JNM: Time-of-flight PET may better detect cancers
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| FDOPA PET brain scan. Image source: Hôpital Neurologique Pierre Wertheimer, Bron, France |
“Time-of-flight (TOF) PET has great potential for the improvement of image quality in whole-body oncologic applications,” explained Georges El Fakhri, PhD, from the department of radiology at Harvard Medical School and Massachusetts General Hospital in Boston, and co-authors. “However, little work has been done on the objective assessment of the benefits of TOF PET for lesion detection in clinical whole-body 18F-FDG oncologic studies,” continued Fakhri and colleagues.
Given the challenges of enrolling sufficient participants and quantitatively assessing lesion detection against a gold standard, the authors fused patient images with realistic 10-mm plastic spheres injected with 18F-FDG, so that the spheres were transposed into images of patients’ lungs and livers to simulate actual lesions. One hundred patients underwent whole-body 18F-FDG PET/CT, with lesion detectability judged as a function of lesion location and contrast, body mass index (BMI) and scan time.
Pooling all image contrasts across all BMIs and scan times, taken at three iterations, TOF PET yielded a significant improvement in lesion detection over non-TOF PET, of 8.3 percent in the liver and 15.1 percent in the lungs. Similar improvements of 20.3 percent, 12.0 percent, 9.2 percent and 7.5 percent in TOF PET were observed across mean contrasts of 2.0:1, 3.2:1, 4.4:1 and 5.7:1, respectively.
TOF showed the greatest improvements compared with non-TOF at lower iterations and demonstrated the strongest detectability of lesions for one-minute scans, as opposed to three-minute scans.
Moreover, the authors found that “the greatest gain in performance is achieved for a BMI of 30 or more; that is, TOF yielded the best improvement in large patients when lesion detection was the most challenging.”
Over all iterations, lesion detection signal-to-noise ratio was significantly higher for TOF than non-TOF in the lung and liver, and TOF PET performance converged faster (at an earlier iteration number) than non-TOF.
“We have determined the gain in performance that can be achieved when TOF PET is applied in whole-body 18F-FDG PET using a task-based metric, that is, lesion detection as measured by a CHO [channelized hotelling observer],” the authors said. “The realistic process of lesion incorporation into normal whole-body TOF PET 18F-FDG studies has yielded a valuable set of lesion-present and lesion-absent studies in 100 patients (i.e., 36,000 studies).”
The authors maintained that their study was unique in that TOF and non-TOF were compared in patients, as opposed to physical phantoms, with widely ranging BMIs and lesion contrasts. Additionally, all physical factors, such as scatter, attenuation, randoms and dead time were included for all body sizes and physiological activity distributions because the authors used clinical data, rather than numerically simulated information.
“Our results show several interesting and important features,” the authors contended. “As expected in clinical studies, the covariance in the images is dominated by variability in patient backgrounds, rather than statistical noise.”
“Another feature of our results is that liver lesions showed higher detectability than lung lesions, because of the higher number of counts in liver background than in lung background and therefore lower noise in liver lesions than in lung lesions,” Fakhri and colleagues pointed out. Their results also showed greater improvement in performance in lung lesions than in the liver using TOF PET, which was due to the lower overall detectability of lung lesions.
Finally, the authors noted that greater improvement was seen from non-TOF to TOF PET for high-BMI patients as opposed to those with low BMIs, suggesting that TOF PET yields the greatest benefit for imaging larger patients.
Given their use of clinical data and control for contrast, BMI and other factors, Fakhri and co-authors said that they were “confident that conclusions drawn from the data can be extrapolated to the clinical setting after confirmation.”