MRI-guided voxel-based automatic semi-quantification of dopamine transporter imaging

Area of Science:

• Nuclear Medicine
• Medical Imaging
• Neuroscience

Background:

• ¹²³I-FP-CIT SPECT imaging is crucial for assessing neurodegenerative diseases.
• Poor spatial resolution leads to partial-volume effects, impacting quantitative accuracy.
• Manual region definition introduces user variability in semi-quantification.

Purpose of the Study:

• Develop an automated method for striatal volume of interest (VOI) definition and partial-volume correction (PVC).
• Utilize patient-specific MRI data for improved accuracy.
• Achieve complete contrast recovery in striatal regions.

Main Methods:

• Spatial pre-processing including image segmentation and multi-modality registration.
• Region-based voxel-wise partial-volume correction technique.
• Validation using an anthropomorphic striatal phantom and 58 patient datasets.

Main Results:

• Automated method achieves high linearity and complete contrast recovery (6.5 mm FWHM).
• Accuracy demonstrated by RMSE < 0.15; reproducibility < 5% for normal uptake ratios.
• Outperformed clinical assessment and showed strong correlation with BasGan software.

Conclusions:

• The automated method effectively corrects partial-volume effects in ¹²³I-FP-CIT SPECT.
• Enhances accuracy and reproducibility of striatal uptake quantification.
• Reduces observer variability and is reproducible with publicly available software.