Molecular Imaging in CANVAS: A Contribution for Differential Diagnosis?

BACKGROUND

Phenotypes of CANVAS are increasingly diversified, including bradykinesia and dysautonomia, so that its primary differential diagnoses are multiple system atrophy-cerebellar type (MSA-c), and spinocerebellar ataxia type 3 (SCA3). This case series aims to highlight key molecular imaging findings in CANVAS.

CASES

We report a case series of six patients with CANVAS who underwent nuclear medicine examinations in our center and 13 patients from the literature. These include 18F-FDG brain positron emission tomography (PET), single photon emission computed tomography (SPECT) of dopamine transporter (DaT) activity, and 123I-MIBG cardiac scintigraphy of noradrenergic transmission.

CONCLUSIONS

In CANVAS, 18F-FDG brain PET mainly shows cerebellar hypometabolism, with preserved brainstem and striatum metabolism, contrasting with SCA3 and MSA-c. Dopaminergic denervation on scintigraphy seems to be associated with clinical parkinsonism, ranging from normal to severely impaired DaT SPECT. Additionally, 123I-MIBG cardiac scintigraphy might show denervation in CANVAS, similar to SCA3, but not in most MSA-c patients.

Dopamine denervation in the functional territories of the striatum: a new MR and atlas-based 123I-FP-CIT SPECT quantification method

Current quantification methods of ¹²³I-FP-CIT SPECT rely on anatomical parcellation of the striatum. We propose here to implement a new method based on MRI segmentation and functional atlas of the basal ganglia (MR-ATLAS) that could provide a reliable quantification within the sensorimotor, associative, and limbic territories of the striatum. Patients with Parkinson's disease (PD), idiopathic rapid eye movement sleep behavioral disorder (iRBD), and healthy controls underwent ¹²³I-FP-CIT SPECT, MRI, motor, and cognitive assessments. SPECT data were corrected for partial volume effects and registered to a functional atlas of the striatum to allow quantification in every functional region of the striatum (nucleus accumbens, limbic, associative, and sensorimotor parts of the striatum). The MR-ATLAS quantification method is proved to be reliable in every territory of the striatum. In addition, good correlations were found between cognitive dysexecutive tests and the binding within the functional (limbic) territories of the striatum using the MR-ATLAS method, slightly better than correlations found using the anatomical quantification method. This new MR-ATLAS method provides a robust and useful tool for studying the dopaminergic system in PD, particularly with respect to cognitive functions. It may also be relevant to further unravel the relationship between dopaminergic denervation and cognitive or behavioral symptoms.

Validation of an automated morphological MRI-based (123)I-FP-CIT SPECT evaluation method

INTRODUCTION

Dopamine transporter imaging with (123)I-FP-CIT single photon emission computed tomography (SPECT) is helpful for the differential diagnosis between Parkinsonian syndrome (PS) and essential tremor (ET). Although visual assessment and time-consuming manual evaluation techniques are readily available, a fully objective and automated dopamine transporter quantification technique is always preferable, at least in research and follow-up investigations. Our aim was to develop a novel automated magnetic resonance imaging (MRI)-based evaluation technique of dopamine transporter SPECT images and to compare its diagnostic accuracy with those of the gold-standard visual grading and manual dopamine transporter binding quantification methods.

METHODS

(123)I-FP-CIT SPECT and MRI sessions were conducted in 33 patients with PS (15 men; mean age: 60.3 ± 9.7 years) and 15 patients with ET (8 men; mean age: 54.7 ± 16.3 years). Striatal dopamine transporter binding was visually classified by 2 independent experts as normal or abnormal grade I, II and III. Caudal and putaminal specific uptake ratios were calculated by both automated MRI-based and manual evaluation techniques.

RESULTS

We found almost perfect agreement (κ = 0.829) between the visual scores by the 2 observers. The automated method showed strong correlation with the visual and manual evaluation techniques and its diagnostic accuracy (sensitivity = 97.0%; specificity = 93.3%) was also comparable to these methods. The automatically determined uptake parameters showed negative correlation with the clinical severity of parkinsonism. Based on ordinal regression modelling, the automated MRI-based method could reliably determine the visual grading scores.

CONCLUSION

The novel MRI-based evaluation of (123)I-FP-CIT SPECT images is useful for the differentiation of PS from ET.