Adaptive data-driven motion detection and optimized correction for brain PET

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

doi:10.1016/j.neuroimage.2022.119031

Cited by 10 publications

(5 citation statements)

References 28 publications

(44 reference statements)

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“…Compared with data-driven methods such as COD and short frame registration (Revilla et al 2022 , Spangler-Bickell et al 2022 ), camera systems such as UMT should provide better performance in the first minutes postinjection because the rapid change in distribution affects performance of these data-driven methods. In addition, the time resolution of data-driven methods must be optimized, unlike the 30 Hz sampling of UMT.…”

Section: Discussionmentioning

confidence: 99%

“…motion within one dynamic frame, cannot be corrected. Data-driven methods using PET raw count data, such as centroid of distribution (COD) and moments of inertia, can achieve great reductions in motion-induced blurring, but generally do not have high temporal resolution and can be inaccurate during large changes in tracer activity (Schleyer et al 2015 , Rezaei et al 2021 , Revilla et al 2022 ). Recently, deep learning for head motion correction (DL-HMC) has demonstrated its feasibility in predicting rigid motion for brain PET (Zeng et al 2022 ), but further advancements are necessary to enhance its robustness.…”

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: Introductionmentioning

confidence: 99%

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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“…The algorithm has the potential to improve the image quality and accuracy of PET scans, particularly for clinical populations where head motion can be a significant challenge, but further validation in larger and more diverse patient populations is needed. This proposed motion correction method yielded −0.3±2.8% and −0.4±3.2% brain region error for 18 F-FDG and 11 C-RAC, respectively, across 10 subjects with larger head motions for each tracer (47).…”

Section: Head Motionmentioning

confidence: 93%

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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“…A COD trace was generated at 1 Hz. By estimating and separating the variation due to count statistics and HM on the COD trace, we could divide the entire study into consecutive HM-free frames (MFFs) separated by the detected HM time points ( 15 ). MFFs shorter than 5 s were discarded and excluded from subsequent processing.…”

Section: Methodsmentioning

confidence: 99%

“…This algorithm, based on a statistics-based method by Revilla et al. ( 15 ), detects HM without parameter tuning and differentiates HM-induced COD changes.…”

mentioning

confidence: 99%