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. Author manuscript; available in PMC: 2011 Jun 1.
Published in final edited form as: J Nucl Med. 2011 Apr 15;52(5):712–719. doi: 10.2967/jnumed.110.086678

Impact of TOF PET on whole-body oncologic studies: a human observer lesion detection and localization study

Suleman Surti 1, Joshua Scheuermann 1, Georges El Fakhri 2, Margaret E Daube-Witherspoon 1, Ruth Lim 2, Nathalie Abi-Hatem 2, Elie Moussallem 2, Francois Benard 3, David Mankoff 4, Joel S Karp 1
PMCID: PMC3104282  NIHMSID: NIHMS291736  PMID: 21498523

Abstract

Phantom studies have shown improved lesion detection performance with time-of-flight (TOF) PET. In this study we evaluate the benefit of fully-3D, TOF PET in clinical whole-body oncology using human observers to localize and detect lesions in realistic patient anatomic backgrounds. Our hypothesis is that with TOF imaging we achieve improved lesion detection and localization for clinically challenging tasks with a bigger impact in large patients.

Methods

100 patient studies with normal 18F-fluoro-deoxyglucose (18F-FDG) uptake were chosen. 10-mm diameter spheres were imaged in air at variable locations in the scanner field-of-view (FOV) corresponding to lung and liver locations within each patient. Sphere data were corrected for attenuation and merged with patient data to produce fused list data files with lesions added to normal patients. All list files were reconstructed with full corrections and with or without the TOF kernel using a list-mode iterative algorithm. The images were presented to readers to localize and report with a confidence level the presence/absence of a lesion. The interpretation results were then analyzed to calculate the probability of correct localization and detection, and the area under the localized receiver operating characteristic (LROC) curve. The results were analyzed as a function of scan time per bed position, patient body-mass index (BMI < 26 and BMI ≥ 26), and type of imaging (TOF and Non-TOF).

Results

Our results showed that longer scan times led to improved area under the LROC curve for all patient sizes. With TOF imaging there was a bigger increase in the area under the LROC curve for larger patients (BMI ≥ 26). Finally, combining longer scan times with TOF imaging we saw smaller differences in the area under the LROC curve for large and small patients.

Conclusion

A combination of longer scan time (3 minutes in this study) together with TOF imaging provides the best performance for imaging large patients and/or a low uptake lesion in small or large patients. This imaging protocol also provides similar performance over all patient sizes for lesions in the same organ type with similar relative uptake, indicating an ability to provide a uniform clinical diagnostic capability in most oncologic lesion detection tasks.

Keywords: Lesion detection, human observers, LROC, Time-of-Flight PET

INTRODUCTION

The last few years have seen the introduction of time-of-flight (TOF) PET for clinical whole-body imaging and the three major PET scanner manufacturers now have commercially-available fully-3D scanners PET/CT scanners with TOF capability (1–3). While TOF PET imaging was originally developed in the 1980s, its current mode of operation in a fully-3D scanner design with improved spatial resolution and iterative image reconstruction algorithms can lead to improvements in image quality that may not be quantified by the previously defined metrics of TOF gain. Past evaluations had shown that with TOF PET imaging, one achieves improved image signal-to-noise ratio (SNR) that is proportional to the square root of the object size and inversely proportional to the square root of the system timing resolution (4, 5). In recent years, the primary clinical imaging application using PET has been in oncology where lesion detection and quantification are the main tasks performed by the physicians. Consequently, recent evaluations of TOF imaging have focused on this area. Simulations as well as measurements have shown faster and more uniform convergence of lesion contrast with TOF PET imaging in physical phantoms (1, 6–9), as well as clinical patient studies (9, 10). In addition, TOF PET lesion studies using numerical observers have shown improved lesion detectability in uniform objects with improving timing resolution (6, 11). A simplification of these early lesion detection studies is the presence of a uniform background, which is not representative of most clinical imaging situations. In these studies, the task was also detection of a lesion at a known position (signal-known exactly task). With statistical noise present in PET images, the task of detecting a lesion at an unknown position represents is more challenging and so signal detection and localization may represent a more clinically relevant task. A recent study attempted to overcome some of these limitations by acquiring data on a TOF PET scanner with a physical anthropomorphic phantom, and using numerical and non-clinician human observers to measure the impact on lesion detection and localization using the localized receiver operating characteristic (LROC) curve methodology to show improved performance with TOF imaging (12).

The goal of our study was to evaluate the benefit of TOF PET imaging for lesion detection and localization as a function of multiple parameters (patient size, lesion location, scan time) in a real patient. Using a previously developed methodology we inserted sphere data in clinical 18F-fluoro-deoxyglucose (18F-FDG) scans of patients of varying sizes (13, 14) to simulate the presence of lesions. Lesions were inserted in two organs (liver and lung) in different locations, and images were reconstructed for two different scan times. Previously we have reported on the accuracy of this technique in generating patient images with simulated lesions, and presented results from a numerical observer analysis for lesion detectability (14). The results of that study directed the current human observer study so that a smaller, and more relevant, sub-section of images were presented to the readers. For this study, we chose to emphasize two clinically challenging situations in patients: (i) detection of small liver lesions with low uptake relative to local liver background, and (ii) detection of small lung lesions with low absolute uptake and low uptake relative to the local lung background. The liver lesions represent detection of lesions in a generally uniform local background, but that are subject to non-uniform attenuation in a patient body and non-uniform activity distribution in the surrounding regions. The lung lesion detection task, on the other hand, represents lesion detection in a non-uniform local background, in addition to being subject to nonuniform attenuation in the patient body and non-uniform activity distribution in the surrounding regions. Our hypothesis is that TOF PET imaging would lead to improved lesion detection and localization for these two clinically challenging tasks compared to Non-TOF PET, with a bigger impact in larger patients.

MATERIALS AND METHODS

Scanner and image reconstruction

All patient scans and lesion measurements were performed on the Gemini TF PET/CT scanner (Philips Healthcare, Highland Heights, OH) which is a TOF capable, fully-3D PET scanner together with a 16-slice Brilliance CT scanner (1). The PET component of this scanner uses 4 × 4 × 22-mm3 lutetium-yttrium oxy-orthosilicate (LYSO) crystals. This scanner has a measured spatial resolution of 4.8 mm near the field-of-view (FOV) center and intrinsic system timing resolution of 585 ps, although the clinical data presented here were acquired with a system timing resolution of 670 ps due to effects of higher count-rates.

The Gemini TF scanner acquires list-mode data, which are reconstructed with and without TOF information using an ordered subsets expectation maximization (OSEM) algorithm with 33 chronologically ordered subsets; for TOF reconstructions, a TOF kernel is incorporated into the forward and backward projections (15). The attenuation map is obtained from a non-contrast CT image acquired with normal patient breathing, while scatter estimation is performed using a TOF-extended single scatter simulation (TOF SSS) (16, 17). Attenuation, detector efficiency/normalization, scatter and random coincidences are incorporated into the system model during image reconstruction to produce fully corrected images.

Patient Studies

For this investigation we selected 100 patient studies with a normal 18F-FDG bio-distribution, and no evidence of abnormal lesions. Based on the standard imaging protocol followed at the time of this study at the PET Center at the University of Pennsylvania, each patient was scanned for 3 minutes/bed position, 60 minutes after the injection of 555 MBq (15 mCi) of 18F -FDG. Since the data were acquired in list-mode, we have the ability to retrospectively reconstruct for scan times shorter then 3 minutes/bed position. A complete patient study typically involves 8–10 overlapping bed positions in order to image the patient from base of the brain to mid-thighs. In this study, for each patient we selected a single bed position that was determined by experienced clinical readers to have normal 18F-FDG uptake in the thoracolumbar region (including lower lungs and upper liver) for insertion of lesion data. The two adjacent bed positions to this data set were also reconstructed in order to perform slice overlapping and thus achieve image noise characteristics similar to a clinical image.

Generation of lesion present images

For every patient data set, we chose specific regions of the lung and liver at which to add 10-mm diameter spherical lesions. Within each organ region, exact lesion position was chosen randomly for each patient, so that the lesions did not appear at the same position in the image for all patients. We used 10-mm diameter plastic spheres (wall thickness of 1-mm) filled with 5–50 MBq/ml of 18F-FDG and acquired data in the scanner with the spheres in air at locations within the scanner FOV that overlap with the chosen region for each patient. Since no attenuation was present in these acquisitions, short scan times (1–2 minutes) were sufficient to acquire a reasonable number of sphere counts. The number of sphere counts needed for addition to patient data was estimated as described below, but the number of counts collected in the sphere data acquisition was always a larger fraction of this number. Random and scatter coincidences in the sphere data acquisition were negligible (< 3%).

Regions-of-interest (ROIs) equal in size to the sphere diameter were drawn in the fully corrected patient image at the specific lesion location to measure the mean activity concentration (CB). A sphere with an activity uptake ratio of u with respect to the local background would emit an additional (u − 1)*CB counts. Since the scanner geometric efficiency would be a function of sphere location, the (u − 1)*CB counts were also appropriately corrected for this effect; the resulting number of counts were extracted from the sphere in air list data for merger with the patient data (extracted sphere list file). Since the sphere data were collected in air, the extracted list file was attenuated based upon the patient transmission map (from CT image) to obtain the attenuated sphere list file. This file was then uniformly merged with the patient list file to obtain a fused, lesion-present data set. The fused data set was reconstructed using the list-mode reconstruction using the same transmission map and scatter estimate as the one generated for the original patient data. Figure 1 schematically shows the steps involved in the generation of the lesion present data set. In our previous work we have successfully verified through a phantom study this process of inserting lesions with a pre-determined activity uptake ratio at a fixed location (14). A consequence of the lesion insertion process for this work was the generation of three single bed position list files (representing a 3 minute scan) for image reconstruction per patient: 1) a normal list file representing the original lesion absent data set, 2) a fused list file representing the addition of a single lesion in the liver, and 3) a fused list file representing the addition of a single lesion in the lung.

Figure 1.

Figure 1

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Flow chart showing the steps involved in generation of fused list files with lesion data inserted in patient data set followed by image reconstruction.

Human observer study

The three list files per patient (no lesion, lesion inserted in liver, and lesion inserted in lung) for a single bed position were reconstructed using TOF and Non-TOF list-mode reconstruction. Lesion activity uptake ratios (relative to local background) were chosen to be 3.5:1 and 3.0:1 for liver and lung lesions, respectively. Since the liver has a higher background uptake, the absolute uptake for the liver lesions was higher, while the absolute lesion uptake in the lung, where normal organ uptake is low, was low. All list data were reconstructed for the full 3 minute scan as well as a 1 minute scan. Clinically, we use three iterations of OSEM reconstruction, and so for this work we decided to restrict our evaluation of all images (TOF and Non-TOF) to three iterations as well. There were three variable parameters for each patient: lesion location (lung, liver, or no lesion), scan time (1 or 3 minutes), and type of reconstruction (TOF or Non-TOF). In addition, the patients were also separated into two equal population body mass index (BMI) categories: BMI < 26 corresponding to small/average patients and BMI ≥ 26 corresponding to average/large patients. Hence a total of 1200 images were created (lesion presence/absence = 3, scan time = 2, reconstruction type = 2, number of patients = 100), that were separated into 24 different sets (lesion presence/absence = 3, scan time = 2, reconstruction type = 2, BMI type = 2) with 50 images per set.

For our image reading we had five readers representing different levels of specialization or expertise. Readers 1 and 2 are board-certified nuclear medicine physicians; while readers 3 and 4 are recent medical graduates doing elective research work in Nuclear Medicine. Reader 5 is a PET imaging physicist with over 25 years of experience in PET image analysis and evaluation, but no direct experience in clinical image interpretation. Each reader was given 30 images from each set, leading to 720 randomly distributed images to be read by every reader. A special viewing program was developed that displays tri-planar views (axial, coronal, sagittal) of PET image sets. The viewing program features scroll bars, performs triangulation between the three planes, and has adjustable window levels that can display images in different color maps. The readers were told that each image t had either no lesion or only one lesion present in the liver or the lung. They were asked to localize the lesion position in 3D by clicking on the image and report with a confidence level (six levels) the presence/absence of a lesion by clicking a button at the bottom of the image viewer. The reading for each image was then written to a text file for further processing. For data analysis, correct lesion localization was defined to be within 10 mm of the known lesion center. Each readers data set was analyzed using Swensson’s LROCFIT program to generate the receiver operating characteristic (ROC) and localized receiver operating characteristic (LROC) curves, as well as the value for area under the two curves (AROC and ALROC)(18) for 16 different categories (2 BMI levels × 2 scan times × 2 types of image reconstruction × 2 lesion locations).

RESULTS

In Figure 2A we show sample reconstructed images from a patient with a BMI of 28.4 with a lesion inserted in the liver region. Figure 2B shows reconstructed images from a different patient with a BMI of 24.6 and a lesion inserted in the lung region. Images are shown for scan durations of 1 and 3 minutes/bed position with TOF and Non-TOF image reconstruction. These images illustrate the type of cases presented to the readers, and the increased challenge of lesion detection for shorter scan times and Non-TOF image reconstruction.

Figure 2.

Figure 2

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Reconstructed images for a: (A) liver lesion present in a patient with BMI of 28.4, and (B) lung lesion present in a patient with BMI of 24.6. The white arrows indicate the location of the inserted lesion. Color scale has been saturated in (B) in order to show the lesion more clearly.

In Figures 3 and 4 we summarize our results for the AROC and ALROC values. Here, we show results for Readers 1 and 2 who are board-certified physicians with the most experience in interpretation of clinical PET images. From Figure 3 we see that heavy patients (BMI ≥ 26) generally had lower AROC values than light patients (BMI < 26). Also, the AROC values for lung lesions were lower than those in the liver for all patient sizes. Our results show that, generally, scans for 3 minutes/bed position compared with scans for 1 minute/bed position led to higher AROC values for all patient sizes, but TOF imaging led to an improvement mainly in heavy patients for both liver and lung lesions. The ALROC values as shown in Figure 4 follow the same trends as seen with the AROC values, but the results are enhanced due to the inclusion of lesion localization effect. For example, the ALROC value for lesions was lower in large patients overall due to poor lesion localization, and it was significantly compromised in the challenging situation of lung lesion detection. Generally, TOF imaging led to an improvement in the ALROC for liver lesions in heavy patients, and lung lesions for both patient sizes (although Reader 1 did not see a big improvement with TOF for lung lesions in light patients after a 1 minute scan).

Figure 3.

Figure 3

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Results for the AROC values obtained from observations of (A) Reader 1 for liver lesions, (B) Reader 1 for lung lesions, (C) Reader 2 for liver lesions, and (D) Reader 2 for lung lesions. Results are shown for TOF (label: T) and Non-TOF (label: NT) images as a function of scan time per bed position of 1 (label: 1m) or 3 (label: 3m) minutes, and patient BMI < 26 (label: L) or BMI ≥ 26 (label: H).

Figure 4.

Figure 4

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Results for the ALROC values obtained from observations of (A) Reader 1 for liver lesions, (B) Reader 1 for lung lesions, (C) Reader 2 for liver lesions, and (D) Reader 2 for lung lesions. Results are shown for TOF (label: T) and Non-TOF (label: NT) images as a function of scan time per bed position of 1 (label: 1m) or 3 (label: 3m) minutes, and patient BMI < 26 (label: L) or BMI ≥ 26 (label: H).

While the absolute performance of all readers varied somewhat, the rank ordering of the different ALROC values for each reader was generally the same within the error limits. A Tukey all-pairs comparison test for statistical significance of differences between the ALROC values for each reader was performed with a Bonferroni correction for multiple comparisons (19). In Table 1 we summarize the number of readers who had a statistically significant (p < 0.01) difference between their ALROC results for TOF versus Non-TOF images. For the readers who showed a statistically significant (p < 0.01) difference, the TOF ALROC value was always higher than the corresponding Non-TOF ALROC value. With TOF imaging all or most readers therefore saw an improvement in the ALROC value for lung lesions over both patient sizes and the two scan times. For liver lesions, TOF imaging did not show a significant improvement in the light patients. These results are in agreement with what we observed earlier in the ALROC results shown in Figure 4 for Readers 1 and 2. In Table 2 we summarize the number of readers who had a statistically significant (p < 0.01) difference between their ALROC results for 1 minute scan versus 3 minute scan images. For the readers who showed a statistically significant (p < 0.01) difference, the 3 minute scan ALROC value was always higher than the corresponding 1 minute scan ALROC value, except for one reader when reading images for BMI < 26 patients. In heavy patients, 3 minute scans therefore led to an improvement for all readers over all imaging categories, while in light patients the gain with 3 minute scans was not always present over the five readers.

Table 1.

Summary of the number of readers who had a statistically significant (p < 0.01) difference between their ALROC results for TOF versus Non-TOF images.

3 minute scan 1 minute scan
Lung lesion Liver lesion Lung lesion Liver lesion
BMI ≥ 26 5 5 4 5
BMI < 26 5 0 4 2
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Table 2.

Summary of the number of readers who had a statistically significant (p < 0.01) difference between their ALROC results for 1 minute versus 3 minute scan per bed position images.

TOF image Non-TOF image
Lung lesion Liver lesion Lung lesion Liver lesion
BMI ≥ 26 5 5 5 5
BMI < 26 4 5 3 5
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In Figure 5 we plot the average ALROC values over all five readers for varying scan times, type of reconstruction, and patient BMI. Results are shown separately for the liver and lung lesions. As noted earlier in Figure 4 for Readers 1 and 2, the average ALROC values for lung lesions were noticeably lower than those for liver lesions. This could be due to the use of a fixed uptake ratio for the inserted lesions relative to the local background, which makes lung lesion detection very challenging since the lesions are located in a much noisier lung background (compared to a more uniform, less noisy liver background). In Tables 3 and 4 we show results from a Tukey all-pairs comparison test for statistical significance of differences between the average ALROC values for all readers. Generally, heavy patients has lower ALROC results compared to light patients, and 3 minute scans led to improved performance compared to 1 minute scans (statistically significant differences). For the liver lesions, we found that TOF imaging led to a statistically significantly (p < 0.01) improved ALROC value in heavy patients for both scan times, while in light patients it led to an improvement only in the 1 minute scans. This is important because overall the ALROC results for all Non-TOF images were statistically significantly (p < 0.01) lower for heavy patients compared to light patients, and so TOF imaging led to improved performance for the heavier patients. The challenging situation of lung lesion detection showed that TOF reconstruction always led to a statistically significantly improved ALROC value. Looking at Table 4 we find that for lung lesions the differences in the ALROC results for 3 minute scans in both light and heavy patients were not statistically significant, indicating a more uniform performance over different patient sizes. However, the ALROC values were overall very low compared to the liver lesions, and only using long scans (3 minute scans here) together with TOF information can one obtain ALROC values in the range measured for the liver lesions.

Figure 5.

Figure 5

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Results for the average ALROC values obtained from all five reader observations for (A) liver and (B) lung lesions. Results are shown for TOF (label: T) and Non-TOF (label: NT) images as a function of scan time per bed position of 1 (label: 1m) or 3 (label: 3m) minutes, and patient BMI < 26 (label: L) or BMI ≥ 26 (label: H).

Table 3.

Results from Tukey all-pairs comparison (p-value with Bonferroni correction for multiple comparisons) of average ALROC results over all readers for liver lesions. The eight categories in the first column (and first row) are arranged in an ascending order based on the ALROC value. The numbers in bold are for categories where the difference between the ALROC values is statistically significant (p < 0.01). Note: (i) 1m and 3m correspond to 1 and 3 minutes/bed position, respectively, (ii) T and NT correspond to TOF and Non-TOF image reconstruction, respectively, and (iii) H and L corresponds to patients with BMI ≥ 26 and BMI < 26, respectively.

1m, NT, H 1m, T, H 3m, NT, H 1m, NT, L 1m, T, L 3m, T, H 3m, NT, L 3m, T, L
1m, NT, H N/A
1m, T, H < 0.01 N/A
3m, NT, H < 0.01 < 0.01 N/A
1m, NT, L < 0.01 < 0.01 < 0.01 N/A
1m, T, L < 0.01 < 0.01 < 0.01 < 0.01 N/A
3m, T, H < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 N/A
3m, NT, L < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 N/A
3m, T, L < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 0.290 N/A
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Table 4.

Results from Tukey all-pairs comparison (p-value with Bonferroni correction for multiple comparisons) of average ALROC results over all readers for lung lesions. The eight categories in the first column (and first row) are arranged in an ascending order based on the ALROC value. The numbers in bold are for categories where the difference between the ALROC values is statistically significant (p < 0.01). Note: (i) 1m and 3m correspond to 1 and 3 minutes/bed position, respectively, (ii) T and NT correspond to TOF and Non-TOF image reconstruction, respectively, and (iii) H and L corresponds to patients with BMI ≥ 26 and BMI < 26, respectively.

1m, NT, H 1m, T, H 1m, NT, L 3m, NT, H 3m, NT, L 1m, T, L 3m, T, H 3m, T, L
1m, NT, H N/A
1m, T, H < 0.01 N/A
1m, NT, L < 0.01 < 0.01 N/A
3m, NT, H < 0.01 < 0.01 < 0.01 N/A
3m, NT, L < 0.01 < 0.01 < 0.01 1 N/A
1m, T, L < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 N/A
3m, T, H < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 N/A
3m, T, L < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 1 N/A
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Discussion

Qualitatively, in Figure 2A (light patient) the lesion is clearly visible in the 1 and 3 minute TOF and 3 minute Non-TOF images (as indicated by the white arrows). In Figure 2B (heavy patient), both TOF images and the 3 minute Non-TOF image provide reasonable confidence in correctly localizing the lesion but the 1 minute Non-TOF image may be challenging to read. Quantitatively, our results suggest that while AROC indicates improved performance with TOF in heavy patients, the accuracy of lesion localization by human observers can be reduced in real patients due to a heterogeneous background. In this situation, we believe that the ALROC metric may provide a better measure of gain in clinical diagnostic situations.

As summarized in Tables 3 and 4, TOF PET was shown to be consistently better in clinical lesion diagnosis compared to Non-TOF PET (statistically significant difference), except for the easiest task of liver lesion detection in a 3 minute scan of light patients. While TOF PET did not have a statistically significant effect on light patient studies of longer duration (3 minute) in the liver, in the more challenging situation of lung lesion detection as set up in this study, it provided improved detection and localization capability for all patient sizes and scan times. Using the average ALROC results over all five readers (Figure 5), we also found, as expected, longer scan times (3 minutes/bed position) provide improved performance for both TOF and Non-TOF, with a more pronounced effect in heavy patients. For lesions in a given organ (liver or lung in this study) the difference in ALROC values between the two BMI levels is less for the long scan time (3 min/bed vs. 1 min/bed), and is reduced further with the addition of TOF. So, a long TOF scan leads to more uniform ALROC values across different patient size and organs. The ALROC values for 1 minute TOF scans were generally close to the ALROC values for 3 minute Non-TOF scans for a given patient size and organ. However, the difference was statistically significant (p < 0.01) and 1 minute TOF scans were always worse than the 3 minute Non-TOF scan except for the case of lung lesion detection in BMI < 26 patients. However, since lung lesions were difficult to detect (low ALROC values), longer scan times together with TOF may be necessary for adequate clinical interpretation. Consequently, with TOF imaging a scan time lying in between 1 and 3 minutes/bed position could provide an optimal clinical performance depending on the task at hand.

When this study was originally conceived, the standard imaging protocol at the University of Pennsylvania PET Center required a 3 minute scan per bed position. Currently, the protocol requires a scan time that varies between 1–3 minutes/bed position based on the patient BMI. This change in imaging protocol was based on a visual impression of image quality versus scan time, and is consistent with the quantitative results derived in this study using human observers and the ALROC metric.

One consideration of our study, as currently conceived, is that only two of our readers had substantial experience in interpretation of clinical PET. While the results from the other three readers generally follow the same ALROC trends, having more experienced readers would be beneficial. Future research that involves subtle, difficult-to-detect lesions should involve a larger number of this type of reader if possible.

For iterative image reconstruction algorithms the image changes as a function of number of iterations and hence readings for lesion detectability can be affected by it. In previous work we evaluated the change in lesion detectability as a function of iteration number using a numerical observer and observed that, generally, TOF images converge faster to a maximum lesion detection signal-to-noise ratio (14). However, while the convergence can vary as a function of patient and lesion size, as well as the lesion uptake and image reconstruction algorithm, in a clinical environment the number of iterations is generally fixed to a value which provides good images over a range of imaging situations. Hence, for this work we decided to restrict our evaluation to three iterations of each reconstruction algorithm. In the previous study we also noticed a small (5%) change in the signal-to-noise ratio (SNR) value for Non-TOF images when reconstructing for more than 3 iterations, while the TOF images are close to the maximum SNR after 3 iterations. So while the absolute value of the ALROC metric may increase a little if Non-TOF images with more iterations were used in this work, the general conclusions derived from this study should not change. Future work will involve further investigating the impact of this parameter on lesion detectability.

In addition, there are two other physiological limitations related to respiratory motion and variability in tumor uptake values, which our study did not take into account. The thoracoabdominal region undergoes a considerable amount of motion due to respiration during PET scanning, and this is a common source of false-negative results for in vivo lesions at the lung base and in the liver dome. The inserted lesions in our study were in a static position during scanning, and thus are likely to be more easily detected than moving in vivo lesions. Future research involving addition of moving lesions and motion-correction techniques could lead to further advances in the detection and characterization of lesions in this challenging anatomic region. Also, the inserted lesions in this study had a fixed local activity ratio, whereas in vivo lesions can vary widely in intensity, and poorly-FDG-avid tumors (e.g. bronchoalveolar carcinoma, hepatocellular carcinoma) can be very difficult to detect, particularly when their location is affected by respiratory motion. Future research using added lesions of varying activity could potentially show whether TOF PET is advantageous over non-TOF PET in the setting of poorly-FDG-avid lesions whose activities are even lower those analyzed in this study.

Conclusion

Based on our results we conclude that in smaller patients, while short scan times (1 minute in this study) may sometimes be adequate for certain clinical diagnoses, longer scan times (3 minutes in this study) still provide better performance for challenging clinical situations. However, when imaging large patients and/or a low uptake lesion in small or large patients, a combination of longer scan time together with TOF imaging provides the best performance. Finally, longer TOF scans in all patients provide similar performance over all patient sizes for lesions in the same organ type with similar relative uptake, indicating an ability to provide a more uniform clinical diagnostic capability in most oncologic lesion detection tasks.

Acknowledgments

This work was supported by the National Institutes of Health grant Nos. R01-CA113941 and R01-EB009056. We would also like to thank Matthew Werner (Radiology, University of Pennsylvania, Philadelphia) for help with generating the images.

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