An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution

Sun, Cheng; Revilla, Enette Mae; Zhang, Jiazhen; Fontaine, Kathryn; Toyonaga, Takuya; Gallezot, Jean‐Dominique; Mulnix, Tim; Onofrey, John A.; Carson, Richard E.; Lu, Yihuan

doi:10.1016/j.neuroimage.2022.119678

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Generally, markerless motion tracking can enhance the subject’s comfort and simplify operator procedures. In addition, the Vicra tool slippage and wobbling can cause motion tracking errors (Lu et al 2020 , Sun et al 2022a ). However, there are still many steps necessary to fully automate and validate the UMT system, including improving the face segmentation and handling non-rigid facial motion.…”

Section: Discussionmentioning

confidence: 99%

“…The central coordinates of the LOR of all events were averaged over 1 s intervals to generate COD traces in X, Y, and Z. A follow-on to COD was motion-corrected COD (MCCOD), which was an objective quality control approach to assess rigid motion information (Sun et al 2022a ); see Supplement for details. MCCOD displacements indicated motion estimation errors.…”

Section: Methodsmentioning

confidence: 99%

“…Several other evaluation methods such as contrast-to-noise ratio (CNR), cross correlation, mutual information and standardized uptake value ratio (SUVR) were also proposed for different motion correction applications (Klen et al 2016 , Chen et al 2018 , Keller et al 2012 , Reilhac et al 2018 ). We recently proposed an objective quality control metric for rigid head motion information, called motion corrected centroid-of-distribution (MCCOD) (Sun et al 2022a ). MCCOD has the advantage over other methods of providing real-time assessment of motion data accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Markerless head motion tracking and event-by-event correction in brain PET

Zeng,

Lu,

Jiang

et al. 2023

Phys. Med. Biol.

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Objective. Head motion correction (MC) is an essential process in brain positron emission tomography (PET) imaging. We have used the Polaris Vicra, an optical hardware-based motion tracking (HMT) device, for PET head MC. However, this requires attachment of a marker to the subject’s head. Markerless HMT (MLMT) methods are more convenient for clinical translation than HMT with external markers. In this study, we validated the United Imaging Healthcare motion tracking (UMT) MLMT system using phantom and human point source studies, and tested its effectiveness on eight 18F-FPEB and four 11C-LSN3172176 human studies, with frame-based region of interest (ROI) analysis. We also proposed an evaluation metric, registration quality (RQ), and compared it to a data-driven evaluation method, motion-corrected centroid-of-distribution (MCCOD). Approach. UMT utilized a stereovision camera with infrared structured light to capture the subject’s real-time 3D facial surface. Each point cloud, acquired at up to 30 Hz, was registered to the reference cloud using a rigid-body iterative closest point registration algorithm. Main results. In the phantom point source study, UMT exhibited superior reconstruction results than the Vicra with higher spatial resolution (0.35 ± 0.27 mm) and smaller residual displacements (0.12 ± 0.10 mm). In the human point source study, UMT achieved comparable performance as Vicra on spatial resolution with lower noise. Moreover, UMT achieved comparable ROI values as Vicra for all the human studies, with negligible mean standard uptake value differences, while no MC results showed significant negative bias. The RQ evaluation metric demonstrated the effectiveness of UMT and yielded comparable results to MCCOD. Significance. We performed an initial validation of a commercial MLMT system against the Vicra. Generally, UMT achieved comparable motion-tracking results in all studies and the effectiveness of UMT-based MC was demonstrated.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Markerless head motion tracking and event-by-event correction in brain PET

Zeng,

Lu,

Jiang

et al. 2023

Phys. Med. Biol.

Self Cite

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Spatial Normalization to Improve Deep Learning-based Head Motion Correction in PET

Zhang,

Lieffrig,

Zeng

et al. 2023

2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor D

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Motion-correction strategies for enhancing whole-body PET imaging

Wang,

Bermudez,

Chen

et al. 2024

Front. Nucl. Med.

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Positron Emission Tomography (PET) is a powerful medical imaging modality widely used for detection and monitoring of disease. However, PET imaging can be adversely affected by patient motion, leading to degraded image quality and diagnostic capability. Hence, motion gating schemes have been developed to monitor various motion sources including head motion, respiratory motion, and cardiac motion. The approaches for these techniques have commonly come in the form of hardware-driven gating and data-driven gating, where the distinguishing aspect is the use of external hardware to make motion measurements vs. deriving these measures from the data itself. The implementation of these techniques help corrects for motion artifacts and improve tracer uptake measurements. With the great impact that these methods have on the diagnostic and quantitative quality of PET images, much research has been performed in this area and this paper outlines the various approaches that have been developed as applied to whole-body PET imaging.

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An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution

Cited by 3 publications

References 24 publications

Markerless head motion tracking and event-by-event correction in brain PET

Markerless head motion tracking and event-by-event correction in brain PET

Spatial Normalization to Improve Deep Learning-based Head Motion Correction in PET

Motion-correction strategies for enhancing whole-body PET imaging

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