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

Verification of the effect of data-driven brain motion correction on PET imaging

INTRODUCTION

Brain positron emission tomography/computed tomography (PET/CT) scans are useful for identifying the cause of dementia by evaluating glucose metabolism in the brain with F-18-fluorodeoxyglucose or Aβ deposition with F-18-florbetaben. However, since imaging time ranges from 10 to 30 minutes, movements during the examination might result in image artifacts, which interfere with diagnosis. To solve this problem, data-driven brain motion correction (DDBMC) techniques are capable of performing motion corrected reconstruction using highly accurate motion estimates with high temporal resolution. In this study, we investigated the effectiveness of DDBMC techniques on PET/CT images using a Hoffman phantom, involving continuous rotational and tilting motion, each expanded up to approximately 20 degrees.

MATERIALS AND METHODS

Listmode imaging was performed using a Hoffman phantom that reproduced rotational and tilting motions of the head. Brain motion correction processing was performed on the obtained data. Reconstructed images with and without brain motion correction processing were compared. Visual evaluations by a nuclear medicine specialist and quantitative parameters of images with correction and reference still images were compared.

RESULTS

Normalized Mean Squared Error (NMSE) results demonstrated the effectiveness of DDBMC in compensating for rotational and tilting motions during PET imaging. In Cases 1 and 2 involving rotational motion, NMSE decreased from 0.15-0.2 to approximately 0.01 with DDBMC, indicating a substantial reduction in differences from the reference image across various brain regions. In the Structural Similarity Index (SSIM), DDBMC improved it to above 0.96 Contrast assessment revealed notable improvements with DDBMC. In continuous rotational motion, % contrast increased from 42.4% to 73.5%, In tilting motion, % contrast increased from 52.3% to 64.5%, eliminating significant differences from the static reference image. These findings underscore the efficacy of DDBMC in enhancing image contrast and minimizing motion induced variations across different motion scenarios.

CONCLUSIONS

DDBMC processing can effectively compensate for continuous rotational and tilting motion of the head during PET, with motion angles of approximately 20 degrees. However, a significant limitation of this study is the exclusive validation of the proposed method using a Hoffman phantom; its applicability to the human brain has not been investigated. Further research involving human subjects is necessary to assess the generalizability and reliability of the presented motion correction technique in real clinical scenarios.

Improving magnetic resonance spectroscopy in the brainstem periaqueductal gray using spectral registration

PURPOSE

Functional understanding of the periaqueductal gray (PAG), a clinically relevant brainstem region, can be advanced using 1 H-MRS. However, the PAG's small size and high levels of physiological noise are methodologically challenging. This study aimed to (1) improve 1 H-MRS quality in the PAG using spectral registration for frequency and phase error correction; (2) investigate whether spectral registration is particularly useful in cases of greater head motion; and (3) examine metabolite quantification using literature-based or individual-based water relaxation times.

METHODS

Spectra were acquired in 33 healthy volunteers (50.1 years, SD = 17.19, 18 females) on a 3 T Philipps MR system using a point-resolved spectroscopy (PRESS) sequence optimized with very selective saturation pulses (OVERPRESS) and voxel-based flip angle calibration (effective volume of interest size: 8.8 × 10.2 × 12.2 mm3 ). Spectra were fitted using LCModel and SNR, NAA peak linewidths and Cramér-Rao lower bounds (CRLBs) were measured after spectral registration and after minimal frequency alignment.

RESULTS

Spectral registration improved SNR by 5% (p = 0.026, median value post-correction: 18.0) and spectral linewidth by 23% (p < 0.001, 4.3 Hz), and reduced the metabolites' CRLBs by 1% to 15% (p < 0.026). Correlational analyses revealed smaller SNR improvements with greater head motion (p = 0.010) recorded using a markerless motion tracking system. Higher metabolite concentrations were detected using individual-based compared to literature-based water relaxation times (p < 0.001).

CONCLUSION

This study demonstrates high-quality 1 H-MRS acquisition in the PAG using spectral registration. This shows promise for future 1 H-MRS studies in the PAG and possibly other clinically relevant brain regions with similar methodological challenges.

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

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 eight18F-FPEB and four11C-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. TheRQevaluation 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.

Robust retrospective motion correction of head motion using navigator-based and markerless motion tracking techniques

PURPOSE

This study investigated the artifacts arising from different types of head motion in brain MR images and how well these artifacts can be compensated using retrospective correction based on two different motion-tracking techniques.

METHODS

MPRAGE images were acquired using a 3 T MR scanner on a cohort of nine healthy participants. Subjects moved their head to generate circular motion (4 or 6 cycles/min), stepwise motion (small and large) and "simulated realistic" motion (nodding and slow diagonal motion), based on visual instructions. One MPRAGE scan without deliberate motion was always acquired as a "no motion" reference. Three dimensional fat-navigator (FatNavs) and a Tracoline markerless device (TracInnovations) were used to obtain motion estimates and images were separately reconstructed retrospectively from the raw data based on these different motion estimates.

RESULTS

Image quality was recovered from both motion tracking techniques in our stepwise and slow diagonal motion scenarios in almost all cases, with the apparent visual image quality comparable to the no-motion case. FatNav-based motion correction was further improved in the case of stepwise motion using a skull masking procedure to exclude non-rigid motion of the neck from the co-registration step. In the case of circular motion, both methods struggled to correct for all motion artifacts.

CONCLUSION

High image quality could be recovered in cases of stepwise and slow diagonal motion using both motion estimation techniques. The circular motion scenario led to more severe image artifacts that could not be fully compensated by the retrospective motion correction techniques used.

Fast Reconstruction for Deep Learning PET Head Motion Correction

Head motion correction is an essential component of brain PET imaging, in which even motion of small magnitude can greatly degrade image quality and introduce artifacts. Building upon previous work, we propose a new head motion correction framework taking fast reconstructions as input. The main characteristics of the proposed method are: (i) the adoption of a high-resolution short-frame fast reconstruction workflow; (ii) the development of a novel encoder for PET data representation extraction; and (iii) the implementation of data augmentation techniques. Ablation studies are conducted to assess the individual contributions of each of these design choices. Furthermore, multi-subject studies are conducted on an 18F-FPEB dataset, and the method performance is qualitatively and quantitatively evaluated by MOLAR reconstruction study and corresponding brain Region of Interest (ROI) Standard Uptake Values (SUV) evaluation. Additionally, we also compared our method with a conventional intensity-based registration method. Our results demonstrate that the proposed method outperforms other methods on all subjects, and can accurately estimate motion for subjects out of the training set. All code is publicly available on GitHub: https://github.com/OnofreyLab/dl-hmc_fast_recon_miccai2023.

Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts

Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.

The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors

We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat.

METHODS

The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers.

RESULTS

MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG.

CONCLUSIONS

The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.

A radiofrequency coil for infants and toddlers

Infants and toddlers are a challenging population upon which to perform magnetic resonance imaging (MRI) of the brain, both in research and clinical settings. Because of the large range in head size during the early years of development, paediatric neuro-MRI requires a radiofrequency (RF) coil, or set of coils, that is tailored to head size to provide the highest image quality. Mitigating techniques must also be employed to reduce and correct for subject motion. This manuscript describes an RF coil with a tailored mechanical-electrical design that can adapt to the head size of 3-month-old infants to 3-year-old toddlers. The RF coil was designed with tight-fitting coil elements to improve the signal-to-noise ratio (SNR) in comparison with commercially available adult head coils, while simultaneously aiding in immobilization. The coil was designed without visual obstruction to facilitate an unimpeded view of the child's face and the potential application of camera or motion-tracking systems. Despite the lack of elements over the face, the paediatric coil produced higher SNR over most of the brain compared with adult coils, including more than twofold in the periphery. Acceleration rates of fourfold in each Cartesian direction could be achieved. High SNR allowed for short acquisition times through accelerated imaging protocols and reduced the probability of motion during a scan. Modification of the acquisition protocol, with immobilization of the head through the adjustable coil geometry, and subsequently being combined with a motion-tracking system, provides a compelling platform for scanning paediatric populations without sedation and with improved image quality.

Motion detection and correction for carotid MRI using a markerless optical system

PURPOSE

Motion related artifact is a challenge for MRI, especially when imaging regions like the carotid artery where complex motion (abrupt and bulk motion) may occur. This study aims to develop a non-contact motion detection and correction system for carotid MRI using a markerless optical tracking system.

METHODS

The proposed markerless optical tracking system consisted of a cross-line laser, an MRI-compatible camera and plastic holders mounted inside the scanner bore. The neck motion of the subject can be captured by monitoring the change of the projected laser position in real-time. The system was used to correct both abrupt motion and bulk motion for carotid MRI. The abrupt motion (e.g. coughing) was compensated by discarding the corrupted k-space lines and re-estimating the missing lines using SPIRiT algorithm. The bulk motion was corrected by phase adjustment of k-space lines according to the measured 1D-translational bulk motion (along anterior-posterior direction) and optimized in-plane translation parameters. Ten volunteers underwent carotid MRI with real-time neck motion detection and retrospective motion correction. Artery sharpness, vessel wall thickness and overall image quality score were compared between the motion-corrupted image and motion-corrected images of different correction strategies.

RESULTS

Both the abrupt motion and the bulk motion during carotid scanning were successfully detected and corrected. The results of ten volunteers demonstrated significant improvement in carotid artery sharpness, vessel wall thickness measurement, and overall image quality score using the proposed markerless optical tracking system and motion correction strategies.

CONCLUSION

The proposed markerless structured light based motion detection and correction system can sensitively detect both abrupt and bulk motion during carotid MR scans. By correcting for both abrupt and bulk motion, vessel wall delineation was improved in carotid MR images, which could potentially facilitate carotid plaque identification and atherosclerosis diagnosis in the future.

Brain PET motion correction using 3D face-shape model: the first clinical study

OBJECTIVE

Head motions during brain PET scan cause degradation of brain images, but head fixation or external-maker attachment become burdensome on patients. Therefore, we have developed a motion correction method that uses a 3D face-shape model generated by a range-sensing camera (Kinect) and by CT images. We have successfully corrected the PET images of a moving mannequin-head phantom containing radioactivity. Here, we conducted a volunteer study to verify the effectiveness of our method for clinical data.

METHODS

Eight healthy men volunteers aged 22-45 years underwent a 10-min head-fixed PET scan as a standard of truth in this study, which was started 45 min after ¹⁸F-fluorodeoxyglucose (285 ± 23 MBq) injection, and followed by a 15-min head-moving PET scan with the developed Kinect based motion-tracking system. First, selecting a motion-less period of the head-moving PET scan provided a reference PET image. Second, CT images separately obtained on the same day were registered to the reference PET image, and create a 3D face-shape model, then, to which Kinect-based 3D face-shape model matched. This matching parameter was used for spatial calibration between the Kinect and the PET system. This calibration parameter and the motion-tracking of the 3D face shape by Kinect comprised our motion correction method. The head-moving PET with motion correction was compared with the head-fixed PET images visually and by standard uptake value ratios (SUVRs) in the seven volume-of-interest regions. To confirm the spatial calibration accuracy, a test-retest experiment was performed by repeating the head-moving PET with motion correction twice where the volunteer's pose and the sensor's position were different.

RESULTS

No difference was identified visually and statistically in SUVRs between the head-moving PET images with motion correction and the head-fixed PET images. One of the small nuclei, the inferior colliculus, was identified in the head-fixed PET images and in the head-moving PET images with motion correction, but not in those without motion correction. In the test-retest experiment, the SUVRs were well correlated (determinant coefficient, r² = 0.995).

CONCLUSION

Our motion correction method provided good accuracy for the volunteer data which suggested it is useable in clinical settings.

Update on the Use of PET/MRI Contrast Agents and Tracers in Brain Oncology: A Systematic Review

The recent advancements in hybrid positron emission tomography–magnetic resonance imaging systems (PET/MRI) have brought massive value in the investigation of disease processes, in the development of novel treatments, in the monitoring of both therapy response and disease progression, and, not least, in the introduction of new multidisciplinary molecular imaging approaches. While offering potential advantages over PET/CT, the hybrid PET/MRI proved to improve both the image quality and lesion detectability. In particular, it showed to be an effective tool for the study of metabolic information about lesions and pathological conditions affecting the brain, from a better tumor characterization to the analysis of metabolic brain networks. Based on the PRISMA guidelines, this work presents a systematic review on PET/MRI in basic research and clinical differential diagnosis on brain oncology and neurodegenerative disorders. The analysis includes literature works and clinical case studies, with a specific focus on the use of PET tracers and MRI contrast agents, which are usually employed to perform hybrid PET/MRI studies of brain tumors. A systematic literature search for original diagnostic studies is performed using PubMed/MEDLINE, Scopus and Web of Science. Patients, study, and imaging characteristics were extracted from the selected articles. The analysis included acquired data pooling, heterogeneity testing, sensitivity analyses, used tracers, and reported patient outcomes. Our analysis shows that, while PET/MRI for the brain is a promising diagnostic method for early diagnosis, staging and recurrence in patients with brain diseases, a better definition of the role of tracers and imaging agents in both clinical and preclinical hybrid PET/MRI applications is needed and further efforts should be devoted to the standardization of the contrast imaging protocols, also considering the emerging agents and multimodal probes.

Performance evaluation of dedicated brain PET scanner with motion correction system

OBJECTIVE

Various motion correction (MC) algorithms for positron emission tomography (PET) have been proposed to accelerate the diagnostic performance and research in brain activity and neurology. We have incorporated MC system-based optical motion tracking into the brain-dedicated time-of-flight PET scanner. In this study, we evaluate the performance characteristics of the developed PET scanner when performing MC in accordance with the standards and guidelines for the brain PET scanner.

METHODS

We evaluate the spatial resolution, scatter fraction, count rate characteristics, sensitivity, and image quality of PET images. The MC evaluation is measured in terms of the spatial resolution and image quality that affect movement.

RESULTS

In the basic performance evaluation, the average spatial resolution by iterative reconstruction was 2.2 mm at 10 mm offset position. The measured peak noise equivalent count rate was 38.0 kcps at 16.7 kBq/mL. The scatter fraction and system sensitivity were 43.9% and 22.4 cps/(Bq/mL), respectively. The image contrast recovery was between 43.2% (10 mm sphere) and 72.0% (37 mm sphere). In the MC performance evaluation, the average spatial resolution was 2.7 mm at 10 mm offset position, when the phantom stage with the point source translates to ± 15 mm along the y-axis. The image contrast recovery was between 34.2 % (10 mm sphere) and 66.8 % (37 mm sphere).

CONCLUSIONS

The reconstructed images using MC were restored to their nearly identical state as those at rest. Therefore, it is concluded that this scanner can observe more natural brain activity.

Tracking of rigid head motion during MRI using an EEG system

Purpose

To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients.

Theory and Methods

Gradient switching during simultaneous EEG–fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high‐impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators.

Results

Head‐pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]‐mm translation, [0.30 0.27 0.22]‐degree rotation). Retrospective correction of 3D gradient‐echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with CapTrack pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees).

Conclusion

Motion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions.

Comparison of prospective and retrospective motion correction in 3D-encoded neuroanatomical MRI

PURPOSE

To compare prospective motion correction (PMC) and retrospective motion correction (RMC) in Cartesian 3D-encoded MPRAGE scans and to investigate the effects of correction frequency and parallel imaging on the performance of RMC.

METHODS

Head motion was estimated using a markerless tracking system and sent to a modified MPRAGE sequence, which can continuously update the imaging FOV to perform PMC. The prospective correction was applied either before each echo train (before-ET) or at every sixth readout within the ET (within-ET). RMC was applied during image reconstruction by adjusting k-space trajectories according to the measured motion. The motion correction frequency was retrospectively increased with RMC or decreased with reverse RMC. Phantom and in vivo experiments were used to compare PMC and RMC, as well as to compare within-ET and before-ET correction frequency during continuous motion. The correction quality was quantitatively evaluated using the structural similarity index measure with a reference image without motion correction and without intentional motion.

RESULTS

PMC resulted in superior image quality compared to RMC both visually and quantitatively. Increasing the correction frequency from before-ET to within-ET reduced the motion artifacts in RMC. A hybrid PMC and RMC correction, that is, retrospectively increasing the correction frequency of before-ET PMC to within-ET, also reduced motion artifacts. Inferior performance of RMC compared to PMC was shown with GRAPPA calibration data without intentional motion and without any GRAPPA acceleration.

CONCLUSION

Reductions in local Nyquist violations with PMC resulted in superior image quality compared to RMC. Increasing the motion correction frequency to within-ET reduced the motion artifacts in both RMC and PMC.

Characterisation of Children's Head Motion for Magnetic Resonance Imaging With and Without General Anaesthesia

Head motion is one of the major reasons for artefacts in Magnetic Resonance Imaging (MRI), which is especially challenging for children who are often intimidated by the dimensions of the MR scanner. In order to optimise the MRI acquisition for children in the clinical setting, insights into children's motion patterns are essential. In this work, we analyse motion data from 61 paediatric patients. We compare structural MRI data of children imaged with and without general anaesthesia (GA), all scanned using the same hybrid PET/MR scanner. We analyse several metrics of motion based on the displacement relative to a reference, decompose the transformation matrix into translation and rotation, as well as investigate whether different regions in the brain are affected differently by the children's motion. Head motion for children without GA was significantly higher, with a median of the mean displacements of 2.19 ± 0.93 mm (median ± standard deviation) during 41.7±7.5 min scans; however, even anaesthetised children showed residual head motion (mean displacement of 1.12±0.35 mm). For both patient groups translation along the z-axis (along the scanner bore) was significantly larger in absolute terms (GA / no GA: 0.87±0.29/0.92 ± 0.49 mm) compared to the other directions. Considering directionality, both patient groups were moving in negative z-direction and thus, out of the scanner. The awake children additionally showed significantly more nodding rotation (0.33±0.20°). In future studies as well as in the clinical setting, these predominant types of motion need to be taken into consideration to limit artefacts and reduce re-scans due to poor image quality.

Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging

Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

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

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 few 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.

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Data-driven respiratory phase-matched PET attenuation correction without CT

We propose a deep learning-based data-driven respiratory phase-matched gated-PET attenuation correction (AC) method that does not need a gated-CT. The proposed method is a multi-step process that consists of data-driven respiratory gating, gated attenuation map estimation using maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm, and enhancement of the gated attenuation maps using convolutional neural network (CNN). The gated MLAA attenuation maps enhanced by the CNN allowed for the phase-matched AC of gated-PET images. We conducted a non-rigid registration of the gated-PET images to generate motion-free PET images. We trained the CNN by conducting a 3D patch-based learning with 80 oncologic whole-body¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT scan data and applied it to seven regional PET/CT scans that cover the lower lung and upper liver. We investigated the impact of the proposed respiratory phase-matched AC of PET without utilizing CT on tumor size and standard uptake value (SUV) assessment, and PET image quality (%STD). The attenuation corrected gated and motion-free PET images generated using the proposed method yielded sharper organ boundaries and better noise characteristics than conventional gated and ungated PET images. A banana artifact observed in a phase-mismatched CT-based AC was not observed in the proposed approach. By employing the proposed method, the size of tumor was reduced by 12.3% and SUV_90%was increased by 13.3% in tumors with larger movements than 5 mm. %STD of liver uptake was reduced by 11.1%. The deep learning-based data-driven respiratory phase-matched AC method improved the PET image quality and reduced the motion artifacts.

Motion correction methods for MRS: experts' consensus recommendations

Long acquisition times due to intrinsically low signal-to-noise ratio and the need for highly homogeneous B₀ field make MRS particularly susceptible to motion or scanner instability compared with MRI. Motion-induced changes in both localization and shimming (ie B₀ 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 B₀ 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 B₀ field correction requires internal navigator methods to measure the B₀ 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. Real-time 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.

Neural correlates of head restraint: Unsolicited neuronal activation and dopamine release

To minimize motion-related distortion of reconstructed images, conventional positron emission tomography (PET) measurements of the brain inevitably require a firm and tight head restraint. While such a restraint is now a routine procedure in brain imaging, the physiological and psychological consequences resulting from the restraint have not been elucidated. To address this problem, we developed a restraint-free brain PET system and conducted PET scans under both restrained and non-restrained conditions. We examined whether head restraint during PET scans could alter brain activities such as regional cerebral blood flow (rCBF) and dopamine release along with psychological stress related to head restraint. Under both conditions, 20 healthy male participants underwent [¹⁵O]H₂O and [¹¹C]Raclopride PET scans during working memory tasks with the same PET system. Before, during, and after each PET scan, we measured physiological and psychological stress responses, including the State-Trait Anxiety Inventory (STAI) scores. Analysis of the [¹⁵O]H₂O-PET data revealed higher rCBF in regions such as the parahippocampus in the restrained condition. We found the binding potential (BP_ND) of [¹¹C]Raclopride in the putamen was significantly reduced in the restrained condition, which reflects an increase in dopamine release. Moreover, the restraint-induced change in BP_ND was correlated with a shift in the state anxiety score of the STAI, indicating that less anxiety accompanied smaller dopamine release. These results suggest that the stress from head restraint could cause unsolicited responses in brain physiology and emotional states. The restraint-free imaging system may thus be a key enabling technology for the natural depiction of the mind.

Prospective motion correction for diffusion weighted EPI of the brain using an optical markerless tracker

PURPOSE

To enable motion-robust diffusion weighted imaging of the brain using well-established imaging techniques.

METHODS

An optical markerless tracking system was used to estimate and correct for rigid body motion of the head in real time during scanning. The imaging coordinate system was updated before each excitation pulse in a single-shot EPI sequence accelerated by GRAPPA with motion-robust calibration. Full Fourier imaging was used to reduce effects of motion during diffusion encoding. Subjects were imaged while performing prescribed motion patterns, each repeated with prospective motion correction on and off.

RESULTS

Prospective motion correction with dynamic ghost correction enabled high quality DWI in the presence of fast and continuous motion within a 10° range. Images acquired without motion were not degraded by the prospective correction. Calculated diffusion tensors tolerated the motion well, but ADC values were slightly increased.

CONCLUSIONS

Prospective correction by markerless optical tracking minimizes patient interaction and appears to be well suited for EPI-based DWI of patient groups unable to remain still including those who are not compliant with markers.

Propeller echo-planar time-resolved imaging with dynamic encoding (PEPTIDE)

PURPOSE

To develop a motion-robust extension to the recently developed echo-planar time-resolved imaging (EPTI) approach, referred to as PROPELLER EPTI with dynamic encoding (PEPTIDE), by incorporating rotations into the rapid, multishot acquisition to enable shot-to-shot motion correction.

METHODS

Echo-planar time-resolved imaging is a multishot EPI-based approach that allows extremely rapid acquisition of distortion-free and blurring-free multicontrast imaging and quantitative mapping. By combining k-space encoding rotations into the EPTI sampling strategy to repeatedly sample the low-resolution k-space center, PEPTIDE enables significant tolerance to shot-to-shot motion and B₀ phase variations. Retrospective PEPTIDE data sets are created through a combination of in vivo EPTI data sets with rotationally acquired protocols, to enable direct comparison of the 2 methods and their robustness to identical motion. The PEPTIDE data sets are also prospectively acquired and again compared with EPTI, in the presence of true subject motion.

RESULTS

The PEPTIDE approach is shown to be motion-robust to even severe subject motion (demonstrated > 30° in-plane rotation, alongside translational and through-plane motion), while maintaining the rapid encoding benefits of the EPTI technique. The technique enables accurate quantitative maps to be calculated from even severe motion data sets. While the performance of the motion correction depends on the type and severity of motion encountered, in all cases PEPTIDE significantly increases image quality in the presence of motion comparative to conventional EPTI.

CONCLUSION

The newly developed PEPTIDE technique combines a high degree of motion tolerance into the EPTI framework, enabling highly rapid acquisition of distortion-free and blurring-free images at multiple TEs in the presence of motion.