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

Yamamoto, Hideo; Takemoto, Shota; Maebatake, Akira; Karube, Shuhei; Yamashiro, Y; Nakanishi, Atsushi; Murakami, Koji

doi:10.1007/s12149-019-01334-z

Cited by 3 publications

(7 citation statements)

References 30 publications

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“…Another difficulty of dynamic WB PET imaging is its long acquisition time. Although various groups attempted to reduce the acquisition time through selecting a fraction of the range of time windows, from 45–60 min post-injection [ 10 , 17 ] to about 0–100 min post-injection [ 18 ] in dynamic whole-body protocols, the acquisition time still remains to be optimized. As demonstrated in [ 16 , 19 – 25 ], the acquisition time is too long for patients to tolerate and hence motion artifacts might be inevitable.…”

Section: Introductionmentioning

confidence: 99%

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

Zaker

Haddad

Faghihi

et al. 2022

Eur J Nucl Med Mol Imaging

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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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Section: Introductionmentioning

confidence: 99%

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

Zaker

Haddad

Faghihi

et al. 2022

Eur J Nucl Med Mol Imaging

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“…Thest ep-and-shoot (SS) method has traditionally been used for PET data acquisition; however, the continuous bed motion (CBM) method was recently developed. In the SS method, multibed data are sequentially acquired only when the bed is stationary, not when the bed is moving (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). More than 1 min is wasted in a whole-body acquisition.…”

mentioning

confidence: 99%

“…Moreover, the axial acquisition range is determined by the number of bed positions, resulting in an unnecessary acquisition range and radiation exposure in a CT scan. In the CBM method, the bed continuously moves to acquire data (2)(3)(4)(5)(6)(7)(8)(9)(10), the axial acquisition range can be determined in a 0.1-mm unit, and the bed speed can be changed according to the body part (2,3,6,8,9). Moreover, generating a whole-body image by adding several fast whole-body scans should be useful if an examination is interrupted by patient motion or pain (7,11).…”

mentioning

confidence: 99%

“…In the CBM method, the bed continuously moves to acquire data (2)(3)(4)(5)(6)(7)(8)(9)(10), the axial acquisition range can be determined in a 0.1-mm unit, and the bed speed can be changed according to the body part (2,3,6,8,9). Moreover, generating a whole-body image by adding several fast whole-body scans should be useful if an examination is interrupted by patient motion or pain (7,11). It has been reported that patients preferred continuous bed movements over SS movements (6).…”

mentioning

confidence: 99%

“…Therefore, the CBM method may replace the SS method because of the flexibility of the PET examination for each patient. The usefulness of the CBM method in PET/CT using a photomultiplier tube system has been studied (2)(3)(4)(5)(6)(7)(8)(9)(10)(11). Differences between the SS and CBM methods did not significantly affect SUV max or SUV mean in phantoms or tumors in clinical examinations (3,4,(6)(7)(8)(9).…”

mentioning

confidence: 99%

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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

Kumamoto

Satō

Tsutsui

et al. 2022

J. Nucl. Med. Technol.

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This study investigated the spatial resolution and image quality of the continuous-bed-motion (CBM) method in a sensitive silicon photomultiplier-based PET/CT system compared with the traditional step-and-shoot (SS) method. Methods: A PET/CT scanner was used in this study. Data acquisition using the SS method was performed for 3 min per bed position. 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-position ranges and 0.8 mm/s for 7-bed-position ranges. The spatial resolution was investigated using 18 F 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 6 spheres 10, 13, 17, 22, 28, and 37 mm in inner diameter. The radioactivity concentration ratio of the 18 F solution in all spheres and the background was approximately 4:1. The detectability of each sphere was visually evaluated using a 5-step score. Image quality was physically evaluated using the noise-equivalent count rate, contrast percentage of the 10-mm hot sphere, background variability percentage, and contrast-to-noise ratio. Results: The spatial resolution was not affected by the difference in acquisition methods or 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-position method. In evaluating image quality, we observed no significant difference in contrast percentage among the acquisition methods or speeds in the CBM method. However, the increasing bed speed in the CBM method increased the background variability percentage and decreased the noise-equivalent count rate. When comparing the SS 2-bedposition method with the CBM method at 0.8 mm/s, we observed no significant differences in any parameters. Conclusion: In whole-body PET images obtained with a silicon photomultiplier-based PET/CT scanner, the CBM method provides spatial resolution and image quality equivalent to the SS method, with the same acquisition time.

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Performance Evaluation of Amplitude and Phase Respiratory Gating Methods on Continuous-Bed-Motion Whole-Body PET Studies

Tsai

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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

Cited by 3 publications

References 30 publications

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

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

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

Performance Evaluation of Amplitude and Phase Respiratory Gating Methods on Continuous-Bed-Motion Whole-Body PET Studies

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