Photon-Counting Detector CT Promising Alternative for Liver Fat Quantification in MASLD, shows research

The team stated, "This study confirmed that photon-counting detector CT can provide accurate, protocol-independent liver fat quantification in metabolic dysfunction-associated steatotic liver disease across different imaging environments."

The authors emphasized that accurately quantifying liver fat is crucial for the treatment and monitoring of metabolic dysfunction-associated steatotic liver disease (MASLD), which affects up to 24% of the U.S. population and can progress to cirrhosis or cancer if untreated. While MRI is a well-established noninvasive method for assessing liver fat, it requires patients to hold their breath for 20 seconds during image acquisition, which can compromise image quality. Additionally, MRI may not be feasible for patients with metal implants or those who are claustrophobic.

In contrast, photon-counting CT offers several advantages, such as improved spatial resolution and spectral separation, and early studies suggest it may effectively quantify liver fat, even in obese patients. However, further robust studies are necessary to validate this technology.

Against the above background, Huimin Lin, From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, and colleagues developed and validated a universal CT to MRI fat conversion formula to enhance fat quantification accuracy across several PCD CT protocols compared to MRI proton density fat fraction (PDFF).

For this purpose, the researchers evaluated how well fat can be measured using different fat levels in phantoms. They enrolled 500 healthy participants and 157 with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) between September 2023 and March 2024. Participants were randomly divided into six groups based on different CT settings for tube voltage (90, 120, or 140 kVp) and radiation dose (standard or low).

In the group with 120 kVp and standard dose, 51% (53 out of 104) were used to train the model, while the rest served as a validation group. A formula to estimate the CT-derived fat fraction (CTFF) from MRI was created using the training group. The researchers then compared how closely the CTFF matched with MRI results in the validation group and looked at variations based on tube voltage, radiation dose, body mass index, and in the MASLD group. They also examined the factors affecting the CTFF's accuracy.

The study led to the following findings:

• In the phantoms, CTFF showed excellent agreement with a nominal fat fraction (intraclass correlation coefficient, 0.98; mean bias, 0.2%).
• The trial included 412 asymptomatic participants and 122 participants with MASLD.
• A CT to MRI fat conversion formula was derived as follows: MRI PDFF (%) = −0.58 · CT (HU) + 43.1.
• Across all comparisons, CTFF demonstrated excellent agreement with PDFF (mean bias values < 1%).
• CTFF error was not influenced by tube voltage, radiation dose, body mass index, or PDFF.
• Agreement between CTFF and PDFF was also found in the MASLD cohort (mean bias, −0.2%).

"The standardized CT value from photon-counting CT demonstrated strong and consistent agreement with MRI proton density fat fraction across different protocols, suggesting it could be a reliable alternative for measuring liver fat," the researchers concluded.

Reference:

DOI: https://pubs.rsna.org/doi/10.1148/radiol.240038