Abstract:

Objective To investigate the consistency between CT-derived fat fraction (CT-FF) based on photoncounting CT material decomposition under different scanning conditions and magnetic resonance imaging proton density fat fraction (MRI-PDFF), thereby developing a CT-based method for liver fat quantification suitable for the Chinese population. Methods From September 2023 to February 2024, a total of 383 healthy volunteers were prospectively recruited at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (176 with MRI-PDFF < 5% and 207 with MRI-PDFF≥ 5%), and randomly assigned to four photon-counting CT scanning groups based on tube voltage (120 kVp/140 kVp) and radiation dose (standard dose/low dose) [Different CT scanning schemes:① 120 kVp+standard dose (n = 123); ② 120 kVp+low dose (n = 71); ③ 140 kVp+standard dose (n = 120); ④ 140 kVp+low dose (n = 69)]. All subjects underwent photoncounting CT liver scanning and MRI examinations, with liver MRI-PDFF used as the reference standard for liver fat quantification. From the standard-dose group (n = 243), this study randomly selected 50 individuals each from the 120 kVp group (n = 123) and 140 kVp group (n = 120) to form a test cohort (n = 100), and the remaining subjects were assigned to the validation cohort (n = 283). Among volunteers with MRI-PDFF < 5% (n = 66) in the test cohort, this study randomly selected 20 individuals each from the 120 kVp group (n = 33) and 140 kVp group (n = 33) to form a threshold adjustment cohort (n = 40). The average CT values of liver and subcutaneous abdominal fat tissues were measured under low and high energy bins to serve as the adjusted thresholds for material decomposition. In the test cohort, the correlation and consistency between CT-FF and MRI-PDFF values obtained using thresholds before (the threshold value provided by the machine itself) and after adjustment (threshold obtained from the adjustment queue) were compared. The performance of the adjusted threshold in measuring liver fat content was evaluated in the validation cohort, as well as the consistency across subgroups with different scanning protocols. Results Based on data from the threshold adjustment cohort, the average CT values of liver tissue at 120 kVp were 65 HU and 70 HU in the low and high energy bins, and so were at 140 kVp. For fat tissue, the average CT values in the low and high energy bins were −127 HU and −96 HU at 120 kVp, and −125 HU and −92 HU at 140 kVp, which were used as the density thresholds for material decomposition. In the test cohort, after threshold adjustment, the correlation (r, 0.98 vs. 0.77), consistency (ICC, 0.980 vs. 0.770; r², 0.96 vs. 0.60), and mean difference (−0.7% vs. −18.1%) between CT-FF and MRI-PDFF values were significantly improved. In the entire validation cohort and subgroups with different tube voltages and radiation doses, CT-FF and MRI-PDFF showed excellent correlation and consistency (r = 0.99, P < 0.001, r² = 0.98, ICC = 0.99), with mean differences not exceeding −0.7%. Conclusion Based on the liver tissue characteristics of the Chinese population, this study optimizes the density thresholds of the photon-counting CT material decomposition algorithm, and develops a fat quantification correction standard applicable to Chinese individuals for the first time, significantly improving measurement accuracy. This method may provide a new non-invasive and precise approach for liver fat quantification.

Key words: Metabolic dysfunction-associated steatotic liver disease, Fat quantification, Photon-counting CT, Magnetic resonance imaging, Proton density fat fraction