Nigrosome 1 visibility and its association with nigrostriatal dopaminergic loss in Parkinson's disease.

BACKGROUND: Nigrosome 1 (NG1), a small cluster of dopaminergic cells in the substantia nigra and visible in the susceptibility map-weighted magnetic resonance image (SMwI), is severely affected in Parkinson's disease (PD). However, the degree of nigrostriatal degeneration according to the visibility of NG1 has not yet been well elucidated. METHODS: We consecutively recruited 138 PD and 78 non-neurodegenerative disease (non-ND) patients, who underwent both 18 F-FP-CIT positron emission tomography (PET) and SMwI. Three neurologists and one radiologist evaluated the visibility of NG1 in SMwI. The participants were thereby grouped into visible, intermediate, and non-visible groups. Nigrostriatal dopaminergic input was calculated using the specific binding ratio (SBR) of the 18 F-FP-CIT PET. We determined the threshold of regional SBR for discriminating NG1 visibility and the probability for NG1 visibility according to regional SBR. RESULTS: Visual rating of NG1 showed excellent interobserver agreements as well as high sensitivity and specificity to differentiate the PD group from the non-ND group. NG1 was visible in seven patients (5.1%) in the PD group, who had relatively short disease duration or less severe loss of striatal dopamine. The threshold of putaminal SBR reduction on the more affected side for the disappearance of NG1 was 45.5%, and the probability for NG1 visibility dropped to 50% after the reduction of putaminal SBR to 41% from the normal mean. CONCLUSIONS: Almost half loss of nigrostriatal dopaminergic input is required to dissipate the hyperintensity of NG1 on SMwI, suggesting its utility in diagnosing PD only after the onset of the motor symptoms.

A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson's disease at 3T: An efficient susceptibility map-weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet).

PURPOSE: Visibility of nigrosome 1 in the substantia nigra (SN) is used as an MR imaging biomarker for Parkinson's disease. Because of lower susceptibility induced tissue contrast and SNR visualization of the SN pars compacta (SNPC) using conventional imaging technique in the clinical field strength (≤3T) has been limited. Susceptibility map-weighted imaging (SMWI) has been proposed to visualize SNPC at 3T. To better visualize nigrosome 1 and SN areas using SMWI, accurate estimation of the quantitative susceptibility mapping (QSM) map is essential. In SMWI processing, however, QSM processing time using conventional algorithms is the most time-consuming step and may limit clinical use. In this study, we introduce an efficient SMWI processing approach using the deep neural network (QSMnet). To improve the processing speed of SMWI while maintaining similar image quality to that obtained with the conventional method, QSMnet was applied to generate a susceptibility mask for SMWI processing. METHODS: To conduct a deep learning-based image to image operation modified QSMnet was utilized. The network was trained with in vivo MR data from 57 healthy controls. To validate SMWI results from QSMnet, four datasets from healthy controls were used as the test datasets. As a preliminary attempt to explore the clinical applicability, Parkinson's disease patient data were additionally tested. The SWMI images generated by QSMnet and conventional model-based QSM outputs were compared. To validate SMWI results, region of interest (ROI) analysis was performed. The mean signal values at the nigrosome 1 and the surrounding regions were measured to calculate contrast-to-noise ratio (CNR). RESULTS: The experimental results confirmed that the proposed approach using QSMnet provided similar QSM and SMWI compared to that obtained with conventional iLSQR while the processing speed was much improved (5.4 times faster). The QSM results from QSMnet show similar tissue contrast with results from iLSQR. When compared, the absolute mean intensity difference between two methods near the nigrosome 1 was 0.015 ppm. SMWI results using susceptibility masks from QSMnet demonstrated signal distribution and tissue contrast that was comparable with those results from the conventional iLSQR method. The absolute difference maps of SMWI were calculated to show the similarity between the two methods. The overall mean absolute difference value in the presented ROIs obtained from healthy controls (n = 4) and a PD patient (n = 1) were 2.31 and 1.81, respectively. Mean CNR values (10 ROIs; n = 5; including both sides; 1.42 for QSMnet; 1.43 for iLSQR) between SN and nigrosome 1 in SMWI results obtained by ROI analysis were similar (P = 0.724). CONCLUSIONS: In this study, we assessed an efficient approach for SMWI visualizing SN and nigrosome 1 on 3T. QSMnet provides a similar SMWI image to that obtained with the conventional iterative QSM algorithm and improves QSM processing speed by avoiding iterative computation. Since QSM is the most time-consuming step of SMWI processing, QSMnet can help to achieve a higher processing speed of SMWI. These results suggest that SMWI imaging with susceptibility masks using QSMnet is a more efficient approach.

Initial diagnostic workup of parkinsonism: Dopamine transporter positron emission tomography versus susceptibility map-weighted imaging at 3T.

BACKGROUND AND PURPOSE: Evaluation of dorsal nigral hyperintensity on MRI can help detect nigrostriatal degeneration. We aimed to compare the diagnostic performance between susceptibility map-weighted imaging (SMWI) and N-3-fluoropropyl-2-ß-carbomethoxy-3-ß-(4-iodophenyl) nortropane (18F-FP-CIT) positron emission tomography (PET) as an initial diagnostic tool of parkinsonism. MATERIALS AND METHODS: This local ethics committee-approved retrospective study enrolled 223 patients with parkinsonism and 15 healthy subjects (mean age, 69.7 years; 135 females) who underwent both SMWI at 3T and 18F-FP-CIT PET. The diagnostic performances of the two tests for nigrostriatal degeneration were compared by evaluating whether the 90% confidence interval (CI) of the difference between the two tests was within the equivalence margin by using the DTComPair package of R. The concordance rate was tested by Cohen's kappa. RESULTS: The diagnostic sensitivities of SMWI and 18F-FP-CIT PET were 94.5% and 100% per SN and 100% and 100% per participant, respectively; their specificities were 95.3% and 86.7% per SN and 94.4% and 84.0% per participant, respectively. While the diagnostic sensitivity was comparable between the two tests for each SN and participant, the lower 90% CI of the differences in the specificity were -0.086 per SN and -0.104 per participant, indicating a higher diagnostic specificity of SMWI than that of 18F-FP-CIT PET. When excluding 20 participants with basal ganglia lesions, the two tests exhibited similar diagnostic performance and had excellent agreement (k = 0.899 per SN; k = 0.945 per participant). CONCLUSION: For patients with parkinsonism, SMWI and 18F-FP-CIT PET exhibit similar diagnostic performance.