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Imaging modalities in differential diagnosis of Parkinson’s disease: opportunities and challenges



Parkinson’s disease (PD) diagnosis is yet largely based on the related clinical aspects. However, genetics, biomarkers, and neuroimaging studies have demonstrated a confirming role in the diagnosis, and future developments might be used in a pre-symptomatic phase of the disease.

Main text

This review provides an update on the current applications of neuroimaging modalities for PD diagnosis. A literature search was performed to find published studies that were involved on the application of different imaging modalities for PD diagnosis. An organized search of PubMed/MEDLINE, Embase, ProQuest, Scopus, Cochrane, and Google Scholar was performed based on MeSH keywords and suitable synonyms. Two researchers (TM and JPI) independently and separately performed the literature search. Our search strategy in each database was done by the following terms: ((Parkinson [Title/Abstract]) AND ((“Parkinsonian syndromes ”[Mesh]) OR Parkinsonism [Title/Abstract])) AND ((PET [Title/Abstract]) OR “SPECT”[Mesh]) OR ((Functional imaging, Transcranial sonography [Title/Abstract]) OR “Magnetic resonance spectroscopy ”[Mesh]). Database search had no limitation in time, and our last update of search was in February 2021. To have a comprehensive search and to find possible relevant articles, a manual search was conducted on the reference list of the articles and limited to those published in English.


Early diagnosis of PD could be vital for early management and adequate neuroprotection. Recent neuroimaging modalities such as SPECT and PET imaging using radiolabeled tracers, MRI, and CT are used to discover the disease. By the modalities, it is possible to early diagnose dopaminergic degeneration and also to differentiate PD from others parkinsonian syndromes, to monitor the natural progression of the disease and the effect of neuroprotective treatments on the progression. In this regard, functional imaging techniques have provided critical insights and roles on PD.


Parkinson’s disease (PD), as the second most popular neurodegenerative disturbance, is a chronic advanced neurodegenerative disorder that causes considerable disability and reduces quality of life also with a significant impact on costs to the healthcare system as well as society [1, 2]. Two major findings when observing the nervous system tissues of patients with PD include loss of neuronal cells as a result of death of dopamine-producing nerve cells as well as manifestation of Lewy bodies in the midbrain [3, 4].

PD diagnosis is quite challenging due to unavailability of biomarkers [5]. Yet, there is no conclusive indicative methodology for PD; hence, the analysis depends on the clinical manifestations of the diseases which is carried out by observing gradual movements (bradykinesia) with symptoms including resting tremor, muscle inflexibility, and postural flimsiness [6]. On the other hand, many symptoms of PD are also common within multiple system atrophy, progressive supra nuclear palsy, corticobasal degeneration, dementia with Lewy bodies, normal pressure hydrocephalus, and Alzheimer’s disease to a range of condition viewpoint, and this may induce missed or misdiagnosis of the disease [7, 8].

This review provides an update on the contribution of imaging modalities in PD diagnosis.

Medical imaging approaches in PD diagnosis

Computed tomography (CT)

CT is not the preferred diagnosis imaging modality for PD because of its limited soft tissue contrast compared to magnetic resonance imaging (MRI). However, this imaging modality can effectively illustrate the patterns of regional volume loss characteristic of multiple system atrophy (MSA), corticobasal degeneration (CBD), or progressive supranuclear palsy (PSP). Although PD diagnosis by CT is nonspecific, meanwhile, it is useful in ruling out focal or regional atrophy, hidden lesions, or vascular diseases. Typically, contrast media is not indicated to the diagnosis [9].

Magnetic resonance imaging

It was shown that raised magnetic field MRI promise to more accurately distinguish healthy subjects from PD patients and also allow improved spatial resolution and increased contrast which provides better visualization of basal ganglia contours and shapes [10, 11]. In comparison to normal controls, a reduction in both magnetization transfer ratio and functional anisotropy in the substantia nigra has also been reported by MRI [12].

Although high gray/white matter contrast is accessible with conventional 3D T1-weighted sequences for cortical and some basal ganglia constructions, there is poor contrast in many structures of interest in PD including substantia nigra (SN), subthalamic nucleus (STN), globus pallidus (GP), and red nucleus (RN) which contains high iron levels leading to shortened T1 and the reduced contrast [13]. Iron load is considered as an advantage in T2/T2*-weighted sequences so that provides an enhanced contrast due to the T2 shortening effects [14,15,16].

Iron load

Physiologically, brain tissue contains iron mostly stored in the form of ferritin. In the basal ganglia (globus pallidus (GP) > putamen > caudate), ferritin accumulates as a function of age in a linear manner. Quantitative susceptibility mapping (QSM) and also iron-sensitive MRI sequences (including SWI, 3D FLAIR, T2*, R2 and R2* relaxation) have been more and more used in PD investigation metabolism and iron content [14].

Increased iron load in PD and T2 relaxometry have shown reduced T2/T2* adiabatic T2ρ relaxation times and increased R2/R2* relaxation rates [15,16,17].

Magnetization transfer imaging (MTI)

MTI is a technique which trusts on the transmission of energy between highly bound protons and mobile protons. The MT measure is consequently associated with myelination and axonal density degree. MT rate can be quantitatively examined by MT imaging. Reduced magnetization transfer ratio (MTR) has been observed in the SN and basal ganglia (GP, the putamen, caudate nucleus) of PD patients [18, 19].

Perfusion imaging

Arterial spin-labeled (ASL) perfusion imaging approach by MRI has been recently presented for perfusion measurements as a noninvasive option in PD [20, 21]. Arterial spin labeling (ASL) as a quantitative and functional imaging method measures tissue perfusion using magnetically labeled protons with radiofrequency (RF) waves in arterial blood water content as an endogenous tracer. ASL is non-invasive and able to quantitatively measure tissue perfusion. Recent technical advances have increased its sensitivity and also extended the potential applications [22]. In PD, it was shown a reduced perfusion in the cortex and either conserved or reduced in the basal ganglia and conserved in the sensorimotor areas [20, 21]. Several studies on ASL-MRI have consistently shown symmetrical cortical hypoperfusion in PD involving predominantly the parieto-occipital areas and the dorsolateral prefrontal cortex [20]. In PD patients with dementia, posterior perfusion deficits were found to be more striking than without dementia [23].

In a study on both FDG-PET metabolism and ASL-MRI perfusion in PD, it was found overlapping metabolic and perfusion deficits [24]. ASL-MRI has the potential to identify PD early in the PD disease course when the patients show disease-specific metabolism patterns with FDG-PET characterized by relatively increased metabolism in the globus pallidus and putamen, thalamus, cerebellum, pons, and sensorimotor cortex and relative decreases in the lateral frontal and parieto-occipital areas [25,26,27].

Diffusion tensor imaging

Diffusion tensor imaging (DTI) is a MRI technique which is widely applied to diagnose several neurodegenerative diseases [28, 29]. DTI provides information about the orientation and integrity of white matter tracts in vivo with the aid of anisotropic water diffusion in white matter [30,31,32].

DTI evaluates the degree of directionality by means of anisotropy (frequently fractional anisotropy [FA]) and also the overall movement of molecules (mean diffusivity [MD]; trace; apparent diffusion coefficient [ADC]) as well. The measurements can either be extracted locally in predefined regions using region of interest (ROI) analysis or, alternatively, globally by voxel-based analysis (VBA) or tract-based spatial statistics (TBSS).

The most widely applied algorithm to extract related fiber data for processing DTI information is tractography [33]. In this procedure, the processed fiber data is evaluated with a connectivity analysis that lets observation of the whole brain as a complex linked network [34, 35]. Resting-state functional MRI (vs-fMRI) was used to observe defects in functional connectivity in the identical circuit [36].

Disruptions to microstructural tissue integrity, such as those seen in the neurodegeneration of parkinsonian syndromes could be affiliated with variations in anisotropy and diffusivity procedures [28, 29].

In spite of uncommon changes visible on conventional MRI imaging including narrowing or disappearance of the pars compacta of the substantia nigra (SN) on usual T2-weighted-imaging, this sign has low sensitivity and specificity and contributes marginally to the diagnosis of PD [37].

Susceptibility weighted imaging (SWI) is a new MRI technique that can be performed on conventional MR scanners with an imaging time comparable with or lower than that of other advanced sequences. The technique is hopeful in PD diagnosis by the improved sensitivity to detect brain mineralization. There is evidence that SWI images improve visualization of the SN due to the increased sensitivity to brain iron concentration and other metals [38]. Brain iron deposition has been proposed to play a key role in the pathogenesis of Parkinson disease (PD) [39].

The iron concentration is quantitatively different in PD and atypical parkinsonian disorders. Increased iron concentration and, more importantly, impaired iron handling are assumed to cause tissue damage via oxidative stress formation of free oxygen radicals [40].

SWI sequences were considered to have a stronger and more accurate correlation with brain iron load than R2 relaxation rate alone [38]. Studies have shown that this sequence is severely sensitive to mineralization and substances with magnetic susceptibility, hence, to be more sensitive than conventional gradient echo sequence in the detection of PD and parkinsonian syndromes [41, 42].

Diffusion-weighted MRI

Diffusion-weighted MRI (DWI) has been utilized in differentiation of PD and other parkinsonian syndromes using water apparent diffusion coefficients (ADC) [43]. ADC relies on both interactions between water molecules as well as the chemical environment and the structural barriers at cellular and subcellular level hindering their motion in vivo [44]. There are several reports on differentiation of MSA-P from PD whom demonstrated respectively high and normal putaminal ADC [40, 45, 46].

There have been a number of reports which showed that DWI method distinguished MSA-P in early stages with Parkinson’s disease and also healthy volunteers on the basis of increased putaminal ADC values which was also related with disease severity [47, 48].

In an investigation by Schocke et al. [47], an expanded diffusivity was additionally found in the caudate nucleus and globus pallidus in MSA-P in contrast with Parkinson’s disease patients and controls that could be reflecting the spreading neurodegeneration in the basal ganglia. Similar results were also obtained for PSP; however, MSA-P and PSP could not be isolated by DWI technique. A further report by Seppi et al. [49] on DWI in MSA with cerebellar feature patients (MSA-C) depicted an increment of the ADC in the pons, in the middle cerebellar peduncle, in the cerebellar white matter, and in the putamen.

In vivo magnetic resonance spectroscopy (MRS) is a further tool that could be used as supplementary to conventional MRI in characterization of the brain metabolism changes in patients with PD and, as an ideal imaging biomarker, has been found to meet plenty of criteria [50]. Indeed, MRS has good constancy (test-retest reliability) and, in comparison with PET and SPECT, is a non-invasive and inexpensive method [50]. Furthermore, in comparison with in vitro molecular imaging, MRS has been shown to be unrestricted to specialized centers for analysis. The metabolites recognizable with proton MRS incorporated the outstanding resonances of N-acetylaspartate (NAA), choline-containing mixes (Cho), creatine + phosphocreatine (Cr), myo-inositol (ml), lactate (Lac), and a variety of different resonances that probably would not be apparently relying upon type and nature of spectra just as on the pathological condition [51].

Studies have shown the efficacy of MRS for differentiating PD in the presence of other atypical parkinsonian disorders (APDs) [52, 53].

MRS has also been shown to be effective for delineating PD in early stage (which is even more difficult to distinguish because of overlapping syndromes of parkinsonism) [54].

Table 1 provides some clinical pointers and radiological features in parkinsonian syndromes by various imaging techniques [55].

Table 1 Clinical pointers and radiological features in parkinsonian syndromes by various imaging techniques [55]

The concentration changes of all metabolites identified by MRS could assist with assessing PD subjects with early motor symptoms, particularly in early differential diagnosis. Single-voxel proton magnetic resonance spectroscopy (1H-MRS) of striatal structures may discriminate PD from APDs by virtue of diminished NAA/Cr proportions in MSA but not in PD. In comparison with normal controls, in patients with PSP, CBD, and MSA, critical decrease of the NAA/Cr ratio in the frontal cortex was seen. Patients with CBD have indicated a significant decrease on the NAA/Cr ratio in the frontal cortex and putamen when contrasted with patients with PD and MSA [58]. On the other hand, patients with CBD have demonstrated clear asymmetry in the putamen when contrasted with controls and also in different patients [58].

Chougar et al. [59] figured out that patients with PSP and MSA-P had lower NAA concentrations in the pallidum, putamen, and lentiform nucleus contrasted with normal controls and patients with PD. However, different MRS reports have indicated diminished NAA/Cr and NAA/Cho proportions in the lentiform nucleus in APD, as well as in PD [60, 61].

Functional connectivity imaging

Functional magnetic resonance imaging (fMRI) was originally proposed for the detection of task-related signal changes in the brain, blood oxygen level dependent (BOLD), and in investigating functional connectivity in distant regions of the brain. Remote regions giving rise to distributed cortical and subcortical networks refer to temporal association of variations of the resting stage fMRI signal [62]. Via computational modeling, these networks are obtained from rsf-MRI data [63, 64]. Anatomical connectivity and resting state functional MRI (rsf-MRI) have been observed using tractography (Fig. 1) [65].

Fig. 1

MRI of nondegenerative “symptomatic” causes of parkinsonism. a Bilateral thalamic grade II glioma. Axial T2-weighted turbo spin echo MR image reveals two hyperintense mass lesions in the thalami. b Axial T2-weighted FLAIR MR image shows marked dilatation of the lateral ventricles exhibiting a ballooned shape and effacement of the cortical sulci. c Sagittal T1 spin echo MR image confirms the dilatation of the lateral ventricle and reveals dilatation of the cerebral aqueduct and fourth ventricle. Note the bowing of the corpus callosum and the effacement of the cortical sulci. Both cases in b and c images have normal pressure hydrocephalus. d Creutzfeldt-Jakob disease. Axial T2-weighted MR image shows symmetric hyperintensity of the putamen and head of caudate. e Axial T1-weighted spin echo and coronal T1-weighted gradient echo. f MR images show marked symmetric hyperintensity of the globus pallidus, putamen, and caudate [63]

Rs-fMRI in PD diagnosis studies have detected irregular functional regional interactions in resting brain networks [66, 67], and hence, it was concluded that PD relates to the variations in cerebral connectivity between the basal ganglia, cortex, or cerebellum [66], and between the STN, cortical motor, and premotor areas [67]. The variations have been observed in the sensorimotor circuit associated with a reduction in functional coupling [68, 69]. Abnormal functional connectivity was also demonstrated in the default-mode network in cognitively unimpaired PD patients which may be associated with cognitive performances in memory and visuospatial tests [70]. The supplementary motor area presents a decreased signal fluctuation in drug-naïve PD patients who were not previously exposed to the therapy or treatment, while an improved functional connectivity was detected in this area with levodopa and in specific frequency band variations [71]. The variations in resting state BOLD fluctuations were successful in estimating the presence of PD, and variations in functional connectivity were distinctively related with symptoms of PD [72]. To predict motor performance, increased amplitude of low-frequency BOLD signal oscillations method was used in the premotor cortex [73]. In PSP, rsf-MRI presented connectivity disruptions among the dorsal midbrain tegmentum and the cerebellum, diencephalon, basal ganglia, and cortex which were related to more critical functional impairment [73, 74], either it was shown an interruption between thalamus and striatum, supplementary motor area, and cerebellum [75]. In general, results with fMRI in resting state refer that dopamine depletion in PD produces remapping of cerebral connectivity which influences predominantly on sensorimotor circuit and sensorimotor integration that was affected by levodopa and differently related to motor and nonmotor symptoms [74].

Positron emission tomography (PET)

PET is a powerful and multipurpose imaging modality which allows in vivo examination of brain processes. It has made valuable contribution to neuroscience research by giving functional information as well as quantitative data of cerebral blood flow, metabolism, and receptor binding. However, its application in clinical neuroscience is confined compared to oncology due to the high costs and need for tremendous supporting facilities such as in site cyclotron, PET scanner, and radiochemical laboratories.

PET provides an impartial in vivo quantification of local radiotracer activity with a very proper sensitivity [23]. It can be used to observe cerebral blood flow and energy metabolism which are based on radiotracers [24].

An accuracy of 90% may be the highest value that can be expected with clinical assessment using current diagnostic criteria of PD [76]. Definitive diagnosis is only by demonstration of intraneuronal Lewy body inclusions in the substantia nigra compacta. PET reports have attracted an enormous recognition due to its ability to detect disease long before beginning of the symptoms [40].

18F-deoxy-glucose (18FDG) PET/CT brain

Despite the increasing use of FDG-PET/CT in clinics and research, its use in PD has been limited due to low spatial resolution and image quality, and high cost. FDG-PET is useful for differentiating PSP from idiopathic PD [77]. In PD-related cognitive decline, 18FDG-PET has a typical template of hypometabolism chiefly affecting the posterior cortical regions [77].

18FDG PET investigations can provide a measure of resting glucose metabolism and thus neuronal activity [78]. Increased glucose metabolism has been shown in the contralateral lentiform nucleus in patients with early unilateral parkinsonism [79]. Covariance investigation areas in PD patients has demonstrated hypermetabolism in the lentiform nucleus and thalamus with hypometabolism in the frontal, parietal, and parieto-occipital (Fig. 2) [56, 79].

Fig. 2

The administration of 18FDG-PET for the diagnosis of Parkinson’s and degenerative parkinsonism disorders. Consequences of the between-group statistical parametric mapping analysis for each diagnostic category shown in the vertical axis as follows: Parkinson’s disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD), vs. healthy controls. The red and yellow colors highlighted regions superimposed on normal T1-weighted MR templates showing areas with significantly reduced FDG uptake relative to the global mean [78]

Most PET 18F-deoxy-glucose (FDG) studies have shown ordinary striatal metabolism in PD, thereby suitable in differentiating PD from other parkinsonian syndromes including progressive supranuclear palsy (PSP) or multiple system atrophy (MSA) [80, 81]. By the technique, metabolic irregularities have been observed in a specific network in PD by investigating regional metabolic covariance patterns [82].

18F-Dopa PET studies

Khamis et al. [83] have shown that 18F-Dopa PET is a valuable tool for identifying reduction of dopaminergic activity in PD patients at a very primary phase. But the uptake might be upregulated in early stage of the disease while expression of DATs might be downregulated.

18F-6-fluoro-l-dopa radiotracer uptake study indicates the dopaminergic nerve density; moreover, it expresses activity of the aromatic amino acid decarboxylase enzyme (AADC) converting dopa into dopamine and the storage of dopamine [84]. This radiotracer allows assessment of presynaptic dopaminergic system viability in the nigrostriatal as well as mesolimbic and mesocortical dopaminergic pathways. In PD, a major decline in striatal 18F-Dopa uptake is usually detected, thereby indicating degeneration of dopaminergic nigrostriatal pathways [85]. The uptake reduction is well correlated with neuronal degeneration as demonstrated in pathological studies by Kroth et al. [86]. Although at the early stages of the disease, false negative cases have been observed as a result of compensatory upregulation of AADC in preserved dopaminergic terminals [86]. The situation has been differed using dopamine transporter ligands such as Br-FECBT, due to the fact that dopamine transporter activity is not regulated as dopa decarboxylase [87]. Ribeiro et al. [88] reported higher sensitivity of DAT imaging compared to 18F-Dopa in detecting dopaminergic degeneration especially in early-stage PD. At advanced stages, the upregulation diminishes. The reduction in striatal 18F-Dopa uptake is not homogeneous in the striatum, and a clear anteroposterior gradient is observed when the caudate is being less affected than the anterior putamen and the anterior putamen less affected than the posterior putamen. Hence, 18F-Dopa PET aids positive diagnosis of parkinsonian syndromes even at its presymptomatic stages [89].

18F-Dopa PET has shown that clinical expression of PD symptoms happen when about 50% of dopamine terminal function is harmed in the posterior putamen [90].

In a typical patient with unilateral parkinsonism, 18F-Dopa could reveal bilaterally decreased putamen dopaminergic function with activity being the most depressed in the putamen contralateral to the affected limb/limbs [73]. Dopamine terminal dysfunction in the asymptomatic relatives of PD patients can be detected by PET [91].

18F-Dopa was used to study 32 members of unrelated familial kindred of which 8 showed reduced uptake in the putamen. Interestingly, 3 out of those 8 ones developed clinical parkinsonism in a 5-year follow-up period [92].

Single photon emission tomography (SPECT)

The combined pre- and postsynaptic as well as clinical criteria using SPECT imaging method could improve the diagnosis of Parkinson’s disease in early stage [93]. The fusion of presynaptic DAT and postsynaptic D2 receptor binding has shown improved diagnostic value in ruling out patients with non-idiopathic parkinsonian syndromes from PD patients [94].

Dopamine transporter scan (DaT scan)

In this test, a radiolabeled tracer, e.g., 123I-ioflupane, is injected into a patient’s veins, circulates around the body, and gets into the brain. When DAT and dopaminergic neurons reduce in PD and other pre-synaptic parkinsonism diseases, SPECT imaging should take place several hours after the tracer has been administrated. In PD, there is a smaller signal in striatum section of the brain where the ends of the dopamine neurons are meant to be [95, 96]. Indeed, the expression of this protein may reflect the functional dopaminergic neuronal density in striatum part, and its decrease in PD is presumed to be in proportion with severity of the illness (Fig. 3) [57, 97, 98].

Fig. 3

DaT scan SPECT images from four patients. Image (a) showing normal “comma” configuration on the striata bilaterally, with a score of 0. Mild progressive loss of dopamine transporters depicted on the right (arrow) (b) score of 1, moderate on the left (arrow) on image (c) score of 2 and severe on the left (arrow) on image (d) score of 3 [96]

DaTscan have no reliable results in the diseases with loss of dopaminergic nerve cells and the resultant decrease in striatal dopamine levels. So, Parkinson-plus syndromes, such as progressive supranuclear palsy (PSP), corticobasal ganglionic degeneration (CBGD), and multiple system atrophy (MSA), cannot be discriminate with DaT scan method, and hence, the mentioned cases are typically demonstrated abnormal [76].

Although DaT scan is not able to distinguish between Parkinson’s disease, PSP, CBGD, and MSA from PD, but there are several reports that confirm the ability of the method to distinguish PD from drug-induced parkinsonism and vascular parkinsonism [99].

123I-ioflupane SPECT imaging

123I-ioflupane-SPECT is an important diagnostic modality for differentiating parkinsonian syndromes (PD, MSA, PSP, CBD) from ET and drug-induced parkinsonism, indicating early stage of the disease in comparison to anatomical modalities including conventional CT or MRI [100]. This approach provides valuable diagnosis based on local binding of presynaptic dopamine transporters (DaTs) with 123I-ioflupane, which has been reported to have high association with progression of PD [101]. The striatum has been the focal point of most investigations making use of 123I-ioflupane-SPECT. It has been shown that PD has significantly reduced dopamine transporter levels in the striatum [100].


123I-fluopane-CIT, an analog of 123I-β-CIT as radiotracer in SPECT imaging, was widely used to investigate the presynaptic dopaminergic system in early diagnosis of parkinsonism and differential diagnosis of PD from ET (Fig. 4) [102, 103].

Fig. 4

Dopamine transporter imaging by administration of 123I-fluopane (FP)-CIT in a patient with Parkinson’s disease (a), an essential tremor (b), and a healthy control (c) [104]

It was found that the accuracy of diagnosis was the same in both clinical exam and using DaT scan [95]. DaT scan is claimed to have enough sensitivity to discriminate changes in the nigrostriatal dopaminergic system of normal controls as well as PD patients [104].

123I-MIBG scintigraphy

Iodine-123 metaiodobenzylguanidine (123I-MIBG) scintigraphy is a noninvasive and secure diagnostic strategy to recognize and assess sympathetic denervation (Fig. 5) [102]. It has reported a diminished uptake of 123I-MIBG in myocardial sympathetic neurons in PD, demonstrating an impaired postganglionic sympathetic innervation in this disorder [105]. The main problem concerning 123I-MIBG/SPECT is its low specificity (37.4%) in spite of its moderately high sensitivity (87.7%) [102].

Fig. 5

The administration of iodine-123 metaiodobenzylguanidine (123I-MIBG) and its uptake in Parkinson’s disease (PD) (left) and multiple system atrophy (MSA) (right). Normal heart uptake in the MSA patient is seen. The image shows extensive liver uptake in both patients [104]


TRODAT-1, a 99mTc-labeled tropane derivative, is a cocaine analogous that can attach to the dopamine transporter (DAT) sites at presynaptic neuron membrane which is beneficial as a potential CNS dopamine transporter imaging agent and can be labeled with 99mTc without difficulty in most nuclear medicine departments all over the world [106].

Some related researchers have discerned that DAT imaging with 99m Tc-TRODAT-1 SPECT has high sensitivity particularity in differentiating PD from ET [107]. PD follows a particular pattern on DAT SPECT demonstrating more decreasing of 99mTc-TRODAT-1 binding on the contralateral to the symptomatic side and a reduction slope in tracer binding with greater/more reduction in putamen as compared to caudate nucleus (Fig. 6) [108]. 99mTc-TRODAT-1 has advantages of easy accessibility of 99mTc, lower cost, optimal energy for imaging, and faster pharmacokinetics, permitting image embodiment within a few hours [109].

Fig. 6

SPECT imaging using 99mTc-TRODAT-1 in normal control (a) and patient with early PD (b). In the case of patient with early PD in comparison to control, images show reduced TRODAT-1 uptake in the striatum [108]

Some of radiotracers used for SPECT imaging in PD diagnosis are illustrated in Table 2.

Table 2 Some radiotracers used for Parkinson’s disease SPECT


There are several diagnostic imaging approaches for screening of PD including PET, SPECT, MRI, and CT. Emission tomography (SPECT or PET) has an important role in PD diagnosis; however, it tends to be costly, with restricted accessibility, and needs radioactive tracers. It seems that the advanced MRI techniques as DWI, DTI, MRS, MTI, and magnetic resonance imaging-based volumetry are more delicate in separating PD from atypical parkinsonian.

Availability of data and materials

Not applicable.




18FDG :


99m Tc-TRODAT-1:

99mTc-labeled tropane derivative


Aromatic amino acid decarboxylase enzyme


Arterial spin labeled


Blood oxygen level dependent


Corticobasal degeneration


Corticobasal ganglionic degeneration


Computed tomography

DaT scan:

Dopamine transporter scan


Globus pallidus


Magnetic resonance imaging


Multiple system atrophy


Magnetization transfer


Parkinson’s disease


Positron emission tomography


Progressive supranuclear palsy


Quantitative susceptibility mapping




Red nucleus


Resting state functional magnetic resonance imaging


Substantia nigra


Single photon emission computed tomography


Subthalamic nucleus


  1. 1.

    Chrischilles EA, Rubenstein LM, Voelker MD, Wallace RB, Rodnitzky RL (1998) The health burdens of Parkinson’s disease. Mov Disord 13(3):406–413.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Tinelli M, Kanavos P, Grimaccia F (2016) The value of early diagnosis and treatment in Parkinson’s disease: a literature review of the potential clinical and socioeconomic impact of targeting unmet needs in Parkinson’s disease. The London School of Economics and Political Science, London

    Google Scholar 

  3. 3.

    Yasuda T, Nakata Y, Mochizuki H (2013) α-Synuclein and neuronal cell death. Mol Neurobiol 47(2):466–483.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Obeso JA, Rodriguez-Oroz MC, Rodriguez M, Lanciego JL, Artieda J, Gonzalo N, Olanow CW (2000) Pathophysiology of the basal ganglia in Parkinson’s disease. Trends Neurosci 23(10 Suppl):S8–S19.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Breen DP, Michell AW, Barker RA (2011) Parkinson’s disease–the continuing search for biomarkers. Clin Chem Lab Med 49(3):393–401.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Pahwa R, Lyons KE (2010) Early diagnosis of Parkinson’s disease: recommendations from diagnostic clinical guidelines. Am J Manag Care 16:94–99

    Google Scholar 

  7. 7.

    Karlsen KH, Tandberg E, Årsland D, Larsen JP (2000) Health related quality of life in Parkinson’s disease: a prospective longitudinal study. J Neurol Neurosurg Psychiatry 69(5):584–589.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Massano J, Bhatia KP (2012) Clinical approach to Parkinson’s disease: features, diagnosis, and principles of management. Cold Spring Harb Perspect Med 2:a008870

    Article  Google Scholar 

  9. 9.

    Wippold FJ II, Brown DC, Broderick DF, Burns J, Corey AS, Deshmukh TK, Douglas AC, Holloway K, Jagadeesan BD, Jurgens JS, Kennedy TA, Patel ND, Perlmutter JS, Rosenow JM, Slavin K, Subramaniam RM (2015) ACR appropriateness criteria dementia and movement disorders. J Am Coll Radiol 12(1):19–28.

    Article  PubMed  Google Scholar 

  10. 10.

    Cosottini M, Frosini D, Pesaresi I, Costagli M, Biagi L, Ceravolo R, Bonuccelli U, Tosetti M (2014) MR imaging of the substantia nigra at 7 T enables diagnosis of Parkinson disease. Radiology 271(3):831–838.

    Article  PubMed  Google Scholar 

  11. 11.

    Oikawa H, Sasaki M, Tamakawa Y, Ehara S, Tohyama K (2002) The substantia nigra in Parkinson disease: proton density-weighted spin-echo and fast short inversion time inversion-recovery MR findings. Am J Neuroradiol 23(10):1747–1756

    PubMed  Google Scholar 

  12. 12.

    Anik Y, Iseri P, Demirci A, Komsuoglu S, Inan N (2007) Magnetization transfer ratio in early period of Parkinson disease. Acad Radiol 14(2):189–192.

    Article  PubMed  Google Scholar 

  13. 13.

    Hardy PA, Gash D, Yokel R, Andersen A, Ai Y, Zhang Z (2005) Correlation of R2 with total iron concentration in the brains of rhesus monkeys. J Magn Reson Imaging 21(2):118–127.

    Article  PubMed  Google Scholar 

  14. 14.

    Langkammer C, Pirpamer L, Seiler S, Deistung A, Schweser F, Franthal S, Homayoon N, Katschnig-Winter P, Koegl-Wallner M, Pendl T, Stoegerer EM, Wenzel K, Fazekas F, Ropele S, Reichenbach JR, Schmidt R, Schwingenschuh P (2016) Quantitative susceptibility mapping in Parkinson’s disease. Plos One 11(9):e0162460.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Wang Y, Butros SR, Shuai X, Dai Y, Chen C, Liu M, Haacke EM, Hu J, Xu H (2012) Different iron-deposition patterns of multiple system atrophy with predominant parkinsonism and idiopathetic Parkinson diseases demonstrated by phase-corrected susceptibility-weighted imaging. Am J Neuroradiol 33(2):266–273.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Heim B, Krismer F, De Marzi R, Seppi K (2017) Magnetic resonance imaging for the diagnosis of Parkinson’s disease. J Neural Transm 124(8):915–964.

    Article  PubMed  Google Scholar 

  17. 17.

    Zhang J, Zhang Y, Wang J, Cai P, Luo C, Qian Z, Dai Y, Feng H (2010) Characterizing iron deposition in Parkinson’s disease using susceptibility-weighted imaging: an in vivo MR study. Brain Res 1330:124–130.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Tambasco N, Belcastro V, Sarchielli P, Floridi P, Pierguidi L, Menichetti C, Castrioto A, Chiarini P, Parnetti L, Eusebi P, Calabresi P, Rossi A (2011) A magnetization transfer study of mild and advanced Parkinson’s disease. Eur J Neurol 18(3):471–477.

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Eckert T, Sailer M, Kaufmann J, Schrader C, Peschel T, Bodammer N, Heinze HJ, Schoenfeld MA (2004) Differentiation of idiopathic Parkinson’s disease, multiple system atrophy, progressive supranuclear palsy, and healthy controls using magnetization transfer imaging. Neuroimage 21(1):229–235.

    Article  PubMed  Google Scholar 

  20. 20.

    Fernández-Seara MA, Mengual E, Vidorreta M, Aznárez-Sanado M, Loayza FR, Villagra F, Irigoyen J, Pastor MA (2012) Cortical hypoperfusion in Parkinson’s disease assessed using arterial spin labeled perfusion MRI. Neuroimage 59(3):2743–2750.

    Article  PubMed  Google Scholar 

  21. 21.

    Melzer TR, Watts R, MacAskill MR, Pearson JF, Rüeger S, Pitcher TL et al (2011) Arterial spin labelling reveals an abnormal cerebral perfusion pattern in Parkinson’s disease. Brain 134(3):845–855.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Heim B, Krismer F, De Marzi R, Seppi K (2017) Magnetic resonance imaging for the diagnosis of Parkinson’s disease. J Neural Transm 124(8):915–964

    Article  Google Scholar 

  23. 23.

    Holtbernd F, Eidelberg D (2014) The utility of neuroimaging in the differential diagnosis of parkinsonian syndromes. Semin Neurol 34(2):202–209.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Teune LK, Renken RJ, de Jong BM, Willemsen AT, van Osch MJ, Roerdink JB, Dierckx RA, Leenders KL (2014) Parkinson’s disease-related perfusion and glucose metabolic brain patterns identified with PCASL-MRI and FDG-PET imaging. Neuroimage Clin 5:240–244.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Detre JA, Rao H, Wang DJ, Chen YF, Wang Z (2012) Applications of arterial spin labeled MRI in the brain. J Magn Reson Imaging 35(5):1026–1037.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Fernández-Seara MA, Mengual E, Vidorreta M, Aznárez-Sanado M, Loayza FR, Villagra F, Irigoyen J, Pastor MA (2012) Cortical hypoperfusion in Parkinson’s disease assessed using arterial spin labeled perfusion MRI. Neuroimage 59(3):2743–2750

    Article  Google Scholar 

  27. 27.

    Madhyastha TM, Askren MK, Boord P, Zhang J, Leverenz JB, Grabowski TJ (2015) Cerebral perfusion and cortical thickness indicate cortical involvement in mild Parkinson’s disease. Mov Disord 30(14):1893–1900.

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Yin X, Liu M, Liao Y, Jin S, Li Q, Zhu C, Hou X, Yang D, Chu X (2020) DTI for Parkinson’s disease: a protocol for systematic review and meta-analysis. Res Square.

  29. 29.

    Deng XY, Wang L, Yang TT, Li R, Yu G (2018) A meta-analysis of diffusion tensor imaging of substantia nigra in patients with Parkinson’s disease. Sci Rep 8(1):1–8

    Google Scholar 

  30. 30.

    Vaillancourt D, Spraker M, Prodoehl J, Abraham I, Corcos D, Zhou X et al (2009) High-resolution diffusion tensor imaging in the substantia nigra of de novo Parkinson disease. Neurology 72:1378–84, 16.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Medina D, deToledo-Morrell L, Urresta F, Gabrieli JD, Moseley M, Fleischman D et al (2006) White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging 27(5):663–672.

    Article  PubMed  Google Scholar 

  32. 32.

    Pavuluri MN, Yang S, Kamineni K, Passarotti AM, Srinivasan G, Harral EM, Sweeney JA, Zhou XJ (2009) Diffusion tensor imaging study of white matter fiber tracts in pediatric bipolar disorder and attention-deficit/hyperactivity disorder. Biol Psychiatry 65(7):586–593.

    Article  PubMed  Google Scholar 

  33. 33.

    Shih MC, Hoexter MQ, LAFd A, Bressan RA (2006) Parkinson’s disease and dopamine transporter neuroimaging: a critical review. São Paulo Med J 124(3):168–175.

    Article  PubMed  Google Scholar 

  34. 34.

    Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A (2000) In vivo fiber tractography using DT-MRI data. Magn Reson Med 44(4):625–632.<625::AID-MRM17>3.0.CO;2-O

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Greicius MD, Supekar K, Menon V, Dougherty RF (2009) Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex 19(1):72–78.

    Article  PubMed  Google Scholar 

  36. 36.

    Lv H, Wang Z, Tong E, Williams LM, Zaharchuk G, Zeineh M, Goldstein-Piekarski AN, Ball TM, Liao C, Wintermark M (2018) Resting-state functional MRI: everything that nonexperts have always wanted to know. AJNR Am J Neuroradiol 39(8):1390–1399

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Péran P, Cherubini A, Assogna F, Piras F, Quattrocchi C, Peppe A et al (2010) Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain 133:3423–3433

    Article  Google Scholar 

  38. 38.

    Halefoglu AM, Yousem DM (2018) Susceptibility weighted imaging: clinical applications and future directions. World J Radiol 10(4):30–45.

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Haller S, Badoud S, Nguyen D, Barnaure I, Montandon ML, Lovblad KO, Burkhard PR (2013) Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): initial results. Eur Radiol 23(1):12–19

    CAS  Article  Google Scholar 

  40. 40.

    Saeed U, Compagnone J, Aviv RI, Strafella AP, Black SE, Lang AE, Masellis M (2017) Imaging biomarkers in Parkinson’s disease and parkinsonian syndromes: current and emerging concepts. Transl Neurodegener 6(1):1–25

    Article  Google Scholar 

  41. 41.

    Kathuria H, Mehta S, Ahuja CK, Chakravarty K, Ray S, Mittal BR, Singh P, Lal V (2021) Utility of imaging of nigrosome-1 on 3T MRI and its comparison with 18F-DOPA PET in the diagnosis of idiopathic Parkinson disease and atypical parkinsonism. Mov Disord Clin Pract 8(2):224–230.

    Article  PubMed  Google Scholar 

  42. 42.

    Bae YJ, Song YS, Choi BS, Kim JM, Nam Y, Kim JH (2021) Comparison of susceptibility-weighted imaging and susceptibility map-weighted imaging for the diagnosis of Parkinsonism with nigral hyperintensity. Eur J Radiol 134:109398.

    Article  PubMed  Google Scholar 

  43. 43.

    Chelban V, Bocchetta M, Hassanein S, Haridy NA, Houlden H, Rohrer JD (2019) An update on advances in magnetic resonance imaging of multiple system atrophy. J Neurol 266(4):1036–1045.

    Article  PubMed  Google Scholar 

  44. 44.

    Chougar L, Pyatigorskaya N, Degos B, Grabli D, Lehéricy S (2020) The role of magnetic resonance imaging for the diagnosis of atypical parkinsonism. Front Neurol 11:665.

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Bajaj S, Krismer F, Palma JA, Wenning GK, Kaufmann H, Poewe W, Seppi K (2017) Diffusion-weighted MRI distinguishes Parkinson disease from the parkinsonian variant of multiple system atrophy: a systematic review and meta-analysis. Plos One 12(12):e0189897

    Article  Google Scholar 

  46. 46.

    Rozhkova Z, Shkliar M (2012) H MRS and DWI for differentiation of Parkinson’s disease (PD) from parkinsonian syndromes (PS). European Congress of Radiology. B-032:1-22.

  47. 47.

    Schocke MF, Seppi K, Esterhammer R, Kremser C, Mair KJ, Czermak BV, Jaschke W, Poewe W, Wenning GK (2004) Trace of diffusion tensor differentiates the Parkinson variant of multiple system atrophy and Parkinson’s disease. Neuroimage 21(4):1443–1451.

    Article  PubMed  Google Scholar 

  48. 48.

    Seppi K, Schocke MF, Donnemiller E, Esterhammer R, Kremser C, Scherfler C, Diem A, Jaschke W, Wenning GK, Poewe W (2004) Comparison of diffusion-weighted imaging and [123I] IBZM-SPECT for the differentiation of patients with the Parkinson variant of multiple system atrophy from those with Parkinson’s disease. Mov Disord 19(12):1438–1445

    Article  Google Scholar 

  49. 49.

    Seppi K, Poewe W (2010) Brain magnetic resonance imaging techniques in the diagnosis of parkinsonian syndromes. Neuroimaging Clin 20(1):29–55

    Article  Google Scholar 

  50. 50.

    Murman DL (2012) Early treatment of Parkinson’s disease: opportunities for managed care. Am J Manag Care 18(7 Suppl):S183–S188

    PubMed  Google Scholar 

  51. 51.

    Nicoletti G, Tonon C, Lodi R, Condino F, Manners D, Malucelli E, Morelli M, Novellino F, Paglionico S, Lanza P, Messina D (2008) Apparent diffusion coefficient of the superior cerebellar peduncle differentiates progressive supranuclear palsy from Parkinson’s disease. Mov Disord 23(16):2370–2376.

    Article  PubMed  Google Scholar 

  52. 52.

    Barbagallo G, Arabia G, Morelli M, Nisticò R, Novellino F, Salsone M, Rocca F, Quattrone A, Caracciolo M, Sabatini U, Cherubini A (2017) Thalamic neurometabolic alterations in tremulous Parkinson’s disease: a preliminary proton MR spectroscopy study. Parkinsonism Relat Disord 43:78–84.

    Article  PubMed  Google Scholar 

  53. 53.

    UK CE (2017) Parkinson’s disease diagnosis. In Parkinson’s disease in adults: diagnosis and management. National Institute for Health and Care Excellence.UK.

  54. 54.

    Zanigni S, Testa C, Calandra-Buonaura G, Sambati L, Guarino M, Gabellini A, Evangelisti S, Cortelli P, Lodi R, Tonon C (2015) The contribution of cerebellar proton magnetic resonance spectroscopy in the differential diagnosis among parkinsonian syndromes. Parkinsonism Relat Disord 21(8):929–937.

    Article  PubMed  Google Scholar 

  55. 55.

    Ali K, Morris HR (2015) Parkinson’s disease: chameleons and mimics. Pract Neurol 15(1):14–25.

    Article  PubMed  Google Scholar 

  56. 56.

    Teune LK, Bartels AL, de Jong BM, Willemsen AT, Eshuis SA, de Vries JJ et al (2010) Typical cerebral metabolic patterns in neurodegenerative brain diseases. Mov Disord 25(14):2395–2404.

    Article  PubMed  Google Scholar 

  57. 57.

    Calle S, Dawood L, Tripathee NR, Cai C, Kaur H, Wan DQ, Ibekwe H, Gayed IW (2019) Identification of patterns of abnormalities seen on DaTscan™ SPECT imaging in patients with non-Parkinson’s movement disorders. Rep Med Imaging 12:9–15.

    Article  Google Scholar 

  58. 58.

    Marino S, Ciurleo R, Di Lorenzo G, Barresi M, De Salvo S, Giacoppo S, Bramanti A, Lanzafame P, Bramanti P (2012) Magnetic resonance imaging markers for early diagnosis of Parkinson’s disease. Neural Regen Res 7(8):611–619.

    Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Chougar L, Pyatigorskaya N, Degos B, Grabli D, Lehéricy S (2020) The role of magnetic resonance imaging for the diagnosis of atypical parkinsonism. Front Neurol 11.

  60. 60.

    Ciurleo R, Di Lorenzo G, Bramanti P, Marino S (2014) Magnetic resonance spectroscopy: an in vivo molecular imaging biomarker for Parkinson’s disease? Biomed Res Int 2014:519816

    Article  Google Scholar 

  61. 61.

    Firbank MJ, Harrison RM, O’Brien JT (2002) A comprehensive review of proton magnetic resonance spectroscopy studies in dementia and, Parkinson’s disease. Dement Geriatr Cogn Disord 14(2):64–76.

    CAS  Article  PubMed  Google Scholar 

  62. 62.

    Cai J, Wang Y, Liu A, McKeown MJ, Wang ZJ (2020) Novel regional activity representation with constrained canonical correlation analysis for brain connectivity network estimation. IEEE Trans Med Imaging 39(7):2363–2373.

    Article  PubMed  Google Scholar 

  63. 63.

    Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198.

    CAS  Article  PubMed  Google Scholar 

  64. 64.

    Ramnani N, Behrens TE, Penny W, Matthews PM (2004) New approaches for exploring anatomical and functional connectivity in the human brain. Biol Psychiatry 56(9):613–619.

    Article  PubMed  Google Scholar 

  65. 65.

    Mascalchi M, Vella A, Ceravolo R (2012) Movement disorders: role of imaging in diagnosis. J Magn Reson Imaging 35(2):239–256.

    Article  PubMed  Google Scholar 

  66. 66.

    Helmich RC, Derikx LC, Bakker M, Scheeringa R, Bloem BR, Toni I (2010) Spatial remapping of cortico-striatal connectivity in Parkinson’s disease. Cereb Cortex 20(5):1175–1186.

    Article  PubMed  Google Scholar 

  67. 67.

    Hacker CD, Perlmutter JS, Criswell SR, Ances BM, Snyder AZ (2012) Resting state functional connectivity of the striatum in Parkinson’s disease. Brain 135(12):3699–3711.

    Article  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Baudrexel S, Witte T, Seifried C, von Wegner F, Beissner F, Klein JC, Steinmetz H, Deichmann R, Roeper J, Hilker R (2011) Resting state fMRI reveals increased subthalamic nucleus–motor cortex connectivity in Parkinson’s disease. Neuroimage 55(4):1728–1738.

    Article  Google Scholar 

  69. 69.

    Sharman M, Valabregue R, Perlbarg V, Marrakchi-Kacem L, Vidailhet M, Benali H, Brice A, Lehéricy S (2013) Parkinson’s disease patients show reduced cortical-subcortical sensorimotor connectivity. Mov Disord 28(4):447–454.

    CAS  Article  PubMed  Google Scholar 

  70. 70.

    Tessitore A, Esposito F, Vitale C, Santangelo G, Amboni M, Russo A, Corbo D, Cirillo G, Barone P, Tedeschi G (2012) Default-mode network connectivity in cognitively unimpaired patients with Parkinson disease. Neurology 79(23):2226–2232.

    Article  Google Scholar 

  71. 71.

    Esposito F, Tessitore A, Giordano A, De Micco R, Paccone A, Conforti R et al (2013) Rhythm-specific modulation of the sensorimotor network in drug-naive patients with Parkinson’s disease by levodopa. Brain 136(3):710–725.

    Article  PubMed  Google Scholar 

  72. 72.

    Long D, Wang J, Xuan M, Gu Q, Xu X, Kong D, Zhang M (2012) Automatic classification of early Parkinson’s disease with multi-modal MR imaging. PloS one 7(11):e47714.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Skidmore F, Yang M, Baxter L, Von Deneen K, Collingwood J, He G et al (2013) Apathy, depression, and motor symptoms have distinct and separable resting activity patterns in idiopathic Parkinson disease. Neuroimage 81:484–495.

    CAS  Article  PubMed  Google Scholar 

  74. 74.

    Gardner RC, Boxer AL, Trujillo A, Mirsky JB, Guo CC, Gennatas ED, Heuer HW, Fine E, Zhou J, Kramer JH, Miller BL, Seeley WW (2013) Intrinsic connectivity network disruption in progressive supranuclear palsy. Ann Neurol 73(5):603–616.

    Article  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Whitwell JL, Avula R, Master A, Vemuri P, Senjem ML, Jones DT, Jack CR Jr, Josephs KA (2011) Disrupted thalamocortical connectivity in PSP: a resting-state fMRI, DTI, and VBM study. Parkinsonism Relat Disord 17(8):599–605.

    Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Hughes AJ, Daniel SE, Lees AJ (2001) Improved accuracy of clinical diagnosis of Lewy body Parkinson’s disease. Neurology 57(8):1497–1499.

    CAS  Article  PubMed  Google Scholar 

  77. 77.

    Walker Z, Gandolfo F, Orini S, Garibotto V, Agosta F, Arbizu J et al (2018) Clinical utility of FDG PET in Parkinson’s disease and atypical parkinsonism associated with dementia. Eur J Nucl Med Mol Imaging 45:1534–1545

    CAS  Article  Google Scholar 

  78. 78.

    Meles SK, Teune LK, de Jong BM, Dierckx RA, Leenders KL (2017) Metabolic imaging in Parkinson disease. J Nucl Med 58(1):23–28.

    CAS  Article  PubMed  Google Scholar 

  79. 79.

    Brooks DJ (2010) Imaging approaches to Parkinson disease. J Nucl Med 51(4):596–609.

    CAS  Article  PubMed  Google Scholar 

  80. 80.

    Baizabal-Carvallo JF, Jankovic J (2012) Movement disorders in autoimmune diseases. Mov Disord 27(8):935–946.

    CAS  Article  PubMed  Google Scholar 

  81. 81.

    Kaasinen V, Kankare T, Joutsa J, Vahlberg T (2019) Presynaptic striatal dopaminergic function in atypical parkinsonism: a meta-analysis of imaging studies. J Nucl Med 60(12):1757–1763.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Boonstra JT, Michielse S, Temel Y, Hoogland G, Jahanshahi A (2020) Neuroimaging detectable differences between Parkinson’s disease motor subtypes: a systematic review. Mov Disord Clin Pract 8(2):175-192.

  83. 83.

    Khamis K, Giladi N, Levine C, Kesler M, Kuten J, Lerman H, Even-Sapir E (2019) The added value of 18F-FDOPA PET/CT in the work-up of patients with movement disorders. Neurotrophic 9(5):344–348.

    Article  Google Scholar 

  84. 84.

    Heiss WD, Hilker R (2004) The sensitivity of 18-fluorodopa positron emission tomography and magnetic resonance imaging in Parkinson’s disease. Eur J Neurol 11(1):5–12.

    Article  PubMed  Google Scholar 

  85. 85.

    Moore RY, Whone AL, Brooks DJ (2008) Extrastriatal monoamine neuron function in Parkinson’s disease: an 18F-dopa PET study. Neurobiol Dis 29(3):381–390.

    CAS  Article  PubMed  Google Scholar 

  86. 86.

    Kroth H, Oden F, Molette J, Schieferstein H, Capotosti F, Mueller A, Berndt M, Schmitt-Willich H, Darmency V, Gabellieri E, Boudou C (2019) Discovery and preclinical characterization of [18 F] PI-2620, a next-generation tau PET tracer for the assessment of tau pathology in Alzheimer’s disease and other tauopathies. Eur J Nucl Med Mol Imaging 46(10):2178–2189.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Ribeiro M-J, Vidailhet M, Loc'h C, Dupel C, Nguyen JP, Ponchant M, Dollé F, Peschanski M, Hantraye P, Cesaro P, Samson Y, Remy P (2002) Dopaminergic function and dopamine transporter binding assessed with positron emission tomography in Parkinson disease. Arch Neurol 59(4):580–586.

    Article  PubMed  Google Scholar 

  88. 88.

    Lee CS, Samii A, Sossi V, Ruth TJ, Schulzer M, Holden JE, Wudel J, Pal PK, de la Fuente-Fernandez R, Calne DB, Stoessl AJ (2000) In vivo positron emission tomographic evidence for compensatory changes in presynaptic dopaminergic nerve terminals in Parkinson’s disease. Ann Neurol 47(4):493–503.<493::AID-ANA13>3.0.CO;2-4

    CAS  Article  PubMed  Google Scholar 

  89. 89.

    Thobois S, Jahanshahi M, Pinto S, Frackowiak R, Limousin-Dowsey P (2004) PET and SPECT functional imaging studies in Parkinsonian syndromes: from the lesion to its consequences. Neuroimage 23(1):1–16.

    CAS  Article  PubMed  Google Scholar 

  90. 90.

    Brooks DJ (2010) Imaging approaches to Parkinson disease. J Nucl Med 51(4):596–609.

    CAS  Article  PubMed  Google Scholar 

  91. 91.

    Emamzadeh FN, Surguchov A (2018) Parkinson’s disease: biomarkers, treatment, and risk factors. Front Neurosci 12:612.

    Article  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Piccini P, Morrish P, Turjanski N, Sawle G, Burn D, Weeks R et al (1997) Dopaminergic function in familial Parkinson’s disease: a clinical and 18F-dopa positron emission tomography study. Ann Neurol 41(2):222–229.

    CAS  Article  PubMed  Google Scholar 

  93. 93.

    Pahuja G, Nagabhushan TN, Prasad B (2019) Early detection of Parkinson’s disease by using SPECT imaging and biomarkers. J Intell Syst 29(1):1329–1344

    Article  Google Scholar 

  94. 94.

    Koch W, Hamann C, Radau PE, Tatsch K (2007) Does combined imaging of the pre- and postsynaptic dopaminergic system increase the diagnostic accuracy in the differential diagnosis of parkinsonism? Eur J Nucl Med Mol imaging 34(8):1265–1273.

    Article  PubMed  Google Scholar 

  95. 95.

    Antonini A, Benti R, De Notaris R, Tesei S, Zecchinelli A, Sacilotto G et al (2003) 123I-Ioflupane/SPECT binding to striatal dopamine transporter (DAT) uptake in patients with Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy. Neurol Sci 24(3):149–150.

    CAS  Article  PubMed  Google Scholar 

  96. 96.

    Booij J, Knol RJ (2007) SPECT imaging of the dopaminergic system in (premotor) Parkinson’s disease. Parkinsonism Relat Disord 13:S425–S4S8.

    Article  PubMed  Google Scholar 

  97. 97.

    Helms G, Draganski B, Frackowiak R, Ashburner J, Weiskopf N (2009) Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps. Neuroimage 47(1):194–198.

  98. 98.

    Huang W-S, Lee M-S, Lin J-C, Chen C-Y, Yang Y-W, Lin S-Z, Wey SP (2004) Usefulness of brain 99m Tc-TRODAT-1 SPET for the evaluation of Parkinson’s disease. Eur J Nucl Med Mol Imaging 31(2):155–161.

    Article  PubMed  Google Scholar 

  99. 99.

    Thanvi B, Treadwell S (2009) Drug induced parkinsonism: a common cause of parkinsonism in older people. Postgrad Med J 85(1004):322–326.

    CAS  Article  PubMed  Google Scholar 

  100. 100.

    Broski SM, Hunt CH, Johnson GB, Morreale RF, Lowe VJ, Peller PJ (2014) Structural and functional imaging in parkinsonian syndromes. Radiographics 34(5):1273–1292.

    Article  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Booth T, Nathan M, Waldman A, Quigley A-M, Schapira A, Buscombe J (2015) The role of functional dopamine-transporter SPECT imaging in parkinsonian syndromes, part 1. Am J Neuroradiol 36(2):229–235.

    CAS  Article  PubMed  Google Scholar 

  102. 102.

    Berardelli A, Wenning G, Antonini A, Berg D, Bloem B, Bonifati V et al (2013) EFNS/MDS-ES recommendations for the diagnosis of Parkinson’s disease. Eur J Neurol 20:16–34, EFNS/MDS-ES recommendations for the diagnosis of Parkinson's disease, 1.

    CAS  Article  PubMed  Google Scholar 

  103. 103.

    Kuya K, Ogawa T, Shinohara Y, Ishibashi M, Fujii S, Mukuda N, Tanabe Y (2018) Evaluation of Parkinson’s disease by neuromelanin-sensitive magnetic resonance imaging and 123I-FP-CIT SPECT. Acta Radiologica 59(5):593–598.

    Article  PubMed  Google Scholar 

  104. 104.

    Tolosa E, Borght TV, Moreno E, Group DCUPSS (2007) Accuracy of DaTSCAN (123I-ioflupane) SPECT in diagnosis of patients with clinically uncertain parkinsonism: 2-year follow-up of an open-label study. Mov Disord 22:2346–2351

    Article  Google Scholar 

  105. 105.

    Hamada K, Hirayama M, Watanabe H, Kobayashi R, Ito H, Ieda T, Koike Y, Sobue G (2003) Onset age and severity of motor impairment are associated with reduction of myocardial 123I-MIBG uptake in Parkinson’s disease. J Neurol Neurosurg Psychiatry 74(4):423–426.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Wu CH, Yang BH, Chou YH, Wang SJ, Chen JC (2018) Effects of 99mTc-TRODAT-1 drug template on image quantitative analysis. Plos one 13(3):e0194503.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Wang L, Zhang Q, Li H, Zhang H (2012) SPECT molecular imaging in Parkinson's disease. BioMed Res Int 2012:412486.

  108. 108.

    Chou K, Hurtig H, Stern M, Colcher A, Ravina B, Newberg A et al (2004) Diagnostic accuracy of [99mTc] TRODAT-1 SPECT imaging in early Parkinson’s disease. Parkinsonism Relat Disord 10(6):375–379.

    CAS  Article  PubMed  Google Scholar 

  109. 109.

    Patel A, Simon S, Elangoven IM, Amalchandran J, Jain AS (2019) Dopamine transporter maging with Tc-99m TRODAT-1 SPECT in Parkinson’s isease and its orrelation with linical isease everity. Asia Ocean J Nucl Med Biol 7:22

    PubMed  PubMed Central  Google Scholar 

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This review study has been produced from a research under the title “improvement of basal ganglia detectability in NCAT phantom brain SPECT by wavelet transformation in image processing domain”, funded by the deputy of Research of Tabriz University of Medical Sciences, Tabriz, Iran (Grant #62741) under the research ethics certificate ID: IR.TBZMED.VCR.REC.1398.207.

The funder had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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TM: literature search, data collection, manuscript drafting. HS: data interpretation, revised the manuscript. BM: data collection, data interpretation, revised the manuscript. JPI: study design, data interpretation, revised the manuscript. All the authors have read and approved the manuscript.

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Correspondence to Jalil Pirayesh Islamian.

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Mortezazadeh, T., Seyedarabi, H., Mahmoudian, B. et al. Imaging modalities in differential diagnosis of Parkinson’s disease: opportunities and challenges. Egypt J Radiol Nucl Med 52, 79 (2021).

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  • PET
  • MRI
  • Diagnostic imaging
  • Parkinson’s disease