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. 2021 Aug 27;90(5):711–719. doi: 10.1002/ana.26187

Single Photon Emission Computed Tomography/Positron Emission Tomography Molecular Imaging for Parkinsonism: A Fast‐Developing Field

Antoine Verger 1,2,, Stephan Grimaldi 3, Maria‐Joao Ribeiro 4, Solène Frismand 5, Eric Guedj 6,7,8
PMCID: PMC9291534  PMID: 34338333

Abstract

The early differential diagnosis of Parkinson disease and atypical parkinsonism is a major challenge. The use of single photon emission computed tomography (SPECT)/positron emission tomography (PET) molecular imaging to investigate parkinsonism is a fast‐developing field. Imaging biomarker research may potentially lead to more accurate disease detection, enabling earlier diagnosis and treatment. This review summarizes recent SPECT/PET advances in radiopharmaceuticals and imaging technologies/analyses that improve the diagnosis of neurodegenerative parkinsonism. We are currently witnessing a turning point in the field. Integrating molecular imaging as a diagnostic technique represents an opportunity to reassess the strategies for diagnosing neurodegenerative parkinsonism. ANN NEUROL 2021;90:711–719


The number of patients with neurodegenerative parkinsonism more than doubled between 1990 and 2015. It is projected to increase again by as much as 56% by 2030. It affects 1 in 120 people older than 45 years. 1 Neurodegenerative parkinsonism exhibits extensive clinical heterogeneity and comprises many different neurodegenerative diseases (Parkinson disease [PD], dementia with Lewy bodies [DLB], multiple system atrophy [MSA], corticobasal degeneration [CBD], progressive supranuclear palsy [PSP]). The differential diagnosis of parkinsonism during early disease still presents major challenges to clinicians.

The differential diagnosis can be narrowed down based on a combination of clinical and paraclinical features. Atypical parkinsonism is characterized by early onset (within the first 5 years) of symptoms that are unusual for PD. 2 In addition, response to dopaminergic treatment is reduced. 3 But it is only definitively diagnosed after postmortem pathological examination of the brain (gold standard for protein anomalies). The international diagnostic criteria for atypical parkinsonism are based on disease cohorts with postmortem brain data. The physician's diagnosis therefore relies on identifying patient features that correspond to the classification criteria. Compared to pathological examinations, this approach has a higher probability of diagnosing PD (>95% correlation) than atypical parkinsonism (approximately 70%). 4 There may also be some degree of variability between the progression of individual forms of atypical parkinsonism. However, these are relatively small compared to the differences in complications (falls, swallowing disorders, severity of dysautonomia) and survival periods between PD and atypical parkinsonism. Atypical parkinsonism requires more regular and multidisciplinary evaluations to prevent, detect, and treat complications compared to PD. The organization of health care therefore needs to be managed differently for these two conditions. Atypical parkinsonism patients have shorter median survival times (ranging from 6 to 7.4 years for PSP compared to 10.7 to >20 years for PD). 5 , 6 Although the multitude of neurodegenerative diseases that constitute parkinsonism are clinically similar, their underlying pathophysiological mechanisms are poorly understood. Molecular imaging that is routinely performed to assess neurodegenerative parkinsonism may provide additional diagnostic information and help clarify specific disease mechanisms (Fig 1).

FIGURE 1.

FIGURE 1

Flow diagram showing the rationale for performing single photon emission computed tomography (SPECT)/positron emission tomography (PET) molecular imaging in a patient with parkinsonism. FDG = fluorodeoxyglucose; MIBG = metaiodobenzylguanidine; MRI = magnetic resonance imaging.

Current Single Photon Emission Computed Tomography and Positron Emission Tomography Imaging in Routine Evaluation of Parkinsonism

Presynaptic Dopaminergic Imaging

The European Association of Nuclear Medicine and the Society of Nuclear Medicine and Molecular Imaging recently published guidelines for dopaminergic imaging of parkinsonism. 7 These guidelines predominantly focus on two commonly used nuclear medicine examinations of presynaptic dopaminergic function, namely, single photon emission computed tomography (SPECT) using 123I‐labeled dopamine transporter ligands and positron emission tomography (PET) using 18F‐fluorodopa (Fig 2A). These differentiate neurodegenerative parkinsonism from other dementias (particularly DLB from Alzheimer disease [AD]), from other forms of parkinsonism (drug‐induced, psychogenic, and vascular parkinsonism, whose diagnosis requires images to be fused with magnetic resonance imaging [MRI]) and from essential tremor. These scans can also detect early degenerative parkinsonism. 7 SPECT, with specific 123I‐labeled dopamine transporter ligands and 18F‐fluorodopa PET, detects different molecular dopaminergic targets. 123I‐labeled dopamine transporter ligands identify presynaptic dopamine active transporters (DATs). 18F‐fluorodopa PET visualizes L‐dopa, which is subsequently decarboxylated to dopamine by the aromatic L‐amino‐acid decarboxylase. Both types of dopaminergic imaging need to take into account radiotracer–drug interactions prior to performing acquisitions. Current guidelines recommend discontinuing antiparkinsonian drugs before 18F‐fluorodopa PET imaging. 7 DAT and L‐dopa targets decrease in parallel but not necessarily synchronously with progression of neurodegenerative parkinsonism. 8 This may be due to the presence of compensatory mechanisms in the presymptomatic and early symptomatic phases: a reduction in the number of presynaptic DATs, which increases the intrasynaptic availability of dopamine, and an upregulation of the fluorodopa to dopamine conversion by aromatic L‐amino acid decarboxylase in nerve terminals. 8 , 9 Clinical 18F‐fluorodopa PET studies focusing on the presymptomatic stage have confirmed the high sensitivity of the method. 10 Albeit performed on a relatively small number of patients, comparative study confirmed that SPECT and PET diagnose presynaptic dopaminergic deficiencies in early stage PD. 11 Fluorodeoxyglucose (18F‐FDG) PET and 123I‐metaiodobenzylguanidine (123I‐MIBG) scintigraphy are also routinely used to evaluate parkinsonism (see Fig 1). 12

FIGURE 2.

FIGURE 2

Representative single photon emission computed tomography (SPECT)/positron emission tomography (PET) images investigating parkinsonism from clinical routine practice radiotracers in normal subjects and patients with parkinsonism. (A) SPECT with 123I‐labeled dopamine transporter ligands (left panel) and 18F‐fluorodopa PET (right panel). (B) 18F‐FDG PET. (C) 123I‐metaiodobenzylguanidine scintigraphy. CBD = corticobasal degeneration; DLB = dementia with Lewy bodies; MSA = multiple system atrophy; PD = Parkinson disease; PSP = progressive supranuclear palsy.

18F‐FDG PET

Brain 18F‐FDG PET images glycolytic metabolism in the brain. It may identify significant levels of brain dysfunction more consistently than any potential brain atrophies detected by MRI. 13 18F‐FDG PET differentially diagnoses PD and parkinsonian “plus” syndromes at sensitivities and specificities greater than 75 and 90%. 14 This is particularly significant, because differential diagnoses based on dopaminergic imaging are somewhat limited for these entities. 15 18F‐FDG PET provides a number of disease‐specific metabolic patterns. DLB is characterized by the preservation of posterior cingulate metabolism (as opposed to posterior associative cortical hypometabolism in the occipital cortex). PSP is distinguished by prefrontal mesial, putamen, and pons hypometabolism. CBD displays asymmetrical frontoparietal hypometabolism. MSA exhibits a somewhat lesser extent of basal ganglia or cerebellum hypometabolism (see Fig 2B). 16

123I‐MIBG Scintigraphy

123I‐MIBG scintigraphy/SPECT imaging targets the peripheral sympathetic noradrenergic stores. It can help differentiate PD/DLB (decreased cardiac binding) from MSA and PSP (normal binding) based on their sympathetic myocardial innervations (see Fig 2C). 123I‐MIBG scintigraphy differentiates PD/DLB from cognitive decline and/or parkinsonism (AD, MSA, PSP, frontotemporal lobe and vascular dementia) with a specificity of 91% and sensitivity of 94%. 17 Advances in 18F‐fluorodopa PET heart scans 18 and PET MIBG radiotracer analogues 19 may further enhance these performances. Current parkinsonism SPECT/PET diagnoses rely on a combination of dopaminergic imaging, 18F‐FDG PET, and 123I‐MIBG scintigraphy. Imaging biomarker research has the potential to improve disease recognition and allow earlier diagnosis and treatment. 20 The development of in vivo biomarkers may be envisaged to follow the progress of personalized drug treatments. In vivo biomarkers may also become integrated in the earliest stages of parkinsonism rehabilitation. This review summarizes recent advances in radiopharmaceuticals and imaging technologies/analyses that improve the diagnosis of parkinsonism.

Advances in Radiopharmaceuticals

The new radiotracers developed for imaging of parkinsonism target either dopaminergic function or a pathological process.

New PET Radiotracers for Dopaminergic Imaging

New dopaminergic PET tracers target presynaptic DATs. 9 , 21 , 22 In addition to overcoming the technical performance limitations of SPECT, PET imaging of DATs with 18F‐FE‐PE2I (N‐[3‐iodoprop‐2E‐enyl]‐2β‐carbo‐18F‐fluoroethoxy‐3β‐[4‐methylphenyl]‐nortropane) allows routine performance of 10‐minute static acquisitions only 30 minutes after administration of radiotracer. This represents a definite advantage over DAT SPECT imaging. 23 Another dopaminergic PET radiotracer that targets DATs, the [18F] (2S,3S)‐methyl‐8‐([E]‐4‐fluorobut‐2‐en‐1‐yl)‐3‐(p‐tolyl)‐8‐azabicyclo(3.2.1)octane‐2‐carboxylate, namely, 18F‐LBT‐999, is being developed (Fig 3A). 24

FIGURE 3.

FIGURE 3

Representative positron emission tomography images investigating parkinsonism with new radiotracers. (A) 18F‐LBT‐999 targeting the dopamine transporters in a normal subject (left panel) and a Parkinson disease (PD) patient (right panel). (B) 18F‐Flortaucipir targeting the tau protein in a normal subject (left panel) and a tauopathy patient (Alzheimer disease, right panel). LBT = Laboratoire Biophysique de Tours.

Additional molecular dopaminergic pathway targets are also being developed. 18F‐FP‐DTBZ ([+]‐α‐9‐O‐[3‐18F‐fluoropropyl]‐dihydrotetrabenazine) is a PET radiotracer that specifically binds to VMAT2 (vesicular monoamine transporter type 2), an integral membrane protein that transports dopamine from the cytoplasm into synaptic vesicles. 25 This PET radiotracer is expected to suffer from the same limitations as 18F‐fluorodopa PET due to potential compensation phenomena at the early stages of PD. The iodobenzamide, 123I‐IBZM, is used for postsynaptic dopaminergic SPECT imaging and 11C‐raclopride, 18F‐fallypride, and 18F‐desmethoxyfallypride for PET imaging. 26 Postsynaptic dopaminergic imaging predominantly aims to differentiate PD from atypical parkinsonism. 26 Although radiotracers for this application are still in the research phase, one report already suggests that 18F‐FDG PET outperforms these postsynaptic dopaminergic radiotracers in terms of the differential diagnosis of PD/DLB and atypical parkinsonism. 15

Tau PET Imaging

The cognitive proteinopathy field 27 has predominantly focused on imaging targets involved in the neurodegenerative process. Tau PET imaging is undoubtedly the best choice for the differential diagnosis of PD/DLB and other forms of atypical parkinsonism (see Fig 3B). The atypical parkinsonism PSP and CBD are tauopathies. PSP patients exhibit increased 18F‐AV‐1451 signals in the pallidum, midbrain, dentate nucleus of the cerebellum, thalamus, caudate nucleus, and frontal regions compared to controls. 28 CBD patients have increased 18F‐THK5351 retention contralateral to the side with the greater cortical dysfunction. 29 These non‐AD tauopathy deposits consist mainly of 4R tau, located in subcortical nuclei. Because AD is associated with 3R and 4R deposits, there is substantial clinical and neuropathological overlap between these tauopathies. Given that many regions of interest in corticobasal syndrome (CBS) and PSP largely coincide with off‐target binding (eg, with monoamine oxidase B in the basal ganglia), there is substantial overlap of tracer binding load across the different diagnostic groups. This is particularly true for 18F‐AV‐1451, which has been tested in the largest number of patients. 30 In CBS and PSP, tau burden determined by neuropathological assessment and in vivo 18F‐AV‐1451 imaging yield inconsistent results. 18F‐AV‐1451 PET, for instance, identifies in vivo areas with high postmortem tau in some brain areas but not others. This presumably reflects a reduced sensitivity of the tracer to non‐AD tau. 31 , 32 In vitro staining and/or autoradiography studies have, however, highlighted that tracers of the THK family do bind to CBS and PSP tau deposits to some extent, which is consistent with in vivo findings. 33 A new generation of more specific 4R tau tracers with less off‐target binding is needed to improve the in vivo diagnosis of non‐AD tauopathies.

α‐Synuclein PET Imaging

Specific PET tracers targeting α‐synuclein aggregates have great potential for diagnosing movement disorders but are currently exclusively in the research phase. 34 The identification of any additional transporter targets that facilitate movement across the blood–brain barrier is also required. The recently identified small, high α‐synuclein affinity molecules are nevertheless not ideal lead compounds for PET radiotracer development. Although other different chemical entities that bind to α‐synuclein, such as anle138b (11C‐MODAG‐001), have been described, their selectivity for β‐amyloid and tau fibrils still needs to be resolved. 35

Neuroinflammation PET Imaging

There are a number of PET radiotracers that target neuroinflammation in parkinsonian disorders. 36 , 37 18F‐DPA‐714 and 18F‐GE‐180 are translocator protein (TSPO) ligands. TSPO is an outer mitochondrial membrane protein expressed in activated microglial cells and macrophages in the brain. It is a marker of nervous system inflammation. Increased 18F‐DPA‐714 and 18F‐GE‐180 radiotracer uptake has been reported in the pons, basal ganglia, frontal and temporal cortex, and midbrain of PD patients compared to normal controls. 36 Although PET neuroinflammation imaging assesses one aspect of neurodegenerative parkinsonian disorder neuropathology (ie, TSPO expression), it is unable to diagnose specific parkinsonian disorders. There is no consensus about the optimal method for analyzing PET data or for selecting an anatomically defined reference region. This is because TSPO is expressed constitutively in the brain and because neurodegenerative pathologies are perhaps more widespread than previously assumed. The recent identification of populations of high‐ and low‐affinity TSPO ligand binders has further complicated the issue. 37 The significance and potential clinical relevance of PET radiotracers targeting neuroinflammation requires further research.

Other innovative treatments that were initially identified in preclinical studies are currently being evaluated in a number of clinical trials. These include antibody‐based treatments (anti–α‐synuclein, antitau), iron chelators, and α‐synuclein and tau aggregation inhibitors. 38 These treatments could potentially influence the course of disease and improve patients' and caregivers' quality of life. It also highlights the importance of developing early PET brain neurodegeneration biomarkers that closely reflect the pathophysiology of these diseases, to rapidly identify patient profiles that would benefit from being included in these therapeutic trials. This would maximize the chances of offering patients a treatment that may potentially improve disease outcome. Figure 4 summarizes the pathophysiological mechanisms of SPECT/PET radiotracers used to assess neurodegenerative parkinsonism.

FIGURE 4.

FIGURE 4

Pathophysiological mechanisms of single photon emission computed tomography (SPECT)/positron emission tomography (PET) radiotracers in neurodegenerative parkinsonism. FDG = fluorodeoxyglucose; MIBG = metaiodobenzylguanidine.

Advances in Imaging Technology

There have been major technological advances in both SPECT and PET imaging that have facilitated the use of neurotransmission imaging in the practice. First and foremost is the widespread deployment of hybrid systems for morphofunctional imaging.

The strongest neuroimaging associations are achieved with MRI. This approach integrates two devices into one system, enabling sequential and simultaneous bimodal acquisitions to be performed. These hybrid PET/MRI systems are, however, costly and currently not widely available. Alternatively, and perhaps more relevant in terms of current clinical practice, the data can also be fused after registering acquisitions performed on separate devices. This combination of information allows identification of striatal lesions that may impact the uptake of the radiotracer and development of bimodal correlations based on morphofunctional information. 39

Large‐field cadmium–zinc–telluride (CZT) SPECT cameras, which provide high performances in terms of count sensitivity, and spatial and energy resolution, are now also used for brain imaging. 40 , 41 Up to 2‐fold shorter acquisition times, due to the lower number of counts required for 123I‐labeled dopamine transporter ligands SPECT imaging with large‐field CZT cameras, have been reported. 40 The 360° detector ring configuration of CZT cameras may further enhance count sensitivity and image quality by providing focus acquisitions. Brain perfusion SPECT with 360° CZT cameras has recently been shown to provide high image quality in high‐speed recording conditions. 41 These improvements allow short acquisitions of as little as 15 minutes, yield high image quality, and may challenge the cost‐effectiveness of dopaminergic PET imaging systems. Additional specific CZT technology advances, such as absolute quantifications and dual isotope acquisitions, may further improve SPECT imaging.

The concomitant evolution of PET systems and digital PET technologies now offers marked improvements in image signal/noise ratios and contrast, and has significant potential for further enhancing spatial resolution. 42 Digital PET systems using silicon photomultipliers improve time‐of‐flight resolution, which facilitates routine dynamic PET recording. 43 This is particularly helpful to compute 18F‐fluorodopa influx constants (Ki) from brain time–activity curves of dynamic PET acquisitions. One advantage of Ki over the conventional ratio approach is that it provides a more direct measure of the 18F‐fluorodopa decarboxylation rate constant. 44 These dynamic acquisitions are also expected to come online for the newer dopaminergic PET radiotracers such as 18F‐FE‐PE2I. 45 Further studies will be required to directly evaluate the additional value of such dynamic acquisitions over conventional static ratios for dopaminergic imaging of parkinsonism.

Advances in Image Analyses

Novel image analysis approaches may further enhance neurotransmission imaging of parkinsonism. Analyzing the metabolic connectivity of 18F‐FDG PET imaging in brain pathologies represents a significant shift from evaluating an underlying pathology of local neuronal function to improving the understanding of long‐distance effects on interconnected neural systems. 46 The 18F‐FDG metabolic connectivity is based on voxelwise or region of interest methods with or without any a priori assumptions about the specific neurodegenerative disease, including PD. 46 , 47 When applied to resting‐state 18F‐FDG PET scans of PD patients, this method identifies abnormal disease‐related spatial covariance patterns (PD‐related metabolic patterns [PDRPs]). PDRPs include numerous corticostriatopallidothalamocortical pathway components and are characterized by increased pallidal, thalamic, and pontine metabolic activity, coupled with relative reductions in the premotor cortex, supplemental motor area, and parietal association areas. 48 18F‐FDG PET and multivariate classifications using PDRP features are objective, sensitive biomarkers of disease stage that may potentially detect treatment effects during PD progression. 49 These disease‐related spatial covariance patterns are starting to be applied to atypical parkinsonism. 48 Prospective application of these models allowed determination of the probability of predicting PD, MSA, and PSP based on metabolic covariance patterns. The models were able to predict MSA with 90% specificity and an 85% positive predictive value. 50 PSP was predicted with 94% specificity and a 94% positive predictive value. 50 When a movement disorder specialist is not readily available, the application of such metabolic patterns improves the diagnostic accuracy by 10 to 15% for PD and 20% for atypical parkinsonism. 51 Compared to functional MRI (fMRI), metabolic 18F‐FDG PET patterns performed at the group level may provide specific advantages. The cerebral metabolic rate of glucose measured by 18F‐FDG PET is known to precede the relative transient decrease in blood oxygen level–dependent signals without any magnetic limitations. 52 Group‐level PET imaging is also expected to yield better signal‐to‐noise ratios, variance concentrations, and out‐of‐sample replications compared to single‐subject–level fMRI. This is due to the unrivaled sensitivity of PET imaging to measure concentrations in the subpicomolar range. The 18F‐FDG radiotracer is also more commonly used and static imaging acquisitions are more easily applicable than dynamic ones. 53 These analytical approaches have now also been applied to dopaminergic imaging 54 and have been further validated in patients with movement disorders. 55 , 56 Molecular connectivity modeling approaches may improve our understanding of neurodegenerative movement disorder pathogeneses and provide a comprehensive strategy to identify the pathological networks involved. A combination of early perfusion scans to characterize spatial covariance patterns in PD and late striatal neuroreceptor binding scans were recently performed using 18F‐FP‐CIT PET imaging. This original approach provides an alternative to 18F‐FDG PET for PD network quantification. This technique evaluates PDRP expression and DAT binding using a single tracer in one scanning session. 57

Any discussion of recent advances in image analyses would be incomplete without the due consideration of artificial intelligence (AI). Radiotracers for SPECT/PET molecular imaging need to provide highly reliable binding specificities to achieve good signal‐to‐noise ratios. All molecular images are a compromise between the number of counts detected for generating a signal and the noise of nonspecific binding counts. This is particularly true for the new radiotracers that provide limited binding specificity (tau PET imaging, α‐synuclein imaging, neuroinflammation imaging), at least until more selective radiochemicals are developed. AI‐based methods may improve diagnostic assessments in the meantime. Several dopaminergic imaging studies using AI have reported accuracy of up to 90% for diagnosing PD. These automated learning approaches involve machine learning methods, based on textural analyses, for (1) differentiating PD and healthy subjects, (2) differentiating PD and vascular parkinsonism, and (3) discriminating between the different forms of atypical parkinsonism. A 2‐center study using a linear support vector machine (SVM) model discriminated PD from healthy subjects with an accuracy of 82.5%. 58 This performance is similar to visual assessment by nuclear physicians. 58 A linear SVM model based on voxel values of statistical parametric images differentiated PD from vascular parkinsonism with 90.4% accuracy. 59 Both Nicastro et al and Iwabuchi et al were able to differentiate between the different forms of atypical parkinsonism by respectively using an SVM model or a classification and regression tree analysis. 60 , 61 Both approaches were based on FP‐CIT uptake in caudate and putamen volumes of interest. CBD was correctly diagnosed with accuracies of up to 83.7% and PSP with accuracies of up to 94.4%. PSP was found to be associated with MIBG scintigraphy uptake. A higher degree of striatal impairment was observed in MSA parkinsonian‐type patients and PSP. CBD patients showed a moderate reduction in uptake and a higher asymmetrical index. 60 DLB was associated with a lower level of impaired putamen to caudate ratio compared to the other forms of parkinsonism. 61 A fully automated artificial neural network based on deep‐learning analyses of dopaminergic imaging has also been shown to diagnose PD with accuracies of >90%. This is similar to accuracies obtained by experienced physicians. 62 Although these deep‐leaning approaches are better than those based on SVM algorithms in terms of diagnostic performance, they do not allow easy extraction of features used to classify patients with parkinsonism. Automated learning approaches also need further validation in clinical practice. This requires multicentric studies on well‐characterized clinical cohorts. Extensive multicentric databases, which include healthy subjects as well as patients, will be needed to collect enough data to accurately perform these types of analyses.

To conclude, SPECT and PET molecular imaging is currently at a turning point and is consolidating its position in the diagnostic strategy of parkinsonism. The development of novel targeted radiopharmaceuticals, the improvement in performance of technical imaging systems, and the rapid evolution of new image analysis approaches improve the diagnosis of neurodegenerative parkinsonism. These innovations may potentially lead to more accurate disease detection, enabling earlier diagnosis and treatment.

Author Contributions

A.V. and E.G. contributed to the conception and design of the review; S.G. and S.F. contributed to the interpretation of studies included in the review; A.V., M.‐J.R., and S.G. contributed to drafting the text and preparing the figures.

Potential Conflicts of Interest

E.G. consults for amyloid PET imaging in AD (General Electric Healthcare).

References

  • 1. Wanneveich M, Moisan F, Jacqmin‐Gadda H, et al. Projections of prevalence, lifetime risk, and life expectancy of Parkinson's disease (2010‐2030) in France: projections of PD in France. Mov Disord 2018;33:1449–1455. [DOI] [PubMed] [Google Scholar]
  • 2. Munhoz RP, Picillo M, Fox SH, et al. Eligibility criteria for deep brain stimulation in Parkinson's disease, tremor, and dystonia. Can J Neurol Sci 2016;43:462–471. [DOI] [PubMed] [Google Scholar]
  • 3. Balestrino R, Schapira AHV. Parkinson disease. Eur J Neurol 2020;27:27–42. [DOI] [PubMed] [Google Scholar]
  • 4. Hughes AJ, Daniel SE, Ben‐Shlomo Y, Lees AJ. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 2002;125:861–870. [DOI] [PubMed] [Google Scholar]
  • 5. Boxer AL, Yu J‐T, Golbe LI, et al. Advances in progressive supranuclear palsy: new diagnostic criteria, biomarkers, and therapeutic approaches. Lancet Neurol 2017;16:552–563. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Roos RAC, Jongen JCF, Van Der Velde EA. Clinical course of patients with idiopathic Parkinson's disease. Mov Disord 1996;11:236–242. [DOI] [PubMed] [Google Scholar]
  • 7. Morbelli S, Esposito G, Arbizu J, et al. EANM practice guideline/SNMMI procedure standard for dopaminergic imaging in Parkinsonian syndromes 1.0. Eur J Nucl Med Mol Imaging 2020;47:1885–1912. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Kaasinen V, Vahlberg T. Striatal dopamine in Parkinson disease: a meta‐analysis of imaging studies. Ann Neurol 2017;82:873–882. [DOI] [PubMed] [Google Scholar]
  • 9. Ribeiro M‐J, Vidailhet M, Loc'h C, et al. Dopaminergic function and dopamine transporter binding assessed with positron emission tomography in Parkinson disease. Arch Neurol 2002;59:580–586. [DOI] [PubMed] [Google Scholar]
  • 10. Sawle GV. The detection of preclinical Parkinson's disease: what is the role of positron emission tomography? Mov Disord 1993;8:271–277. [DOI] [PubMed] [Google Scholar]
  • 11. Eshuis SA, Maguire RP, Leenders KL, et al. Comparison of FP‐CIT SPECT with F‐DOPA PET in patients with de novo and advanced Parkinson's disease. Eur J Nucl Med Mol Imaging 2006;33:200–209. [DOI] [PubMed] [Google Scholar]
  • 12. Thobois S, Prange S, Scheiber C, Broussolle E. What a neurologist should know about PET and SPECT functional imaging for parkinsonism: a practical perspective. Parkinsonism Relat Disord 2019;59:93–100. [DOI] [PubMed] [Google Scholar]
  • 13. Albrecht F, Ballarini T, Neumann J, Schroeter ML. FDG‐PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: a whole‐brain multimodal imaging meta‐analysis. Neuroimage Clin 2019;21:101594. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Eckert T, Barnes A, Dhawan V, et al. FDG PET in the differential diagnosis of parkinsonian disorders. Neuroimage 2005;26:912–921. [DOI] [PubMed] [Google Scholar]
  • 15. Hellwig S, Amtage F, Kreft A, et al. [18F]FDG‐PET is superior to [123I]IBZM‐SPECT for the differential diagnosis of parkinsonism. Neurology 2012;79:1314–1322. [DOI] [PubMed] [Google Scholar]
  • 16. Walker Z, Gandolfo F, Orini S, et al. Clinical utility of FDG PET in Parkinson's disease and atypical parkinsonism associated with dementia. Eur J Nucl Med Mol Imaging 2018;45:1534–1545. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. King AE, Mintz J, Royall DR. Meta‐analysis of 123I‐MIBG cardiac scintigraphy for the diagnosis of Lewy body‐related disorders. Mov Disord 2011;26:1218–1224. [DOI] [PubMed] [Google Scholar]
  • 18. Kuten J, Linevitz A, Lerman H, et al. [18F] FDOPA PET may confirm the clinical diagnosis of Parkinson's disease by imaging the nigro‐striatal pathway and the sympathetic cardiac innervation: proof‐of‐concept study. J Integr Neurosci 2020;19:489–494. [DOI] [PubMed] [Google Scholar]
  • 19. Pandit‐Taskar N, Zanzonico P, Staton KD, et al. Biodistribution and dosimetry of 18F‐meta‐fluorobenzylguanidine: a first‐in‐human PET/CT imaging study of patients with neuroendocrine malignancies. J Nucl Med 2018;59:147–153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. McFarland NR. Diagnostic approach to atypical parkinsonian syndromes. Continuum (Minneap Minn) 2016;22(4 Movement Disorders):1117–1142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Jucaite A, Odano I, Olsson H, et al. Quantitative analyses of regional [11C]PE2I binding to the dopamine transporter in the human brain: a PET study. Eur J Nucl Med Mol Imaging 2006;33:657–668. [DOI] [PubMed] [Google Scholar]
  • 22. Sasaki T, Ito H, Kimura Y, et al. Quantification of dopamine transporter in human brain using PET with 18F‐FE‐PE2I. J Nucl Med 2012;53:1065–1073. [DOI] [PubMed] [Google Scholar]
  • 23. Jakobson Mo S, Axelsson J, Jonasson L, et al. Dopamine transporter imaging with [18F]FE‐PE2I PET and [123I]FP‐CIT SPECT—a clinical comparison. EJNMMI Res 2018;8:100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Ribeiro M‐J, Vercouillie J, Arlicot N, et al. Usefulness of PET with [18F]LBT‐999 for the evaluation of presynaptic dopaminergic neuronal loss in a clinical environment. Front Neurol 2020;11:754. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Lin K‐J, Weng Y‐H, Wey S‐P, et al. Whole‐body biodistribution and radiation dosimetry of 18F‐FP‐(+)‐DTBZ (18F‐AV‐133): a novel vesicular monoamine transporter 2 imaging agent. J Nucl Med 2010;51:1480–1485. [DOI] [PubMed] [Google Scholar]
  • 26. Van Laere K, Varrone A, Booij J, et al. EANM procedure guidelines for brain neurotransmission SPECT/PET using dopamine D2 receptor ligands, version 2. Eur J Nucl Med Mol Imaging 2010;37:434–442. [DOI] [PubMed] [Google Scholar]
  • 27. Allegri RF. Moving from neurodegenerative dementias, to cognitive proteinopathies, replacing “where” by “what”…. Dement Neuropsychol 2020;14:237–242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Whitwell JL, Lowe VJ, Tosakulwong N, et al. [18 F]AV‐1451 tau positron emission tomography in progressive supranuclear palsy. Mov Disord 2017;32:124–133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Kikuchi A, Okamura N, Hasegawa T, et al. In vivo visualization of tau deposits in corticobasal syndrome by 18 F‐THK5351 PET. Neurology 2016;87:2309–2316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Leuzy A, Chiotis K, Lemoine L, et al. Tau PET imaging in neurodegenerative tauopathies—still a challenge. Mol Psychiatry 2019;24:1112–1134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Josephs KA, Whitwell JL, Tacik P, et al. [18F]AV‐1451 tau‐PET uptake does correlate with quantitatively measured 4R‐tau burden in autopsy‐confirmed corticobasal degeneration. Acta Neuropathol 2016;132:931–933. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Marquié M, Normandin MD, Meltzer AC, et al. Pathological correlations of [F‐18]‐AV‐1451 imaging in non‐Alzheimer tauopathies. Ann Neurol 2017;81:117–128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Ishiki A, Harada R, Kai H, et al. Neuroimaging‐pathological correlations of [18F]THK5351 PET in progressive supranuclear palsy. Acta Neuropathol Commun 2018;6:53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Kuebler L, Buss S, Leonov A, et al. [11C]MODAG‐001‐towards a PET tracer targeting α‐synuclein aggregates. Eur J Nucl Med Mol Imaging 2020;48:1759–1772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Kotzbauer PT, Tu Z, Mach RH. Current status of the development of PET radiotracers for imaging alpha synuclein aggregates in Lewy bodies and Lewy neurites. Clin Transl Imaging 2017;5:3–14. [Google Scholar]
  • 36. Ouchi Y, Yoshikawa E, Sekine Y, et al. Microglial activation and dopamine terminal loss in early Parkinson's disease. Ann Neurol 2005;57:168–175. [DOI] [PubMed] [Google Scholar]
  • 37. Gerhard A, Trender‐Gerhard I, Turkheimer F, et al. In vivo imaging of microglial activation with [11C](R)‐PK11195 PET in progressive supranuclear palsy. Mov Disord 2006;21:89–93. [DOI] [PubMed] [Google Scholar]
  • 38. Silva M, Caro V, Guzmán C, et al. α‐Synuclein and tau, two targets for dementia. Studies in natural products chemistry, Amsterdam, Netherlands: Elsevier; 2021:1‐25. [Google Scholar]
  • 39. Rodriguez‐Rojas R, Pineda‐Pardo JA, Martinez‐Fernandez R, et al. Functional impact of subthalamotomy by magnetic resonance‐guided focused ultrasound in Parkinson's disease: a hybrid PET/MR study of resting‐state brain metabolism. Eur J Nucl Med Mol Imaging 2020;47:425–436. [DOI] [PubMed] [Google Scholar]
  • 40. Bani Sadr A, Testart N, Tylski P, Scheiber C. Reduced scan time in 123I‐FP‐CIT SPECT imaging using a large‐field cadmium‐zinc‐telluride camera. Clin Nucl Med 2019;44:568–569. [DOI] [PubMed] [Google Scholar]
  • 41. Bordonne M, Chawki MB, Marie P‐Y, et al. High‐quality brain perfusion SPECT images may be achieved with a high‐speed recording using 360° CZT camera. EJNMMI Phys 2020;7:65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Salvadori J, Imbert L, Perrin M, et al. Head‐to‐head comparison of image quality between brain 18F‐FDG images recorded with a fully digital versus a last‐generation analog PET camera. EJNMMI Res 2019;9:61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Salvadori J, Odille F, Karcher G, et al. Fully digital PET is unaffected by any deterioration in TOF resolution and TOF image quality in the wide range of routine PET count rates. EJNMMI Phys 2021;8:1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Takikawa S, Dhawan V, Chaly T, et al. Input functions for 6‐[fluorine‐18]fluorodopa quantitation in parkinsonism: comparative studies and clinical correlations. J Nucl Med 1994;35:955–963. [PubMed] [Google Scholar]
  • 45. Fazio P, Svenningsson P, Forsberg A, et al. Quantitative analysis of 18F‐(E)‐N‐(3‐Iodoprop‐2‐enyl)‐2β‐carbofluoroethoxy‐3β‐(4′‐methyl‐phenyl) nortropane binding to the dopamine transporter in Parkinson disease. J Nucl Med 2015;56:714–720. [DOI] [PubMed] [Google Scholar]
  • 46. Sala A, Perani D. Brain molecular connectivity in neurodegenerative diseases: recent advances and new perspectives using positron emission tomography. Front Neurosci 2019;13:617. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Spetsieris PG, Eidelberg D. Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson's disease. Neuroimage 2021;226:117568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Niethammer M, Eidelberg D. Network imaging in parkinsonian and other movement disorders: network dysfunction and clinical correlates. Int Rev Neurobiol 2019;144:143–184. [DOI] [PubMed] [Google Scholar]
  • 49. Matthews DC, Lerman H, Lukic A, et al. FDG PET Parkinson's disease‐related pattern as a biomarker for clinical trials in early stage disease. Neuroimage Clin 2018;20:572–579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Tripathi M, Tang CC, Feigin A, et al. Automated differential diagnosis of early parkinsonism using metabolic brain networks: a validation study. J Nucl Med 2016;57:60–66. [DOI] [PubMed] [Google Scholar]
  • 51. Rus T, Tomše P, Jensterle L, et al. Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated ‐ metabolic brain patterns' based approach. Eur J Nucl Med Mol Imaging 2020;47:2901–2910. [DOI] [PubMed] [Google Scholar]
  • 52. Magistretti PJ, Pellerin L. Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Philos Trans R Soc Lond B Biol Sci 1999;354:1155–1163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Verger A, Guedj E. The renaissance of functional 18F‐FDG PET brain activation imaging. Eur J Nucl Med Mol Imaging 2018;45:2338–2341. [DOI] [PubMed] [Google Scholar]
  • 54. Verger A, Horowitz T, Chawki MB, et al. From metabolic connectivity to molecular connectivity: application to dopaminergic pathways. Eur J Nucl Med Mol Imaging 2020;47:413–424. [DOI] [PubMed] [Google Scholar]
  • 55. Cervenka S, Varrone A, Fransén E, et al. PET studies of D2‐receptor binding in striatal and extrastriatal brain regions: biochemical support in vivo for separate dopaminergic systems in humans. Synapse 2010;64:478–485. [DOI] [PubMed] [Google Scholar]
  • 56. Caminiti SP, Presotto L, Baroncini D, et al. Axonal damage and loss of connectivity in nigrostriatal and mesolimbic dopamine pathways in early Parkinson's disease. Neuroimage Clin 2017;14:734–740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57. Peng S, Tang C, Schindlbeck K, et al. Dynamic 18 F‐FPCIT PET: quantification of Parkinson's disease metabolic networks and nigrostriatal dopaminergic dysfunction in a single imaging session. J Nucl Med, (online ahead of print). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58. Dotinga M, van Dijk JD, Vendel BN, et al. Clinical value of machine learning‐based interpretation of I‐123 FP‐CIT scans to detect Parkinson's disease: a two‐center study. Ann Nucl Med 2021;35:378–385. [DOI] [PubMed] [Google Scholar]
  • 59. Huertas‐Fernández I, García‐Gómez FJ, García‐Solís D, et al. Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [(123)I]FP‐CIT SPECT. Eur J Nucl Med Mol Imaging 2015;42:112–119. [DOI] [PubMed] [Google Scholar]
  • 60. Nicastro N, Wegrzyk J, Preti MG, et al. Classification of degenerative parkinsonism subtypes by support‐vector‐machine analysis and striatal 123I‐FP‐CIT indices. J Neurol 2019;266:1771–1781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61. Iwabuchi Y, Kameyama M, Matsusaka Y, et al. A diagnostic strategy for parkinsonian syndromes using quantitative indices of DAT SPECT and MIBG scintigraphy: an investigation using the classification and regression tree analysis. Eur J Nucl Med Mol Imaging 2021;48:1833–1841. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62. Choi H, Ha S, Im HJ, et al. Refining diagnosis of Parkinson's disease with deep learning‐based interpretation of dopamine transporter imaging. Neuroimage Clin 2017;16:586–594. [DOI] [PMC free article] [PubMed] [Google Scholar]

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