Markerless motion tracking and correction for PET, MRI, and simultaneous PET/MRI

Slipsager, Jakob Mølkjær; Ellegaard, Andreas H.; Glimberg, Stefan Lemvig; Paulsen, Rasmus Reinhold; Tisdall, M. Dylan; Wighton, Paul; Kouwe, André van der; Marner, Lisbeth; Henriksen, Otto Mølby; Law, Ian; Olesen, Ole Vendelin

doi:10.1371/journal.pone.0215524

Cited by 35 publications

(44 citation statements)

References 32 publications

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“…Metria Innovation (Wauwatosa, WI) provides camera systems based on the Moiré phase tracking principle, which are very sensitive to rotations 15,45,71,72 . Most recently, the TCL system (TracInnovations, Copenhagen, Denmark) provides a markerless tracking system that eliminates possible marker attachment problems using surface tracking of the face and computer vision algorithms to directly track head motion 13,73 . All these systems provide rapid and continuous position updates independent of the MR sequence, and these positions can be fed back to the scanner to provide real‐time correction, or the time‐synchronized information can be used to correct MR data offline.…”

Section: Motion Correction Methodsmentioning

confidence: 99%

“…15,45,71,72 Most recently, the TCL system (TracInnovations, Copenhagen, Denmark) provides a markerless tracking system that eliminates possible marker attachment problems using surface tracking of the face and computer vision algorithms to directly track head motion. 13,73 All these systems provide rapid and continuous position updates independent of the MR sequence, and these positions can be fed back to the scanner to provide real-time correction, or the time-synchronized information can be used to correct MR data offline. A comparison between optical tracking and MR-based tracking methods discovered a high degree of correlation between the motion estimates provided by the two tracking modalities.…”

Section: Optical Trackingmentioning

confidence: 99%

“…While various motion correction methods are commonly used for MRI, [10][11][12][13] currently these have not been widely used in the MRS community outside of a few research laboratories, despite the fact that MRS is more susceptible to motion degradation compared with MRI. This is mainly due to the limited availability of methods that are easy to integrate and use with the MRS acquisition and processing, or they are suboptimal because they do not mitigate B 0 field changes that are critical in MRS.…”

Section: Introductionmentioning

confidence: 99%

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Motion correction methods for MRS: experts' consensus recommendations

et al. 2020

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Long acquisition times due to intrinsically low signal-to-noise ratio and the need for highly homogeneous B 0 field make MRS particularly susceptible to motion or scanner instability compared with MRI. Motion-induced changes in both localization and shimming (ie B 0 homogeneity) degrade MRS data quality. To mitigate the effects of motion three approaches can be employed: (1) subject immobilization, (2) retrospective correction, and (3) prospective real-time correction using internal and/or external tracking methods. Prospective real-time correction methods can simultaneously update localization and the B 0 field to improve MRS data quality. While localization errors can be corrected with both internal (navigators) and external (optical camera, NMR probes) tracking methods, the B 0 field correction requires internal navigator methods to measure the B 0 field inside the imaged volume and the possibility to update the scanner shim hardware in real time. Internal and external tracking can rapidly update the MRS localization with submillimeter and subdegree precision, while scanner frequency and first-order shims of scanner hardware can be updated by internal methods every sequence repetition. These approaches are most well developed for neuroimaging, for which rigid transformation is primarily applicable. Realtime correction greatly improves the stability of MRS acquisition and quantification, as shown in clinical studies on subjects prone to motion, including children and patients with movement disorders, enabling robust measurement of metabolite signals including those with low concentrations, such as gamma-aminobutyric acid and glutathione. Thus, motion correction is recommended for MRS users and calls for tighter integration and wider availability of such methods by MR scanner manufacturers.

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Section: Motion Correction Methodsmentioning

confidence: 99%

Section: Optical Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Motion correction methods for MRS: experts' consensus recommendations

et al. 2020

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“…Recent studies have utilized external optical signals or electromagnetic motion trackers to estimate changes in head pose, often with more than one camera or with markers attached to the study participant [9][10][11][12][13][14]. Our proposed approach is an attractive solution to the problem of head motion because eye trackers are widely available in clinical research centers and our technique does not require placing markers on the study participant.…”

Section: Introductionmentioning

confidence: 99%

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video

Pardoe¹,

Martin²,

Zhao

et al. 2021

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Introduction In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are currently no widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. Methods 21 healthy controls (age 32 ± 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R²). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. Results Video-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. Conclusions We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

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“…With the correct protocol, it is possible to extract these same features from an MR image, simplifying cross-calibration. Prospective motion correction using a novel markerless motion tracker has been demonstrated for 3D gradient echo, 27,28 3D fast spin echo, 28 and 2D fast spin echo and inversion recovery sequences. 29 In this work, we investigate the utility of optical markerless motion tracking to prospectively correct DWI of the brain using well-established imaging techniques.…”

Section: Introductionmentioning

confidence: 99%