Dose Reduction in Pediatric Oncology Patients with Delayed Total-Body [18F]FDG PET/CT
=====================================================================================

* Clemens Mingels
* Benjamin A. Spencer
* Hande Nalbant
* Negar Omidvari
* Mehrad Rokni
* Axel Rominger
* Fatma Sen
* Simon R. Cherry
* Ramsey D. Badawi
* Yasser G. Abdelhafez
* Lorenzo Nardo

## Visual Abstract

![Figure1](http://jnm.snmjournals.org/https://jnm.snmjournals.org/content/jnumed/65/7/1101/F1.medium.gif)

[Figure1](http://jnm.snmjournals.org/content/65/7/1101/F1)

## Abstract

Our aim was to define a lower limit of reduced injected activity in delayed [18F]FDG total-body (TB) PET/CT in pediatric oncology patients. **Methods:** In this single-center prospective study, children were scanned for 20 min with TB PET/CT, 120 min after intravenous administration of a 4.07 ± 0.49 MBq/kg dose of [18F]FDG. Five randomly subsampled low-count reconstructions were generated using ¼, ⅛, ![Formula][1]</img>, and ![Formula][2]</img> of the counts in the full-dose list-mode reference standard acquisition (20 min), to simulate dose reduction. For the 2 lowest-count reconstructions, smoothing was applied. Background uptake was measured with volumes of interest placed on the ascending aorta, right liver lobe, and third lumbar vertebra body (L3). Tumor lesions were segmented using a 40% isocontour volume-of-interest approach. Signal-to-noise ratio, tumor-to-background ratio, and contrast-to-noise ratio were calculated. Three physicians identified malignant lesions independently and assessed the image quality using a 5-point Likert scale. **Results:** In total, 113 malignant lesions were identified in 18 patients, who met the inclusion criteria. Of these lesions, 87.6% were quantifiable. Liver SUVmean did not change significantly, whereas a lower signal-to-noise ratio was observed in all low-count reconstructions compared with the reference standard (*P* < 0.0001) because of higher noise rates. Tumor uptake (SUVmax), tumor-to-background ratio, and total lesion count were significantly lower in the reconstructions with ![Formula][3]</img> and ![Formula][4]</img> of the counts of the reference standard (*P* < 0.001). Contrast-to-noise ratio and clinical image quality were significantly lower in all low-count reconstructions than with the reference standard. **Conclusion:** Dose reduction for delayed [18F]FDG TB PET/CT imaging in children is possible without loss of image quality or lesion conspicuity. However, our results indicate that to maintain comparable tumor uptake and lesion conspicuity, PET centers should not reduce the injected [18F]FDG activity below 0.5 MBq/kg when using TB PET/CT in pediatric imaging at 120 min after injection.

*   total-body PET/CT
*   dose reduction
*   pediatric oncology
*   long-axial-field-of-view PET/CT
*   [18F]FDG

PET/CT has undergone rapid development since its first introduction (1). The latest paradigm shift was achieved by the introduction of long-axial-field-of-view and total-body (TB) PET/CT systems (2–5). The increased signal collection efficiency of these novel systems enables significant reduction of the injected radiopharmaceutical activity (dose reduction). In addition, delayed imaging time points (e.g., 120 min after injection) are possible, allowing further clearance of the radiotracer (4*,*6–11). With short-axial-field-of-view PET/CT, it has been shown that some tumors demonstrated higher [18F]FDG uptake on delayed images (12*,*13) whereas benign lesions showed reduced tracer uptake over time (14). Therefore, delayed imaging was routinely established with the introduction of TB PET/CT to increase the image quality, which is of particular interest in pediatric patients (10).

Children with oncologic diseases such as lymphoma, sarcoma, and other soft-tissue malignancies are frequently referred to PET centers for staging or restaging (15). Accurate imaging is crucial because the therapy regime often relies on the PET/CT result (16). However, the scanning of children—compared with adults—harbors specific challenges. Accurate coregistration of PET and CT is needed to provide the high image quality necessary for interpretation. Because of patient motion, accurate coregistration is often not possible, especially in younger children. Moreover, children are more vulnerable to radiation, with an increased risk of developing secondary malignancies (17). It has been reported that the risk of developing childhood leukemia increases by 12%/mSv of cumulative bone marrow dose (18) and that 9% of all thyroid cancer cases are related to ionizing radiation (19). Therefore, many attempts have been made to reduce the radiation exposure of children undergoing frequent diagnostic procedures involving ionizing radiation (20–22).

First experiences with TB PET/CT have proven efficient in dose reduction (4). Chen et al. reported that half-dose imaging with [18F]FDG TB PET/CT was sufficient in a cohort of 100 children, even with an acquisition of only 60 s, when 600 s was the reference standard (22). Consequently, ultra-low-dose [18F]FDG PET/CT is theoretically possible with only ![Formula][5]</img> of the clinical reference injected activity, which would reduce the effective dose of a pediatric PET scan to 0.18–0.26 mSv (22–24).

However, in late imaging protocols, which help reduce background and blood-pool activity but also diminish the overall count rate because of a higher amount of decayed activity, the feasibility of dose reduction has not yet been evaluated (25). The first aim of this study was to provide the methodology to evaluate the best reconstruction parameters for reduced injected activity in delayed imaging of pediatric oncology patients with TB PET/CT. The second aim was to assess the full potential of dose reduction clinically. The primary endpoint was to determine the lower limit of dose reduction with an image reconstruction protocol optimized for late [18F]FDG TB PET/CT imaging.

## MATERIALS AND METHODS

This study was approved by the University of California Davis institutional review board (approval 1470016). The study was performed in accordance with the Declaration of Helsinki.

### Patient Population, Radiopharmaceutical, and Imaging

This was a retrospective analysis of prospectively collected registry-type data. Nineteen pediatric subjects’ data were available for this study after written informed consent was obtained by the parents or guardian, providing access to the subjects’ primary staging and follow-up examinations. From this cohort of 19, the initial staging examination was used for image evaluation, whereas the follow-up TB PET/CT scans from a subcohort of 12 of the 19 patients were used to optimize the reconstruction parameters. All patients underwent clinical routine [18F]FDG PET/CT on a TB PET/CT scanner (uEXPLORER; United Imaging Healthcare). Subjects fasted for at least 6 h before a 4.07 ± 0.49 MBq/kg intravenous injection of [18F]FDG. All patients underwent TB PET/CT scans 120 min after injection. A 20-min list-mode acquisition was recorded for all scans.

Inclusion criteria for the analysis were an age of less than 18 y, biopsy-proven oncologic disease, and the ability to successfully complete the clinical 20-min TB PET/CT scan without significant intrascan motion or use of anesthesia.

### Data Preparation Using List-Mode Subsampling

Using short-frame reconstructions of a PET dataset for simulating low-count imaging will result in inherent interframe differences in the count distribution because of motion, physiologic uptake changes, and radioactive decay. To overcome this issue for a quantitative evaluation of low-count imaging scenarios, randomly subsampled low-count reconstructions were generated from the originally acquired raw list-mode data to simulate 4 reduced-dose datasets with approximately ¼, ⅛, ![Formula][6]</img>, and ![Formula][7]</img> of the total counts. The subsampling was performed by, first, randomly shuffling the list-mode coincidence events in the PET raw data and, subsequently, running a 4-frame dynamic reconstruction on the randomly shuffled dataset consisting of ¼, ⅛, ![Formula][8]</img>, and ![Formula][9]</img> of the reference standard. The dynamic frames had no overlap (i.e., a unique set of events used within each frame), to avoid interframe noise correlation in the quantitative comparison. The methodology is displayed in Figure 1. All standard data corrections were included in all reconstructions.

![FIGURE 1.](http://jnm.snmjournals.org/https://jnm.snmjournals.org/content/jnumed/65/7/1101/F2.medium.gif)

[FIGURE 1.](http://jnm.snmjournals.org/content/65/7/1101/F2)

FIGURE 1. 
Graphical visualization of data shuffling method. Available full data were rearranged and consecutively subsampled into portions of total injected activity.

### Reconstruction Parameter Optimization Study

The reconstruction parameter optimization study was performed by subsampling low-count reconstructions on the follow-up examinations of 12 pediatric subjects, with 4 reconstruction parameter sets for each subsampled dose, to provide a designated optimized reconstruction parameter set to be used in the image evaluation study of the 18 pediatric subjects. The 4 tested reconstruction parameter sets were chosen by the TB PET imaging team. This led to a dataset with 12 subjects, each with 4 subsampled acquisitions and 4 reconstruction parameter sets (12 × 4 × 4). For each subject and each subsampled acquisition, 3 nuclear medicine physicians reviewed the 4 image volumes side by side in randomized order and ranked them from best to worst. The preferred reconstruction parameter set was selected by consensus by 3 nuclear medicine physicians and was subsequently used for each reduced dose of the 18 initial staging examinations, providing a total of 18 × 4 subsampled low-count reconstructions (Table 1).

View this table:
[TABLE 1.](http://jnm.snmjournals.org/content/65/7/1101/T1)

TABLE 1. 
Selected Reconstruction Parameters for Each Reduced Dose by Consensus from 3 Nuclear Medicine Physicians During Reconstruction Parameter Optimization

### Semiquantitative Image Analysis

Tumor lesion uptake was quantified using a 40% isocontour volume of interest placed on the lesion as previously described (4*,*26). SUVmax and SUVpeak were used to assess the target lesion uptake. The background activity concentration was measured by placing a 30-mm-diameter spheric volume of interest in the right lobe of healthy liver tissue (27). An additional 10-mm-diameter sphere was placed on the ascending aorta and on the third lumbar vertebral body (L3) to characterize healthy organ uptake. In all cases, volumes of interest were initially placed on the reference standard image and then copied to the same anatomic locations in different reconstructions to avoid errors.

The signal-to-noise ratio (SNR) was defined as the reciprocal coefficient of variation of the liver background as follows:![Formula][10]</img>where σ is the SD of the background volume of interest and μ is the SUVmean of the background volume of interest (4). Tumor-to-background ratio (TBR) was defined from the ratio of tumor uptake (SUVmax) to liver uptake (SUVmean) as follows:![Formula][11]</img>

Contrast-to-noise ratio (CNR) was calculated as follows:![Formula][12]</img>as previously published (28). For semiquantitative image analysis, OsiriX version 13.0 (Pixmeo SARL) was used.

### Visual Assessments of Lesion Detectability and Image Quality

Three nuclear medicine physicians evaluated lesion detectability. The number of malignant lesions per patient was counted to a maximum of 10 lesions. Image quality was rated on a 5-point Likert scale: 1 was classified as nondiagnostic and 5 as the state of the art (29). To minimize bias, reconstructions were anonymized and displayed randomly during the clinical evaluation.

### Statistical Analysis

Statistical analysis was performed using Excel (Microsoft) and GraphPad Prism version 10 (30). Semiquantitative data are presented as mean ± SD or as median and range. Reconstructions were analyzed using paired *t* testing after checking for a normal distribution. *P* values of less than 0.05 were considered statistically significant.

## RESULTS

Eighteen of the participants met the inclusion criteria. One patient was excluded because a 20-min acquisition was not possible in the clinical setting. From the total of 18 pediatric oncology patients included, 13 were male and 5 female, with a median age of 12 y (range, 5–17 y) and an average body mass index of 22.33 ± 4.93 kg/m2. Patient characteristics are outlined in the supplemental materials (available at [http://jnm.snmjournals.org](http://jnm.snmjournals.org)). In the reference standard images, 113 malignant lesions were identified. Of these, 99 (87.6%) could be analyzed with the 40% isocontour approach in all reconstructions. The study flowchart is shown in the supplemental materials.

### Reconstruction Parameter Optimization Study

Table 1 presents the optimized reconstruction parameters selected by the 3 nuclear medicine physicians. Increased smoothing was preferred for the reduced-dose reconstructions, specifically for ![Formula][13]</img>- and ![Formula][14]</img>-count levels. For the ¼ and ⅛ reductions, the nuclear medicine physicians preferred the standard clinical reconstruction parameters except with the incorporation of point-spread function modeling.

### Physiologic Uptake and SNR

Although liver SUVmean did not significantly differ between the low-count reconstructions and the 20-min reference standard acquisition, liver SUVmax and SD were significantly higher in all low-count reconstructions (*P* < 0.001). Furthermore, liver SNR was significantly lower in all low-count reconstructions than with the reference standard (*P* < 0.0001) (Fig. 2). There was no significant difference in liver SNR among the low-count reconstructions. Blood pool and bone (L3) uptake and SNRs are outlined in the supplemental materials.

![FIGURE 2.](http://jnm.snmjournals.org/https://jnm.snmjournals.org/content/jnumed/65/7/1101/F3.medium.gif)

[FIGURE 2.](http://jnm.snmjournals.org/content/65/7/1101/F3)

FIGURE 2. 
Liver SUVmean (A) and SUVmax (B) in all low-count reconstructions, compared with full injected activity images, and SD (C) and SNR (D) in liver volumes of interest. **P* < 0.05. ***P* < 0.01. \***|*P* < 0.001. \**\*|\**P* < 0.0001.

### Tumor Uptake and Contrast

In total, 99 lesions were analyzed. Tumor uptake quantification was significantly affected in the 2 lowest-count reconstructions, showing a significantly lower (*P* < 0.001) tumor SUVmax in the ![Formula][15]</img> and ![Formula][16]</img> images than with the reference standard (Fig. 3). Tumor-to-background ratio was also significantly lower in these reconstructions (![Formula][17]</img> and ![Formula][18]</img>, *P* < 0.0001), whereas the tumor-to-background ratio of the images with ¼ and ⅛ of the counts of the reference standard showed no significant change from the full-dose image (*P* = 0.46 and 0.16) (Fig. 3). However, contrast-to-noise ratio was significantly lower in all low-count reconstructions than with the full-dose reference standard (*P* < 0.0001) (Fig. 3).

![FIGURE 3.](http://jnm.snmjournals.org/https://jnm.snmjournals.org/content/jnumed/65/7/1101/F4.medium.gif)

[FIGURE 3.](http://jnm.snmjournals.org/content/65/7/1101/F4)

FIGURE 3. 
(A) Tumor SUVmax in all reconstructions, compared with full-injected-activity images. (B) Tumor-to-background ratio (TBR) in all reconstructions, compared with full-injected-activity images. (C) Contrast-to-noise ratio (CNR) in all reconstructions, compared with full-injected-activity images. **P* < 0.05. ***P* < 0.01. \***|*P* < 0.001. \**\*|\**P* < 0.0001.

### Clinical Image Evaluation and Number of Identified Lesions

The visual image quality assessments of all low-count reconstructions showed significantly lower Likert scale ratings than with the clinical reference standard (Fig. 4). The median rating of the full-dose reference image was 5 and was not affected in the ¼-dose image. The median rating decreased to 4 for the ⅛-dose image, and the most impaired images (*P* < 0.0001) were found at ![Formula][19]</img> (median, 3) and ![Formula][20]</img> (median, 3) of the counts, as expected.

![FIGURE 4.](http://jnm.snmjournals.org/https://jnm.snmjournals.org/content/jnumed/65/7/1101/F5.medium.gif)

[FIGURE 4.](http://jnm.snmjournals.org/content/65/7/1101/F5)

FIGURE 4. 
(A) Image quality on Likert scale (1–5) identified by 3 independent readers for all reconstructions, compared with full-injected-activity images. (B) Number of lesions identified by 3 independent readers for all reconstructions, compared with full-injected-activity images. **P* < 0.05. ***P* < 0.01. \***|*P* < 0.001. \**\*|\**P* < 0.0001.

The visual lesion detectability evaluation revealed differences in the number of identified lesions throughout the different low-count reconstructions. Although a maximum of 113 lesions was identified in the clinical reference standard images by the 3 different readers independently, a maximum of 107 and 105 lesions was identified in the ¼- and ⅛-dose reconstructions, respectively. These differences in lesion count were not significantly different from the reference standard (*P* = 0.17 and *P* = 0.11, respectively). However, we found significant differences in the number of identified lesions in the reconstructions with ![Formula][21]</img> and ![Formula][22]</img> of the counts of the reference standard. In the ![Formula][23]</img>-count reconstruction, a total of 102 lesions was identified (*P* = 0.04), and in the ![Formula][24]</img>-count reconstruction, readers identified only 88 lesions (*P* < 0.0001). Figure 4 shows the number of lesions identified by the 3 readers per patient, in which the medians were 8, 5.5, 5, 5, and 3.5 lesions per patient for the reference standard and for ¼, ⅛, ![Formula][25]</img>, and ![Formula][26]</img> of the reference standard, respectively.

Figure 5 illustrates an example of a 9-y-old patient for whom the detectability of the axillary lymphoma lesions was impaired by the increased noise level in the 2 lowest-count reconstructions, resulting in an inability to detect the lesion visually.

![FIGURE 5.](http://jnm.snmjournals.org/https://jnm.snmjournals.org/content/jnumed/65/7/1101/F6.medium.gif)

[FIGURE 5.](http://jnm.snmjournals.org/content/65/7/1101/F6)

FIGURE 5. 
Different reconstructions for 9-y-old lymphoma patient: ![Formula][27]</img> dose (A), ![Formula][28]</img> dose (B) ⅛ dose (C), ¼ dose (D), full dose (E). A–E show coronal slices in middle of body. Arrows indicate axillary lymphoma lesions.

## DISCUSSION

In this study investigating the lower limit for injected [18F]FDG activity reduction in pediatric oncology patients, we evaluated the first data for delayed TB PET/CT in a prospective cohort. Showing that reduced injected activities are possible even in delayed imaging protocols may have the advantage of reducing absorbed doses in children, may reduce costs for the PET center, and may reduce diagnostic error due to higher tracer uptake in some malignant lesions and less uptake in some benign ones (13).

In our new method, we randomly shuffled the list-mode clinical reference standard acquisition of 20 min after injection to simulate reduction of injected activity. Clinical evaluation concluded that after a limit of ⅛ of the reference standard, additional smoothing is required to compensate for the increased noise level (Table 1). This finding could be verified in the semiquantitative and qualitative image analysis of this study.

Liver SNR significantly decreased in the low-count reconstructions compared with the reference standard acquisition. However, we found no significant difference in the liver SNR among the reduced low-count reconstructions (⅛, ![Formula][29]</img>, and ![Formula][30]</img>), indicating that the reconstruction parameter optimization was mitigating this SNR degradation, as intended. The stable SNR was achieved by completing the first goal of the study to establish the methodology for reconstruction parameter optimization. We note that past studies missed this approach of reconstruction optimization (24*,*31–33). Therefore, this study ensured that these low-count datasets are a realistic representation of how dose reduction would be seen clinically.

This optimization was necessary because reducing the counts in any PET imaging protocol directly leads to increased noise (34). Furthermore, it is not expected that the reconstruction parameters suitable for a full-dose 20-min acquisition are also suitable for an acquisition with far lower count statistics (e.g., with ![Formula][31]</img> or ![Formula][32]</img> of the reference standard). However, previous approaches toward determining a lower limit for imaging pediatric oncology patients have not implemented image reconstruction optimization and, therefore, did not exploit the full potential of TB PET/CT in pediatric oncology (24*,*35).

Thus, even with the optimization of especially the low-count reconstructions (![Formula][33]</img> and ![Formula][34]</img>), we noted significantly decreased tumor uptake (*P* < 0.001), whereas the tumor uptake of ¼ or ⅛ of the reference standard was not significantly reduced (Fig. 3). This finding sets the lower limit of count reduction to ⅛ (i.e., 0.5 MBq/kg) of our clinical reference standard.

In agreement with the limit of injecting a 0.5 MBq/kg dose of [18F]FDG in delayed TB PET/CT, the lesion conspicuity in the 2 lowest-count reconstructions was impaired, resulting in a significantly lower lesion count by the 3 independent readers (![Formula][35]</img>, 90.3% of lesions identified [*P* = 0.04]; ![Formula][36]</img>, 77.9% of lesions identified [*P* < 0.0001]). Moreover, these reconstructions (![Formula][37]</img> and ![Formula][38]</img>) were found to be barely diagnostic, with ill-defined lesion conspicuity and impairment of diagnostic confidence. The subjective image noise was rated as increased to excessive and was found to be worse than average, according to the applied 5-point Likert scale (29). The clinical benefit of these 2 reconstructions is—despite the applied reconstruction optimization protocol—doubtful.

Our findings on the lower limits of count reduction for pediatric oncologic imaging agree well with the current [18F]FDG PET/CT oncologic examination guideline published by the European Association of Nuclear Medicine, which suggests a time–activity product of at least 7 MBq·min·bed−1·kg−1 for tumor imaging at 60 min after injection with short-axial-field-of-view PET/CT scanners (36). We showed that image quality is still acceptable, without significant changes in tumor uptake, tumor-to-background ratio, or lesion count, with an injected activity of 0.5 MBq/kg for delayed TB PET/CT scans in pediatric patients. A 0.5 MBq/kg injected activity translates to a time–activity product of 10 MBq·min·kg−1 for our delayed imaging protocol. Notably, this would correspond to 7 MBq·min·kg−1 for an [18F]FDG TB PET/CT scan at 60 min after injection (36), showing even more potential to reduce the injected [18F]FDG activity with TB PET/CT both at 60 min after injection and in the delayed protocols.

Some limitations of our study should be acknowledged. First, the sample size of 18 patients who met the inclusion criteria was small. However, our lesion-based analysis detected 113 malignant lesions and 99 analyzed lesions, strengthening our findings. Moreover, the study was not designed as a lesion detectability study. Nevertheless, our approach was sufficient to discriminate differences among the 4 subsampled datasets. Second, our study investigated potential use of lower injected activities using the subsampled list-mode data from a 20-min acquisition. Given that the subsampled list-mode data contain the same randoms rate and dead-time losses as the reference standard injected activity (4.07 MBq/kg), our method is an approximation. On the basis of Spencer et al., we estimate that this would equate to approximately a 4% improvement in true count rates (37). Therefore, in a real clinical setting we expect slightly better image quality than in our subsampled images because of a lower randoms rate and less dead time. The conclusions regarding reduced dose may be underestimated, and in a real clinical setting even lower injected activities might still yield diagnostic quality. Further studies may focus on true reduction of injected activities and compare the data with early PET/CT scans or scans from an adult patient.

In this preliminary study, only 4 low-count scans were investigated. This enabled us to define a limit of possible dose reductions at ⅛ of the reference standard. However, there might still be a possibility to reduce the injected activity between the limit of ⅛ and the next lower-count reconstruction (![Formula][39]</img>) further. It is noteworthy that the patient cohort, with a median age of 12 y (range, 5–17 y) and an average body mass index of 22.33 ± 4.93 kg/m2, was relatively mature. This patient cohort was selected because of the inclusion criteria and clinical imaging settings, which did not allow sedation during the examination.

## CONCLUSION

Injected activity reduction with delayed [18F]FDG TB PET/CT imaging in children is possible without loss of image quality or lesion conspicuity. This study supported the reduction of injected radiotracer activity, especially in children who may undergo multiple follow-up studies. Our results indicate that the lower limit for delayed [18F]FDG TB PET/CT imaging in pediatric oncology patients is 0.5 MBq/kg. Dose reduction can be generalized to other long-axial-field-of-view scanners; however, the precise magnitude of the reduced injected activity may be scanner-specific.

## DISCLOSURE

Research reported in this publication was supported by the National Institutes of Health under award R01CA249422. The work was also supported by the In Vivo Translational Imaging Shared Resources with funds from NCI P30CA093373 and by the Fred and Julia Rusch Foundation for Nuclear Medicine Research and Education. Hande Nalbant’s funding is partially provided by United Imaging Healthcare’s UIH Fellowship Gift. Axel Rominger has received research support and speaker honoraria from Siemens. Lorenzo Nardo is the principal investigator of a service agreement with United Imaging Healthcare. Lorenzo Nardo is the site principal investigator of clinical trials supported by Novartis Pharmaceuticals Corp. Lorenzo Nardo is the principal investigator of clinical trials supported by Telix Pharmaceuticals, Lantheus Medical Imaging, and GE Healthcare. Lorenzo Nardo is coprincipal investigator of a clinical trial supported by Lilly. Ramsey Badawi is the principal investigator of a clinical trial supported by Lilly. Simon Cherry and Ramsey Badawi received research support from United Imaging Healthcare during the course of this work. The University of California Davis has a revenue sharing agreement with United Imaging Healthcare. Fatma Sen is the principal investigator of clinical research sponsored by Biogen. No other potential conflict of interest relevant to this article was reported.

#### KEY POINTS

**QUESTIONS:** Can we reduce the injected activity in delayed imaging protocols scanning pediatric oncology patients with [18F]FDG TB PET/CT?

**PERTINENT FINDINGS:** In this prospective single-center study, delayed [18F]FDG TB PET/CT in pediatric oncology patients enabled a reduction in the injected radioactivity (0.5 MBq/kg with a 20-min acquisition).

**IMPLICATIONS FOR PATIENT CARE:** [18F]FDG TB PET/CT with delayed imaging protocols in pediatric oncology patients can decrease ionized radiation exposure.

## ACKNOWLEDGMENTS

We thank the EXPLORER Molecular Imaging Center clinical research and compliance team, especially Phu Huynh, Dana Little, Ofilio Vigil, Lynda Painting, and Anh Nguyen.

## Footnotes

*   Published online Apr. 25, 2024.

*   © 2024 by the Society of Nuclear Medicine and Molecular Imaging.

## REFERENCES

1.  1.Beyer T, Townsend DW, Brun T, et al. A combined PET/CT scanner for clinical oncology. J Nucl Med. 2000;41:1369–1379.
    
    [Abstract/FREE Full Text](http://jnm.snmjournals.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Njoiam51bWVkIjtzOjU6InJlc2lkIjtzOjk6IjQxLzgvMTM2OSI7czo0OiJhdG9tIjtzOjIyOiIvam51bWVkLzY1LzcvMTEwMS5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=) 

2.  2.Karp JS, Viswanath V, Geagan MJ, et al. PennPET Explorer: design and preliminary performance of a whole-body imager. J Nucl Med. 2020;61:136–143.
    
    [Abstract/FREE Full Text](http://jnm.snmjournals.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Njoiam51bWVkIjtzOjU6InJlc2lkIjtzOjg6IjYxLzEvMTM2IjtzOjQ6ImF0b20iO3M6MjI6Ii9qbnVtZWQvNjUvNy8xMTAxLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

3.  3.Badawi RD, Shi H, Hu P, et al. First human imaging studies with the EXPLORER total-body PET scanner. J Nucl Med. 2019;60:299–303.
    
    [Abstract/FREE Full Text](http://jnm.snmjournals.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Njoiam51bWVkIjtzOjU6InJlc2lkIjtzOjg6IjYwLzMvMjk5IjtzOjQ6ImF0b20iO3M6MjI6Ii9qbnVtZWQvNjUvNy8xMTAxLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

4.  4.Alberts I, Hünermund J-N, Prenosil G, et al. Clinical performance of long axial field of view PET/CT: a head-to-head intra-individual comparison of the Biograph Vision Quadra with the Biograph Vision PET/CT. Eur J Nucl Med Mol Imaging. 2021;48:2395–2404.
    
    [CrossRef](http://jnm.snmjournals.org/lookup/external-ref?access_num=10.1007/s00259-021-05282-7&link_type=DOI) 

5.  5.Mingels C, Caobelli F, Alavi A, et al. Total-body PET/CT or LAFOV PET/CT? Axial field-of-view clinical classification. Eur J Nucl Med Mol Imaging. 2024;51:951–953.
    
    

6.  6.Alberts I, Sari H, Mingels C, et al. Long-axial field-of-view PET/CT: perspectives and review of a revolutionary development in nuclear medicine based on clinical experience in over 7000 patients. Cancer Imaging. 2023;23:28.
    
    

7.  7.Alberts I, Prenosil G, Mingels C, et al. Feasibility of late acquisition [68Ga]Ga-PSMA-11 PET/CT using a long axial field-of-view PET/CT scanner for the diagnosis of recurrent prostate cancer: first clinical experiences. Eur J Nucl Med Mol Imaging. 2021;48:4456–4462.
    
    

8.  8.Mingels C, Weidner S, Sari H, et al. Impact of the new ultra-high sensitivity mode in a long axial field-of-view PET/CT. Ann Nucl Med. 2023;37:310–315.
    
    

9.  9.Alberts I, Sachpekidis C, Prenosil G, et al. Digital PET/CT allows for shorter acquisition protocols or reduced radiopharmaceutical dose in [18F]-FDG PET/CT. Ann Nucl Med. 2021;35:485–492.
    
    

10. 10.Nardo L, Abdelhafez YG, Spencer BA, Badawi RD. Clinical implementation of total-body PET/CT at University of California, Davis. PET Clin. 2021;16:1–7.
    
    

11. 11.Zhang Q, Hu Y, Zhou C, et al. Reducing pediatric total-body PET/CT imaging scan time with multimodal artificial intelligence technology. EJNMMI Phys. 2024;11:1.
    
    

12. 12.Cheng G, Torigian DA, Zhuang H, Alavi A. When should we recommend use of dual time-point and delayed time-point imaging techniques in FDG PET? Eur J Nucl Med Mol Imaging. 2013;40:779–787.
    
    [CrossRef](http://jnm.snmjournals.org/lookup/external-ref?access_num=10.1007/s00259-013-2343-9&link_type=DOI) 
    
    [PubMed](http://jnm.snmjournals.org/lookup/external-ref?access_num=23361859&link_type=MED&atom=%2Fjnumed%2F65%2F7%2F1101.atom) 

13. 13.Kubota K, Itoh M, Ozaki K, et al. Advantage of delayed whole-body FDG-PET imaging for tumour detection. Eur J Nucl Med. 2001;28:696–703.
    
    [CrossRef](http://jnm.snmjournals.org/lookup/external-ref?access_num=10.1007/s002590100537&link_type=DOI) 
    
    [PubMed](http://jnm.snmjournals.org/lookup/external-ref?access_num=11440029&link_type=MED&atom=%2Fjnumed%2F65%2F7%2F1101.atom) 

14. 14.Basu S, Kung J, Houseni M, Zhuang H, Tidmarsh GF, Alavi A. Temporal profile of fluorodeoxyglucose uptake in malignant lesions and normal organs over extended time periods in patients with lung carcinoma: implications for its utilization in assessing malignant lesions. Q J Nucl Med Mol Imaging. 2009;53:9–19.
    
    [PubMed](http://jnm.snmjournals.org/lookup/external-ref?access_num=18337683&link_type=MED&atom=%2Fjnumed%2F65%2F7%2F1101.atom) 

15. 15.McCarville MB. PET-CT imaging in pediatric oncology. Cancer Imaging. 2009;9:35–43.
    
    [CrossRef](http://jnm.snmjournals.org/lookup/external-ref?access_num=10.1102/1470-7330.2009.0008&link_type=DOI) 
    
    [PubMed](http://jnm.snmjournals.org/lookup/external-ref?access_num=19602470&link_type=MED&atom=%2Fjnumed%2F65%2F7%2F1101.atom) 

16. 16.Vali R, Alessio A, Balza R, et al. SNMMI procedure standard/EANM practice guideline on pediatric 18F-FDG PET/CT for oncology 1.0. J Nucl Med. 2021;62:99–110.
    
    [Abstract/FREE Full Text](http://jnm.snmjournals.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Njoiam51bWVkIjtzOjU6InJlc2lkIjtzOjc6IjYyLzEvOTkiO3M6NDoiYXRvbSI7czoyMjoiL2pudW1lZC82NS83LzExMDEuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

17. 17.Kutanzi KR, Lumen A, Koturbash I, Miousse IR. Pediatric exposures to ionizing radiation: carcinogenic considerations. Int J Environ Res Public Health. 2016;13:1057.
    
    

18. 18.Kendall GM, Little MP, Wakeford R, et al. A record-based case-control study of natural background radiation and the incidence of childhood leukaemia and other cancers in Great Britain during 1980-2006. Leukemia. 2013;27:3–9.
    
    [CrossRef](http://jnm.snmjournals.org/lookup/external-ref?access_num=10.1038/leu.2012.151&link_type=DOI) 
    
    [PubMed](http://jnm.snmjournals.org/lookup/external-ref?access_num=22766784&link_type=MED&atom=%2Fjnumed%2F65%2F7%2F1101.atom) 
    
    [Web of Science](http://jnm.snmjournals.org/lookup/external-ref?access_num=000313511400002&link_type=ISI) 

19. 19.Ron E, Kleinerman RA, Boice JD. LiVolsi VA, Flannery JT, Fraumeni JF. A population-based case-control study of thyroid cancer. J Natl Cancer Inst. 1987;79:1–12.
    
    [PubMed](http://jnm.snmjournals.org/lookup/external-ref?access_num=3474436&link_type=MED&atom=%2Fjnumed%2F65%2F7%2F1101.atom) 
    
    [Web of Science](http://jnm.snmjournals.org/lookup/external-ref?access_num=A1987J069300001&link_type=ISI) 

20. 20.Tang S, Hu Y, Zeng J, et al. Significant CT dose reduction of 2-[18F]FDG PET/CT in pretreatment pediatric lymphoma without compromising the diagnostic and staging efficacy. Eur Radiol. 2023;33:2248–2257.
    
    

21. 21.Waelti S, Skawran S, Sartoretti T, et al. A third of the radiotracer dose: two decades of progress in pediatric [18F]fluorodeoxyglucose PET/CT and PET/MR imaging. Eur Radiol. October 19, 2023 [Epub ahead of print].
    
    

22. 22.Chen W, Liu L, Li Y, et al. Evaluation of pediatric malignancies using total-body PET/CT with half-dose [18F]-FDG. Eur J Nucl Med Mol Imaging. 2022;49:4145–4155.
    
    

23. 23.Tan H, Qi C, Cao Y, et al. Ultralow-dose [18F]FDG PET/CT imaging: demonstration of feasibility in dynamic and static images. Eur Radiol. 2023;33:5017–5027.
    
    

24. 24.Zhao Y-M, Li Y-H, Chen T, et al. Image quality and lesion detectability in low-dose pediatric 18F-FDG scans using total-body PET/CT. Eur J Nucl Med Mol Imaging. 2021;48:3378–3385.
    
    

25. 25.Costantini DL, Vali R, Chan J, McQuattie S, Charron M. Dual-time-point FDG PET/CT for the evaluation of pediatric tumors. AJR. 2013;200:408–413.
    
    [CrossRef](http://jnm.snmjournals.org/lookup/external-ref?access_num=10.2214/AJR.12.8930&link_type=DOI) 
    
    [PubMed](http://jnm.snmjournals.org/lookup/external-ref?access_num=23345365&link_type=MED&atom=%2Fjnumed%2F65%2F7%2F1101.atom) 

26. 26.Lee H, Paeng JC, Hong SH, et al. Appropriate margin thresholds for isocontour metabolic volumetry of fluorine-18 fluorodeoxyglucose PET in sarcoma: a hybrid PET/MRI study. Nucl Med Commun. 2016;37:1088–1094.
    
    

27. 27.Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50(suppl 1):122S–150S.
    
    [Abstract/FREE Full Text](http://jnm.snmjournals.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Njoiam51bWVkIjtzOjU6InJlc2lkIjtzOjE1OiI1MC9TdXBwbF8xLzEyMlMiO3M6NDoiYXRvbSI7czoyMjoiL2pudW1lZC82NS83LzExMDEuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

28. 28.Yan J, Schaefferkoetter J, Conti M, Townsend D. A method to assess image quality for low-dose PET: analysis of SNR, CNR, bias and image noise. Cancer Imaging. 2016;16:26.
    
    

29. 29.Calderón E, Schmidt FP, Lan W, et al. Image quality and quantitative PET parameters of low-dose [18F]FDG PET in a long axial field-of-view PET/CT scanner. Diagnostics (Basel). 2023;13:3240.
    
    

30. 30.Mingels C, Sachpekidis C, Bohn KP, et al. The influence of colour scale in lesion detection and patient-based sensitivity in [68Ga]Ga-PSMA-PET/CT. Nucl Med Commun. 2021;42:495–502.
    
    

31. 31.Li Y, Wang J, Hu J, et al. PET/CT scan without sedation: how to use total-body PET/CT to salvage child’s involuntary movement? Eur J Nucl Med Mol Imaging. 2023;50:2912–2913.
    
    

32. 32.Chen W, Liu L, Li Y, et al. Evaluation of pediatric malignancies using total-body PET/CT with half-dose [18F]-FDG. Eur J Nucl Med Mol Imaging. 2022;49:4145–4155.
    
    

33. 33.Dickson J, Eberlein U, Lassmann M. The effect of modern PET technology and techniques on the EANM paediatric dosage card. Eur J Nucl Med Mol Imaging. 2022;49:1964–1969.
    
    

34. 34.MacDonald LR, Harrison RL, Alessio AM, Hunter WCJ, Lewellen TK, Kinahan PE. Effective count rates for PET scanners with reduced and extended axial field of view. Phys Med Biol. 2011;56:3629–3643.
    
    [CrossRef](http://jnm.snmjournals.org/lookup/external-ref?access_num=10.1088/0031-9155/56/12/011&link_type=DOI) 
    
    [PubMed](http://jnm.snmjournals.org/lookup/external-ref?access_num=21610291&link_type=MED&atom=%2Fjnumed%2F65%2F7%2F1101.atom) 

35. 35.Mohammadi N, Akhlaghi P. Evaluation of radiation dose to pediatric models from whole body PET/CT imaging. J Appl Clin Med Phys. 2022;23:e13545.
    
    

36. 36.Boellaard R, Delgado-Bolton R, Oyen WJG, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–354.
    
    [CrossRef](http://jnm.snmjournals.org/lookup/external-ref?access_num=10.1007/s00259-014-2961-x&link_type=DOI) 
    
    [PubMed](http://jnm.snmjournals.org/lookup/external-ref?access_num=25452219&link_type=MED&atom=%2Fjnumed%2F65%2F7%2F1101.atom) 

37. 37.Spencer BA, Berg E, Schmall JP, et al. Performance evaluation of the uEXPLORER total-body PET/CT scanner based on NEMA NU 2-2018 with additional tests to characterize PET scanners with a long axial field of view. J Nucl Med. 2021;62:861–870.
    
    [Abstract/FREE Full Text](http://jnm.snmjournals.org/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Njoiam51bWVkIjtzOjU6InJlc2lkIjtzOjg6IjYyLzYvODYxIjtzOjQ6ImF0b20iO3M6MjI6Ii9qbnVtZWQvNjUvNy8xMTAxLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

*   Received for publication January 29, 2024.
*   Accepted for publication March 25, 2024.

 [1]: /embed/mml-math-1.gif
 [2]: /embed/mml-math-2.gif
 [3]: /embed/mml-math-3.gif
 [4]: /embed/mml-math-4.gif
 [5]: /embed/mml-math-5.gif
 [6]: /embed/mml-math-6.gif
 [7]: /embed/mml-math-7.gif
 [8]: /embed/mml-math-8.gif
 [9]: /embed/mml-math-9.gif
 [10]: /embed/mml-math-12.gif
 [11]: /embed/mml-math-13.gif
 [12]: /embed/mml-math-14.gif
 [13]: /embed/mml-math-15.gif
 [14]: /embed/mml-math-16.gif
 [15]: /embed/mml-math-17.gif
 [16]: /embed/mml-math-18.gif
 [17]: /embed/mml-math-19.gif
 [18]: /embed/mml-math-20.gif
 [19]: /embed/mml-math-21.gif
 [20]: /embed/mml-math-22.gif
 [21]: /embed/mml-math-23.gif
 [22]: /embed/mml-math-24.gif
 [23]: /embed/mml-math-25.gif
 [24]: /embed/mml-math-26.gif
 [25]: /embed/mml-math-27.gif
 [26]: /embed/mml-math-28.gif
 [27]: F6/embed/mml-math-29.gif
 [28]: F6/embed/mml-math-30.gif
 [29]: /embed/mml-math-31.gif
 [30]: /embed/mml-math-32.gif
 [31]: /embed/mml-math-33.gif
 [32]: /embed/mml-math-34.gif
 [33]: /embed/mml-math-35.gif
 [34]: /embed/mml-math-36.gif
 [35]: /embed/mml-math-37.gif
 [36]: /embed/mml-math-38.gif
 [37]: /embed/mml-math-39.gif
 [38]: /embed/mml-math-40.gif
 [39]: /embed/mml-math-41.gif