Nonalcoholic　fatty　liver　disease　is　a　type　of　hepatic　steatosis　that　is　not　only　associated　with　critical　metabolic　risk　factors　but　can　also　result　in　advanced　liver　diseases.　Ultrasound　parametric　imaging,　which　is　based　on　statistical　models,　assesses　fatty　liver　changes,　using　quantitative　visualization　of　hepatic-steatosis-caused　variations　in　the　statistical　properties　of　backscattered　signals.　One　constraint　with　using　statistical　models　in　ultrasound　imaging　is　that　ultrasound　data　must　conform　to　the　distribution　employed.　Small-window　entropy　imaging　was　recently　proposed　as　a　non-model-based　parametric　imaging　technique　with　physical　meanings　of　backscattered　statistics.　In　this　study,　we　explored　the　feasibility　of　using　small-window　entropy　imaging　in　the　assessment　of　fatty　liver　disease　and　evaluated　its　performance　through　comparisons　with　parametric　imaging　based　on　the　Nakagami　distribution　model　(currently　the　most　frequently　used　statistical　model).　Liver　donors　(n　=　53)　and　patients　(n　=　142)　were　recruited　to　evaluate　hepatic　fat　fractions　(HFFs),　using　magnetic　resonance　spectroscopy　and　to　evaluate　the　stages　of　fatty　liver　disease　(normal,　mild,　moderate　and　severe),　using　liver　biopsy　with　histopathology.　Livers　were　scanned　using　a　3-MHz　ultrasound　to　construct　B-mode,　small-window　entropy　and　Nakagami　images　to　correlate　with　HFF　analyses　and　fatty　liver　stages.　The　diagnostic　values　of　the　imaging　methods　were　evaluated　using　receiver　operating　characteristic　curves.　The　results　demonstrated　that　the　entropy　value　obtained　using　small-window　entropy　imaging　correlated　well　with　log(10)(HFF),　with　a　correlation　coefficient　r　=　0.74,　which　was　higher　than　those　obtained　for　the　B-scan　and　Nakagami　images.　Moreover,　small-window　entropy　imaging　also　resulted　in　the　highest　area　under　the　receiver　operating　characteristic　curve　(0.80　for　stages　equal　to　or　more　severe　than　mild;　0.90　for　equal　to　or　more　severe　than　moderate;　0.89　for　severe),　which　indicated　that　non　model-based　entropy　imaging-using　the　small-window　technique-performs　more　favorably　than　other　techniques　in　fatty　liver　assessment.　(c)　2018　World　Federation　for　Ultrasound　in　Medicine　&　Biology.　All　rights　reserved.