PET data inherently contains information about patient motion; information that is not currently being utilized. We have shown that a respiratory signal can be extracted from raw PET data in potentially real-time and in a fully automated manner. This signal correlates well with hardware based signal for a large percentage of scans, and avoids the efforts and complications associated with hardware. The proposed method to extract a respiratory signal can be implemented on existing scanners and, if properly integrated, can be applied without changes to routine clinical procedures.

We could not detect regionally increased [(18)F]FDDNP binding and regionally decreased FDG binding in the brains of Tg2576 transgenic versus wild-type mice. However, small group differences in signal might have been masked by inter-animal variability. In addition, technical limitations of the applied method (partial volume effect, spatial resolution) for measurements in such small organs as mouse brain have to be taken into consideration.

Positron emission tomography (PET) is increasingly used for the detection, characterization, and follow-up of tumors located in the thorax. However, patient respiratory motion presents a unique limitation that hinders the application of high-resolution PET technology for this type of imaging. Efforts to transcend this limitation have been underway for more than a decade, yet PET remains for practical considerations a modality vulnerable to motion-induced image degradation. Respiratory motion control is not employed in routine clinical operations. In this article, we take an opportunity to highlight some of the recent advancements in data-driven motion control strategies and how they may form an underpinning for what we are presenting as a fully automated data-driven motion control framework. This framework represents an alternative direction for future endeavors in motion control and can conceptually connect individual focused studies with a strategy for addressing big picture challenges and goals.Electronic supplementary materialThe online version of this article (doi:10.1186/2197-7364-1-8) contains supplementary material, which is available to authorized users.

Purpose To assess the performance of hardware- and software-gating technologies in terms of qualitative and quantitative characteristics of respiratory motion in positron emission tomography (PET) imaging. Materials and Methods Between 2010 and 2013, 219 fluorine 18 fluorodeoxyglucose PET examinations were performed in 116 patients for assessment of pulmonary nodules. All patients provided informed consent in this institutional review board-approved study. Acquisitions were reconstructed as respiratory-gated images by using hardware-derived respiratory triggers and software-derived signal (via an automated postprocessing method). Asymmetry was evaluated in the joint distribution of reader preference, and linear mixed models were used to evaluate differences in outcomes according to gating type. Results In blind reviews of reconstructed gated images, software was selected as superior 16.9% of the time (111 of 657 image sets; 95% confidence interval [CI]: 14.0%, 19.8%), and hardware was selected as superior 6.2% of the time (41 of 657 image sets; 95% CI: 4.4%, 8.1%). Of the image sets, 76.9% (505 of 657; 95% CI: 73.6%, 80.1%) were judged as having indistinguishable motion quality. Quantitative analysis demonstrated that the two gating strategies exhibited similar performance, and the performance of both was significantly different from that of nongated images. The mean increase ± standard deviation in lesion maximum standardized uptake value was 42.2% ± 38.9 between nongated and software-gated images, and lesion full width at half maximum values decreased by 9.9% ± 9.6. Conclusion Compared with vendor-supplied respiratory-gating hardware methods, software gating performed favorably, both qualitatively and quantitatively. Fully automated gating is a feasible approach to motion correction of PET images. (©) RSNA, 2016 Online supplemental material is available for this article.

Purpose: The increasing interest and availability of non-standard positron-emitting radionuclides has heightened the relevance of radionuclide choice in the development and optimization of new positron emission tomography (PET) imaging procedures, both in preclinical research and clinical practice. Differences in achievable resolution arising from positron range can largely influence application suitability of each radionuclide, especially in small-ring preclinical PET where system blurring factors due to annihilation photon acollinearity and detector geometry are less significant. Some resolution degradation can be mitigated with appropriate range corrections implemented during image reconstruction, the quality of which is contingent on an accurate characterization of positron range. Procedures: To address this need, we have characterized the positron range of several standard and non-standard PET radionuclides (As-72, F-18, Ga-68, Mn-52, Y-86, and Zr-89) through imaging of small-animal quality control phantoms on a benchmark preclinical PET scanner. Further, the Particle and Heavy Ion Transport code System (PHITS v3.02) code was utilized for Monte Carlo modeling of positron range-dependent blurring effects.

Respiratory motion in PET degrades image quality and limits detectability of small or low-contrast lesions. Although image quality can be improved using respiratory-gating, this adds to the complexity and expense of acquiring PET data. We aimed to develop a data-driven method, based on individual voxel signal fluctuations, for accomplishing electronic respiratory gating of clinical PET data, requiring no additional hardware or end-user input. We tested our methods using both simulated PET scans and actual human PET acquisitions. For the simulations, our methods correctly identified the start frame of each respiratory cycle defined for the phantom. Resultant gated images demonstrated improved effective resolution and increased measured uptake for lesions located in the thorax. For human PET data, we were able to recover respiratory phase information with a high signal-to-noise ratio. We report here a method to achieve fully automated voxel-based respiratory gating of PET images, without the need for gating hardware or additional user input, capable of improving effective resolution and increasing lesion detectability.

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BackgroundRespiratory gating and gate optimization strategies present solutions for overcoming image degradation caused by respiratory motion in PET and traditionally utilize hardware systems and/or employ complex processing algorithms. In this work, we aimed to advance recently emerging data-driven gating methods and introduce a new strategy for optimizing the four-dimensional data based on information contained in that data. These algorithms are combined to form an automated motion correction workflow.MethodsSoftware-based gating methods were applied to a nonspecific population of 84 small-animal rat PET scans to create respiratory gated images. The gated PET images were then optimized using an algorithm we introduce as ‘gating+’ to reduce noise and optimize signal; the technique was also tested using simulations. Gating+ is based on a principle of only using gated information if and where it adds a net benefit, as evaluated in temporal frequency space. Motion-corrected images were assessed quantitatively and qualitatively.ResultsOf the small-animal PET scans, 71% exhibited quantifiable motion after software gating. The mean liver displacement was 3.25 mm for gated and 3.04 mm for gating+ images. The (relative) mean percent standard deviations measured in background ROIs were 1.53, 1.05, and 1.00 for the gated, gating+, and ungated values, respectively. Simulations confirmed that gating+ image voxels had a higher probability of being accurate relative to the corresponding ungated values under varying noise and motion scenarios. Additionally, we found motion mapping and phase decoupling models that readily extend from gating+ processing.ConclusionsRaw PET data contain information about motion that is not currently utilized. In our work, we showed that through automated processing of standard (ungated) PET acquisitions, (motion-) information-rich images can be constructed with minimal risk of noise introduction. Such methods have the potential for implementation with current PET technology in a robust and reproducible way.