Impact of Data-driven Respiratory Gating in Clinical                     PET

Büther, Florian; Vehren, Thomas; Schäfers, Klaus P.; Schäfers, Michael

doi:10.1148/radiol.2017164041

Cited by 28 publications

(27 citation statements)

References 24 publications

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“…Of these, the data driven gating approaches of PET images promise to yield comparable results with less discomfort for the patients than hardware driven approaches . Recent works have highlighted their applicability in clinical settings . The development of synergistic algorithms, which encompass entire workflows, or account for motion simultaneously with segmentation, are also expected …”

Section: Discussion Of Segmentation Limitations Dependencies and Immentioning

confidence: 99%

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

et al. 2017

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Purpose The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. Approach A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. Findings A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. Conclusions Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to ...

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Section: Discussion Of Segmentation Limitations Dependencies and Immentioning

confidence: 99%

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

et al. 2017

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“…DDG methods are promising alternatives to conventional hardware‐based gating approaches. They have already proven to perform similarly well in first clinical trials . On the other hand, there are still challenges that need to be investigated.…”

Section: Discussionmentioning

confidence: 99%

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

Section: Introductionmentioning

confidence: 89%

“…A signal‐to‐noise (SNR) metric that was already used to assess the quality of DDG methods in earlier publications is as follows:

S N R [R_{raw} false(t false)] = \frac{f_{3} - f_{2}}{f_{1} - f_{0}} \cdot \frac{\int_{f_{0}}^{f_{1}} |FT [R_{raw} (t)]| d f}{\int_{f_{2}}^{f_{3}} |FT [R_{raw} (t)]| d f}

with FT denoting the Fourier transform, [ f 0 , f 1 ] = [1/12 Hz, 1/3 Hz] representing an interval of typical respiratory frequencies (corresponding to respiratory periods of 3 s to 12 s), and [ f 2 , f 3 ] = [3 Hz, f max ] representing high frequencies caused by noise alone . This concept is similar to the “quality factor” defined in Ref.…”

Section: Methodsmentioning

confidence: 99%

“…This parameter was used as a metric for the evaluation of motion resolution as it is robust against noise effects, as opposed to standard image intensity values like SUV max or derived values like lesion volumes. It has thus been used to characterize gating methods in the past . Furthermore, an exact knowledge of the average shift vector between end‐expiratory 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 …”

Section: Methodsmentioning

confidence: 99%

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

Büther

Ernst²,

Frohwein

et al. 2018

Medical Physics

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The SNR can serve as a general metric to assess the success of COM-based DDG, even in different scanners and patients. The derived formula for motion resolution can be used to estimate the actual motion extent reasonably well in cases of limited PET raw data statistics. This may be of interest for individualized radiotherapy treatment planning procedures of target structures subjected to respiratory motion.

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Data‐driven optimal binning for respiratory motion management in PET

et al. 2017

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Purpose: Respiratory gating has been used in PET imaging to reduce the amount of image blurring caused by patient motion. Optimal binning is an approach for using the motion-characterized data by binning it into a single, easy to understand/use, optimal bin. To date, optimal binning protocols have utilized externally driven motion characterization strategies that have been tuned with populationderived assumptions and parameters. In this work, we are proposing a new strategy with which to characterize motion directly from a patient's gated scan, and use that signal to create a patient/instance-specific optimal bin image. Methods: Two hundred and nineteen phase-gated FDG PET scans, acquired using data-driven gating as described previously, were used as the input for this study. For each scan, a phase-amplitude motion characterization was generated and normalized using principle component analysis. A patient-specific "optimal bin" window was derived using this characterization, via methods that mirror traditional optimal window binning strategies. The resulting optimal bin images were validated by correlating quantitative and qualitative measurements in the population of PET scans. Results: In 53% (n = 115) of the image population, the optimal bin was determined to include 100% of the image statistics. In the remaining images, the optimal binning windows averaged 60% of the statistics and ranged between 20% and 90%. Tuning the algorithm, through a single acceptance window parameter, allowed for adjustments of the algorithm's performance in the population toward conservation of motion or reduced noise-enabling users to incorporate their definition of optimal. In the population of images that were deemed appropriate for segregation, average lesion SUV max were 7.9, 8.5, and 9.0 for nongated images, optimal bin, and gated images, respectively. The Pearson correlation of FWHM measurements between optimal bin images and gated images were better than with nongated images, 0.89 and 0.85, respectively. Generally, optimal bin images had better resolution than the nongated images and better noise characteristics than the gated images. Discussion: We extended the concept of optimal binning to a data-driven form, updating a traditionally one-size-fits-all approach to a conformal one that supports adaptive imaging. This automated strategy was implemented easily within a large population and encapsulated motion information in an easy to use 3D image. Its simplicity and practicality may make this, or similar approaches ideal for use in clinical settings.

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Impact of Data-driven Respiratory Gating in Clinical PET

Abstract: first line of Materials and Methods should read as follows: This retrospective study was approved by the ethics committee of the Ärztekammer Westfalen-Lippe and the University of Münster (Az 2014-217-fN).

Cited by 28 publications

References 24 publications

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

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

Data‐driven optimal binning for respiratory motion management in PET

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