Abstract
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.
Highlights
Introduction ofDD geometric sensitivity method 1 + sim2009 Schleyer et al [23] PMBRetrospective data-driven
Communication The issue of patient respiratory motion in nuclear medicine imaging has been recognized as a significant problem [1,2]
Respiratory motion has been acknowledged as a problem in nuclear medicine imaging, and there have been calls for the development of effective robust solutions for handling motion control
Summary
Communication The issue of patient respiratory motion in nuclear medicine imaging has been recognized as a significant problem [1,2]. Beyond the ability to replace hardware, data-driven algorithms can provide a foundation for an entirely new paradigm of motion control strategies, a paradigm in which we focus on minimal impact and maximal benefit at both ends of the best case scenario (images exhibit obvious benefit) and worst case scenario (effort provides no benefit) spectrum. This is achieved through the integration of both data-driven information capture and information utilization strategies into black box workflows. Patients move in different patterns and to different extents, and available count statistics vary from patient to patient and scanner to scanner
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