A data-driven head motion correction (MoCo) method was tested on C-11-Methionine brain positron emission tomography (PET) images in a cohort of 44 pediatric patients with brain tumors referred to a PET/MR study. Its impact was investigated both qualitatively and quantitatively. For each patient, PET images were reconstructed offline both with (PETn0M0C0) and without (PETn0M0C0) MoCo algorithm. An expert Nuclear Medicine physician qualitatively evaluated PET images, and segmented PET positive lesions in both datasets, extracting the following PET parameters: maximum and mean standardized uptake value (SUVmax and SUVmean, respectively), and metabolic tumor volume (MTV). PET parameters before and after MoCo were compared and their absolute percentage difference was calculated (A%). Contrast-to-noise (CNR) and its difference (A) before and after MoCo were calculated. Thirty-one patients had a "low" level of motion, 8 patients "medium" and 5 patients "high." Twenty-one patients out of forty-four had positive 11C-Methionine uptake (26 lesions). Qualitatively, no difference was evident in negative patients, while two PET positive lesions could be better defined after MoCo. Quantitatively, CNR increased significantly after MoCo for "medium+high" lesions, while none of the PET parameters showed significant difference. Increasing the sample of patients might confirm these results.

ABSTRACT:We report a case of a 33-year-old man with epilepsy and equivocal EEG, MRI signs of mesiotemporal sclerosis, and nondiagnostic standard FDG-PET imaging. The patient underwent repeat FDG-PET/MRI to clarify the sidedness of the epileptogenic focus and to confirm the suspected MTS. The standard PET reconstruction using block sequential regularized expectation maximization failed to provide evidence of a clear epileptogenic focus. However, using MR-guided PET reconstruction, circumscribed hypometabolism was observed in the right-sided entorhinal cortex, compatible with the epileptogenic focus. The MR-guided PET reconstruction provided significantly improved gray/white matter differentiation, enhancing confidence in imaging interpretation.

ABSTRACT:A 51-year-old man with severe multifactorial neurocognitive disorders subsequent to delirium, benzodiazepine withdrawal, and preexisting psychiatric illness was referred for 18 F-FDG PET/CT brain imaging in order to rule out an underlying neurodegenerative cause of the symptoms, particularly frontotemporal lobar degeneration. Imaging was impaired by severe motion artifacts, leading to a false-positive result. However, utilizing retrospective data-driven motion correction facilitated a change in diagnosis, ruling out the presence of neurodegenerative disease. The implementation of motion correction of the 18 F-FDG PET dataset proved crucial for the patient, as the exclusion of frontotemporal lobar degeneration formed the basis for continuing psychiatric and psychotherapeutic treatment.

Abstract We report a case of a 33-year-old man with epilepsy and equivocal EEG, MRI signs of mesiotemporal sclerosis, and nondiagnostic standard FDG-PET imaging. The patient underwent repeat FDG-PET/MRI to clarify the sidedness of the epileptogenic focus and to confirm the suspected MTS. The standard PET reconstruction using block sequential regularized expectation maximization failed to provide evidence of a clear epileptogenic focus. However, using MR-guided PET reconstruction, circumscribed hypometabolism was observed in the right-sided entorhinal cortex, compatible with the epileptogenic focus. The MR-guided PET reconstruction provided significantly improved gray/white matter differentiation, enhancing confidence in imaging interpretation.

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 F-18-fluorodeoxyglucose (FDG) and F-18-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 x 10(5) 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. (C) 2021 American Association of Physicists in Medicine

The 90Y PET images used for post-treatment dosimetry in SIRT of liver cancer are impacted by respiratory motion. Liver motion due to respiration will result in blurred images and can lead to inaccuracies of the dosimetric measures. Due to the low count statistics in 90Y PET data, the data-driven gating method does not work well, making the deviceless respiratory motion correction method unusable. The purpose of this study was to develop an alternative data-driven gating method to derive triggers directly from the list-mode data in order to produce respiratory motion compensated 90Y PET images and to assess the impact of respiratory motion on 90Y dosimetry. The steps for performing dosimetry have been adapted to improve dosimetry on the motion corrected PET images. Two different motion correction methods called Q.Static and Q.Rigid were investigated and evaluated on both NCAT phantom and patient data. The corrected images were compared to uncorrected respiratory motion PET images. Results demonstrate that both correction techniques lead to an improvement in dosimetry calculation for the liver tumor. Doses delivered to tumor were increased by 18.3% and 37.4% for Q.Static and Q.Rigid respectively for NCAT phantom; tumor doses were increased by 7.12 ± 12.04% and 8.76 ± 12.97% for Q.Static and Q.Rigid respectively, for patient data.

Objective . Positron emission tomography (PET) imaging of tau deposition using [ 18 F]-MK6240 often involves long acquisitions in older subjects, many of whom exhibit dementia symptoms. The resulting unavoidable head motion can greatly degrade image quality. Motion increases the variability of PET quantitation for longitudinal studies across subjects, resulting in larger sample sizes in clinical trials of Alzheimer’s disease (AD) treatment. Approach . After using an ultra-short frame-by-frame motion detection method based on the list-mode data, we applied an event-by-event list-mode reconstruction to generate the motion-corrected images from 139 scans acquired in 65 subjects. This approach was initially validated in two phantoms experiments against optical tracking data. We developed a motion metric based on the average voxel displacement in the brain to quantify the level of motion in each scan and consequently evaluate the effect of motion correction on images from studies with substantial motion. We estimated the rate of tau accumulation in longitudinal studies (51 subjects) by calculating the difference in the ratio of standard uptake values in key brain regions for AD. We compared the regions’ standard deviations across subjects from motion and non-motion-corrected images. Main results . Individually, 14% of the scans exhibited notable motion quantified by the proposed motion metric, affecting 48% of the longitudinal datasets with three time points and 25% of all subjects. Motion correction decreased the blurring in images from scans with notable motion and improved the accuracy in quantitative measures. Motion correction reduced the standard deviation of the rate of tau accumulation by −49%, −24%, −18%, and −16% in the entorhinal, inferior temporal, precuneus, and amygdala regions, respectively. Significance . The list-mode-based motion correction method is capable of correcting both fast and slow motion during brain PET scans. It leads to improved brain PET quantitation, which is crucial for imaging AD.

Cardiac imaging in PET is susceptible to image degradation from multiple types of motion: myocardial motion due to the cardiac cycle, respiratory motion (which can vary greatly over the course of a scan), and bulk body movements. A comprehensive approach to cardiac motion correction has been developed which addresses each of these types of motion. First, the effect of respiratory motion and bulk body movements on the myocardium is modelled as rigid translation motion; these translations are estimated in image space from short-duration (0.5 s) frames reconstructed over the full duration of the scan. This accounts for variation in respiratory motion because no periodicity is assumed. Next, an ECG signal is used for phase-based gating of the myocardial cycle. The cardiac gated reconstructions incorporate event-by-event motion correction from the estimated translations. The resulting cardiac gated series presents the heart at a fixed position, with only cardiac motion between gates. Finally, the cardiac gated images are combined into a single volume via non-rigid registration using the Q.Freeze2 package from GE HealthCare. This approach provides a single fully motion-corrected, quantitative image of the myocardium that utilizes all counts. Results from this approach are shown on NH 3 data with the reconstructions showing improved contrast and signal-to-noise.

Standard clinical reconstructions usually require several minutes to complete, and this time is mostly independent of the duration of the data being reconstructed. Applications such as data-driven motion estimation, which require many short frames over the duration of the scan, become unfeasible with such long reconstruction times. In this work, we present an infrastructure whereby ultra-fast list-mode reconstructions of very short frames (≤1 s) are performed. With this infrastructure, it is possible to have a dynamic series of frames that can be used for various applications, such as data-driven motion estimation, whole-body surveys, quick reconstructions of gated data to select the optimal gate for a given attenuation map, and, if the infrastructure runs simultaneously with the scan, real-time display of the reconstructed data during the scan and automated alerts for patient motion. Methods: A fast ray-tracing time-of-flight projector was implemented and parallelized. The reconstruction parameters were optimized to allow for fast performance: only a few iterations are performed, without point-spread-function modeling, and scatter correction is not used. The resulting reconstructions are thus not quantitative but are acceptable for motion estimation and visualization purposes. Data-driven motion can be estimated using image registration, with the resultant motion data being used in a fully motion-corrected list-mode reconstruction. Results: The infrastructure provided images that can be used for visualization and gating purposes and for motion estimation using image registration. Several case studies are presented, including data-driven motion estimation and correction for brain studies, abdominal studies in which respiratory and cardiac motion is visible, and a whole-body survey. Conclusion: The presented infrastructure provides the capability to quickly create a series of very short frames for PET data that can be used in a variety of applications.

High resolution PET imaging is being driven by recent advances in both scanner hardware and image reconstruction. As a result, images are being reconstructed with smaller pixel sizes and little or no post-reconstruction filtering. In certain circumstances, this combination can lead to distracting image artifacts due to high frequencies introduced by the projectors. We present a method that utilizes PET point spread function (PSF) processing to mitigate these artifacts while preserving image resolution. Our approach includes a "hybrid-space PSF," with components in both sinogram-space and image space. Radial smoothing of the sinogram data (or a broadened line of response if performing list mode reconstruction) is combined with a spatially invariant image smoothing. Unlike fully-image-based PSF approaches, this method allows a PSF kernel specific to each crystal pair, even when corrected for patient motion. The hybrid-space PSF approach also opens the door to isotope-dependent positron range imaging, by allowing the use of different image-based kernels for isotopes with different positron range, while preserving the same projection-based kernel (which is a function of detector design only). The effectiveness of the hybrid-space PSF approach is demonstrated with a brain 18 F-FDG dataset and spatial resolution point sources.

A data-driven method is proposed for rigid motion estimation directly from time-of-flight (TOF)-positron emission tomography (PET) emission data. Rigid motion parameters (translations and rotations) are estimated from the first and second moments of the emission data masked in a spherical volume. The accuracy of the method is analyzed on 3D analytical simulations of the PET-SORTEO brain phantom, and subsequently tested on18F-FDG as well as11C-PIB brain datasets acquired on a TOF-PET/CT scanner. The estimated inertia-based motion is later compared to rigid motion parameters obtained by directly registering the short frame backprojections. We find that the method provides sub mm/degree accuracies for the estimated rigid motion parameters for counts corresponding to typical 0.5 s, 1 s, and 2 s18F-FDG brain scans, with the current TOF resolutions clinically available. The method provides robust motion estimation for different types of patient motion, most notably for a continuous patient motion case where conventional frame-based approaches which rely on little to no intra-frame motion of short time intervals could fail. The method relies on the detection of stable eigenvectors for accurate motion estimation, and a monitoring of this condition can reveal time-frames where the motion estimation is less accurate, such as in dynamic PET studies.

59 Introduction: Head motion during a brain PET scan can cause motion blurring and loss of image quality. In this work we present a completely data-driven motion estimation approach with full event-by-event motion corrected list-mode (LM) reconstruction. A reader study and atlas-based quantitative analysis comparing the motion corrected (MoCo) and uncorrected (nMoCo) reconstructions were performed on 50 clinical FDG brain scans. Methods: Ultra-fast LM reconstructions of very short frames (0.6 - 1.8 sec) were performed. Head motion was estimated using image registration [1]. Event-by-event motion corrected LM reconstruction was then performed on the entire acquisition. Normalization, deadtime, attenuation, scatter, and randoms corrections were performed during reconstruction. A cohort of 50 retrospective clinical metabolic FDG brain scans was obtained from 3 scanners: SIGNA PET/MR (n=21), DMI PET/CT (n=11), and D710 PET/CT (n=18) (GE Healthcare, Chicago, IL). Experienced readers evaluated the MoCo and nMoCo images in a blinded read using a Likert scale (1-5) for image sharpness and diagnostic quality. Atlas-based quantitation analysis was performed on the SUVmax values of 9 regions-of-interest (ROIs) [2]. Paired Wilcoxon tests were used to assess differences in reader scores and ROI-wise SUVmax. A Levene test was used to assess if the variance of the relative difference of MoCo and nMoCo was different between motion groups. Statistical tests were corrected for multiple comparisons. Results: The range of estimated motion in the cohort, as quantified by a point within the brain displaced by the motion, is shown in Figure 1. Figure 1 also shows how the cohort was divided into 4 motion groups. Figure 2 and 3 show examples of MoCo applied to 2 data sets. Figure 4 shows the results of the reader study. Figure 5 shows the results of the ROI analysis. Conclusions: MoCo improved both quantitative and qualitive assessment of the images. Using an atlas-based approach we demonstrated that, when motion occurs, MoCo makes a significant difference in the quantitation of the reconstruction. The reader study demonstrated that MoCo improved diagnostic quality in 10% of the data sets, with 8% changing from diagnostically “unacceptable” to “acceptable” with MoCo. When there is little motion, MoCo reconstructions are not significantly different to the nMoCo reconstructions, as expected. The developed approach was completely data-driven, yielding motion estimates with ~1 Hz frame rate utilizing an event-by-event LM reconstruction, and is compatible with routine imaging workflows suggesting that motion-robust PET is clinically attainable. Fig. 1: (Left) Box plots of estimated motion. Shaded regions indicate motion groups: “Offset” includes low motion during the scan with an offset between PET and attenuation map of >2 mm. (Right) Initial offset between PET and attenuation map. Fig. 2: Example data set in the “High” motion group. (Top) Translations of the motion (rotations not shown). (Bottom) The nMoCo and MoCo images. The nMoCo image is visibly blurred. Fig. 3: Example data set in the “Offset” motion group. (Top) Translations of the estimated motion. (Bottom) The nMoCo and MoCo images, and a sagittal difference image showing a relative gradient. Fig. 4: Difference of the averaged reader scores between the MoCo and nMoCo images, grouped according to motion group for (Top) image sharpness and (Middle) diagnostic quality. (Bottom) Significant differences in sharpness and quality for MoCo and nMoCo reconstructions were found for the “High” motion group and the pooled cohort data (“All”). Fig. 5: (Top) Relative differences between the SUVmax values of the MoCo and nMoCo images in 9 ROIs. (Bottom) Significant differences in SUVmax were present in many ROIs between the MoCo and nMoCo images. Similarly, the variance of the relative difference between MoCo and nMoCo was significantly different for many ROIs in the “Offset”, “Med”, and “High” motion groups compared to the “Low” motion group.