FLAIR超分注意：小病灶主要被抹除而非幻觉。ADNI 29例上ECLARE最好，INR不如三次插值。
研究用ADNI队列29人的1mm各向同性高清FLAIR扫描，模拟3mm和5mm层厚退化。对比多对比隐式神经表示、单对比自监督ECLARE和三次插值三种超分方法。在MARS-WMH分割下，超分的主导效应是抹除小的真实病灶，而非幻觉生成，且层厚越厚抹除越严重。所有重建方法都比原始厚层扫描提升病灶检测，其中ECLARE在两种层厚下恢复小病灶信号最好，INR效果不优于三次插值。
Does FLAIR super-resolution erase or hallucinate small white-matter lesions?
White matter hyperintensities (WMH), bright regions on Fluid-attenuated Inversion Recovery (FLAIR) scans are associated with cerebrovascular pathology and neurodegeneration. FLAIR is usually acquired with thick slices in clinical settings, giving it poor through-plane resolution. Super-resolution (SR) is a widely used method for recovering an isotropic volume from an anisotropic scan. Yet whether applying it prior to WMH segmentation preserves lesion content remains unknown: a model may erase small real lesions or hallucinate absent ones. We used 1-mm isotropic high-resolution (HR) FLAIR scans from 29 individuals in the ADNI cohort, each manually segmented for WMH by an expert. Then, we degraded each to simulated 3 and 5 mm through-plane acquisitions. Multi-contrast implicit neural representation (INR), a single-contrast self-supervised model (ECLARE), and cubic interpolation were used to upsample them onto the HR grid. WMH segmentation from a simulated thick slice and the original HR FLAIR set the floor and ceiling, respectively, for the per-lesion analysis. Of four WMH segmentation methods (WMH-SynthSeg, segcsvd, MARS-WMH, TrUE-Net), we ran the analysis under the most sensitive one to small lesions on HR (MARS-WMH) with the evaluation metrics of detection sensitivity, erasure rate (HR-detected lesions lost after reconstruction), and hallucination rate (predicted components absent from both the manual and HR segmentation). The dominant effect of SR was erasure of small real lesions, not hallucination, and it increased with slice thickness, though every reconstruction still improved lesion detection over the raw thick slice. ECLARE recovered small lesion signal best at both thicknesses, while the INR was no better than cubic interpolation.