Fat quantification map facilitates intra-tumoral microscopic fat visualization, detection and quantification

Motivation: The identification of microscopic fat by chemical shift imaging through signal reduction on out-phase is diagnostically significant in differentiating angiomyolipoma (AML) from renal cell carcinoma (RCC). Goal(s): This study evaluated the efficacy of fat quantification maps in detecting intra-tumoral microscopic fat and using histogram-based quantitative parameters to differentiate RCC from AML. Approach: The results demonstrated that fat quantification maps significantly reduce inter-observer variability in evaluating lesional fat content. Additionally, RCC and AML showed distinct intensity distribution patterns based on histogram-derived parameters. Results: These findings confirm that fat quantification maps, combined with quantitative measurements, effectively aid in the differential diagnosis of AML and RCC. Impact: Fat-quantification maps can improve the indentification and quantification of microscopic fat, enhancing differentiation of angiomyolipoma and renal cell carcinoma, which can be integrated seamlessly into clinical workflows for a systematic approach.