Motion correction of PET data with a joint registration functional for multi-modal data

Motion correction of neurological 3D PET data by the multiple acquisition frame (MAF) method presents some challenges that do not apply to the same extent in 2D. One of the most significant is that 3D PET scans have a much higher scatter component, and the inclusion of valid correction for scatter is essential for accurate quantification. This paper describes a technique for motion correction of PET data acquired in 3D that uses information provided by an optical motion tracking system, and includes corrections for temporal variations in attenuation and scatter. The technique is demonstrated in 3D dynamic and list mode scans of a Hoffman 3D brain phantom performed on a Siemens/CTI ECAT EXACT HR+ scanner in which multiple arbitrary movements were applied to the phantom during data acquisition. The MAF method cannot correct for motion that occurs during the acquisition of an individual frame. The feasibility of avoiding this limitation by acquiring the data in list mode, and re-framing into a dynamic scan in which a new frame is commenced whenever significant motion occurs was investigated. Both approaches provided a major reduction in motion distortion when compared to a conventional reconstruction without motion correction. It appears that effective motion compensation is achievable in 3D neurological PET.

The impact of a method for MR-based respiratory motion correction of PET data on lesion visibility and quantification in patients with oncologic findings in the lung was evaluated. Twenty patients with one or more lesions in the lung were included. Hybrid imaging was performed on an integrated PET/MR system using 18F-FDG as radiotracer. The standard thoracic imaging protocol was extended by a free-breathing self-gated acquisition of MR data for motion modelling. PET data was acquired simultaneously in list-mode for 5-10 mins. One experienced radiologist and one experienced nuclear medicine specialist evaluated and compared the post-processed data in consensus regarding lesion visibility (scores 1–4, 4 being best), image noise levels (scores 1–3, 3 being lowest noise), SUVmean and SUVmax. Motion-corrected (MoCo) images were additionally compared with gated images. Non-motion-corrected free-breathing data served as standard of reference in this study. Motion correction generally improved lesion visibility (3.19 ± 0.63) and noise ratings (2.95 ± 0.22) compared to uncorrected (2.81 ± 0.66 and 2.95 ± 0.22, respectively) or gated PET data (2.47 ± 0.93 and 1.30 ± 0.47, respectively). Furthermore, SUVs (mean and max) were compared for all methods to estimate their respective impact on the quantification. Deviations of SUVmax were smallest between the uncorrected and the MoCo lesion data (average increase of 9.1% of MoCo SUVs), while SUVmean agreed best for gated and MoCo reconstructions (MoCo SUVs increased by 1.2%). The studied method for MR-based respiratory motion correction of PET data combines increased lesion sharpness and improved lesion activity quantification with high signal-to-noise ratio in a clinical setting. In particular, the detection of small lesions in moving organs such as the lung and liver may thus be facilitated. These advantages justify the extension of the PET/MR imaging protocol by 5–10 minutes for motion correction.

In normal free-breathing PET/CT imaging protocol, a single CT image of the patient is obtained before the PET scan, which is inadequate for attenuation correction of the free-breathing PET data. The aim of this study is to design an automatic workflow to correct mismatch and reduce motion blurring of free-breathing PET and single-scan CT data. First, the PET data was divided into four equal-count respiratory frames using data-driven respiratory gating method. Modified attenuation map was used in during reconstruction to obtain mismatch-free gated PET images. Second, the respiratory phase of the original CT image was identified automatically using mutual information (MI) as the similarity metric. Two regions of interests (ROIs) near the lungs were segmented from CT image. The mutual information (MI) inside the ROIs of each gated PET reconstruction and the CT image was measured for all gates and the phase with highest MI value is identified as the correct respiratory phase of CT image. Third, using the respiratory phase of the CT image as the reference gate, MVFs from every other gate to the reference gate was estimated using a B-spline based multi-resolution image registration algorithm. Fourth, the original CT image with identified respiratory phase was transformed to every other frame using estimated MVFs to obtain attenuation maps for all respiratory frames. Finally, image reconstruction of gated PET data was repeated using phase-matched gated PET-CT data pairs. MVFs estimated in step 3 were applied to this set of gated PET images to obtain motion compensated PET image. This method was applied to clinical datasets using both F18-FDG and Ga68 -DOTA-NOC tracer. The proposed method successfully identified the respiratory phase of the CT image and generated phase-matched CT images for all respiratory phases of PET data. Final reconstructed PET data using the proposed method showed much-reduced mismatch artifacts and motion blurring, as well as improved statistics.

Neocortical atrophy reduces PET signal intensity, potentially affecting the diagnostic efficacy of β-amyloid (Aβ) brain PET imaging. This study investigated whether partial-volume effect correction (PVEC), adjusting for this atrophy bias, improves the accuracy of (18)F-florbetaben Aβ PET. We analyzed (18)F-florbetaben PET and MRI data obtained from 3 cohorts. The first was 10 patients with probable Alzheimer disease (AD) and 10 age-matched healthy controls (HCs), the second was 31 subjects who underwent in vivo imaging and postmortem histopathology for Aβ plaques, and the third was 5 subjects who underwent PET and MRI at baseline and 1 y later. The imaging data were coregistered and segmented. PVEC was performed using the voxel-based modified Müller-Gärtner method (PVELab, SPM8). From the PET data, regional and composite SUV ratios (SUVRs) with and without PVEC were obtained. In the MRI data, mesial temporal lobe atrophy was determined by the Scheltens mesial temporal atrophy scale and gray matter volumes by voxel-based morphometry. In cohort 1, PVEC increased the effect on AD-versus-HC discrimination from a Cohen d value of 1.68 to 2.0 for composite SUVRs and from 0.04 to 1.04 for mesial temporal cortex SUVRs. The PVEC-related increase in mesial temporal cortex SUVR correlated with the Scheltens score (r = 0.84, P < 0.001), and that of composite SUVR correlated with the composite gray matter volume (r = -0.75, P < 0.001). In cohort 2, PVEC increased the correlation coefficient between mesial temporal cortex SUVR and histopathology score for Aβ plaque load from 0.28 (P = 0.09) to 0.37 (P = 0.03). In cohort 3, PVEC did not affect the composite SUVR dynamics over time for the Aβ-negative subject. This finding was in contrast to the 4 Aβ-positive subjects, in 2 of whom PVEC changed the composite SUVR dynamics. The influence of PVEC on (18)F-florbetaben PET data is associated with the degree of brain atrophy. Thus, PVEC increases the ability of (18)F-florbetaben PET to discriminate between AD patients and HCs, to detect Aβ plaques in the atrophic mesial temporal cortex, and potentially to evaluate changes in brain Aβ load over time. As such, the use of PVEC should be considered for quantitative (18)F-florbetaben PET scans, especially in assessing patients with brain atrophy.

The PET/MRI hybrid system became eligible for staging the diagnosis of malignant tumors, metastasis, and recurrence using 18F-Fluorodeoxyglucose (FDG). Inserting PET in an MRI is technically challenging and requires PET detectors to be compatible with the magnetic field. The key technology that enables this is the semiconductor-based photomultiplier tube. The advantage of the PET/MRI hybrid system is not simply that MRI has replaced computed tomography (CT), but rather that PET and MRI data can be acquired simultaneously. The high-precision fusion images show improved diagnostic accuracy. Positional misalignment due to movements, such as respiratory motion, as seen on PET/CT, can be minimized through simultaneous acquisition, and higher-precision fusion images can be obtained by performing PET and MRI acquisitions under respiratory gating. One of the disadvantages of PET/MRI over PET/CT is that CT cannot be used for gamma-ray attenuation correction, which is necessary for PET image reconstruction. Although a method has been established to generate a pseudo-CT image from MRI for attenuation correction, generating a pseudo-CT image that includes bone components is particularly difficult. In PET/MRI systems approved for clinical use, bone components are not considered for attenuation correction outside the head region. Recently, active research has been conducted utilizing deep learning to generate pseudo-CT images with bone components from MRI, regardless of the anatomical region, or to perform attenuation correction using PET data alone. Recently, new PET tracers have been approved in Japan. As PET/MRI can produce highly accurate fusion images with MRI, these tracers and target lesions are likely to benefit more from PET/MRI than from PET/CT. As such, the scope of PET/MRI applications is expected to continue to expand, and efforts should be made to promote its widespread adoption so that more facilities can utilize this technology.

Recently, AmyloidIQ was introduced as a new canonical image-based algorithm to quantify amyloid PET scans and demonstrated increased power over traditional SUV ratio (SUVR) approaches when assessed in cross-sectional and longitudinal analyses. We build further on this mathematical framework to develop a TauIQ algorithm for the quantitative analysis of the more complex spatial distribution displayed by tau PET radiotracers. Methods: Cross-sectional (n = 615) and longitudinal (n = 149) 18F-flortaucipir data were obtained from the Alzheimer's Disease Neuroimaging Initiative along with necessary adjunct amyloid PET and T1-weighted structural MRI data. A subset of these data were used to derive a chronological tau dataset, using AmyloidIQ analysis of associated amyloid PET data to calculate the subject's temporal position in the canonical AD disease process, from which canonical images for the nonspecific and specific binding components of 18F-flortaucipir in AD were calculated. These 2 canonical images were incorporated into the TauIQ algorithm that enables the quantification of both global and local tau outcome measures using an image-based regression and statistical parametric analysis of the initial residual image. Performance of the TauIQ algorithm was compared with SUVR approaches for cross-sectional analyses, longitudinal analyses, and correlation with clinical measures (Alzheimer Disease Assessment Scale-Cognitive Subscale [ADAS-Cog], Clinical Dementia Rating scale-sum of boxes [CDR-SB], and Mini-Mental State Examination [MMSE]). Results: TauIQ successfully calculated global tau load (TauL) in all 791 scans analyzed (range, -3.5% to 185.2%; mean ± SD, 23% ± 20.5%) with a nonzero additional local tau component being required in 31% of all scans (cognitively normal [CN], 22%; mild cognitive impairment [MCI], 35%; dementia, 72%). TauIQ was compared with the best SUVR approach in the cross-sectional analysis (TauL increase in effect size: CN- vs. CN+, +45%; CN- vs. MCI+, -5.6%; CN- vs. dementia+, +2.3%) (+/- indicates amyloid-positive or -negative) and correlation with clinical scores (TauL increase in r2: CDR-SB+, 7%; MMSE+, 38%; ADAS-Cog+, 0%). TauIQ substantially outperformed SUVR approaches in the longitudinal analysis (TauIQ increase in power: CN+, >3.2-fold; MCI+, >2.2-fold; dementia+, >2.9-fold). Conclusion: TauL as calculated by TauIQ provides a superior approach for the quantification of tau PET data. In particular, it provides a substantial improvement in power for longitudinal analyses and the early detection of tau deposition and thus should have significant value for clinical imaging trials in AD that are investigating the attenuation of tau deposition with novel therapies.

Catheter-placement in liver metastases is difficult when the lesions are not visible on plain images. We evaluated the use of image fusion using CT and PET data, providing information on anatomy and liver lesions, respectively. Plain CT of the liver and whole body FDG-PET were performed in 28 patients with colorectal carcinoma in preparation for laser-induced thermotherapy. Fusions of image data and 2D-visualisation were performed and evaluated with regard to quality of the registration, number of detected lesions and influence of the procedure on laser-induced thermotherapy. Image fusion was performed using an algorithm which was developed by our group. 84 focal liver lesions were visible on CT, 107 on PET images. This means that CT guided catheter placement would have to be performed "blindly" in 23 lesions. In 6 patients previously unknown extra-hepatic tumor deposits were seen on PET images (local recurrence, 2 extrahepatic metastases, 2 second primaries). Hot spots from metastases, which are difficult to locate on PET images alone, may be identified on images generated by fusion of CT and PET data. These images facilitate an interventional approach to liver lesions which are not visible on plain CT. As PET allows for whole body screening, it helps identifying patients who would not profit from laser-induced thermotherapy.

Objectives: PET data is usually corrected for scatter and attenuation among other factors during image reconstruction. The aim of this abstract is to study the accuracy of scatter and attenuation correction using different tissue equivalent and object sizes in PET/CT imaging. Methods: A phantom containing three cylindrical inserts (Air, water and Teflon) each of 40mm diameter was scanned on a GE DRX PET/CT scanner. The inserts were used to simulate air, soft tissue, and bone at 511 keV. The activity concentration (AC) in the phantom background was set to 0.12uCi/cc to emulate background AC found in clinical studies. PET data was acquired in 3D mode for 6 minutes (2 FOVs, 3 min per FOV). The same experiment was then repeated twice while wrapping the phantom with one and two layers of saline bags to simulate patients with medium and large cross sections. Each layer of saline bags added 4cm to the phantom diameter. All PET images were reconstructed using OSEM (2 iterations 21 subsets). A 20mm diameter ROI was drawn on each of the three cylinders and background as well as ±6 slices and the mean AC was calculated. In addition, cold contrast defined as one minus the ratio of cylinder to background AC was calculated for each cylinder type and phantom size. Results: Contrast in the air cylinder was constant for all phantom sizes, but decreased for water and Teflon with increasing phantom sizes. The decrease was 17% and 15% for Teflon and water respectively between the small and large phantom sizes. The highest contrast was observed in the Teflon cylinder for all phantom sizes. Conclusion: Current scatter and attenuation correction techniques do not fully recover the true AC in tissue particularly in large objects which might impact the accuracy of quantification in PET imaging.

Amyloid-β (Aβ) and [18F]FDG PET are established as amyloid pathology and neuronal injury biomarkers. Early after administration Aβ PET images have the potential to replace [18F]FDG PET images allowing dual biomarker delivery by the administration of a single tracer. For [18F]FDG PET data, a correlation with cognitive performance is known. The aim of this study was to investigate whether early after administration [11C]PiB PET data also correlate with cognitive performance. The early after administration [11C]PiB PET data of 31 patients with cognitive impairment were evaluated. CERAD subtests were summarized to five cognitive domains. The resulting z scores were correlated with the PET data on a voxel- and VOI-based approach. Additional subgroup analyses (MCI versus dementia, Aβ-positive versus Aβ-negative subjects) were performed. Significant correlations between cognitive performance and early after administration [11C]PiB PET data were found between left temporo-parietal SUVR and language domain, bilateral occipital as well as left temporal SUVR and executive function, left pre- and postcentral SUVRs, and visuospatial abilities. For the episodic and immediate memory domains, the analysis at the high significance level did not show any correlated cluster, however, the exploratory analysis did. Our study revealed correlations between deficits in different cognitive domains and regional early after administration [11C]PiB PET data similar to those known from [18F]FDG PET studies. Thus, our data support the assumption that early [11C]PiB PET data have a potential as neuronal injury biomarker. Head-to-head double-tracer studies of larger cohorts are needed to confirm this assumption.

Purpose: We propose to characterize the spatial distribution of PET‐derived voxel statistics within anatomically‐defined Regions Of Interest (ROIs) as a means to characterize disease‐related changes. Methods: We used 3D moment invariants (3DMIs) to characterize the spatial distribution of PET data ([11C]Raclopride, [11C]Tetrabenazine and [18F]Fluorodopa) recorded from subjects with Parkinsonˈs Disease (PD) and healthy controls. 3DMIs are mathematical spatial descriptors designed to be invariant to scaling, translation and rotation. In fMRI studies, assessing the spatial characteristics of voxel‐based statistics has recently been shown to utilized be a powerful and sensitive method to characterize brain activation. Crucially, this allows characterization of the spatial characteristics of activation without the need to warp brain images to a common brain template. Analogously, we propose characterizing the spatial distribution of PET‐derived voxel statistics within anatomically‐defined ROIs using 3DMIs. The 3DMIs used here were a combination of terms describing spatial variance, skewness and kurtosis. Subjects also underwent MRI scans so that the T1‐weighted MRI data could be used to anatomically delineate basal ganglia ROIs to act as binary masks on the PET data. Results: 3DMIs were found to accurately describe the “3D texture” of PET images despite changes in the size and orientation of these regions across subjects. In addition, we were able to find differences in the 3DMIs of PD patients distinct from those of healthy volunteers. These changes suggest that disease‐related variations in the spatial distribution measured using PET can be quantitatively described with the proposed method Conclusions: This method shows great promise to extract additional information from PET data with a wealth of potential applications to disease diagnosis, staging, treatment assessment and more. The quantification of the observed disease‐related changes for PD subjects is currently under way.

Several investigators have shown that noise equivalent count rate (NECR) is linearly proportional to the square of image signal-to-noise ratio (SNR) when PET images are reconstructed using filtered back-projection. However, to our knowledge, none have shown a similar relationship in fully 3D ordered-subset expectation maximization (OSEM) reconstruction. This paper has two aims. The first is to investigate the NECR-SNR relationship for 3D-OSEM reconstruction using phantom studies while the second aim is to evaluate the NECR-SNR relationship using patient data. An anthropomorphic phantom was scanned on a GE Discovery-STE (DSTE) PET∕CT scanner in 3D mode with an initial activity concentration of 66.34 kBq∕cc. PET data were acquired over the lower chest∕upper abdomen region in dynamic mode. The experiment was repeated with the same activity concentration on a GE Discovery-RX (DRX) scanner. Care was taken to place the phantom at identical positions in both scanners. PET data were then reconstructed using 3D Reprojection (3D-RP) and 3D-OSEM with different reconstruction parameters and the NECR and SNR for each frame∕image were calculated. SNR(2) was then plotted versus the NECR for each scanner, reconstruction method and parameters. In addition, 40 clinical PET∕CT studies from the two scanners (20 patients∕scanner) were evaluated retrospectively. The patient studies from each scanner were further divided into two subgroups of body mass indices (BMI). Each PET study was acquired in 3D mode and reconstructed using both 3D-OSEM and 3D-RP. The NECR and SNR of the bed position covering the patient liver were calculated for each patient and averaged for each subgroup. Comparisons of the NECR and SNR between scanner types and BMIs were performed using a t-test and a p value less than 0.05 was considered significant. Phantom results showed that SNR(2) versus NECR was linear for 3D-RP reconstruction across all activity concentration on both scanners, as expected. However, when 3D-OSEM was used, this relationship was nonlinear at activity concentrations beyond the peak NECR on both scanners. On the other hand, the plot of SNR(2) versus trues count rate was linear for 3D-OSEM across all activity concentrations on both scanners independent of reconstruction parameters used. In addition, for activity concentrations <30kBq∕cc, phantom results showed a higher SNR (by 12 ± 10%; p < 0.05) and NECR for the DRX scanner compared to DSTE for 3D-RP reconstruction. However, for 3D-OSEM reconstruction, these two scanners had similar SNRs (different by 2% ± 9%; p > 0.05), despite having different NECRs. Patient studies showed a statistically significant difference in NECR as well as the SNR for 3D-RP reconstruction between the two scanners. However, no statistically significant difference was found for 3D-OSEM. A statistically significant difference in both NECR and SNR were found between the different BMI subgroups for both 3D-RP and 3D-OSEM reconstructions. For the scanners and reconstruction algorithm used in this study, our results suggest that the image SNR cannot be predicted by the NEC when using 3D-OSEM reconstruction particularly for those clinical applications requiring high activity concentration. Instead, our results suggest that image SNR varies with activity concentration and is dominated by the 3D-OSEM reconstruction algorithm and its associated parameters, while not being affected by the scanner type for the range of activity concentrations usually found in the clinic.

Motion Freeze for Respiration Motion Correction in PET/CT: A Preliminary Investigation With Lung Cancer Patient Data

Purpose. Respiratory motion presents significant challenges for accurate PET/CT. It often introduces apparent increase of lesion size, reduction of measured standardized uptake value (SUV), and the mismatch in PET/CT fusion images. In this study, we developed the motion freeze method to use 100% of the counts collected by recombining the counts acquired from all phases of gated PET data into a single 3D PET data, with correction of respiration by deformable image registration. Methods. Six patients with diagnosis of lung cancer confirmed by oncologists were recruited. PET/CT scans were performed with Discovery STE system. The 4D PET/CT with the Varian real-time position management for respiratory motion tracking was followed by a clinical 3D PET/CT scan procedure in the static mode. Motion freeze applies the deformation matrices calculated by optical flow method to generate a single 3D effective PET image using the data from all the 4D PET phases. Results. The increase in SUV and decrease in tumor size with motion freeze for all lesions compared to the results from 3D and 4D was observed in the preliminary data of lung cancer patients. In addition, motion freeze substantially reduced tumor mismatch between the CT image and the corresponding PET images. Conclusion. Motion freeze integrating 100% of the PET counts has the potential to eliminate the influences induced by respiratory motion in PET data.

e19578 Background: The presence of multiple MRI focal lesions is a key diagnostic criterion for multiple myeloma (MM). Some patients with smoldering multiple myeloma (SMM) may progress to MM. This study aims to evaluate the role of MRI lesions in predicting progression from SMM to MM and to characterize their clinical significance in both the overall SMM cohort and in the subset of patients who progressed to MM. Methods: We conducted a large retrospective study of patients diagnosed with SMM up to 2024 at the University of Arkansas for Medical Sciences (UAMS) Myeloma Center. SMM patients were followed regularly with repeat myeloma marker assessments and advanced imaging, including MRI, MRI diffusion-weighted imaging with background suppression (MRI DWIBS), and positron emission tomography (PET) scans. A total of 746 SMM patients with MRI data were analyzed, stratified by the number of MRI focal lesions (0, 1, 2, and &gt;2). A subset of 315 patients (42%) who progressed to MM was further evaluated. Demographics, genetic risk factors, lesion location, disease progression rates, and survival outcomes were assessed. Results: With a median follow-up of 9.8 years (95% confidence interval [CI]: 9.2–10.6 years), no significant differences were found in age, sex, or race across the different MRI lesion cohorts (p&gt;0.05). The median number of MRI studies per patient before MM diagnosis was 5, with a range of 1–49, and PET scans were performed a median of 4 times, with a range of 1–44. Patients with &gt;2 MRI focal lesions had a significantly higher likelihood of progression to MM (80.4%) compared to those with no lesions (28.7%), one lesion (47.7%), or two lesions (67%) (p&lt;0.001). In patients who developed MM, a higher UAMS gene expression profile score was associated with a greater number of MRI lesions. MRI lesions were most commonly observed in the vertebrae (22.7%), pelvis (12.2%), humerus (6%), femur (5.6%), and ribs (3.4%). Among those with a single MRI lesion, the most frequent locations were the pelvis (78.5%), vertebrae (52.3%), femur (6.8%), and humerus (4.5%). Additionally, having at least two lesions was associated with a shorter time to progression to MM (p=0.032). Notably, some patients with no MRI lesions showed focal lesion avidity on PET scans, while others with MRI lesions did not show corresponding focal lesions on PET scans (20.6%). No difference in overall survival was observed based on the presence of MRI focal lesions (p&gt;0.05). Conclusions: The number of MRI focal lesions is a strong predictor of progression to MM. Advanced imaging techniques, such as MRI and PET, are essential for comprehensive evaluation of smoldering myeloma, as some lesions may be missed by a single imaging modality. Specifically, some patients without MRI-detected lesions showed focal lesion avidity on PET scans, while others with MRI lesions had no corresponding findings on PET, highlighting the importance of multimodal imaging for accurate disease monitoring and prognostication.

Traumatic brain injury (TBI) has been promoted as a risk factor for Alzheimer disease (AD). There is evidence of elevated β-amyloid (Aβ) and tau, the pathologic hallmarks of AD, immediately following TBI. It is not clear whether Aβ and tau remain elevated in the chronic period. To address this issue, we assessed Aβ and tau burden in long-term TBI survivors and healthy controls using PET imaging. Using a cross-sectional design, we recruited individuals following a single moderate to severe TBI at least 10 years previously from an inpatient rehabilitation program. A demographically similar healthy control group was recruited from the community. PET data were acquired using 18F-NAV4694 (Aβ) and 18F-MK6240 (tau) tracers. Aβ deposition was quantified using the Centiloid scale. Tau deposition was quantified using the standardized uptake value ratio (SUVR) in 4 regions of interest (ROIs). As a secondary measure, PET scans were also visually read as positive or negative. We examined PET data in relation to time since injury and age at injury. PET data were analyzed in a series of regression analyses. The sample comprised 87 individuals with TBI (71.3% male; 28.7% female; mean 57.53 years, SD 11.53) and 59 controls (59.3% male; 40.7% female; mean 60.34 years, SD 11.97). Individuals with TBI did not have significantly higher 18F-NAV4694 Centiloid values (p = 0.067) or 18F-MK6240 tau SUVRs in any ROI (p ≤ 0.001; SUVR greater for controls). Visual assessment was consistent with the quantification; individuals with TBI were not more likely than controls to have a positive Aβ (p = 0.505) or tau scan (p = 0.221). No associations were identified for Aβ or tau burden with time since injury (p = 0.057 to 0.332) or age at injury. A single moderate to severe TBI was not associated with higher burden of Aβ or tau pathologies in the chronic period relative to healthy controls. Aβ and tau burden did not show a significant increase with years since injury, and burden did not appear to be greater for those who were older at the time of injury.