Non-alcoholic fatty liver disease (NAFLD) is currently the most common liver condition worldwide; approximately 55% of the world population will have NAFLD by 2040. NAFLD is an unwanted side effect of common cancer therapies, such as chemotherapy. Ultrasound can detect NAFLD via measurement of the backscatter coefficient (BSC). It is an attractive technique because of its low cost and availability, potentially enabling earlier detection of NAFLD in a larger population through screening. This approach has shown promise in detecting NAFLD but is limited by variability in measurement due to several factors. Measurement of the BSC requires assumptions about the nature of the tissue being measured; if these assumptions are incorrect, they can lead to inaccurate BSC measurements. To improve accuracy, an algorithm (COSIE) was developed to quantify the suitability of tissue for BSC analysis. The investigators believe COSIE will enable more reliable BSC measurements by selecting the optimal regions of tissue to measure. By measuring the BSC in the livers of healthy volunteers and patients with evidence of fatty liver, the algorithm can be evaluated against liver fat percentage values obtained from MRI imaging. This study will assess whether applying the COSIE algorithm enhances the reliability of BSC measurements, bringing them closer in quality to MRI imaging.

Inclusion Criteria:

Healthy Volunteers:

• Healthy male or female adults (18 or above).
• Volunteers must consider themselves fit and healthy.
• RMH and/or ICR employees.

Patients:

• Patients undergoing liver MRI as part of clinical standard of care with evidence of steatosis on preceding MRI.
• Aged 18 and above.

Exclusion Criteria:

Healthy Volunteers:

• Volunteers that are under investigation or planning to consult their GP to seek investigation for an undiagnosed condition, particularly regarding pelvic or abdominal disease or injury.
• Healthy volunteers who do not have an NHS number or who are not registered with a GP will be excluded.

Patients:

• Severe liver fibrosis (fibrosis stage F4) or severe cirrhosis.

It is assumed that these volunteers will display a range of liver fat fractions and that there may be bias towards the low-fat fractions (< 5%). Patients with known fatty liver are included to present a broader range of cases to consider, thereby improving the analysis. If a patient is MR scanned and it is found that they have low fat fraction, we will aim to replace them with another patient that does have a high fat fraction.

This number has been selected without a priori predictions of the expected variance in results. This study will help to determine the necessary study power required for future investigations. This population size has been selected based on practical considerations, i.e. cost of MR and length of the study.

Anonymised ultrasound and MR images will be shared upon request.