Automated estimation of perivascular space and brain morphometry from deep learning-reconstructed three-dimensional T1-weighted MRI: comparison with the conventional technique

Clinical Snapshot

Key Findings

• P Value: P < 0.001 for all comparisons

• Effect Size: PVS volumes reduced by ~50% with DLR across all severity groups (mild: 738.4 vs 1408.1 mm³; severe: 1328.5 vs 2711.7 mm³)

• Primary Outcome: PVS volumes and brain morphometry measurements comparing DLR vs conventional T1-WI

• Nnt Or Sensitivity: Variability reduction: mild PVS 47% lower (224.4 vs 420.9 mm³), severe PVS 41% lower (382.4 vs 653.6 mm³)

• Confidence Interval: Not reported

Clinical Application

Requires deep learning reconstruction capability and specialized software (FreeSurfer), may not be widely available in all centers Technology adoption would depend on TGA approval of DLR systems and integration with existing MRI infrastructure; potential applications in neurodegenerative disease research and clinical assessment Patients requiring brain MRI for morphometric analysis or perivascular space assessment, particularly in research settings

Abstract

PURPOSE: To evaluate the utility of deep learning-based reconstruction (DLR) three-dimensional T1-weighted imaging (T1-WI) in improving fine structural delineation through the automated estimation of brain morphometry and perivascular space (PVS). MATERIALS AND METHODS: In this retrospective cohort study, participants who underwent T1-WI between August 2021 and June 2022 were included. Participants were categorized into normal and disease groups based on the presence of visible cerebral lesions. Cortical thicknesses and cerebral volumes from DLR T1-WI were estimated using FreeSurfer and compared to those derived from conventional T1-WI. Additionally, participants were grouped by MRI-visible PVS severity according to the number of enlarged PVS in centrum semiovale and basal ganglia. PVS volumes from DLR and conventional T1-WI were compared across these groups. Statistical comparisons were performed using a paired independent t-test with Benjamin-Hochberg adjustment. RESULTS: 240 participants (mean age: 40.8 ± 17 years) were included. In the normal group (n = 144), cortical thicknesses and volumes were significantly greater in DLR T1-WI compared to conventional T1-WI (P < 0.001). This trend was also observed in the disease group (n = 96) and in external datasets (n = 63). Across all three PVS severity groups (mild, n = 111; moderate, n = 71; severe, n = 58), DLR T1-WI significantly reduced PVS volumes and variability compared to conventional T1-WI. DLR T1-WI demonstrated PVS volumes (mild: 738.4 mm³ vs. 1408.1 mm³; moderate: 919.0 mm³ vs. 1895.8 mm³; severe: 1328.5 mm³ vs. 2711.7 mm³; all P < 0.001) and variability (mild: 224.4 mm3 vs. 420.9 mm³; moderate: 229.7 mm3 vs. 462.9 mm³; severe: 382.4 mm3 vs. 653.6 mm³) that were consistently smaller. CONCLUSION: DLR T1-WI yielded cerebral cortical thickness and volume measurements that were more consistent with expected anatomical values than those from conventional T1-WI.PVS volumes measured from DLR T1-WI exhibited less variability and better concordance with radiologists' visual assessments.

References

1. 1.Yang, S. J., Cheong, E. N., Choi, Y., Kim, Y., Nam, Y., Jeong, E., Park, J. E., & Kim, H. S. (2026). Automated estimation of perivascular space and brain morphometry from deep learning-reconstructed three-dimensional T1-weighted MRI: comparison with the conventional technique. Journal of Neuroradiology, 101550. https://doi.org/10.1016/j.neurad.2026.101550