sphere with a bed speed of 2.2 mm/s or faster was significantly inferior to that of the SS 2-bed method. In evaluating image quality, no significant difference in the contrast percentage was observed among the acquisition methods and speeds in the CBM method. However, the increasing bed speed in the CBM method increased the N10mm and decreased the NECphantom. When comparing the SS 2-bed method with the CBM method at 0.8 mm/s, no significant differences in all parameters were observed. Conclusion: In a SiPM-based PET/CT scanner, the CBM method provides equivalent spatial resolution and image quality in whole body PET images with same acquisition time using the SS method.