Validation of Software Gating: A Practical Technology for Respiratory Motion Correction in PET

Kesner, Adam; Chung, Jonathan H.; Lind, Kimberly E.; Kwak, Jennifer; Lynch, David A.; Burckhardt, Darrell; Koo, Phillip J.

doi:10.1148/radiol.2016152105

Cited by 57 publications

(67 citation statements)

References 31 publications

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“…Therefore, these hybrid images are subject to involuntary patient motion during the acquisition [179]. The induced respiratory motion as well as cardiac contraction introduce motion blur in the acquired images (Figure 5), which may affect the clinical interpretation of the acquired images [182].…”

Section: Motion Compensationmentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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State-of-the-art patient management frequently requires the use of non-invasive imaging methods to assess the anatomy, function or molecular-biological conditions of patients or study subjects. Such imaging methods can be singular, providing either anatomical or molecular information, or they can be combined, thus, providing "anato-metabolic" information. Hybrid imaging denotes image acquisitions on systems that physically combine complementary imaging modalities for an improved diagnostic accuracy and confidence as well as for increased patient comfort. The physical combination of formerly independent imaging modalities was driven by leading innovators in the field of clinical research and benefited from technological advances that permitted the operation of PET and MR in close physical proximity, for example. This review covers milestones of the development of various hybrid imaging systems for use in clinical practice and small-animal research. Special attention is given to technological advances that helped the adoption of hybrid imaging, as well as to introducing methodological concepts that benefit from the availability of complementary anatomical and biological information, such as new types of image reconstruction and data correction schemes. The ultimate goal of hybrid imaging is to provide useful, complementary and quantitative information during patient work-up. Hybrid imaging also opens the door to multi-parametric assessment of diseases, which will help us better understand the causes of various diseases that currently contribute to a large fraction of healthcare costs.

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Section: Motion Compensationmentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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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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“…Respiratory motion compensation in PET has received substantial attention in the literature with applications in specific thoracic areas like the lungs, the heart and more generic approaches . In SPECT, respiratory motion compensation techniques are largely focused on myocardial imaging, which is still its major application area.…”

Section: Introductionmentioning

confidence: 99%