Automated striatal uptake analysis of ¹⁸F-FDOPA PET images applied to Parkinson's disease patients

Beyond Visual Assessment of Basal Ganglia Uptake: Can Automated Method and Pineal Body Uptake Assessment Improve Identification of Nigrostriatal Dysfunction on 18F-DOPA PET/CT?

The interpretation of 18F-DOPA PET/CT performed for assessing nigrostriatal dysfunction (NSD) is usually based on visual assessment of the uptake in the basal ganglia (VA-BG). In the present study, we evaluate the diagnostic performance of an automated method that assesses BG uptake (AM-BG) and of methods that assess pineal body uptake, and examine whether these methods can enhance the diagnostic performance of VA-BG alone. We retrospectively included 112 scans performed in patients with clinically suspected NSD who also had a subsequent final clinical diagnosis provided by a movement disorder specialist (69 NSD and 43 non-NSD patients). All scans were categorized as positive or negative based on (1) VA-BG, (2) AM-BG, and (3) qualitative and semiquantitative assessment of pineal body uptake. VA-BG, AM-BG, assessment of pineal body 18F-DOPA uptake by VA (uptake > background), by SUVmax (≥0.72), and by pineal to occipital ratio (POR ≥ 1.57) could all significantly differentiate NSD from non-NSD patients (Pv < 0.01 for all five methods). Of these methods, VA-BG provided the highest sensitivity (88.4%) and accuracy (90.2%). Combining VA-BG with AM-BG did not improve diagnostic accuracy. An interpretation algorithm that combines VA-BG with pineal body uptake assessment by POR calculation increased sensitivity to 98.5%, at the expense of decreased specificity. In conclusion, an automated method that assesses 18F-DOPA uptake in the BG and assessment of pineal body 18F-DOPA uptake can significantly separate NSD from non-NSD patients, with apparent inferior diagnostic performance when applied alone compared with VA-BG. When VA-BG categorizes a scan as negative or equivocal, assessment of the 18F-DOPA uptake in the pineal body has the potential to minimize the rate of false negative reports. Further research is essential to validate this approach and to study the pathophysiologic relationship between 18F-DOPA uptake in the pineal body and nigrostriatal dysfunction.

A Multi-Atlas-Based [18F]9-Fluoropropyl-(+)-Dihydrotetrabenazine Positron Emission Tomography Image Segmentation Method for Parkinson's Disease Quantification

Objectives

[18F]9-fluoropropyl-(+)-dihydrotetrabenazine ([18F]-FP-DTBZ) positron emission tomography (PET) provides reliable information for the diagnosis of Parkinson's disease (PD). In this study, we proposed a multi-atlas-based [18F]-FP-DTBZ PET image segmentation method for PD quantification assessment.

Methods

A total of 99 subjects from Xuanwu Hospital of Capital Medical University were included in this study, and both brain PET and magnetic resonance (MR) scans were conducted. Data from 20 subjects were used to generate atlases, based on which a multi-atlas-based [18F]-FP-DTBZ PET segmentation method was developed especially for striatum and its subregions. The proposed method was compared with the template-based method through striatal subregion parcellation performance and the standard uptake value ratio (SUVR) quantification accuracy. Discriminant analysis between healthy controls (HCs) and PD patients was further performed.

Results

Segmentation results of the multi-atlas-based method showed better consistency than the template-based method with the ground truth, yielding a dice coefficient of 0.81 over 0.73 on the full striatum. The SUVRs calculated by the multi-atlas-based method had an average interclass correlation coefficient (ICC) of 0.953 with the standardized result, whereas the template-based method only reached 0.815. The SUVRs of HCs were generally higher than that of patients with PD and showed significant differences in all of the striatal subregions (all p < 0.001). The median and posterior putamen performed best in discriminating patients with PD from HCs.

Conclusion

The proposed multi-atlas-based [18F]-FP-DTBZ PET image segmentation method achieved better performance than the template-based method, indicating great potential in improving accuracy and efficiency for PD diagnosis in clinical routine.

Pharmacokinetic Modeling of 18F-FDOPA PET in the Human Brain for Early Parkinson's Disease

Objectives:

Early detection is essential for the treatment approaches of Parkinson’s disease (PD). Clinical criteria alone may be insufficient to distinguish early PD from other conditions. This study aimed to investigate the transfer rate constants of 6-¹⁸F-fluoro-L-dopa (¹⁸F-FDOPA) in positron emission tomography (PET) brain images as a sensitive parameter to detect early PD.

Methods:

Retrospective ¹⁸F-FDOPA PET data of five patients with early PD were collected. PET data were acquired for 90 min after intravenous injection of 306-379 MBq ¹⁸F-FDOPA, and reconstructed into a series of 18 five-minute frames. Reoriented PET images were coregistered and normalized with the PET brain template on the statistical parametric mapping. The ¹⁸F-FDOPA activity concentrations were measured in the striatum, caudate, and putamen on both sides: Contralateral (as PD) and ipsilateral (as control) to the main motor symptoms. The pharmacokinetic model was generated using the SAAM II simulation software. The transfer rate constants across the blood-brain barrier (forward, K₁ and reverse, k₂) and decarboxylation rate constants (k₃) were estimated in these regions.

Results:

The activity uptakes in the contralateral striatum (0.0323%±0.0091%) and putamen (0.0169%±0.0054%) were significantly lower than the control (0.0353%±0.0086%, 0.0199%±0.0054%, respectively). The K₁ and k₃ were significantly lower in the contralateral striatum and putamen (p<0.05). There were no significant differences in any transfer rate constants in the caudate.

Conclusion:

The transfer rate constants (K₁ and k₃) of ¹⁸F-FDOPA on the contralateral striatum and putamen were significantly lower than the control. These biokinetic data could be potential indicators for quantitative detection of early PD diagnosis.

The role of the deep convolutional neural network as an aid to interpreting brain [18F]DOPA PET/CT in the diagnosis of Parkinson's disease

OBJECTIVES

To test the performance of a 3D convolutional neural network (CNN) in analysing brain [¹⁸F]DOPA PET/CT in order to identify patients with nigro-striatal neurodegeneration. We evaluated the robustness of the 3D CNN by testing it against a manual regional analysis of the striata by using a striatal-to-occipital ratio (SOR).

METHODS

We analyzed patients who had undergone [¹⁸F]DOPA PET/CT from 2016 to 2018. Two examiners interpreted PET/CT images as positive or negative. Only patients with at least 2 years of follow-up and an ascertained neurological diagnosis were included. A 3D CNN was developed to evaluate [¹⁸F]DOPA PET/CT and refine the diagnosis of movement disorder. This system required training and testing, which were carried out on 2/3 and 1/3 of patients, respectively. A regional analysis was also conducted by drawing region of interest on T1-weighted 3D MRI scans, on which the [¹⁸F]DOPA PET images were first co-registered.

RESULTS

Ninety-eight patients were enrolled: 43 presented nigro-striatal degeneration and 55 negative cases used as controls. After training on 69 patients, the diagnostic performance of the 3D CNN was then calculated in 29 patients. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 100%, 89%, 100%, 85% and 93%, respectively. When we compared the 3D CNN results with the SOR analysis, we found that the two patients falsely classified as positive by the 3D CNN procedure showed SOR values ≤ 5^th percentile of the negative cases' distribution.

CONCLUSIONS

3D CNNs are able to interpret [¹⁸F]DOPA PET/CT properly, revealing patients affected by Parkinson's disease.

KEY POINTS

• [¹⁸F]DOPA PET/CT is a sensitive diagnostic tool to identify patients with nigro-striatal neurodegeneration. • A semiquantitative evaluation of the images allows a more confident interpretation of the PET findings. • 3D convolutional neural network allows an accurate interpretation of 18F-DOPA PET/CT images, revealing patients affected by Parkinson's disease.

A Novel Automatic Approach for Calculation of the Specific Binding Ratio in [I-123]FP-CIT SPECT

A fully automatic method for specific binding ratio (SBR) calculation in [¹²³I]ioflupane single-photon emission computed tomography (SPECT) studies was proposed by creating volumes of interest of the striatum (VOI_st) and reference region (VOI_ref) without manual handling to avoid operator-induced variability. The study involved 105 patients (72 ± 10 years) suspected of parkinsonian syndrome (PS) who underwent [¹²³I]ioflupane SPECT. The 200 images from our previous study were used for evaluation and validation of the new program. All patients were classified into PS and non-PS groups according to the results of clinical follow-up. A trapezoidal volume of interest (VOI_t) containing all striatal intensive counts was created automatically, followed by VOI_st setting using the previous method. SBR values were calculated from the mean values of VOI_st and VOI_ref determined by the whole brain outside of VOI_t. The low count voxels in the VOI_ref were excluded using an appropriate threshold. The SBR values from the new method were compared with the previous semi-automatic method and the Tossici–Bolt (TB) method. The SBRs from the semi- and fully automatic methods showed a good linear correlation (r > 0.98). The areas under the curves (AUCs) of receiver operating characteristic analysis showed no significant difference between the two methods for both our previous (AUC > 0.99) and new (AUC > 0.95) data. The diagnostic accuracy of the two methods showed similar results (>92%), and both were better than the TB method. The proposed method successfully created the automatic VOIs and calculated SBR rapidly (9 ± 1 s/patient), avoiding operator-induced variability and providing objective SBR results.

Intercomparison of MR-informed PET image reconstruction methods

PURPOSE

Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET-unique regions). To address this, further developments for MR-informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade-off between the suppression of noise and the retention of unique features present in the PET data.

METHODS

The reconstruction methods investigated were: the MR-informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR-informed Bowsher and Gaussian MR-guided MAP methods; and the PET-MR-informed hybrid kernel and anato-functional MAP methods. The trade-off between improving the reconstruction of the whole brain region and the PET-unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [18 F] fluorodeoxyglucose (FDG) three-dimensional datasets were used. The real [18 F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET-MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level.

RESULTS

For the high-count simulated and real data studies, the anato-functional MAP method performed better than the other methods under investigation (MR-informed, PET-MR-informed and postsmoothed MLEM), in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato-functional MAP method enables the reconstruction of PET-unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato-functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the other methods under investigation in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation.

CONCLUSION

For the reconstruction of noisy data, multiple MR-informed methods produce favorable whole brain vs PET-unique region trade-off in terms of the image quality metrics of SSIM and NRMSE, comfortably outperforming the whole image denoising of postsmoothed MLEM.

Enhancement of Partial Volume Correction in MR-Guided PET Image Reconstruction by Using MRI Voxel Sizes

Positron emission tomography (PET) suffers from poor spatial resolution which results in quantitative bias when evaluating the radiotracer uptake in small anatomical regions, such as the striatum in the brain which is of importance in this paper of neurodegenerative diseases. These partial volume effects need to be compensated for by employing partial volume correction (PVC) methods in order to achieve quantitatively accurate images. Two important PVC methods applied during the reconstruction are resolution modeling, which suffers from Gibbs artifacts, and penalized likelihood using anatomical priors. The introduction of clinical simultaneous PET-MR scanners has attracted new attention for the latter methods and brought new opportunities to use MRI information to assist PET image reconstruction in order to improve image quality. In this context, MR images are usually down-sampled to the PET resolution before being used in MR-guided PET reconstruction. However, the reconstruction of PET images using the MRI voxel size could achieve a better utilization of the high resolution anatomical information and improve the PVC obtained with these methods. In this paper, we evaluate the importance of the use of MRI voxel sizes when reconstructing PET images with MR-guided maximum a posteriori (MAP) methods, specifically the modified Bowsher method. We also propose a method to avoid the artifacts that arise when PET reconstructions are performed in a higher resolution matrix than the standard for a given scanner. The MR-guided MAP reconstructions were implemented with a modified Lange prior that included anatomical information with the Bowsher method. The methods were evaluated with and without resolution modeling for simulated and real brain data. We show that the use of the MRI voxel sizes when reconstructing PET images with MR-guided MAP enhances PVC by improving the contrast and reducing the bias in six different regions of the brain using regional metrics for a single simulated data set and ensemble metrics for ten noise realizations. Similar results were obtained for real data, where a good enhancement of the contrast was achieved. The combination of MR-guided MAP reconstruction with point-spread function modeling and MRI voxel sizes proved to be an attractive method to achieve considerable enhancement of PVC, while reducing and controlling the noise level and Gibbs artifacts.

Quantitative study of 18F-(+)DTBZ image: comparison of PET template-based and MRI based image analysis

[¹⁸F]9-fluoropropyl-(+)-dihydrotetrabenazine (¹⁸F-(+)DTBZ) is a recently developed PET tracer to investigate the vesicular monoamine transporter type 2 (VMAT2) activity in measuring dopaminergic degeneration in vivo and monitoring the severity of Parkinson’s disease (PD). However, manual drawing of the striatal regions is time consuming and prone to human bias. In the current study, we developed an automated method to quantify the signals of the striatum on ¹⁸F-(+)DTBZ images. 39 patients with PD and 26 controls were enrolled. Traditional brain magnetic resonance imaging (MRI) and ¹⁸F-(+)DTBZ PET were acquired. Both indirect normalization of native PET images to the standard space through individual brain MRI and directly coregistration of native images to the transporter-specific PET template in standard space were performed. Specific uptake ratios (SURs) in 10 predefined regions were used as indicators of VMAT2 activities to correlate with motor severity. Our results showed patients with PD had significant lower SURs in the bilateral putamina, caudates and globus pallidi than controls. SURs in the caudate and putamen were significantly correlated with motor severity. The contralateral putaminal region performed best in discriminating between PD patients and controls. Finally, the results from the application of the ¹⁸F-(+)DTBZ PET template were comparable to those derived from the traditional MRI based method. Thus, ¹⁸F-(+)DTBZ PET imaging holds the potential to effectively differentiate PD patients from controls. The ¹⁸F-(+)DTBZ PET template-based method for automated quantification of presynaptic VMAT2 transporter density is easier to implement and may facilitate efficient, robust and user-independent image analysis.

Fully Automated Quantification of the Striatal Uptake Ratio of [(99m)Tc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson's Disease and the Temporal Regression of Striatal Tracer Uptake

Purpose. We aimed at improving the existing methods for the fully automatic quantification of striatal uptake of [^99mTc]-TRODAT with SPECT imaging. Procedures. A normal [^99mTc]-TRODAT template was first formed based on 28 healthy controls. Images from PD patients (n = 365) and nPD subjects (28 healthy controls and 33 essential tremor patients) were spatially normalized to the normal template. We performed an inverse transform on the predefined striatal and reference volumes of interest (VOIs) and applied the transformed VOIs to the original image data to calculate the striatal-to-reference ratio (SRR). The diagnostic performance of the SRR was determined through receiver operating characteristic (ROC) analysis. Results. The SRR measured with our new and automatic method demonstrated excellent diagnostic performance with 92% sensitivity, 90% specificity, 92% accuracy, and an area under the curve (AUC) of 0.94. For the evaluation of the mean SRR and the clinical duration, a quadratic function fit the data with R² = 0.84. Conclusions. We developed and validated a fully automatic method for the quantification of the SRR in a large study sample. This method has an excellent diagnostic performance and exhibits a strong correlation between the mean SRR and the clinical duration in PD patients.

Comparison of PET template-based and MRI-based image processing in the quantitative analysis of C11-raclopride PET

Background

Quantitative measures of ¹¹C-raclopride receptor binding can be used as a correlate of postsynaptic D₂ receptor density in the striatum, allowing ¹¹C-raclopride positron emission tomography (PET) to be used for the differentiation of Parkinson’s disease from atypical parkinsonian syndromes. Comparison with reference values is recommended to establish a reliable diagnosis. A PET template specific to raclopride may facilitate direct computation of parametric maps without the need for an additional MR scan, aiding automated image analysis.

Methods

Sixteen healthy volunteers underwent a dynamic ¹¹C-raclopride PET and a high-resolution T1-weighted MR scan of the brain. PET data from eight healthy subjects was processed to generate a raclopride-specific PET template normalized to standard space. Subsequently, the data processing based on the PET template was validated against the standard magnetic resonance imaging (MRI)-based method in 8 healthy subjects and 20 patients with suspected parkinsonian syndrome. Semi-quantitative image analysis was performed in Montreal Neurological Institute (MNI) and in original image space (OIS) using VOIs derived from a probabilistic brain atlas previously validated by Hammers et al. (Hum Brain Mapp, 15:165–174, 2002).

Results

The striatal-to-cerebellar ratio (SCR) of ¹¹C-raclopride uptake obtained using the PET template was in good agreement with the MRI-based image processing method, yielding a Lin’s concordance coefficient of 0.87. Bland-Altman analysis showed that all measurements were within the ±1.96 standard deviation range. In all 20 patients, the PET template-based processing was successful and manual volume of interest optimization had no further impact on the diagnosis of PD in this patient group. A maximal difference of <5% was found between the measured SCR in MNI space and OIS.

Conclusions

The PET template-based method for automated quantification of postsynaptic D₂ receptor density is simple to implement and facilitates rapid, robust and reliable image analysis. There was no significant difference between the SCR values obtained with either PET- or MRI-based image processing. The method presented alleviates the clinical workflow and facilitates automated image analysis.

A simple algorithm for subregional striatal uptake analysis with partial volume correction in dopaminergic PET imaging

OBJECTIVE

In positron emission tomography (PET) of the dopaminergic system, quantitative measurements of nigrostriatal dopamine function are useful for differential diagnosis. A subregional analysis of striatal uptake enables the diagnostic performance to be more powerful. However, the partial volume effect (PVE) induces an underestimation of the true radioactivity concentration in small structures. This work proposes a simple algorithm for subregional analysis of striatal uptake with partial volume correction (PVC) in dopaminergic PET imaging.

METHODS

The PVC algorithm analyzes the separate striatal subregions and takes into account the PVE based on the recovery coefficient (RC). The RC is defined as the ratio of the PVE-uncorrected to PVE-corrected radioactivity concentration, and is derived from a combination of the traditional volume of interest (VOI) analysis and the large VOI technique. The clinical studies, comprising 11 patients with Parkinson's disease (PD) and 6 healthy subjects, were used to assess the impact of PVC on the quantitative measurements. Simulations on a numerical phantom that mimicked realistic healthy and neurodegenerative situations were used to evaluate the performance of the proposed PVC algorithm. In both the clinical and the simulation studies, the striatal-to-occipital ratio (SOR) values for the entire striatum and its subregions were calculated with and without PVC.

RESULTS

In the clinical studies, the SOR values in each structure (caudate, anterior putamen, posterior putamen, putamen, and striatum) were significantly higher by using PVC in contrast to those without. Among the PD patients, the SOR values in each structure and quantitative disease severity ratings were shown to be significantly related only when PVC was used. For the simulation studies, the average absolute percentage error of the SOR estimates before and after PVC were 22.74% and 1.54% in the healthy situation, respectively; those in the neurodegenerative situation were 20.69% and 2.51%, respectively.

CONCLUSIONS

We successfully implemented a simple algorithm for subregional analysis of striatal uptake with PVC in dopaminergic PET imaging. The PVC algorithm provides an accurate measure of the SOR in the entire striatum and its subregions, and improves the correlation between the SOR values and the clinical disease severity of PD patients.