Improved frame-based estimation of head motion in PET brain imaging

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.

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

Head motion presents a continuing problem in brain PET studies. A wealth of motion correction (MC) algorithms had been proposed in the past, including both hardware-based methods and data-driven methods. However, in most real brain PET studies, in the absence of ground truth or gold standard of motion information, it is challenging to objectively evaluate MC quality. For MC evaluation, image-domain metrics, e.g., standardized uptake value (SUV) change before and after MC are commonly used, but this measure lacks objectivity because 1) other factors, e.g., attenuation correction, scatter correction and parameters used in the reconstruction, will confound MC effectiveness; 2) SUV only reflects final image quality, and it cannot precisely inform when an MC method performed well or poorly during the scan time period; 3) SUV is tracer-dependent and head motion may cause increases or decreases in SUV for different tracers, so evaluating MC effectiveness is complicated. Here, we present a new algorithm, i.e., motion corrected centroid-of-distribution (MCCOD) to perform objective quality control for measured or estimated rigid motion information. MCCOD is a three-dimensional surrogate trace of the center of tracer distribution after performing rigid MC using the existing motion information. MCCOD is used to inform whether the motion information is accurate, using the PET raw data only, i.e., without PET image reconstruction, where inaccurate motion information typically leads to abrupt changes in the MCCOD trace. MCCOD was validated using simulation studies and was tested on real studies acquired from both time-of-flight (TOF) and non-TOF scanners. A deep learning-based brain mask segmentation was implemented, which is shown to be necessary for non-TOF MCCOD generation. MCCOD is shown to be effective in detecting abrupt translation motion errors in slowly varying tracer distribution caused by the motion tracking hardware and can be used to compare different motion estimation methods as well as to improve existing motion information.

Assessment of motion and model bias on the detection of dopamine response to behavioral challenge

Compartmental modeling analysis of ¹¹C-raclopride (RAC) PET data can be used to measure the dopaminergic response to intra-scan behavioral tasks. Bias in estimates of binding potential (BP_ND) and its dynamic changes (ΔBP_ND) can arise both when head motion is present and when the compartmental model used for parameter estimation deviates from the underlying biology. The purpose of this study was to characterize the effects of motion and model bias within the context of a behavioral task challenge, examining the impacts of different mitigation strategies. Seventy healthy adults were administered bolus plus constant infusion RAC during a simultaneous PET/magnetic resonance (MR) scan with a reward task experiment. BP_ND and ΔBP_ND were estimated using an extension of the Multilinear Reference Tissue Model (E-MRTM2) and a new method (DE-MRTM2) was proposed to selectively discount the contribution of the initial uptake period. Motion was effectively corrected with a standard frame-based approach, which performed equivalently to a more complex reconstruction-based approach. DE-MRTM2 produced estimates of ΔBP_ND in putamen and nucleus accumbens that were significantly different from those estimated from E-MRTM2, while also decoupling ΔBP_ND values from first-pass k₂' estimation and removing skew in the spatial bias distribution of parametric ΔBP_ND estimates within the striatum.

Evaluating different methods of MR-based motion correction in simultaneous PET/MR using a head phantom moved by a robotic system

Background

Due to comparatively long measurement times in simultaneous positron emission tomography and magnetic resonance (PET/MR) imaging, patient movement during the measurement can be challenging. This leads to artifacts which have a negative impact on the visual assessment and quantitative validity of the image data and, in the worst case, can lead to misinterpretations. Simultaneous PET/MR systems allow the MR-based registration of movements and enable correction of the PET data. To assess the effectiveness of motion correction methods, it is necessary to carry out measurements on phantoms that are moved in a reproducible way. This study explores the possibility of using such a phantom-based setup to evaluate motion correction strategies in PET/MR of the human head.

Method

An MR-compatible robotic system was used to generate rigid movements of a head-like phantom. Different tools, either from the manufacturer or open-source software, were used to estimate and correct for motion based on the PET data itself (SIRF with SPM and NiftyReg) and MR data acquired simultaneously (e.g. MCLFIRT, BrainCompass). Different motion estimates were compared using data acquired during robot-induced motion. The effectiveness of motion correction of PET data was evaluated by determining the segmented volume of an activity-filled flask inside the phantom. In addition, the segmented volume was used to determine the centre-of-mass and the change in maximum activity concentration.

Results

The results showed a volume increase between 2.7 and 36.3% could be induced by the experimental setup depending on the motion pattern. Both, BrainCompass and MCFLIRT, produced corrected PET images, by reducing the volume increase to 0.7–4.7% (BrainCompass) and to -2.8–0.4% (MCFLIRT). The same was observed for example for the centre-of-mass, where the results show that MCFLIRT (0.2–0.6 mm after motion correction) had a smaller deviation from the reference position than BrainCompass (0.5–1.8 mm) for all displacements.

Conclusions

The experimental setup is suitable for the reproducible generation of movement patterns. Using open-source software for motion correction is a viable alternative to the vendor-provided motion-correction software.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00442-6.

Optimizing the frame duration for data-driven rigid motion estimation in brain PET imaging

PURPOSE

Data-driven rigid motion estimation for PET brain imaging is usually performed using data frames sampled at low temporal resolution to reduce the overall computation time and to provide adequate signal-to-noise ratio in the frames. In recent work it has been demonstrated that list-mode reconstructions of ultrashort frames are sufficient for motion estimation and can be performed very quickly. In this work we take the approach of using image-based registration of reconstructions of very short frames for data-driven motion estimation, and optimize a number of reconstruction and registration parameters (frame duration, MLEM iterations, image pixel size, post-smoothing filter, reference image creation, and registration metric) to ensure accurate registrations while maximizing temporal resolution and minimizing total computation time.

METHODS

Data from 18 F-fluorodeoxyglucose (FDG) and 18 F-florbetaben (FBB) tracer studies with varying count rates are analyzed, for PET/MR and PET/CT scanners. For framed reconstructions using various parameter combinations interframe motion is simulated and image-based registrations are performed to estimate that motion.

RESULTS

For FDG and FBB tracers using 4 × 105 true and scattered coincidence events per frame ensures that 95% of the registrations will be accurate to within 1 mm of the ground truth. This corresponds to a frame duration of 0.5-1 sec for typical clinical PET activity levels. Using four MLEM iterations with no subsets, a transaxial pixel size of 4 mm, a post-smoothing filter with 4-6 mm full width at half maximum, and averaging two or more frames to create the reference image provides an optimal set of parameters to produce accurate registrations while keeping the reconstruction and processing time low.

CONCLUSIONS

It is shown that very short frames (≤1 sec) can be used to provide accurate and quick data-driven rigid motion estimates for use in an event-by-event motion corrected reconstruction.

Physiological skin FDG uptake: A quantitative and regional distribution assessment using PET/MRI

Purpose

To retrospectively assess the repeatability of physiological F-18 labeled fluorodeoxyglucose (FDG) uptake in the skin on positron emission tomography/magnetic resonance imaging (PET/MRI) and explore its regional distribution and relationship with sex and age.

Methods

Out of 562 examinations with normal FDG distribution on whole-body PET/MRI, 74 repeated examinations were evaluated to assess the repeatability and regional distribution of physiological skin uptake. Furthermore, 224 examinations were evaluated to compare differences in the uptake due to sex and age. Skin segmentation on PET was performed as body-surface contouring on an MR-based attenuation correction map using an off-line reconstruction software. Bland–Altman plots were created for the repeatability assessment. Kruskal–Wallis test was performed to compare the maximum standardized uptake value (SUVmax) with regional distribution, age, and sex.

Results

The limits of agreement for the difference in SUVmean and SUVmax of the skin were less than 30%. The highest SUVmax was observed in the face (3.09±1.04), followed by the scalp (2.07±0.53). The SUVmax in the face of boys aged 0–9 years and 10–20 years (1.33±0.64 and 2.05±1.00, respectively) and girls aged 0–9 years (0.98±0.38) was significantly lower than that of men aged ≥20 years and girls aged ≥10 years (p<0.001). In women, the SUVmax of the face (2.31±0.71) of ≥70-year-olds was significantly lower than that of 30–39-year-olds (3.83±0.82) (p<0.05).

Conclusion

PET/MRI enabled the quantitative analysis of skin FDG uptake with repeatability. The degree of physiological FDG uptake in the skin was the highest in the face and varied between sexes. Although attention to differences in body habitus between age groups is needed, skin FDG uptake also depended on age.

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic 18F-FDG PET Brain Studies

This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic ¹⁸F-FDG brain studies. Methods: Ten healthy volunteers (5 men/5 women; mean age ± SD, 27 ± 7 y; weight, 70 ± 10 kg) underwent a test–retest ¹⁸F-FDG PET/MRI examination of the brain (n = 20). The imaging protocol consisted of a 60-min PET list-mode acquisition contemporaneously acquired with MRI, including MR navigators and a 3-dimensional time-of-flight MR angiography sequence. Arterial blood samples were collected as a reference standard representing the arterial input function (AIF). Training of the cGAN was performed using 70% of the total datasets (n = 16, randomly chosen), which was corrected for motion using MR navigators. The resulting cGAN mappings (between individual frames and the reference frame [55–60 min after injection]) were then applied to the test dataset (remaining 30%, n = 6), producing artificially generated low-noise images from early high-noise PET frames. These low-noise images were then coregistered to the reference frame, yielding 3-dimensional motion vectors. Performance of cGAN-aided motion correction was assessed by comparing the image-derived input function (IDIF) extracted from a cGAN-aided motion-corrected dynamic sequence with the AIF based on the areas under the curves (AUCs). Moreover, clinical relevance was assessed through direct comparison of the average cerebral metabolic rates of glucose (CMRGlc) values in gray matter calculated using the AIF and the IDIF. Results: The absolute percentage difference between AUCs derived using the motion-corrected IDIF and the AIF was (1.2% + 0.9%). The gray matter CMRGlc values determined using these 2 input functions differed by less than 5% (2.4% + 1.7%). Conclusion: A fully automated data-driven motion-compensation approach was established and tested for ¹⁸F-FDG PET brain imaging. cGAN-aided motion correction enables the translation of noninvasive clinical absolute quantification from PET/MR to PET/CT by allowing the accurate determination of motion vectors from the PET data itself.

Different preprocessing strategies lead to different conclusions: A [11C]DASB-PET reproducibility study

Positron emission tomography (PET) neuroimaging provides unique possibilities to study biological processes in vivo under basal and interventional conditions. For quantification of PET data, researchers commonly apply different arrays of sequential data analytic methods ("preprocessing pipeline"), but it is often unknown how the choice of preprocessing affects the final outcome. Here, we use an available data set from a double-blind, randomized, placebo-controlled [¹¹C]DASB-PET study as a case to evaluate how the choice of preprocessing affects the outcome of the study. We tested the impact of 384 commonly used preprocessing strategies on a previously reported positive association between the change from baseline in neocortical serotonin transporter binding determined with [¹¹C]DASB-PET, and change in depressive symptoms, following a pharmacological sex hormone manipulation intervention in 30 women. The two preprocessing steps that were most critical for the outcome were motion correction and kinetic modeling of the dynamic PET data. We found that 36% of the applied preprocessing strategies replicated the originally reported finding (p < 0.05). For preprocessing strategies with motion correction, the replication percentage was 72%, whereas it was 0% for strategies without motion correction. In conclusion, the choice of preprocessing strategy can have a major impact on a study outcome.

Body motion detection and correction in cardiac PET: Phantom and human studies

PURPOSE

Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We propose and evaluate a novel method to detect, estimate, and correct body motion in cardiac PET.

METHODS

Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction. For motion detection, we first divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences' distribution in each bin. We then compute the covariance matrix within a 25-s sliding window over the COM signals inside the window. The sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into "static" (i.e., body motion free) and "moving" (i.e. contaminated by body motion) frames. Each moving frame is further divided into a number of evenly spaced sub-frames (referred to as "sub-moving" frames), in which motion is assumed to be negligible. For motion estimation, we first reconstruct the data in each static and sub-moving frame using a rapid back-projection technique. We then select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration. For motion-compensated image reconstruction, we reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix. We evaluated the performance of our approach in both phantom and human studies.

RESULTS

Visually, the motion-corrected (MC) PET images obtained using the proposed method have better quality and fewer motion artifacts than the images reconstructed without motion correction (NMC). Quantitative analysis indicates that MC yields higher myocardium to blood pool concentration ratios. MC also yields sharper myocardium than NMC.

CONCLUSIONS

The proposed body motion correction method improves image quality of cardiac PET.

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

Objective

We demonstrate and evaluate the first markerless motion tracker compatible with PET, MRI, and simultaneous PET/MRI systems for motion correction (MC) of brain imaging.

Methods

PET and MRI compatibility is achieved by careful positioning of in-bore vision extenders and by placing all electronic components out-of-bore. The motion tracker is demonstrated in a clinical setup during a pediatric PET/MRI study including 94 pediatric patient scans. PET MC is presented for two of these scans using a customized version of the Multiple Acquisition Frame method. Prospective MC of MRI acquisition of two healthy subjects is demonstrated using a motion-aware MRI sequence. Real-time motion estimates are accompanied with a tracking validity parameter to improve tracking reliability.

Results

For both modalities, MC shows that motion induced artifacts are noticeably reduced and that motion estimates are sufficiently accurate to capture motion ranging from small respiratory motion to large intentional motion. In the PET/MRI study, a time-activity curve analysis shows image improvements for a patient performing head movements corresponding to a tumor motion of ±5-10 mm with a 19% maximal difference in standardized uptake value before and after MC.

Conclusion

The first markerless motion tracker is successfully demonstrated for prospective MC in MRI and MC in PET with good tracking validity.

Significance

As simultaneous PET/MRI systems have become available for clinical use, an increasing demand for accurate motion tracking and MC in PET/MRI scans has emerged. The presented markerless motion tracker facilitate this demand.

Robustness of post-reconstruction and direct kinetic parameter estimates under rigid head motion in dynamic brain PET imaging

OBJECTIVE

Dynamic PET imaging is extensively used in brain imaging to estimate parametric maps. Inter-frame motion can substantially disrupt the voxel-wise time-activity curves (TACs), leading to erroneous maps during kinetic modelling. Therefore, it is important to characterize the robustness of kinetic parameters under various motion and kinetic model related factors.

METHODS

Fully 4D brain simulations ([¹⁵O]H₂O and [¹⁸F]FDG dynamic datasets) were performed using a variety of clinically observed motion patterns. Increasing levels of head motion were investigated as well as varying temporal frames of motion initiation. Kinetic parameter estimation was performed using both post-reconstruction kinetic analysis and direct 4D image reconstruction to assess bias from inter-frame emission blurring and emission/attenuation mismatch.

RESULTS

Kinetic parameter bias heavily depends on the time point of motion initiation. Motion initiated towards the end of the scan results in the most biased parameters. For the [¹⁸F]FDG data, k₄ is the more sensitive parameter to positional changes, while K₁ and blood volume were proven to be relatively robust to motion. Direct 4D image reconstruction appeared more sensitive to changes in TACs due to motion, with parameter bias spatially propagating and depending on the level of motion.

CONCLUSION

Kinetic parameter bias highly depends upon the time frame at which motion occurred, with late frame motion-induced TAC discontinuities resulting in the least accurate parameters. This is of importance during prolonged data acquisition as is often the case in neuro-receptor imaging studies. In the absence of a motion correction, use of TOF information within 4D image reconstruction could limit the error propagation.

From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies

Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state-of-the-art MR-PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR-PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR-PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR-PET neuroimaging research studies.