A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology

From SPECT/CT towards absolute quantification? - the case of unilateral condylar hyperplasia of the mandible

BACKGROUND

Unilateral condylar hyperplasia (UCH) of the mandible is a rare condition characterized by asymmetric growth of the mandibular condyles. Bone scintigraphy with SPECT(/CT) is commonly used to diagnose UCH and guide treatment. Still, varying results have been reported using the traditional threshold of 55%:45% in relative tracer uptake. While absolute quantification of uptake on SPECT/CT could improve results, optimal correction and reconstruction settings are currently unknown.

METHODS

Three anthropomorphic phantoms representing UCH were developed from patient CT volumes and produced using 3D printing technology. Fillable spherical inserts of different sizes (Ø: 8-15 mm) were placed in the condylar positions representing symmetrical and asymmetrical distributions. Recovery coefficients were determined for SPECT/CT using various reconstruction corrections, including attenuation and scatter correction (ACSC), resolution modeling (RM), and partial volume correction (PVC) using phantom measurements. Uptake ratios between condyles and condyle to clivus were evaluated. Finally, the impact of these correction techniques on absolute activity and diagnostic accuracy was assessed in a retrospective patient cohort for the diagnostic threshold of 55%:45%.

RESULTS

The activity was only partially recovered in all spherical inserts (range: 22.5-64.9%). However, RM improved relative recovery by 20.2-62.3% compared to ACSC. In the symmetric phantoms, the 95% confidence interval (CI) of condyle ratios included the diagnostic threshold (57.6%:42.4%) for UCH when using ACSC potentially leading to false positives, but not for ACSCRM datasets. Partial volume corrections coefficients from the NEMA IQ phantom was positionally dependent, with improvements seen performing PVC using coefficients derived from anthropomorphic phantoms. Retrospective application in a patient cohort showed only a weak linear correlation (R²: 0.25-0.67) and large limits of agreement (9.6-12.5%) between different reconstructions. Up to 44% of patients were reclassified using the 55%:45% threshold. Using clinical outcome data, ACSCRM had highest sensitivity (91%; 95% CI 59-100%) and specificity (66%; 95% CI 47-81%), significantly improving specificity (P = 0.038).

CONCLUSIONS

Anthropomorphic phantoms were shown to be essential in determining optimal settings for acquisition, reconstruction, and analysis. SPECT/CT reconstructions with attenuation and scatter correction and resolution modeling are recommended and could improve specificity when using the 55%:45% threshold to assess condylar growth.

Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs

We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions.

Deep denoiser prior driven relaxed iterated Tikhonov method for low-count PET image restoration

Objective. Low-count positron emission tomography (PET) imaging is an efficient way to promote more widespread use of PET because of its short scan time and low injected activity. However, this often leads to low-quality PET images with clinical image reconstruction, due to high noise and blurring effects. Existing PET image restoration (IR) methods hinder their own restoration performance due to the semi-convergence property and the lack of suitable denoiser prior.Approach. To overcome these limitations, we propose a novel deep plug-and-play IR method called Deep denoiser Prior driven Relaxed Iterated Tikhonov method (DP-RI-Tikhonov). Specifically, we train a deep convolutional neural network denoiser to generate a flexible deep denoiser prior to handle high noise. Then, we plug the deep denoiser prior as a modular part into a novel iterative optimization algorithm to handle blurring effects and propose an adaptive parameter selection strategy for the iterative optimization algorithm.Main results. Simulation results show that the deep denoiser prior plays the role of reducing noise intensity, while the novel iterative optimization algorithm and adaptive parameter selection strategy can effectively eliminate the semi-convergence property. They enable DP-RI-Tikhonov to achieve an average quantitative result (normalized root mean square error, structural similarity) of (0.1364, 0.9574) at the stopping iteration, outperforming a conventional PET IR method with an average quantitative result of (0.1533, 0.9523) and a state-of-the-art deep plug-and-play IR method with an average quantitative result of (0.1404, 0.9554). Moreover, the advantage of DP-RI-Tikhonov becomes more obvious at the last iteration. Experiments on six clinical whole-body PET images further indicate that DP-RI-Tikhonov successfully reduces noise intensity and recovers fine details, recovering sharper and more uniform images than the comparison methods.Significance. DP-RI-Tikhonov's ability to reduce noise intensity and effectively eliminate the semi-convergence property overcomes the limitations of existing methods. This advancement may have substantial implications for other medical IR.

The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.

PET quantification performance of the oversize-volume-of-interest approach in the context of tumour dosimetry in radionuclide therapy planning

Objective.The partial-volume effect (PVE) is an important factor impairing tumour quantification in molecular imaging. The commonly used contour-volume-of-interest (contour-VOI) approach to correct for this effect employs phantom-based recovery coefficients. Applying oversize-VOIs could offer superior quantification accuracy in small lesions. The oversize-VOI approach uses a large oversize volume to determine the total tumour activity after applying a background correction. Aims of this study were to provide a procedure for the application of the oversize-VOI approach and to compare its performance to the contour-VOI approach in PET imaging.Approach.A sphere tumour model was simulated to determine the oversize diameter that contained 90%, 95%, and 98% of the total activity as a function of the tumour size. Experimental investigations involving phantom and clinical data were conducted on a digital PET/CT scanner. In the phantom investigation, 12 spherical tumour inserts (diameters ranging from 3.7 to 37.4 mm) containing18F-solution were used. The accuracy of the contour- and oversize-VOI approach was evaluated for different signal-to-background ratios (20-3). Clinically, both approaches were applied on PET/CT images acquired with18F-labelled prostate-specific membrane antigen in prostate cancer patients.Main results.From the tumour model, we deduced that an oversize-VOI of two PET spatial resolutions larger than the physical lesion diameter contains at least 98% of the total activity for lesions with diameters down to one PET spatial resolution, while minimizing the background contribution. Both approaches were robust against varying phantom and clinical imaging conditions. Performance of the oversize-VOI approach was favorable for lesions below 10 mm in diameter, whereas the contour-VOI approach was slightly more accurate for sizes above 10 mm.Significance.The oversize-VOI approach facilitates image quantification of small tumours. It is simple and effective to correct for the PVE and may be used in pre-therapeutic (small) tumour dosimetry.

A Comparison of Arterial Input Function Interpolation Methods for Patlak Plot Analysis of 68Ga-PSMA-11 PET Prostate Cancer Studies

Positron emission tomography (PET) imaging enables quantitative assessment of tissue physiology. Dynamic pharmacokinetic analysis of PET images requires accurate estimation of the radiotracer plasma input function to derive meaningful parameter estimates, and small discrepancies in parameter estimation can mimic subtle physiologic tissue variation. This study evaluates the impact of input function interpolation method on the accuracy of Patlak kinetic parameter estimation through simulations modeling the pharmacokinetic properties of [68Ga]-PSMA-11. This study evaluated both trained and untrained methods. Although the mean kinetic parameter accuracy was similar across all interpolation models, the trained node weighting interpolation model estimated accurate kinetic parameters with reduced overall variability relative to standard linear interpolation. Trained node weighting interpolation reduced kinetic parameter estimation variance by a magnitude approximating the underlying physiologic differences between normal and diseased prostatic tissue. Overall, this analysis suggests that trained node weighting improves the reliability of Patlak kinetic parameter estimation for [68Ga]-PSMA-11 PET.

Position dependence of recovery coefficients in 177Lu-SPECT/CT reconstructions - phantom simulations and measurements

BACKGROUND

Although the importance of quantitative SPECT has increased tremendously due to newly developed therapeutic radiopharmaceuticals, there are still no accreditation programs to harmonize SPECT imaging. Work is currently underway to develop an accreditation for quantitative 177Lu SPECT/CT. The aim of this study is to verify whether the positioning of the spheres within the phantom has an influence on the recovery and thus needs to be considered in SPECT harmonization. In addition, the effects of these recovery coefficients on a potential partial volume correction as well as absorbed-dose estimates are investigated.

METHODS

Using a low-dose CT of a SPECT/CT acquisition, a computerized version of the NEMA body phantom was created using a semi-automatic threshold-based method. Based on the mass-density map, the detector orbit, and the sphere centers, realistic SPECT acquisitions of all possible 720 sphere configurations of both the PET and the SPECT versions of the NEMA Body Phantom were generated using Monte Carlo simulations. SPECT reconstructions with different numbers of updates were performed without (CASToR) and with resolution modeling (STIR). Recovery coefficients were calculated for all permutations, reconstruction methods, and phantoms, and their dependence on the sphere positioning was investigated. Finally, the simulation-based findings were validated using SPECT/CT acquisitions of six different sphere configurations.

RESULTS

Our analysis shows that sphere positioning has a significant impact on the recovery for both of the reconstruction methods and the phantom type. Although resolution modeling resulted in significantly higher recovery, the relative variation in recovery within the 720 permutations was even larger. When examining the extreme values of the recovery, reconstructions without resolution modeling were influenced primarily by the sphere position, while with resolution modeling the volume of the two adjacent spheres had a larger influence. The SPECT measurements confirmed these observations, and the recovery curves showed good overall agreement with the simulated data.

CONCLUSION

Our study shows that sphere positioning has a significant impact on the recovery obtained in NEMA sphere phantom measurements and should therefore be considered in a future SPECT accreditation. Furthermore, the single-measurement method normally performed for PVC should be reconsidered to account for the position dependency.

Construction and Evaluation of an AI-based CBCT Resolution Optimization Technique for Extracted Teeth

INTRODUCTION

In dental clinical practice, cone-beam computed tomography (CBCT) is commonly used to assist practitioners to recognize the complex morphology of root canal systems; however, because of its resolution limitations, certain small anatomical structures still cannot be accurately recognized on CBCT. The purpose of this study was to perform image super-resolution (SR) processing on CBCT images of extracted human teeth with the help of a deep learning model, and to compare the differences among CBCT, super-resolution computed tomography (SRCT), and micro-computed tomography (Micro-CT) images through three-dimensional reconstruction.

METHODS

The deep learning model (Basicvsr++) was selected and modified. The dataset consisted of 171 extracted teeth that met inclusion criteria, with 40 maxillary first molars as the training set and 40 maxillary first molars as well as 91 teeth from other tooth positions as the external test set. The corresponding CBCT, SRCT, and Micro-CT images of each tooth in test sets were reconstructed using Mimics Research 17.0, and the root canal recognition rates in the 3 groups were recorded. The following parameters were measured: volume of hard tissue (V1), volume of pulp chamber and root canal system (V2), length of visible root canals under orifice (VL-X, where X represents the specific root canal), and intersection angle between coronal axis of canal and long axis of tooth (∠X, where X represents the specific root canal). Data were statistically analyzed between CBCT and SRCT images using paired sample t-test and Wilcoxon test analysis, with the measurement from Micro-CT images as the gold standard.

RESULTS

Images from all tested teeth were successfully processed with the SR program. In 4-canal maxillary first molar, identification of MB2 was 72% (18/25) in CBCT group, 92% (23/25) in SRCT group, and 100% (25/25) in Micro-CT group. The difference of hard tissue volume between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT in all tested teeth except 4-canal mandibular first molar (P < .05). Similar results were obtained in volume of pulp chamber and root canal system in all tested teeth (P < .05). As for length of visible root canals under orifice, the difference between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT (P < .05) in most root canals.

CONCLUSIONS

The deep learning model developed in this study helps to optimize the root canal morphology of extracted teeth in CBCT. And it may be helpful for the identification of MB2 in the maxillary first molar.

A Deep-Learning-Based Partial-Volume Correction Method for Quantitative 177Lu SPECT/CT Imaging

With the development of new radiopharmaceutical therapies, quantitative SPECT/CT has progressively emerged as a crucial tool for dosimetry. One major obstacle of SPECT is its poor resolution, which results in blurring of the activity distribution. Especially for small objects, this so-called partial-volume effect limits the accuracy of activity quantification. Numerous methods for partial-volume correction (PVC) have been proposed, but most methods have the disadvantage of assuming a spatially invariant resolution of the imaging system, which does not hold for SPECT. Furthermore, most methods require a segmentation based on anatomic information. Methods: We introduce DL-PVC, a methodology for PVC of 177Lu SPECT/CT imaging using deep learning (DL). Training was based on a dataset of 10,000 random activity distributions placed in extended cardiac-torso body phantoms. Realistic SPECT acquisitions were created using the SIMIND Monte Carlo simulation program. SPECT reconstructions without and with resolution modeling were performed using the CASToR and STIR reconstruction software, respectively. The pairs of ground-truth activity distributions and simulated SPECT images were used for training various U-Nets. Quantitative analysis of the performance of these U-Nets was based on metrics such as the structural similarity index measure or normalized root-mean-square error, but also on volume activity accuracy, a new metric that describes the fraction of voxels in which the determined activity concentration deviates from the true activity concentration by less than a certain margin. On the basis of this analysis, the optimal parameters for normalization, input size, and network architecture were identified. Results: Our simulation-based analysis revealed that DL-PVC (0.95/7.8%/35.8% for structural similarity index measure/normalized root-mean-square error/volume activity accuracy) outperforms SPECT without PVC (0.89/10.4%/12.1%) and after iterative Yang PVC (0.94/8.6%/15.1%). Additionally, we validated DL-PVC on 177Lu SPECT/CT measurements of 3-dimensionally printed phantoms of different geometries. Although DL-PVC showed activity recovery similar to that of the iterative Yang method, no segmentation was required. In addition, DL-PVC was able to correct other image artifacts such as Gibbs ringing, making it clearly superior at the voxel level. Conclusion: In this work, we demonstrate the added value of DL-PVC for quantitative 177Lu SPECT/CT. Our analysis validates the functionality of DL-PVC and paves the way for future deployment on clinical image data.

Quantifying partial volume effect in SPECT and planar imaging: optimizing region of interest for activity concentration estimation in different sphere sizes

INTRODUCTION

To quantify the partial volume effect in single photon emission tomography (SPECT) and planar images of Carlson phantom as well as providing an optimum region of interest (ROI) required to more accurately estimate the activity concentration for different sphere sizes.

METHODS

131 I solution with the 161.16 kBq/ml concentration was uniformly filled into the different spheres of Carlson phantom (cold background condition) with the diameters of 7.3, 9.2, 11.4, 14.3, 17.9, 22.4 and 29.9 mm, and there was no background activity. In the hot background condition, the spheres were filled with the solution of 131 I with the 1276.5 kBq/ml addition to the background activity concentration of 161.16 kBq/ml in all the phantoms. The spheres were mounted inside the phantom and underwent SPECT and planar images. ROI was drawn closely on the boundary of each sphere image and it was extended to extract the true count.

RESULTS

In the cold background condition, the recovery coefficient (RC) value for SPECT images ranged between 0.8 and 1.03. However, in planar imaging, the RC value was 0.72 for the smallest sphere size and it increased for larger spheres until 0.98 for 29.9 mm. In the hot background condition, the RC value for sphere diameters larger than 20 mm was overestimated more than in the cold background condition. The ROI/size required to more accurately determine activity concentration for the cold background ranged from 1.18 to 2.7. However, in the hot background condition, this ratio varied from 1.34 to 4.05.

CONCLUSION

In the quantification of partial volume effects, the spill-out effect seems to play a crucial role in the distribution of the image counts beyond the boundaries of the image pixels. However, more investigations are needed to accurately characterize limitations regarding the object size, background levels, and other factors.

Image denoising and model-independent parameterization for IVIM MRI

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versusb-value curve.Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial 'pseudokernels'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis.Main results. Image denoising improved blind image quality metrics, smoothed the signal versusb-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising.AUCparameters were more correlated with dose and had higher relative importance than exponential model parameters.Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versusb-value curve with model-independent parameters like theAUCand preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

Relationship between neuroimaging and cognition in frontotemporal dementia: An FDG-PET and structural MRI study

BACKGROUND AND PURPOSE

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative condition with a prevalence comparable to Alzheimer's disease for patients under 65 years of age. Limited studies have examined the association between cognition and neuroimaging in FTD using different imaging modalities.

METHODS

We examined the association of cognition using Montreal Cognitive Assessment (MoCA) with both gray matter (GM) volume and glucose metabolism using magnetic resonance imaging and fluorodeoxyglucose (FDG)-PET in 21 patients diagnosed with FTD. Standardized uptake value ratio (SUVR) using the brainstem as a reference region was the primary outcome measure for FDG-PET. Partial volume correction was applied to PET data to account for disease-related atrophy.

RESULTS

Significant positive associations were found between whole-cortex GM volume and MoCA scores (r = 0.46, p = .04). The association between whole-cortex FDG SUVR and MoCA scores was not significant (r = 0.37, p = .09). GM volumes of the frontal cortex (r = 0.54, p = .01), caudate (r = 0.62, p<.01), and insula (r = 0.57, p<.01) were also significantly correlated with MoCA, as were SUVR values of the insula (r = 0.51, p = .02), thalamus (r = 0.48, p = .03), and posterior cingulate cortex (PCC) (r = 0.47, p = .03).

CONCLUSIONS

Whole-cortex atrophy is associated with cognitive dysfunction, and this association is larger than for whole-cortex hypometabolism as measured with FDG-PET. At the regional level, focal atrophy and/or hypometabolism in the frontal cortex, insula, PCC, thalamus, and caudate seem to be important for the decline of cognitive function in FTD. Furthermore, these results highlight how functional and structural changes may not overlap and might contribute to cognitive dysfunction in FTD in different ways.

Neural blind deconvolution for deblurring and supersampling PSMA PET

Objective. To simultaneously deblur and supersample prostate specific membrane antigen (PSMA) positron emission tomography (PET) images using neural blind deconvolution.Approach. Blind deconvolution is a method of estimating the hypothetical 'deblurred' image along with the blur kernel (related to the point spread function) simultaneously. Traditionalmaximum a posterioriblind deconvolution methods require stringent assumptions and suffer from convergence to a trivial solution. A method of modelling the deblurred image and kernel with independent neural networks, called 'neural blind deconvolution' had demonstrated success for deblurring 2D natural images in 2020. In this work, we adapt neural blind deconvolution to deblur PSMA PET images while simultaneous supersampling to double the original resolution. We compare this methodology with several interpolation methods in terms of resultant blind image quality metrics and test the model's ability to predict accurate kernels by re-running the model after applying artificial 'pseudokernels' to deblurred images. The methodology was tested on a retrospective set of 30 prostate patients as well as phantom images containing spherical lesions of various volumes.Main results. Neural blind deconvolution led to improvements in image quality over other interpolation methods in terms of blind image quality metrics, recovery coefficients, and visual assessment. Predicted kernels were similar between patients, and the model accurately predicted several artificially-applied pseudokernels. Localization of activity in phantom spheres was improved after deblurring, allowing small lesions to be more accurately defined.Significance. The intrinsically low spatial resolution of PSMA PET leads to partial volume effects (PVEs) which negatively impact uptake quantification in small regions. The proposed method can be used to mitigate this issue, and can be straightforwardly adapted for other imaging modalities.

Partial volume correction in longitudinal tau PET studies: is it really needed?

BACKGROUND

[18F]flortaucipir (FTP) tau PET quantification is known to be affected by non-specific binding in off-target regions. Although partial volume correction (PVC) techniques partially account for this effect, their inclusion may also introduce noise and variability into the quantification process. While the impact of these effects has been studied in cross-sectional designs, the benefits and drawbacks of PVC on longitudinal FTP studies is still under scrutiny. The aim of this work was to study the performance of the most common PVC techniques for longitudinal FTP imaging.

METHODS

A cohort of 247 individuals from the Alzheimer's Disease Neuroimaging Initiative with concurrent baseline FTP-PET, amyloid-beta (Aβ) PET and structural MRI, as well as with follow-up FTP-PET and MRI were included in the study. FTP-PET scans were corrected for partial volume effects using Meltzer's, a simple and popular analytical PVC, and both the region-based voxel-wise (RBV) and the iterative Yang (iY) corrections. FTP SUVR values and their longitudinal rates of change were calculated for regions of interest (ROI) corresponding to Braak Areas I-VI, for a temporal meta-ROI and for regions typically displaying off-target FTP binding (caudate, putamen, pallidum, thalamus, choroid plexus, hemispheric white matter, cerebellar white matter, and cerebrospinal fluid). The longitudinal correlation between binding in off-target and target ROIs was analysed for the different PVCs. Additionally, group differences in longitudinal FTP SUVR rates of change between Aβ-negative (A-) and Aβ-positive (A+), and between cognitively unimpaired (CU) and cognitively impaired (CI) individuals, were studied. Finally, we compared the ability of different partial-volume-corrected baseline FTP SUVRs to predict longitudinal brain atrophy and cognitive decline.

RESULTS

Among off-target ROIs, hemispheric white matter showed the highest correlation with longitudinal FTP SUVR rates from cortical target ROIs (R2=0.28-0.82), with CSF coming in second (R2=0.28-0.42). Application of voxel-wise PVC techniques minimized this correlation, with RBV performing best (R2=0.00-0.07 for hemispheric white matter). PVC also increased group differences between CU and CI individuals in FTP SUVR rates of change across all target regions, with RBV again performing best (No PVC: Cohen's d = 0.26-0.66; RBV: Cohen's d = 0.43-0.74). These improvements were not observed for differentiating A- from A+ groups. Additionally, voxel-wise PVC techniques strengthened the correlation between baseline FTP SUVR and longitudinal grey matter atrophy and cognitive decline.

CONCLUSION

Quantification of longitudinal FTP SUVR rates of change is affected by signal from off-target regions, especially the hemispheric white matter and the CSF. Voxel-wise PVC techniques significantly reduce this effect. PVC provided a significant but modest benefit for tasks involving the measurement of group-level longitudinal differences. These findings are particularly relevant for the estimations of sample sizes and analysis methodologies of longitudinal group studies.

Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging

BACKGROUND

Positron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images.

METHODS

We trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PVC images by six different PVC methods as the target. These six PVC approaches include geometric transfer matrix (GTM), multi-target correction (MTC), region-based voxel-wise correction (RBV), iterative Yang (IY), reblurred Van-Cittert (RVC), and Richardson-Lucy (RL). The proposed models were evaluated using standard criteria, such as peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity index (SSIM), relative bias, and absolute relative bias.

RESULTS

Different levels of error were observed for these partial volume correction methods, which were relatively smaller for GTM with a SSIM of 0.63 for LD and 0.29 for FD, IY with an SSIM of 0.63 for LD and 0.67 for FD, RBV with an SSIM of 0.57 for LD and 0.65 for FD, and RVC with an SSIM of 0.89 for LD and 0.94 for FD PVC approaches. However, large quantitative errors were observed for multi-target MTC with an RMSE of 2.71 for LD and 2.45 for FD and RL with an RMSE of 5 for LD and 3.27 for FD PVC approaches.

CONCLUSIONS

We found that the proposed framework could effectively perform joint de-noising and partial volume correction for PET images with LD and FD input PET data (LD vs. FD). When no magnetic resonance imaging (MRI) images are available, the developed deep learning models could be used for partial volume correction on LD or standard PET-computed tomography (PET-CT) scans as an image quality enhancement technique.

Attention-based deep neural network for partial volume correction in brain 18F-FDG PET imaging

PURPOSE

This work set out to propose an attention-based deep neural network to predict partial volume corrected images from PET data not utilizing anatomical information.

METHODS

An attention-based convolutional neural network (ATB-Net) is developed to predict PVE-corrected images in brain PET imaging by concentrating on anatomical areas of the brain. The performance of the deep neural network for performing PVC without using anatomical images was evaluated for two PVC methods, including iterative Yang (IY) and reblurred Van-Cittert (RVC) approaches. The RVC and IY PVC approaches were applied to PET images to generate the reference images. The training of the U-Net network for the partial volume correction was trained twice, once without using the attention module and once with the attention module concentrating on the anatomical brain regions.

RESULTS

Regarding the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and root mean square error (RMSE) metrics, the proposed ATB-Net outperformed the standard U-Net model (without attention compartment). For the RVC technique, the ATB-Net performed just marginally better than the U-Net; however, for the IY method, which is a region-wise method, the attention-based approach resulted in a substantial improvement. The mean absolute relative SUV difference and mean absolute relative bias improved by 38.02 % and 91.60 % for the RVC method and 77.47 % and 79.68 % for the IY method when using the ATB-Net model, respectively.

CONCLUSIONS

Our results propose that without using anatomical data, the attention-based DL model could perform PVC on PET images, which could be employed for PVC in PET imaging.

The regional pattern of age-related synaptic loss in the human brain differs from gray matter volume loss: in vivo PET measurement with [11C]UCB-J

PURPOSE

Aging is a major societal concern due to age-related functional losses. Synapses are crucial components of neural circuits, and synaptic density could be a sensitive biomarker to evaluate brain function. [11C]UCB-J is a positron emission tomography (PET) ligand targeting synaptic vesicle glycoprotein 2A (SV2A), which can be used to evaluate brain synaptic density in vivo.

METHODS

We evaluated age-related changes in gray matter synaptic density, volume, and blood flow using [11C]UCB-J PET and magnetic resonance imaging (MRI) in a wide age range of 80 cognitive normal subjects (21-83 years old). Partial volume correction was applied to the PET data.

RESULTS

Significant age-related decreases were found in 13, two, and nine brain regions for volume, synaptic density, and blood flow, respectively. The prefrontal cortex showed the largest volume decline (4.9% reduction per decade: RPD), while the synaptic density loss was largest in the caudate (3.6% RPD) and medial occipital cortex (3.4% RPD). The reductions in caudate are consistent with previous SV2A PET studies and likely reflect that caudate is the site of nerve terminals for multiple major tracts that undergo substantial age-related neurodegeneration. There was a non-significant negative relationship between volume and synaptic density reductions in 16 gray matter regions.

CONCLUSION

MRI and [11]C-UCB-J PET showed age-related decreases of gray matter volume, synaptic density, and blood flow; however, the regional patterns of the reductions in volume and SV2A binding were different. Those patterns suggest that MR-based measures of GM volume may not be directly representative of synaptic density.

Loss of cholinergic input to the entorhinal cortex is an early indicator of cognitive impairment in natural aging of humans and mice

In a series of translational experiments using fully quantitative positron emission tomography (PET) imaging with a new tracer specific for the vesicular acetylcholine transporter ([18F]VAT) in vivo in humans, and genetically targeted cholinergic markers in mice, we evaluated whether changes to the cholinergic system were an early feature of age-related cognitive decline. We found that deficits in cholinergic innervation of the entorhinal cortex (EC) and decline in performance on behavioral tasks engaging the EC are, strikingly, early features of the aging process. In human studies, we recruited older adult volunteers that were physically healthy and without prior clinical diagnosis of cognitive impairment. Using [18F]VAT PET imaging, we demonstrate that there is measurable loss of cholinergic inputs to the EC that can serve as an early signature of decline in EC cognitive performance. These deficits are specific to the cholinergic circuit between the medial septum and vertical limb of the diagonal band (MS/vDB; CH1/2) to the EC. Using diffusion imaging, we further demonstrate impaired structural connectivity in the tracts between the MS/vDB and EC in older adults with mild cognitive impairment. Experiments in mouse, designed to parallel and extend upon the human studies, used high resolution imaging to evaluate cholinergic terminal density and immediate early gene (IEG) activity of EC neurons in healthy aging mice and in mice with genetic susceptibility to accelerated accumulation amyloid beta plaques and hyperphosphorylated mouse tau. Across species and aging conditions, we find that the integrity of cholinergic projections to the EC directly correlates with the extent of EC activation and with performance on EC-related object recognition memory tasks. Silencing EC-projecting cholinergic neurons in young, healthy mice during the object-location memory task impairs object recognition performance, mimicking aging. Taken together we identify a role for acetylcholine in normal EC function and establish loss of cholinergic input to the EC as an early, conserved feature of age-related cognitive decline in both humans and rodents.

Partial volume correction of PET image data using geometric transfer matrices based on uniform B-splines

Objective. Most methods for partial volume correction (PVC) of positron emission tomography (PET) data employ anatomical segmentation of images into regions of interest. This approach is not optimal for exploratory functional imaging beyond regional hypotheses. Here, we describe a novel method for unbiased voxel-wise PVC.Approach.B-spline basis functions were combined with geometric transfer matrices to enable a method (bsGTM) that provides PVC or alternatively provides smoothing with minimal regional crosstalk. The efficacy of the proposed method was evaluated using Monte Carlo simulations, human PET data, and murine functional PET data.Main results.In simulations, bsGTM provided recovery of partial volume signal loss comparable to iterative deconvolution, while demonstrating superior resilience to noise. In a real murine PET dataset, bsGTM yielded much higher sensitivity for detecting amphetamine-induced reduction of [11C]raclopride binding potential. In human PET data, bsGTM smoothing enabled increased signal-to-noise ratios with less degradation of binding potentials relative to Gaussian convolution or non-local means.Significance.bsGTM offers improved performance for PVC relative to iterative deconvolution, the current method of choice for voxel-wise PVC, especially in the common PET regime of low signal-to-noise ratio. The new method provides an anatomically unbiased way to compensate partial volume errors in cases where anatomical segmentation is unavailable or of questionable relevance or accuracy.

Anatomy-based correction of kidney PVE on [Formula: see text] SPECT images

BACKGROUND

In peptide receptor radionuclide therapy (PRRT), accurate quantification of kidney activity on post-treatment SPECT images paves the way for patient-specific treatment. Due to the limited spatial resolution of SPECT images, the partial volume effect (PVE) is a significant source of quantitative bias. In this study, we aimed to evaluate the performance and robustness of anatomy-based partial volume correction (PVC) algorithms to recover the accurate activity concentration of realistic kidney geometries on [Formula: see text]Lu SPECT images recorded under clinical conditions.

METHODS

Based on the CT scan data from patients, three sets of fillable kidneys with surface-to-volume (S:V) ratios ranging from 1.5 to 2.8 cm-1, were 3D printed and attached in a IEC phantom. Quantitative [Formula: see text]Lu SPECT/CT acquisitions were performed on a GE Discovery NM CT 870 DR camera for the three modified IEC phantoms and for 6 different Target-To-Background ratios (TBRs: 2, 4, 6, 8, 10, 12). Two region-based (GTM and Labbé) and five voxel-based (GTM + MTC, Labbé + MTC, GTM + RBV, Labbé + RBV and IY) methods were evaluated with this data set. Additionally, the robustness of PVC methods to Point Spread Function (PSF) discrepancies, registration mismatches and background heterogeneity was evaluated.

RESULTS

Without PVC, the average kidney RCs across all TBRs ranged from 0.66 ± 0.05 (smallest kidney) to 0.80 ± 0.03 (largest kidney). For a TBR of 12, all anatomy-based method were able to recover the kidneys activity concentration with an error < 6%. All methods result in a comparable decline in RC restoration with decreasing TBR. The Labbé method was the most robust against PSF and registration mismatches but was also the most sensitive to background heterogeneity. Among the voxel-based methods, MTC images were less uniform than RBV and IY images at the outer edge of high uptake areas (kidneys and spheres).

CONCLUSION

Anatomy-based PVE correction allows for accurate SPECT quantification of the [Formula: see text]Lu activity concentration with realistic kidney geometries. Combined with recent progress in deep-learning algorithms for automatic anatomic segmentation of whole-body CT, these methods could be of particular interest for a fully automated OAR dosimetry pipeline with PVE correction.

In vivo imaging of synaptic density in neurodegenerative disorders with positron emission tomography: A systematic review

Positron emission tomography (PET) with radiotracers that bind to synaptic vesicle glycoprotein 2 A (SV2A) enables quantification of synaptic density in the living human brain. Assessing the regional distribution and severity of synaptic density loss will contribute to our understanding of the pathological processes that precede atrophy in neurodegeneration. In this systematic review, we provide a discussion of in vivo SV2A PET imaging research for quantitative assessment of synaptic density in various dementia conditions: amnestic Mild Cognitive Impairment and Alzheimer's disease, Frontotemporal dementia, Progressive supranuclear palsy and Corticobasal degeneration, Parkinson's disease and Dementia with Lewy bodies, Huntington's disease, and Spinocerebellar Ataxia. We discuss the main findings concerning group differences and clinical-cognitive correlations, and explore relations between SV2A PET and other markers of pathology. Additionally, we touch upon synaptic density in healthy ageing and outcomes of radiotracer validation studies. Studies were identified on PubMed and Embase between 2018 and 2023; last searched on the 3rd of July 2023. A total of 36 studies were included, comprising 5 on normal ageing, 21 clinical studies, and 10 validation studies. Extracted study characteristics were participant details, methodological aspects, and critical findings. In summary, the small but growing literature on in vivo SV2A PET has revealed different spatial patterns of synaptic density loss among various neurodegenerative disorders that correlate with cognitive functioning, supporting the potential role of SV2A PET imaging for differential diagnosis. SV2A PET imaging shows tremendous capability to provide novel insights into the aetiology of neurodegenerative disorders and great promise as a biomarker for synaptic density reduction. Novel directions for future synaptic density research are proposed, including (a) longitudinal imaging in larger patient cohorts of preclinical dementias, (b) multi-modal mapping of synaptic density loss onto other pathological processes, and (c) monitoring therapeutic responses and assessing drug efficacy in clinical trials.

Relationship between Neuroimaging and Cognition in Frontotemporal Dementia: A [18 F]FDG PET and Structural MRI Study

Background

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous condition with a prevalence comparable to Alzheimer's Disease for patients under sixty-five years of age. Gray matter (GM) atrophy and glucose hypometabolism are important biomarkers for the diagnosis and evaluation of disease progression in FTD. However, limited studies have systematically examined the association between cognition and neuroimaging in FTD using different imaging modalities in the same patient group.

Methods

We examined the association of cognition using Montreal Cognitive Assessment (MoCA) with both GM volume and glucose metabolism using structural magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography scanning ([18F]FDG PET) in 21 patients diagnosed with FTD. Standardized uptake value ratio (SUVR) using the brainstem as a reference region was the primary outcome measure for [18F]FDG PET. Partial volume correction was applied to PET data to account for disease-related atrophy.

Results

Significant positive associations were found between whole-cortex GM volume and MoCA scores (r = 0.461, p = 0.035). The association between whole-cortex [18F]FDG SUVR and MoCA scores was not Significant (r = 0.374, p = 0.094). GM volumes of the frontal cortex (r = 0.540, p = 0.011), caudate (r = 0.616, p = 0.002), and insula (r = 0.568, p = 0.007) were also Significantly correlated with MoCA, as were SUVR values of the insula (r = 0.508, p = 0.018), thalamus (r = 0.478, p = 0.028), and posterior cingulate cortex (PCC) (r = 0.472, p = 0.030).

Discussion

Whole-cortex atrophy is associated with cognitive dysfunction, and this effect is larger than for cortical hypometabolism as measured with [18F]FDG PET. At the regional level, focal atrophy and/or hypometabolism in the frontal lobe, insula, PCC, thalamus, and caudate seem to imply the importance of these regions for the decline of cognitive function in FTD. Furthermore, these results highlight how functional and structural changes may not overlap and might contribute to cognitive dysfunction in FTD in different ways. Our findings provide insight into the relationships between structural, metabolic, and cognitive changes due to FTD.

Assessment of Gray Matter Microstructure and Synaptic Density in Alzheimer's Disease: A Multimodal Imaging Study With DTI and SV2A PET

OBJECTIVE

Multimodal imaging techniques have furthered our understanding of how different aspects of Alzheimer's disease (AD) pathology relate to one another. Diffusion tensor imaging (DTI) measures such as mean diffusivity (MD) may be a surrogate measure of the changes in gray matter structure associated with AD. Positron emission tomography (PET) imaging of synaptic vesicle glycoprotein 2A (SV2A) has been used to quantify synaptic loss, which is the major pathological correlate of cognitive impairment in AD. In this study, we investigated the relationship between gray matter microstructure and synaptic density.

METHODS

DTI was used to measure MD and [11C]UCB-J PET to measure synaptic density in 33 amyloid-positive participants with AD and 17 amyloid-negative cognitively normal (CN) participants aged 50-83. Univariate regression analyses were used to assess the association between synaptic density and MD in both the AD and CN groups.

RESULTS

Hippocampal MD was inversely associated with hippocampal synaptic density in participants with AD (r = -0.55, p <0.001, df = 31) but not CN (r = 0.13, p = 0.62, df = 15). Exploratory analyses across other regions known to be affected in AD suggested widespread inverse associations between synaptic density and MD in the AD group.

CONCLUSION

In the setting of AD, an increase in gray matter MD is inversely associated with synaptic density. These co-occurring changes may suggest a link between synaptic loss and gray matter microstructural changes in AD. Imaging studies of gray matter microstructure and synaptic density may allow important insights into AD-related neuropathology.

Synaptic density affects clinical severity via network dysfunction in syndromes associated with frontotemporal lobar degeneration

There is extensive synaptic loss from frontotemporal lobar degeneration, in preclinical models and human in vivo and post mortem studies. Understanding the consequences of synaptic loss for network function is important to support translational models and guide future therapeutic strategies. To examine this relationship, we recruited 55 participants with syndromes associated with frontotemporal lobar degeneration and 24 healthy controls. We measured synaptic density with positron emission tomography using the radioligand [11C]UCB-J, which binds to the presynaptic vesicle glycoprotein SV2A, neurite dispersion with diffusion magnetic resonance imaging, and network function with task-free magnetic resonance imaging functional connectivity. Synaptic density and neurite dispersion in patients was associated with reduced connectivity beyond atrophy. Functional connectivity moderated the relationship between synaptic density and clinical severity. Our findings confirm the importance of synaptic loss in frontotemporal lobar degeneration syndromes, and the resulting effect on behaviour as a function of abnormal connectivity.

New Perspectives in Radiological and Radiopharmaceutical Hybrid Imaging in Progressive Supranuclear Palsy: A Systematic Review

Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by four-repeat tau deposition in various cell types and anatomical regions, and can manifest as several clinical phenotypes, including the most common phenotype, Richardson's syndrome. The limited availability of biomarkers for PSP relates to the overlap of clinical features with other neurodegenerative disorders, but identification of a growing number of biomarkers from imaging is underway. One way to increase the reliability of imaging biomarkers is to combine different modalities for multimodal imaging. This review aimed to provide an overview of the current state of PSP hybrid imaging by combinations of positron emission tomography (PET) and magnetic resonance imaging (MRI). Specifically, combined PET and MRI studies in PSP highlight the potential of [18F]AV-1451 to detect tau, but also the challenge in differentiating PSP from other neurodegenerative diseases. Studies over the last years showed a reduced synaptic density in [11C]UCB-J PET, linked [11C]PK11195 and [18F]AV-1451 markers to disease progression, and suggested the potential role of [18F]RO948 PET for identifying tau pathology in subcortical regions. The integration of quantitative global and regional gray matter analysis by MRI may further guide the assessment of reduced cortical thickness or volume alterations, and diffusion MRI could provide insight into microstructural changes and structural connectivity in PSP. Challenges in radiopharmaceutical biomarkers and hybrid imaging require further research targeting markers for comprehensive PSP diagnosis.

Amphetamine pretreatment blunts dopamine-induced D2/D3-receptor occupancy by an arrestin-mediated mechanism: A PET study in internalization compromised mice

While all reversible receptor-targeting radioligands for positron emission tomography (PET) can be displaced by competition with an antagonist at the receptor, many radiotracers show limited occupancies using agonists even at high doses. [11C]Raclopride, a D2/D3 receptor radiotracer with rapid kinetics, can identify the direction of changes in the neurotransmitter dopamine, but quantitative interpretation of the relationship between dopamine levels and radiotracer binding has proven elusive. Agonist-induced receptor desensitization and internalization, a homeostatic mechanism to downregulate neurotransmitter-mediated function, can shift radioligand-receptor binding affinity and confound PET interpretations of receptor occupancy. In this study, we compared occupancies induced by amphetamine (AMP) in drug-naive wild-type (WT) and internalization-compromised β-arrestin-2 knockout (KO) mice using a within-scan drug infusion to modulate the kinetics of [11C]raclopride. We additionally performed studies at 3 h following AMP pretreatment, with the hypothesis that receptor internalization should markedly attenuate occupancy on the second challenge, because dopamine cannot access internalized receptors. Without prior AMP treatment, WT mice exhibited somewhat larger binding potential than KO mice but similar AMP-induced occupancy. At 3 h after AMP treatment, WT mice exhibited binding potentials that were 15 % lower than KO mice. At this time point, occupancy was preserved in KO mice but suppressed by 60 % in WT animals, consistent with a model in which most receptors contributing to binding potential in WT animals were not functional. These results demonstrate that arrestin-mediated receptor desensitization and internalization produce large effects in PET [11C]raclopride occupancy studies using agonist challenges.

Challenges and innovations in brain PET analysis of neurodegenerative disorders: a mini-review on partial volume effects, small brain region studies, and reference region selection

Positron Emission Tomography (PET) brain imaging is increasingly utilized in clinical and research settings due to its unique ability to study biological processes and subtle changes in living subjects. However, PET imaging is not without its limitations. Currently, bias introduced by partial volume effect (PVE) and poor signal-to-noise ratios of some radiotracers can hamper accurate quantification. Technological advancements like ultra-high-resolution scanners and improvements in radiochemistry are on the horizon to address these challenges. This will enable the study of smaller brain regions and may require more sophisticated methods (e.g., data-driven approaches like unsupervised clustering) for reference region selection and to improve quantification accuracy. This review delves into some of these critical aspects of PET molecular imaging and offers suggested strategies for improvement. This will be illustrated by showing examples for dopaminergic and cholinergic nerve terminal ligands.

Future trends for patient-specific dosimetry methodology in molecular radiotherapy

Molecular radiotherapy is rapidly expanding, and new radiotherapeutics are emerging. The majority of treatments is still performed using empirical fixed activities and not tailored for individual patients. Molecular radiotherapy dosimetry is often seen as a promising candidate that would allow personalisation of treatments as outcome should ultimately depend on the absorbed doses delivered and not the activities administered. The field of molecular radiotherapy dosimetry has made considerable progress towards the feasibility of routine clinical dosimetry with reasonably accurate absorbed-dose estimates for a range of molecular radiotherapy dosimetry applications. A range of challenges remain with respect to the accurate quantification, assessment of time-integrated activity and absorbed dose estimation. In this review, we summarise a range of technological and methodological advancements, mainly focussed on beta-emitting molecular radiotherapeutics, that aim to improve molecular radiotherapy dosimetry to achieve accurate, reproducible, and streamlined dosimetry. We describe how these new technologies can potentially improve the often time-consuming considered process of dosimetry and provide suggestions as to what further developments might be required.

Averaging of absorbed doses: How matter matters

BACKGROUND

Dosimetry in radionuclide therapy often requires the calculation of average absorbed doses within and between spatial regions, for example, for voxel-based dosimetry methods, for paired organs, or across multiple tumors. Formation of such averages can be made in different ways, starting from different definitions.

PURPOSE

The aim of this study is to formally specify different averaging strategies for absorbed doses, and to compare their results when applied to absorbed dose distributions that are non-uniform within and between regions.

METHODS

For averaging within regions, two definitions of the average absorbed dose are considered: the simple average over the region (the region average) and the average when weighting by the mass density (density-weighted region average). The latter is shown to follow from the definition of mean absorbed dose according to the ICRU, and to be consistent with the MIRD formalism. For averaging between different spatial regions, three definitions follow: the volume-weighted, the mass-weighted, and the unweighted average. With respect to characterizing non-uniformity, the different average definitions lead to the use of dose-volume histograms (DVHs) (region average), dose-mass histograms (DMHs) (density-weighted region average), and unweighted histograms (unweighted average). Average absorbed doses are calculated for three worked examples, starting from the different definitions. The first, schematic, example concerns the calculation of the average absorbed dose between two regions with different volumes or mass densities. The second, stylized, example concerns voxel-based dosimetry, for which the average absorbed-dose rate within a region is calculated. The geometries studied include three 177 Lu-filled voxelized spheres, where the sphere masses are held constant while the material compositions, densities, and volumes are varied. For comparison, the mean absorbed-dose rates obtained using unit-density sphere S-values are also included. The third example concerns SPECT/CT-based tumor dosimetry for five patients undergoing therapy with 177 Lu-PSMA and six patients undergoing therapy with 177 Lu-DOTA-TATE, for which the average absorbed-dose rates across multiple tumors are calculated. For the second and third examples, analyses also include representations by histograms.

RESULTS

Example 1 shows that the average absorbed doses, calculated using different definitions, can differ considerably if the masses and absorbed doses for two regions are markedly different. From example 2 it is seen that the density-weighted region average is stable under different activity and density distributions and is also in line with results using S-values. In contrast, the region average varies as function of the activity distribution. In example 3, the absorbed dose rates for individual tumors differ by (1.1 ± 4.3)% and (-0.1 ± 0.4)% with maximum deviations of +34.4% and -1.4% for 177 Lu-PSMA and 177 Lu-DOTA-TATE, respectively, when calculated as region averages or density-weighted region averages, with largest deviations obtained when the density is non-uniform. The average absorbed doses calculated across all tumors are similar when comparing mass-weighted and volume-weighted averages but these differ substantially from unweighted averages.

CONCLUSION

Different strategies for averaging of absorbed doses within and between regions can lead to substantially different absorbed-dose estimates. At reporting of radionuclide therapy dosimetry, it is important to specify the averaging strategy applied.

Patient-specific biokinetics and hybrid 2D/3D approach integration in OEDIPE software: Application to radioiodine therapy

BACKGROUND

The progression of targeted radionuclide therapy requires the development of dosimetry software accounting for patient-specific biokinetics. New functionalities were thus developed in the OEDIPE software, to deal with multiple 3D images or multiple planar images and a SPECT image.

MATERIEL & METHOD

Methods were implemented to recover patient biokinetics in volumes of interest. If several 3D SPECT images are available, they are registered to a reference CT scan. When several planar images and a single SPECT are available, the planar images are registered to the SPECT and counts of the planar images converted to activity. To validate these developments, six SPECT/CT and planar images of a Jaszczak phantom containing I-131 were acquired at different dates. Cumulated activity was estimated in each sphere using the SPECT/CT images only or the planar series associated to one SPECT/CT. Biokinetics and doses in lesions and in the lungs of a patient treated with I-131 for differentiated thyroid cancer were then estimated using four planar images and a SPECT/CT scan. Whole-body retention data were used to compare the biokinetics obtained from the planar and SPECT data.

RESULTS

Activities and cumulated activities estimated using OEDIPE in the phantom spheres agreed well with the reference values for both approaches. Results obtained for the patient compared well with those derived from whole-body retention data.

CONCLUSION

The implemented features allow automatic evaluation of patient-specific biokinetics from different series of patient images, enabling patient-specific dosimetry without the need for external software to estimate the cumulated activities in different VOIs.

No impact of attenuation and scatter correction on the interpretation of dopamine transporter SPECT in patients with clinically uncertain parkinsonian syndrome

PURPOSE

The benefit from attenuation and scatter correction (ASC) of dopamine transporter (DAT)-SPECT for the detection of nigrostriatal degeneration in clinical routine is still a matter of debate. The current study evaluated the impact of ASC on visual interpretation and semi-quantitative analysis of DAT-SPECT in a large patient sample.

METHODS

One thousand seven hundred forty consecutive DAT-SPECT with 123I-FP-CIT from clinical routine were included retrospectively. SPECT images were reconstructed iteratively without and with ASC. Attenuation correction was based on uniform attenuation maps, scatter correction on simulation. All SPECT images were categorized with respect to the presence versus the absence of Parkinson-typical reduction of striatal 123I-FP-CIT uptake by three independent readers. Image reading was performed twice to assess intra-reader variability. The specific 123I-FP-CIT binding ratio (SBR) was used for automatic categorization, separately with and without ASC.

RESULTS

The mean proportion of cases with discrepant categorization by the same reader between the two reading sessions was practically the same without and with ASC, about 2.2%. The proportion of DAT-SPECT with discrepant categorization without versus with ASC by the same reader was 1.66% ± 0.50% (1.09-1.95%), not exceeding the benchmark of 2.2% from intra-reader variability. This also applied to automatic categorization of the DAT-SPECT images based on the putamen SBR (1.78% discrepant cases between without versus with ASC).

CONCLUSION

Given the large sample size, the current findings provide strong evidence against a relevant impact of ASC with uniform attenuation and simulation-based scatter correction on the clinical utility of DAT-SPECT to detect nigrostriatal degeneration in patients with clinically uncertain parkinsonian syndrome.

Sex differences in 123I-mIBG scintigraphy imaging techniques in patients with heart failure

BACKGROUND

123I-mIBG-scintigraphy could be a useful stratifying tool for patients with heart failure (HF). The purpose of this retrospective study is to evaluate whether there are differences between men and women with HF in terms of the prediction of cardiac arrhythmic events (AE).

RESEARCH AND METHODS

A total of 306 patients, before implantable-cardioverter-defibrillator (ICD) implantation, were evaluated. They underwent 123I-mIBG-scintigraphy and an evaluation of the results was performed after 85 months of follow-up. Early and late planar and SPECT cardiac images were acquired. Heart-to-mediastinum ratio (HM) for planar images and the sum of the segmental scores (SS) for SPECT were calculated.

RESULTS

In the general population, age, early SS (ESS), late SS (LSS), and ejection fraction (EF) were statistically significant for the prediction of AE at Cox regression, while early and late HM (eHM,lHM) were not significative for the prediction of AE. Population was divided into females and males and univariate analysis was conducted separately for the two cohorts: no significant variables for prediction of AE were found in females. For males, ESS, LSS, EF, and late HM were statistically significant predictors of AE. The overall survival was similar in males and females, but the risk of AE is lower in males than in females.

CONCLUSIONS

123I-mIBG represents a more effective tool for the prediction of AE in male patients than in women.

Principal component analysis of synaptic density measured with [11C]UCB-J PET in early Alzheimer's disease

BACKGROUND

Synaptic loss is considered an early pathological event and major structural correlate of cognitive impairment in Alzheimer's disease (AD). We used principal component analysis (PCA) to identify regional patterns of covariance in synaptic density using [11C]UCB-J PET and assessed the association between principal components (PC) subject scores with cognitive performance.

METHODS

[11C]UCB-J binding was measured in 45 amyloid + participants with AD and 19 amyloid- cognitively normal participants aged 55-85. A validated neuropsychological battery assessed performance across five cognitive domains. PCA was applied to the pooled sample using distribution volume ratios (DVR) standardized (z-scored) by region from 42 bilateral regions of interest (ROI).

RESULTS

Parallel analysis determined three significant PCs explaining 70.2% of the total variance. PC1 was characterized by positive loadings with similar contributions across the majority of ROIs. PC2 was characterized by positive and negative loadings with strongest contributions from subcortical and parietooccipital cortical regions, respectively, while PC3 was characterized by positive and negative loadings with strongest contributions from rostral and caudal cortical regions, respectively. Within the AD group, PC1 subject scores were positively correlated with performance across all cognitive domains (Pearson r = 0.24-0.40, P = 0.06-0.006), PC2 subject scores were inversely correlated with age (Pearson r = -0.45, P = 0.002) and PC3 subject scores were significantly correlated with CDR-sb (Pearson r = 0.46, P = 0.04). No significant correlations were observed between cognitive performance and PC subject scores in CN participants.

CONCLUSIONS

This data-driven approach defined specific spatial patterns of synaptic density correlated with unique participant characteristics within the AD group. Our findings reinforce synaptic density as a robust biomarker of disease presence and severity in the early stages of AD.

Activity recovery for differently shaped objects in quantitative SPECT

Objective.The aim was to theoretically and experimentally investigate recovery in SPECT images with objects of different shapes. Furthermore, the accuracy of volume estimation by thresholding was studied for those shapes.Approach.Nine spheres, nine oblate spheroids, and nine prolate spheroids phantom inserts were used, of which the six smaller spheres were part of the NEMA IEC body phantom and the rest of the inserts were 3D-printed. The inserts were filled with99mTc and177Lu. When filled with99mTc, SPECT images were acquired in a Siemens Symbia Intevo Bold gamma camera and when filled with177Lu in a General Electric NM/CT 870 DR gamma camera. The signal rate per activity (SRPA) was determined for all inserts and represented as a function of the volume-to-surface ratio and of the volume-equivalent radius using VOIs defined according to the sphere dimensions and VOIs defined using thresholding. Experimental values were compared with theoretical curves obtained analytically (spheres) or numerically (spheroids), starting from the convolution of a source distribution with a point-spread function. Validation of the activity estimation strategy was performed using four 3D-printed ellipsoids. Lastly, the threshold values necessary to determine the volume of each insert were obtained.Main results.Results showed that SRPA values for the oblate spheroids diverted from the other inserts, when SRPA were represented as a function of the volume-equivalent radius. However, SRPA values for all inserts followed a similar behaviour when represented as a function of the volume-to-surface ratio. Results for ellipsoids were in agreement with those results. For the three types of inserts the volume could be accurately estimated using a threshold method for volumes larger than 25 ml.Significance.Determination of SRPA independently of lesion or organ shape should decrease uncertainties in estimated activities and thereby, in the long term, be beneficial to patient care.

Monitoring the biodistribution of radiolabeled therapeutics in mice

Radiopharmaceutical therapy is a rapidly growing field for the treatment of cancer due to its high specificity and ability to target individual affected cells. A key component of the pre-clinical development of a new therapeutic radiopharmaceutical is the determination of its time-dependent distribution in tumors, normal tissues, and the whole body in mouse tumor models. Here, we provide an overview of the available instrumentation for the novice in radiation measurement. We also detail the methodology for assessing distribution and kinetics of a radiopharmaceutical and calculating radiation absorbed dose in mice using a gamma counter or a PET or SPECT camera.

A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information

PURPOSE

Partial volume effect (PVE) is a consequence of the limited spatial resolution of PET scanners. PVE can cause the intensity values of a particular voxel to be underestimated or overestimated due to the effect of surrounding tracer uptake. We propose a novel partial volume correction (PVC) technique to overcome the adverse effects of PVE on PET images.

METHODS

Two hundred and twelve clinical brain PET scans, including 50 18F-Fluorodeoxyglucose (18F-FDG), 50 18F-Flortaucipir, 36 18F-Flutemetamol, and 76 18F-FluoroDOPA, and their corresponding T1-weighted MR images were enrolled in this study. The Iterative Yang technique was used for PVC as a reference or surrogate of the ground truth for evaluation. A cycle-consistent adversarial network (CycleGAN) was trained to directly map non-PVC PET images to PVC PET images. Quantitative analysis using various metrics, including structural similarity index (SSIM), root mean squared error (RMSE), and peak signal-to-noise ratio (PSNR), was performed. Furthermore, voxel-wise and region-wise-based correlations of activity concentration between the predicted and reference images were evaluated through joint histogram and Bland and Altman analysis. In addition, radiomic analysis was performed by calculating 20 radiomic features within 83 brain regions. Finally, a voxel-wise two-sample t-test was used to compare the predicted PVC PET images with reference PVC images for each radiotracer.

RESULTS

The Bland and Altman analysis showed the largest and smallest variance for 18F-FDG (95% CI: - 0.29, + 0.33 SUV, mean = 0.02 SUV) and 18F-Flutemetamol (95% CI: - 0.26, + 0.24 SUV, mean =  - 0.01 SUV), respectively. The PSNR was lowest (29.64 ± 1.13 dB) for 18F-FDG and highest (36.01 ± 3.26 dB) for 18F-Flutemetamol. The smallest and largest SSIM were achieved for 18F-FDG (0.93 ± 0.01) and 18F-Flutemetamol (0.97 ± 0.01), respectively. The average relative error for the kurtosis radiomic feature was 3.32%, 9.39%, 4.17%, and 4.55%, while it was 4.74%, 8.80%, 7.27%, and 6.81% for NGLDM_contrast feature for 18F-Flutemetamol, 18F-FluoroDOPA, 18F-FDG, and 18F-Flortaucipir, respectively.

CONCLUSION

An end-to-end CycleGAN PVC method was developed and evaluated. Our model generates PVC images from the original non-PVC PET images without requiring additional anatomical information, such as MRI or CT. Our model eliminates the need for accurate registration or segmentation or PET scanner system response characterization. In addition, no assumptions regarding anatomical structure size, homogeneity, boundary, or background level are required.

Characterization of the partial volume effect along the axial field-of-view of the Biograph Vision Quadra total-body PET/CT system for multiple isotopes

BACKGROUND

Total-body PET scanners with axial field of views (FOVs) longer than 1 m enable new applications to study multiple organs (e.g., the brain-gut-axis) simultaneously. As the spatial resolution and the associated partial volume effect (PVE) can vary significantly along the FOV, detailed knowledge of the contrast recovery coefficients (CRCs) is a prerequisite for image analysis and interpretation of quantitative results. The aim of this study was to determine the CRCs, as well as voxel noise, for multiple isotopes throughout the 1.06 m axial FOV of the Biograph Vision Quadra PET/CT system (Siemens Healthineers).

MATERIALS AND METHODS

Cylindrical phantoms equipped with three different sphere sizes (inner diameters 7.86 mm, 28 and 37 mm) were utilized for the PVE evaluation. The 7.86 mm sphere was filled with F-18 (8:1 and 4:1), Ga-68 (8:1) and Zr-89 (8:1). The 28 mm and 37 mm spheres were filled with F-18 (8:1). Background concentration in the respective phantoms was of ~ 3 kBq/ml. The phantoms were measured at multiple positions in the FOV (axial: 0, 10, 20, 30, 40 and 50 cm, transaxial: 0, 10, 20 cm). The data were reconstructed with the standard clinical protocol, including PSF correction and TOF information with up to 10 iterations for maximum ring differences (MRDs) of 85 and 322; CRCs, as well as voxel noise levels, were determined for each position.

RESULTS

F-18 CRCs (SBR 8:1 and 4:1) of the 7.86 mm sphere decreased up to 18% from the center FOV (cFOV) toward the transaxial edge and increased up to 17% toward the axial edge. Noise levels were below 15% for the default clinical reconstruction parameters. The larger spheres exhibited a similar pattern. Zr-89 revealed ~ 10% lower CRCs than F-18 but larger noise (9.1% (F-18), 19.1% (Zr-89); iteration 4, cFOV) for the default reconstruction. Zr-89 noise levels in the cFOV significantly decreased (~ 28%) when reconstructing the data with MRD322 compared with MRD85 along with a slight decrease in CRC values. Ga-68 exhibited the lowest CRCs for the three isotopes and noise characteristics comparable to those of F-18.

CONCLUSIONS

Distinct differences in the PVE within the FOV were detected for clinically relevant isotopes F-18, Ga-68 and Zr-89, as well as for different sphere sizes. Depending on the positions inside the FOV, the sphere-to-background ratios, count statistics and isotope used, this can result in an up to 50% difference between CRCs. Hence, these changes in PVE can significantly affect the quantitative analysis of patient data. MRD322 resulted in slightly lower CRC values, especially in the center FOV, whereas the voxel noise significantly decreased compared with MRD85.

Segmentation-Free PVC for Cardiac SPECT Using a Densely-Connected Multi-Dimensional Dynamic Network

In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective. However, such anatomical-guided methods typically require tedious image registration and segmentation steps. Accurately segmented organ templates are also hard to obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid SPECT/CT scanners with high-end CT and associated motion artifacts. Slight mis-registration/mis-segmentation would result in severe degradation in image quality after PVC. In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation. The proposed network involves a densely-connected multi-dimensional dynamic mechanism, allowing the convolutional kernels to be adapted based on the input images, even after the network is fully trained. Intramyocardial blood volume (IMBV) is introduced as an additional clinical-relevant loss function for network optimization. The proposed network demonstrated promising performance on 28 canine studies acquired on a GE Discovery NM/CT 570c dedicated cardiac SPECT scanner with a 64-slice CT using Technetium-99m-labeled red blood cells. This work showed that the proposed network with densely-connected dynamic mechanism produced superior results compared with the same network without such mechanism. Results also showed that the proposed network without anatomical information could produce images with statistically comparable IMBV measurements to the images generated by anatomical-guided PVC methods, which could be helpful in clinical translation.

Development of a validation imaging dataset for Molecular Radiotherapy dosimetry multicenter intercomparison exercises based on anthropomorphic phantoms

Validation of a Molecular Radiotherapy (MRT) dosimetry system requires imaging data for which an accompanying "ground truth" pharmacokinetic model and absorbed dose calculation are known.

METHODS

We present a methodology for production of a validation dataset for image based ¹⁷⁷Lu dotatate dosimetry calculations. A pharmacokinetic model is presented with activity concentrations corresponding to common imaging timepoints. Anthropomorphic 3D printed phantoms, corresponding to the organs at risk, have been developed to provide SPECT/CT and Whole Body imaging with known organ activities corresponding to common clinical timepoints.

RESULTS

Results for the accuracy of phantom filling reproduce the activity concentrations from the pharmacokinetic model for all timepoints and organs within measurement uncertainties, with a mean deviation of 0.6(8)%. The imaging dataset, ancillary data and phantoms designs are provided as a source of well characterized input data for the validation of clinical MRT dosimetry systems.

CONCLUSIONS

The combination of pharmacokinetic modelling with the use of anthropomorphic 3D printed phantoms are a promising procedure to provide data for the validation of Molecular Radiotherapy Dosimetry systems, allowing multicentre comparisons.

Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT

PURPOSE

This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on ¹⁸F-FDG PET/CT images.

METHODS

128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC.

RESULTS

There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods.

CONCLUSION

Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.

Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images

When no arterial input function is available, quantification of dynamic PET images requires a previous step devoted to the extraction of a reference time-activity curve (TAC). Factor analysis is often applied for this purpose. This paper introduces a novel approach that conducts a new kind of nonlinear factor analysis relying on a compartment model, and computes the kinetic parameters of specific binding tissues jointly. To this end, it capitalizes on data-driven parametric imaging methods to provide a physical description of the underlying PET data, directly relating the specific binding with the kinetics of the non-specific binding in the corresponding tissues. This characterization is introduced into the factor analysis formulation to yield a novel nonlinear unmixing model designed for PET image analysis. This model also explicitly introduces global kinetic parameters that allow for a direct estimation of a binding potential that represents the ratio at equilibrium of specifically bound radioligand to the concentration of nondisplaceable radioligand in each non-specific binding tissue. The performance of the method is evaluated on synthetic and real data to demonstrate its potential interest.

Data-Driven Phenotyping of Alzheimer's Disease under Epigenetic Conditions Using Partial Volume Correction of PET Studies and Manifold Learning

Alzheimer's disease (AD) is the most common form of dementia. An increasing number of studies have confirmed epigenetic changes in AD. Consequently, a robust phenotyping mechanism must take into consideration the environmental effects on the patient in the generation of phenotypes. Positron Emission Tomography (PET) is employed for the quantification of pathological amyloid deposition in brain tissues. The objective is to develop a new methodology for the hyperparametric analysis of changes in cognitive scores and PET features to test for there being multiple AD phenotypes. We used a computational method to identify phenotypes in a retrospective cohort study (532 subjects), using PET and Magnetic Resonance Imaging (MRI) images and neuropsychological assessments, to develop a novel computational phenotyping method that uses Partial Volume Correction (PVC) and subsets of neuropsychological assessments in a non-biased fashion. Our pipeline is based on a Regional Spread Function (RSF) method for PVC and a t-distributed Stochastic Neighbor Embedding (t-SNE) manifold. The results presented demonstrate that (1) the approach to data-driven phenotyping is valid, (2) the different techniques involved in the pipelines produce different results, and (3) they permit us to identify the best phenotyping pipeline. The method identifies three phenotypes and permits us to analyze them under epigenetic conditions.

Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council

PURPOSE

The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches.

METHODS

In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives.

CONCLUSION

Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.

Synaptic Loss in Frontotemporal Dementia Revealed by [11 C]UCB-J Positron Emission Tomography

OBJECTIVE

Synaptic loss is an early feature of neurodegenerative disease models, and is severe in post mortem clinical studies, including frontotemporal dementia. Positron emission tomography (PET) with radiotracers that bind to synaptic vesicle glycoprotein 2A enables quantification of synaptic density in vivo. This study used [11 C]UCB-J PET in participants with behavioral variant frontotemporal dementia (bvFTD), testing the hypothesis that synaptic loss is severe and related to clinical severity.

METHODS

Eleven participants with clinically probable bvFTD and 25 age- and sex-matched healthy controls were included. Participants underwent dynamic [11 C]UCB-J PET, structural magnetic resonance imaging, and a neuropsychological battery, including the revised Addenbrooke Cognitive Examination, and INECO frontal screening. General linear models compared [11 C]UCB-J binding potential maps and gray matter volume between groups, and assessed associations between synaptic density and clinical severity in patients. Analyses were also performed using partial volume corrected [11 C]UCB-J binding potential from regions of interest (ROIs).

RESULTS

Patients with bvFTD showed severe synaptic loss compared to controls. [11 C]UCB-J binding was reduced bilaterally in medial and dorsolateral frontal regions, inferior frontal gyri, anterior and posterior cingulate gyrus, insular cortex, and medial temporal lobe. Synaptic loss in the frontal and cingulate regions correlated significantly with cognitive impairments. Synaptic loss was more severe than atrophy. Results from ROI-based analyses mirrored the voxelwise results.

INTERPRETATION

In accordance with preclinical models, and human postmortem evidence, there is widespread frontotemporal loss of synapses in symptomatic bvFTD, in proportion to severity. [11 C]UCB-J PET could support translational studies and experimental medicine strategies for new disease-modifying treatments for neurodegeneration. ANN NEUROL 2023;93:142-154.

Quantification and dosimetry of small volumes including associated uncertainty estimation

BACKGROUND

Accurate quantification of radioactivity in a source of interest relies on accurate registration between SPECT and anatomical images, and appropriate correction of partial volume effects (PVEs). For small volumes, exact registration between the two imaging modalities and recovery factors used to correct for PVE are unreliable. There is currently no guidance relating to quantification or the associated uncertainty estimation for small volumes.

MATERIAL AND METHODS

A method for quantification of small sources of interest is proposed, which uses multiple oversized volumes of interest. The method was applied to three Na[¹³¹I]I activity distributions where a Na[¹³¹I]I capsule was situated within a cylindrical phantom containing either zero background, uniform background or non-uniform background and to a scenario with small lesions placed in an anthropomorphic phantom. The Na[¹³¹I]I capsule and lesions were quantified using the proposed method and compared with measurements made using two alternative quantification methods. The proposed method was also applied to assess the absorbed dose delivered to a bone metastasis following [¹³¹I]mIBG therapy for neuroblastoma including the associated uncertainty estimation.

RESULTS

The method is accurate across a range of activities and in varied radioactivity distributions. Median percentage errors using the proposed method in no background, uniform backgrounds and non-uniform backgrounds were - 0.4%, - 0.3% and 1.7% with median associated uncertainties of 1.4%, 1.4% and 1.6%, respectively. The technique is more accurate and robust when compared to currently available alternative methods.

CONCLUSIONS

The proposed method provides a reliable and accurate method for quantification of sources of interest, which are less than three times the spatial resolution of the imaging system. The method may be of use in absorbed dose calculation in cases of bone metastasis, lung metastasis or thyroid remnants.

Synaptic density and cognitive performance in Alzheimer's disease: A PET imaging study with [11 C]UCB-J

INTRODUCTION

For 30 years synapse loss has been referred to as the major pathological correlate of cognitive impairment in Alzheimer's disease (AD). However, this statement is based on remarkably few patients studied by autopsy or biopsy. With the recent advent of synaptic vesicle glycoprotein 2A (SV2A) positron emission tomography (PET) imaging, we have begun to evaluate the consequences of synaptic alterations in vivo.

METHODS

We examined the relationship between synaptic density measured by [11 C]UCB-J PET and neuropsychological test performance in 45 participants with early AD.

RESULTS

Global synaptic density showed a significant positive association with global cognition and performance on five individual cognitive domains in participants with early AD. Synaptic density was a stronger predictor of cognitive performance than gray matter volume.

CONCLUSION

These results confirm neuropathologic studies demonstrating a significant association between synaptic density and cognitive performance, and suggest that this correlation extends to the early stages of AD.

Quantify total activity by volume-of-interest expansion with clinical SPECT/CT systems, a phantom study

PURPOSE

Quantitative measurements of activity in SPECT are important for radioisotope therapy planning and disease diagnosis. The aim of this manuscript is to develop a robust method to quantify the total activity in a volume-of-interest (VOI) of different quantitative SPECT reconstructions and validate the estimation accuracy.

METHODS

We customized an IEC body phantom using 3D printing technology and made six sphere inserts of 1-6 cm in diameter with at least 3 cm separation. The activity concentration within the spheres was in the range of patient lesion/organ activity. The background activity was then increased from zero to a sphere/background activity concentration of 8:1, 4:1, and 2:1. SPECT data were acquired with Philips Brightview and GE Discovery 670 SPECT/computed tomography (CT) systems under clinical acquisition protocols. Quantitative SPECT images were reconstructed with Hermes SUV-SPECT (both Philips and GE data) and GE Q.Metrix (GE data only). The quantitative SPECT reconstructions are iterative with scatter, CT attenuation correction, and resolution recovery. We quantified the total activity by expanding the sphere VOI to include a spill-out region. Background correction was applied by sampling a region outside the spill-out region. The true fractions (TFs) (total activity/true activity) were measured for all six spheres for all SPECT acquisitions.

RESULTS

The TF is close to 100% for 2-6 cm spheres for zero background, 8:1 and 4:1 sphere/background activity ratios. The TF was found to be unreliable for the 1-cm sphere because of the limit of phantom design. TF accuracy for 2:1 sphere/background ratio was degraded due to significantly large background, inadequate scatter correction and detector count loss.

CONCLUSIONS

The results demonstrated that the proposed quantification method is accurate for objects of different sizes in currently clinical quantitative reconstruction and has the potential for improving the accuracy for therapeutic treatment planning or radiation dosimetry calculations.

Increased white matter glycolysis in humans with cerebral small vessel disease

White matter lesions in cerebral small vessel disease are related to ischemic injury and increase the risk of stroke and cognitive decline. Pathological changes due to cerebral small vessel disease are increasingly recognized outside of discrete lesions, but the metabolic alterations in nonlesional tissue has not been described. Aerobic glycolysis is critical to white matter myelin homeostasis and repair. In this study, we examined cerebral metabolism of glucose and oxygen as well as blood flow in individuals with and without cerebral small vessel disease using multitracer positron emission tomography. We show that glycolysis is relatively elevated in nonlesional white matter in individuals with small vessel disease relative to healthy, age-matched controls. On the other hand, in young healthy individuals, glycolysis is relatively low in areas of white matter susceptible to lesion formation. These results suggest that increased white matter glycolysis is a marker of pathology associated with small vessel disease.