From the Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No 197 Ruijin 2nd Rd, Huangpu District, Shanghai 200025, China (H.L., X.X., R.D., X.C., H.D., F.Y.); CT Collaboration, Siemens Healthineers, Shanghai, China (Z.X.); and College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China (F.Y.).
Radiology. 2024 Sep;312(3):e240038. doi: 10.1148/radiol.240038.
Background Traditional energy-integrating detector CT has limited utility in accurately quantifying liver fat due to protocol-induced CT value shifts, but this limitation can be addressed by using photon-counting detector (PCD) CT, which allows for a standardized CT value. Purpose To develop and validate a universal CT to MRI fat conversion formula to enhance fat quantification accuracy across various PCD CT protocols relative to MRI proton density fat fraction (PDFF). Materials and Methods In this prospective study, the feasibility of fat quantification was evaluated in phantoms with various nominal fat fractions. Five hundred asymptomatic participants and 157 participants with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) were enrolled between September 2023 and March 2024. Participants were randomly assigned to six groups with different CT protocols regarding tube voltage (90, 120, or 140 kVp) and radiation dose (standard or low). Of the participants in the 120-kVp standard-dose asymptomatic group, 51% (53 of 104) were designated as the training cohort, with the rest of the asymptomatic group serving as the validation cohort. A CT to MRI fat quantification formula was derived from the training cohort to estimate the CT-derived fat fraction (CTFF). CTFF agreement with PDFF and its error were evaluated in the asymptomatic validation cohort and subcohorts stratified by tube voltage, radiation dose, and body mass index, and in the MASLD cohort. The factors influencing CTFF error were further evaluated. Results In the phantoms, CTFF showed excellent agreement with nominal fat fraction (intraclass correlation coefficient, 0.98; mean bias, 0.2%). A total of 412 asymptomatic participants and 122 participants with MASLD were included. 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%). Conclusion Standardized CT value from PCD CT showed a robust and remarkable agreement with MRI PDFF across various protocols and may serve as a precise alternative for liver fat quantification. © RSNA, 2024 See also the editorial by Wildman-Tobriner in this issue.
传统的能量积分探测器 CT 由于协议诱导的 CT 值偏移，在准确量化肝脏脂肪方面的应用有限，但这一限制可以通过使用光子计数探测器（PCD）CT 来解决，PCD CT 允许标准化 CT 值。目的：开发和验证一种通用的 CT 至 MRI 脂肪转换公式，以提高相对于 MRI 质子密度脂肪分数（PDFF）的各种 PCD CT 协议下的脂肪定量准确性。材料与方法：本前瞻性研究评估了不同名义脂肪分数的体模中脂肪定量的可行性。2023 年 9 月至 2024 年 3 月期间，共纳入 500 名无症状参与者和 157 名疑似代谢功能障碍相关脂肪性肝病（MASLD）的参与者。参与者被随机分配到六个具有不同 CT 协议的组，涉及管电压（90、120 或 140 kVp）和辐射剂量（标准或低）。在 120 kVp 标准剂量无症状组的参与者中，51%（104 名中的 53 名）被指定为训练队列，其余无症状组作为验证队列。从训练队列中得出一个 CT 至 MRI 脂肪定量公式，以估计 CT 衍生的脂肪分数（CTFF）。在无症状验证队列及其按管电压、辐射剂量和体重指数分层的子队列以及 MASLD 队列中，评估 CTFF 与 PDFF 的一致性及其误差。进一步评估了影响 CTFF 误差的因素。结果：在体模中，CTFF 与名义脂肪分数显示出极好的一致性（组内相关系数，0.98；平均偏差，0.2%）。共纳入 412 名无症状参与者和 122 名 MASLD 参与者。得出了以下 CT 至 MRI 脂肪转换公式：MRI PDFF（%）=-0.58·CT（HU）+43.1。在所有比较中，CTFF 与 PDFF 具有极好的一致性（平均偏差值<1%）。CTFF 误差不受管电压、辐射剂量、体重指数或 PDFF 的影响。在 MASLD 队列中也发现了 CTFF 与 PDFF 之间的一致性（平均偏差，-0.2%）。结论：PCD CT 的标准化 CT 值在各种协议下与 MRI PDFF 具有强大而显著的一致性，可能成为肝脏脂肪定量的精确替代方法。
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