Impact of Data-driven Respiratory Gating in Clinical PET

Abstract

Purpose To study the feasibility and impact of respiratory gating in positron emission tomographic (PET) imaging in a clinical trial comparing conventional hardware-based gating with a data-driven approach and to describe the distribution of determined parameters. Materials and Methods This prospective study was approved by the ethics committee of the University Hospital of Münster (AZ 2014-217-f-N). Seventy-four patients suspected of having abdominal or thoracic fluorine 18 fluorodeoxyglucose (FDG)-positive lesions underwent clinical whole-body FDG PET/computed tomographic (CT) examinations. Respiratory gating was performed by using a pressure-sensitive belt system (belt gating [BG]) and an automatic data-driven approach (data-driven gating [DDG]). PET images were analyzed for lesion uptake, metabolic volumes, respiratory shifts of lesions, and diagnostic image quality. Results Forty-eight patients had at least one lesion in the field of view, resulting in a total of 164 lesions analyzed (range of number of lesions per patient, one to 13). Both gating methods revealed respiratory shifts of lesions (4.4 mm ± 3.1 for BG vs 4.8 mm ± 3.6 for DDG, P = .76). Increase in uptake of the lesions compared with nongated values did not differ significantly between both methods (maximum standardized uptake value [SUVmax], +7% ± 13 for BG vs +8% ± 16 for DDG, P = .76). Similarly, gating significantly decreased metabolic lesion volumes with both methods (-6% ± 26 for BG vs -7% ± 21 for DDG, P = .44) compared with nongated reconstructions. Blinded reading revealed significant improvements in diagnostic image quality when using gating, without significant differences between the methods (DDG was judged to be inferior to BG in 22 cases, equal in 12 cases, and superior in 15 cases; P = .32). Conclusion Respiratory gating increases diagnostic image quality and uptake values and decreases metabolic volumes compared with nongated acquisitions. Data-driven approaches are clinically applicable alternatives to belt-based methods and might help establishing routine respiratory gating in clinical PET/CT. (©) RSNA, 2016 Online supplemental material is available for this article.

Data‐driven gating in PET: Influence of respiratory signal noise on motion resolution

“…17,18 Recently, the first studies have been published investigating the effect of DDG in PET scans. 19,20 The results demonstrate that DDG methods on average perform as well as conventional methods in typical clinical settings.…”

“…They have already proven to perform similarly well in first clinical trials. 19,20 On the other hand, there are still challenges that need to be investigated. One is the question of how the statistical quality of the acquired PET data influences the outcome of gating.…”

“…A signal-to-noise (SNR) metric that was already used to assess the quality of DDG methods in earlier publications is as follows: 19,25 This concept is similar to the "quality factor" defined in Ref. [13].…”

“…It has thus been used to characterize gating methods in the past. 10,12,19,20 Furthermore, an exact knowledge of the average shift vector between endexpiratory and end-inspiratory phases of target structures is expected to be of benefit in radiotherapy treatment planning procedures to account for respiratory movements of target structures, and DDG PET-based motion delineation may have advantages over alternative methods. 27,28 In order to investigate any general relationship between SNR and motion resolution, every plot of d against SNR was analyzed and fitted using the curve fitting tool of MATLAB (version R2016a; MathWorks, Natick, Mass, USA) to the following expression:…”

The physics of radioembolization

“…In order to further refine personalized dosimetry in clinical practice, new methods need to be fast in terms of scan time, labor extensive, robust, and standardized. Some examples of developments in this direction are fully data-driven respiratory motion compensation [ 127 , 128 ] and fast lung absorbed dose estimation [ 129 ]. These methods have in common that they are faster, often more reliable and robust and more usable in clinical practice than existing methods.…”