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

Büther, Florian; Ernst, Iris; Frohwein, Lynn Johann; Pouw, Joost Jacob; Schäfers, Klaus P.; Stegger, Lars

doi:10.1002/mp.12987

Cited by 16 publications

(12 citation statements)

References 32 publications

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“…It may be possible to devise improved measures of the suitability of DDG waveforms for respiratory gating, as well as the benefit that may be expected from applying such gating. Different metrics which have some similarity to R have been used in the evaluation of other DDG algorithms [19, 20]. The phantom study confirmed that larger motion amplitudes do lead to higher R values but also showed that the increase was not linear.…”

Section: Discussionmentioning

confidence: 98%

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

et al. 2019

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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.

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Section: Discussionmentioning

confidence: 98%

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

et al. 2019

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“…On the other hand, list‐mode event‐driven methods were shown to have the capability to detect respiratory and/or cardiac motion signals . A similar technique was applied to detect rigid head movement with time‐of‐flight (TOF) information .…”

Section: Introductionmentioning

confidence: 99%

Body motion detection and correction in cardiac PET: Phantom and human studies

et al. 2019

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Purpose: Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We propose and evaluate a novel method to detect, estimate, and correct body motion in cardiac PET. Methods: Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction. For motion detection, we first divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences' distribution in each bin. We then compute the covariance matrix within a 25-s sliding window over the COM signals inside the window. The sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into "static" (i.e., body motion free) and "moving" (i.e. contaminated by body motion) frames. Each moving frame is further divided into a number of evenly spaced sub-frames (referred to as "sub-moving" frames), in which motion is assumed to be negligible. For motion estimation, we first reconstruct the data in each static and sub-moving frame using a rapid back-projection technique. We then select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration. For motion-compensated image reconstruction, we reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix. We evaluated the performance of our approach in both phantom and human studies. Results: Visually, the motion-corrected (MC) PET images obtained using the proposed method have better quality and fewer motion artifacts than the images reconstructed without motion correction (NMC). Quantitative analysis indicates that MC yields higher myocardium to blood pool concentration ratios. MC also yields sharper myocardium than NMC. Conclusions: The proposed body motion correction method improves image quality of cardiac PET.

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“…In 2010, our group published an article discussing potential of a real-time DDMC system and demonstrated this can be achieved by collapsing pre-reconstructed data space into more manageable data structures [5]. Since then, there have been numerous additions and contributions to developing DDMC ([6–11]). These articles demonstrate that robust DDMC is possible, though its practical, clinical implementation remains a systems integration challenge.…”

Section: Introductionmentioning

confidence: 99%

Real-time data-driven motion correction in PET

2019

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PET imaging has been, and continues to be, an evolving diagnostic technology. In recent years, the modernizing digital landscape has opened new opportunities for data-driven innovation. One such facet has been data-driven motion correction (DDMC) in PET. As both research and industry propel this technology forward, we can recognize prospects and opportunities for further development. The concept of clinical practicality is supported by DDMC approaches—it is what sets them apart from traditional hardware-driven motion correction strategies that have largely not gained acceptance in routine diagnostic PET; the ease of use of DDMC may help propel acceptance of motion correction solutions in clinical practice. As we reflect on the present field, we should consider that DDMC can be made even more practical, and likely more impactful, if further developed to fit within a real-time acquisition framework. This vision for development is not new, but has been made more feasible with contemporary electronics, and has begun to be revisited in contemporary literature. The opportunities for development lie on a new forefront of innovation where medical physics integrates with engineering, data science, and modern computing capacities. Real-time DDMC is a systems integration challenge, and achieving it will require cooperation between hardware and software developers, and likely academia and industry. While challenges for development do exist, it is likely that we will see real-time DDMC come to fruition in the coming years. This effort may establish groundwork for developing similar innovations in the emerging digital innovation age.

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

Cited by 16 publications

References 32 publications

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

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

Body motion detection and correction in cardiac PET: Phantom and human studies

Real-time data-driven motion correction in PET

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