Ultrasound and Attenuation Imaging

Overview

Fatty liver disease affects almost a 3rd of the United Kingdom (UK) population. Currently, this is being investigated using a type of MRI scan that reports how fatty or not a person's liver is, with a liver fat score, but many patients cannot have this type of scan due to time, cost or because they have a condition which makes Magnetic Resonance Imaging (MRI) an unsafe scan for them to have. An ultrasound scan is cheaper, faster and safer for a wider variety of patients. New developments in ultrasound technology mean that instead of a Sonographer, or ultrasound operator, deciding whether your liver is fatty or not based on what they see on the scan, the ultrasound machine gives a liver fat score. This study will look at the relationship between the Sonographers' view on how fatty the liver is, compared with the liver fat score given by the new development in ultrasound technology.

Non-alcoholic fatty liver disease (NAFLD) affects almost a 3rd of the population of the UK and current best practice is to evaluate this using Magnetic Resonance Imaging proton density fat fraction (MRI PDFF), however, this is inaccessible to many patients due to limited availability, cost and a large exclusion criteria. Ultrasound offers an affordable, faster and more inclusive method of liver evaluation but only subjective assessment of fatty liver disease until a recent development- Attenuation Imaging (AI). Attenuation imaging gives a numerical score for fatty liver disease. Attenuation imaging has previously only been compared to MRI PDFF, however, when many patients do not have MRI PDFF it is essential to compare it to common practice, conventional ultrasound. This study aims to assess the agreement between conventional ultrasound assessment of the liver and AI using a cross-sectional method. Convenience sampling will be used to select 95 participants who will have their AI score carried out alongside their routine ultrasound scan with the sonographer blinded to the AI results. The inter-rater agreement will be determined using Kappa statistics. The results will demonstrate the strength of relationship between the two methods. A strong agreement between the two may facilitate further investigation into the uses of AI, such as use as a teaching tool or in aiding decisions in cases that Sonographers find more difficult to assess such as those patients with a high BMI.