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Investigating early-stage parkinsonism using a dedicated MRI protocol and decision tree
Samy Abo Seada1, Anke W. van der Eerden1, Agnita J.W. Boon2, and Juan Antonio Hernandez-Tamames1,3
1Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands, 2Department of Neurology, Erasmus MC, Rotterdam, Netherlands, 3Department of Imaging Physics, TU Delft, Delft, Netherlands

Synopsis

Keywords: Parkinson's Disease, Multi-Contrast

A dedicated MRI protocol and decision tree are presented for differentially diagnosing parkinsonisms. Decision rules are based on existing literature which identified biomarkers with a sensitivity and specificity around or above 80%. Initial in-vivo results from patients are presented, and their MRI findings are partially in agreement with existing literature. Our goal is to test our method on patients at an early-stage when their clinical diagnosis is unclear.

Introduction

Differentiating between atypical parkinsonisms is a challenge in clinical settings. Quantitative MRI can aid the diagnostic decision making, yet a single quantitative biomarker is unlikely to be sufficiently powerful for differential diagnosis (1). A recent review (2) has identified several MRI-based biomarkers which have sensitivities and specificities around or above 80% for differentiating between Parkinson’s Disease (PD), progressive supranuclear palsy (PSP) and multiple system atrophy (MSA).

We developed a decision tree made up of a sequence of decision rules which could perform differential diagnosis, and describe a dedicated multi-contrast MRI protocol to acquire the necessary information. We present a study design to test our novel method for differentially diagnosing parkinsonisms, which we aim to test on early-stage patients with a clinically unclear presentation at the time of scanning.

Methods

We reviewed the literature surrounding the use of MRI for differentially diagnosing atypical parkinsonisms (3-16). Based on existing techniques and findings, we propose a decision tree with 7 decision rules (Figure 1) and an MRI protocol which are based on published findings, summarised in Figure 2. Such a decision tree could be trained to perform patient outcome prediction, however it would require a population of approximately 50 subjects per cohort. The aim of our study is to test individual decision rules for group differences and report their significance, to justify a larger cohort study.

Our study was approved by the local ethics board. To illustrate the study design, three patients were recruited from the movement disorder clinic with PD, MSA, and PSP (2 females, 1 male, aged 64-66, disease duration 1.16-3.39 years) as well as a healthy volunteer (female, 62). After informed consent they were scanned on a 3T system (Signa Premier, GE Healthcare, USA). Whole-brain T1-weighted (0.9mm isotropic) and T2-weighted (1x1x3mm3) were acquired. SWI and QSM were produced from the same 3D multi-echo gradient-echo sequence (0.7x0.7x1mm3, 88 slices, TE=13-37.6ms). FA was produced from a DTI sequence (1.75x1.75x2mm3, 56 slices, TE=77.9ms, SMS2+GRAPPA2, b1000, 64 directions) and neuromelanin MRI to image the substantia nigra (SN) and locus coeruleus (LC) with a 2D MT-weighted sequence (0.4x0.4x3mm3, 12 slices, TE=7.5ms, NEX=3).

QSM processing was performed using the MEDI toolbox (17), and DTI data was corrected for off-resonance effects (18) and fit using FSL dtifit.

Results

In-vivo data illustrating relevant biomarkers are shown for a healthy control and three patients.

Figure 3 shows T1-weighted images about the brainstem and third ventricle, which have shown to be useful for quantifying midbrain and ventricular atrophy for differentiating PSP from PD and MSA patients. Subfigures 3d and 3h show increased atrophy in the PSP patient compared to other subjects, which should aid rule #3.

Figure 4 shows neuromelanin MRI at the level of the SN and LC . Reduced neuromelanin can be seen in the SN for all patients compared to the control. The LC seems most visible in the healthy control, but also in the patients (rule #2).

Figure 5 shows QSM and DTI based markers in the brainstem and superior cerebral peduncle (SCP). All window levels are consistent as these are quantitative images. Increased susceptibility can be seen in the substantia nigra (SN) (7,10) of all patients compared to the healthy control (rule #1).
Increased susceptibility in the subthalamic nucleus (STN) (14,15) is seen in the MSA patient in comparison with the healthy control and PD patient, in agreement with decision rule #6. Reduction in FA in the SCP (13) cannot be confirmed without quantitative analysis, (rule #4).

Discussion

We propose a decision tree and an MRI protocol to differentially diagnosing atypical parkinsonisms (Figure 1), based on published literature with sensitivities and specificities around or above 80% (Figure 2). The dedicated parkinsonism protocol will be tested on diagnosed patients as well as patients with an unclear clinical presentation at the time of the MRI scan. Leng et al. (19) showed promising results using a similar stratified approach based using data from diagnosed patients. This work’s novelty is to apply such an approach at an early-stage when the clinical diagnosis is unclear.

Initial in-vivo data show promising qualitative results for performing differentiation on diagnosed patients in agreement with existing literature. Future work will compare results on early-stage patients for whom the diagnosis is unclear. Further image analysis is required to report quantitative comparisons.

Conclusion

We present a decision tree, dedicated MRI protocol and example data for differentially diagnosing atypical parkinsonisms using quantitative MRI.

Acknowledgements

This project was sponsored by a research grant from Parkinson NL.

This project was funded by Erasmus MC - TU Delft Convergence for health and technology.

References

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Figures

Figure 1 – Decision tree with numbered decision rules, the type of biomarker and the objective differentiation. The decision rules are based on existing literature, summarised in Figure 2.

Figure 2: Decision rules, biomarkers and references as well as relevant results. Decision rules (numbered in the first column as in Figure 1) in the decision tree are based on these results. “et al.” is omitted for readability. PD: Parkinson’s Disease; PSP: progressive supranuclear palsy; MSA: multiple system atrophy; SWI: Susceptibility Weighted Imaging; QSM: Quantitative Susceptibility Mapping; MRPI: Magnetic Resonance Parkinsonian Index; NM: neuromelanin; DTI: diffusion tensor imaging; SN: substantia nigra; LC: locus coeruleus.

Figure 3: Features on a T1-weighted MR image for a healthy control, MSA, PD and PSP patient about the brainstem (a-d) and third ventricle (e-h). For the PSP patient, increased atrophy of the midbrain (d) can be seen, as well as increased width of the third ventricle (red arrows) (h) compared to others.

Figure 4: Neuromelanin MRI for a healthy control and three patients. Neuromelanin in the substantia nigra is reduced for all patients compared to the healthy volunteer. For the MSA patient a signal dropout is observed (red arrow), which could be due to the high amount of iron present (Figure 5b). The locus coeruleus (e), green arrows), is more clearly visible in the healthy control, but still visible in patients. Note that the CSF signal in the case of the PD patient (g) makes LC visualisation harder, while the data from the PSP patient (d,h) suffers from a lot of motion.

Figure 5: Quantitative MRI markers related to decision rules #1, 3, 4 and 6 showing QSM around the substantia nigra (a-d), red nucleus (e-h). Higher susceptibility values in the SN are found in all patients compared to healthy control (green arrow in a vs b,c,d) for #1. A higher susceptibility is also seen in an anatomy, which may be the SN or STN, of the MSA compared to PD and HC (red arrow in f vs e,g,h) for #6). DTI fractional anisotropy (FA) of the SCP (i-l) is not visibly lower for the PSP patient (white arrows in l) for #4.

Proc. Intl. Soc. Mag. Reson. Med. 31 (2023)
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DOI: https://doi.org/10.58530/2023/2275