Does FLAIR super-resolution erase or hallucinate small white-matter lesions?
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TL;DR - A study testing whether super-resolution (SR) of thick-slice clinical FLAIR MRI distorts white-matter hyperintensity (WMH) content before segmentation, finding that SR mainly erases small real lesions rather than hallucinating fake ones. This matters because SR is routinely used as a preprocessing step for clinical neuroimaging pipelines where small lesions carry diagnostic signal.
- Setup: 29 ADNI subjects with 1-mm isotropic HR FLAIR and expert manual WMH labels; scans degraded to simulated 3 mm and 5 mm through-plane acquisitions, then upsampled with multi-contrast implicit neural representation (INR), self-supervised single-contrast ECLARE, and cubic interpolation.
- Evaluation used simulated thick-slice segmentation as the floor and original HR as the ceiling, with per-lesion detection sensitivity, erasure rate (HR-detected lesions lost after reconstruction), and hallucination rate (components absent from both manual and HR labels).
- Segmentation method chosen by screening four tools (WMH-SynthSeg, segcsvd, MARS-WMH, TrUE-Net) and running the analysis under MARS-WMH, the most sensitive to small lesions on HR.
- Findings: erasure dominated over hallucination and worsened with thicker slices, yet every reconstruction still beat raw thick-slice detection; ECLARE best recovered small-lesion signal at both thicknesses, while INR was no better than cubic interpolation.
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Does FLAIR super-resolution erase or hallucinate small white-matter lesions?
TL;DR - A study testing whether super-resolution (SR) of thick-slice clinical FLAIR MRI distorts white-matter hyperintensity (WMH) content before segmentation, finding that SR mainly erases small real lesions rather than hallucinating fake ones. This matters because SR is routinely used as a preprocessing step for clinical neuroimaging pipelines where small lesions carry diagnostic signal.
- Setup: 29 ADNI subjects with 1-mm isotropic HR FLAIR and expert manual WMH labels; scans degraded to simulated 3 mm and 5 mm through-plane acquisitions, then upsampled with multi-contrast implicit neural representation (INR), self-supervised single-contrast ECLARE, and cubic interpolation.
- Evaluation used simulated thick-slice segmentation as the floor and original HR as the ceiling, with per-lesion detection sensitivity, erasure rate (HR-detected lesions lost after reconstruction), and hallucination rate (components absent from both manual and HR labels).
- Segmentation method chosen by screening four tools (WMH-SynthSeg, segcsvd, MARS-WMH, TrUE-Net) and running the analysis under MARS-WMH, the most sensitive to small lesions on HR.
- Findings: erasure dominated over hallucination and worsened with thicker slices, yet every reconstruction still beat raw thick-slice detection; ECLARE best recovered small-lesion signal at both thicknesses, while INR was no better than cubic interpolation.