Editorial for “Mapping Neurodegenerative Changes in Clinically Uncertain Parkinsonian Syndrome Patients Using Fast MR Spin TomogrAphy in Time‐Domain (MR‐STAT) Relaxometry at 3 T”

Differentiating dopaminergic neurodegenerative from non‐neurodegenerative parkinsonism in patients with clinically uncertain parkinsonian syndrome (CUPS) is a critical step in guiding therapy, predicting prognosis, and optimizing patient outcomes [1]. CUPS encompasses a heterogeneous spectrum of motor and non‐motor symptoms that often overlap between Parkinson's disease and atypical parkinsonian syndromes, such as multiple system atrophy or progressive supranuclear palsy [2]. Errors in classification at this stage can delay appropriate management, thus exposing patients to ineffective therapies and complicating prognostication. While many imaging studies have focused on distinguishing Parkinson's disease from healthy controls, the clinically relevant problem is fundamentally different: patients often present with overlapping symptoms, and the key diagnostic question is whether parkinsonism reflects underlying neurodegeneration. Currently, dopamine transporter single photon emission computed tomography (DAT‐SPECT) serves as the standard tool for detecting nigrostriatal degeneration [3]. However, DAT‐SPECT is not universally available. It can be costly and exposes patients to ionizing radiation. Moreover, most patients with suspected parkinsonism undergo conventional MRI before referral for DAT‐SPECT imaging.

Conventional MRI is typically qualitative and performed to exclude secondary causes of parkinsonism, such as vascular lesions or structural abnormalities. More advanced semi‐quantitative methods, including diffusion‐weighted, susceptibility‐weighted, and neuromelanin‐sensitive imaging, have demonstrated utility in detecting microstructural and biochemical changes in established Parkinson's disease and atypical Parkinsonian syndromes [4]. Multiparametric MRI approaches, such as magnetic resonance fingerprinting [5] (MRF) and Magnetic Resonance Spin TomogrAphy in Time‐domain (MR‐STAT) relaxometry [6], enable the simultaneous estimation of quantitative parameters such as T1 and T2 within relatively short acquisition times. While MRF is typically based on variable acquisition schemes and dictionary matching, MR‐STAT relies on a model‐based reconstruction framework and may be implemented using more conventional sampling strategies, such as Cartesian acquisitions. Both approaches nonetheless depend on dedicated post‐processing to extract quantitative maps, which remain an important consideration for protocol design. From a clinical point of view, the quantitative maps obtained with multiparametric MRI may capture microstructural changes associated with neurodegeneration, including neuronal loss, gliosis, or iron accumulation, potentially serving as sensitive biomarkers for differentiating parkinsonian syndromes.

In this issue of JMRI, Schilder et al. [7] applied MR‐STAT to 109 patients and 10 healthy controls, all of whom underwent DAT‐SPECT before MRI. Importantly, the study focused on patients with CUPS rather than pre‐defined diagnostic groups, directly addressing the clinically relevant question of differentiating neurodegenerative from non‐neurodegenerative parkinsonism. After demonstrating the excellent repeatability of T1 and T2 measurements in healthy controls, the study showed significant differences in T1 values between the two groups of parkinsonian patients. The differences were mostly observed in regions including the putamen and globus pallidus, which is consistent with areas affected by dopaminergic neurodegeneration. T2 differences were not statistically significant, but this might be due to the noisier nature of the measured T2 maps and their lower reproducibility compared to T1 maps. These results suggest that T1 obtained using MR‐STAT may serve as a primary sensitive biomarker to differentiate neurodegenerative from non‐neurodegenerative parkinsonism in patients with CUPS. At the same time, the broader multiparametric potential of MR‐STAT remains to be fully explored.

The study is strengthened by a relatively large cohort, with neurodegenerative and non‐neurodegenerative diagnoses represented in roughly equal numbers, and by the most welcome inclusion of healthy controls to assess the reproducibility of MR‐STAT measurements. Nevertheless, one could argue that the retrospective design introduced potential limitations: the average 1.8‐year interval between scans may have allowed disease progression to influence the findings, and some patients lacked definitive diagnoses at the time of MRI, requiring an external evaluation during the study that could affect the observed differences.

Although multiparametric MRI approaches have been investigated for differentiating neurodegenerative from non‐neurodegenerative parkinsonism, most studies focus on patients with established diagnoses rather than CUPS [4]. This study showed that T1 maps obtained through MR‐STAT might be a promising non‐invasive approach for extracting accessible and quantitative biomarkers in these diagnostically challenging cases. Future studies should prospectively validate these findings, compare MR‐STAT T1 maps with conventional T1 imaging, MRF, and DAT‐SPECT, and investigate additional parameters such as T2 or quantitative susceptibility. Integration of machine learning methods could further enhance diagnostic performance by further empowering multiparametric data.