sFRC for assessing hallucinations in medical image restoration

ArXi:2603.04673v2 Announce Type: replace Deep learning (DL) methods are currently being explored to re images from sparse-view-, limited-data-, and undersampled-based acquisitions in medical applications. Although outputs from DL may appear visually appealing based on likability/subjective criteria (such as less noise, smooth features), they may also suffer from hallucinations. This issue is further exacerbated by a lack of easy-to-use techniques and robust metrics for the identification of hallucinations in DL outputs.