Evaluation of data-driven respiratory gating waveforms for clinical PET imaging

Abstract

BackgroundWe aimed to evaluate the clinical robustness of a commercially developed data-driven respiratory gating algorithm based on principal component analysis, for use in routine PET imaging.MethodsOne hundred fifty-seven adult FDG PET examinations comprising a total of 1149 acquired bed positions were used for the assessment. These data are representative of FDG scans currently performed at our institution. Data were acquired for 4 min/bed position (3 min/bed for legs). The data-driven gating (DDG) algorithm was applied to each bed position, including those where minimal respiratory motion was expected. The algorithm provided a signal-to-noise measure of respiratory-like frequencies within the data, denoted as R. Qualitative evaluation was performed by visual examination of the waveforms, with each waveform scored on a 3-point scale by two readers and then averaged (score S of 0 = no respiratory signal, 1 = some respiratory-like signal but indeterminate, 2 = acceptable signal considered to be respiratory). Images were reconstructed using quiescent period gating and compared with non-gated images reconstructed with a matched number of coincidences. If present, the SUVmax of a well-defined lesion in the thorax or abdomen was measured and compared between the two reconstructions.ResultsThere was a strong (r = 0.86) and significant correlation between R and scores S. Eighty-six percent of waveforms with R ≥ 15 were scored as acceptable for respiratory gating. On average, there were 1.2 bed positions per patient examination with R ≥ 15. Waveforms with high R and S were found to originate from bed positions corresponding to the thorax and abdomen: 90% of waveforms with R ≥ 15 had bed centres in the range 5.6 cm superior to 27 cm inferior from the dome of the liver. For regions where respiratory motion was expected to be minimal, R tended to be < 6 and S tended to be 0. The use of DDG significantly increased the SUVmax of focal lesions, by an average of 11% when considering lesions in bed positions with R ≥ 15.ConclusionsThe majority of waveforms with high R corresponded to the part of the patient where respiratory motion was expected. The waveforms were deemed suitable for respiratory gating when assessed visually, and when used were found to increase SUVmax in focal lesions.

Data-Driven Respiratory Gating Outperforms Device-Based Gating for Clinical 18F-FDG PET/CT

“…For a detailed discussion of such methods, the reader is referred to previous publications (6,9,20).…”

“…For the DDG-retro reconstruction, the algorithm makes an assessment of the signal-to-noise of respiratory frequencies within the waveform. This quality metric is defined as the R value of the waveform (9). The waveforms were also inspected and scored by a medical physicist similar to our previous work (9).…”

“…This quality metric is defined as the R value of the waveform (9). The waveforms were also inspected and scored by a medical physicist similar to our previous work (9). No threshold based on the R value (or visual score) was applied, with DDG applied to all datasets.…”

“…The commercial version has been evaluated in phantom studies where it was found to reliably provide a respiratory waveform (8). The quality of the waveforms extracted by the algorithm have been assessed from clinical 18 F-FDG PET data which shows an example of suitable and unsuitable waveforms (9). Clinical examples have also been…”

Impact of PET data driven respiratory motion correction and BSREM reconstruction of 68Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS)

“…Unlike externally driven gating, which usually relies on infrared camera tracking of patient motion, data-driven gating methods use solely PET raw data in combination with dimensionality reduction techniques, in order to extract the respiratory signal. In our study, we used a commercially available DDG algorithm (MOTIONFREE, GE Healthcare), in combination with a motion correction algorithm (Q.STATIC, GE Healthcare) that utilizes the quiescent phase of the respiratory cycle [quiescent period gating (QPG)] 34 – 37 .…”