Affiliation:
1. Medical Radiation Physics, Department of Translational Medicine Lund University Malmö Sweden
2. Department of Radiology & Nuclear Medicine Erasmus MC, University Medical Center Rotterdam The Netherlands
3. Clinical Epidemiology Unit, Orthopedics, Department of Clinical Sciences Lund Lund University Lund Sweden
4. Imaging and Physiology Skåne University Hospital Lund Sweden
Abstract
PurposeTo demonstrate the feasibility and accuracy of chemical shift–encoded imaging of the fatty acid composition (FAC) of human bone marrow adipose tissue at 7 T, and to determine suitable image‐acquisition parameters using simulations.MethodsThe noise performance of FAC estimation was investigated using simulations with a range of inter‐echo time, and accuracy was assessed using a phantom experiment. Furthermore, one knee of 8 knee‐healthy subjects (ages 35–54 years) was imaged, and the fractions of saturated fatty acids (SFA) and polyunsaturated fatty acids (PUFA) were mapped. Values were compared between reconstruction methods, and between anatomical regions.ResultsBased on simulations, ΔTE = 0.6 ms was chosen. The phantom experiment demonstrated high accuracy of especially SFA using a constrained reconstruction model (slope = 1.1, average bias = −0.2%). The lowest accuracy was seen for PUFA using a free model (slope = 2.0, average bias = 9.0%). For in vivo images, the constrained model resulted in lower intersubject variation compared with the free model (e.g., in the femoral shaft, the SFA percent‐point range was within 1.0% [vs. 3.0%]). Furthermore, significant regional FAC differences were detected. For example, using the constrained approach, the femoral SFA in the medial condyle was lower compared with the shaft (median [range]: 27.9% [27.1%, 28.4%] vs. 32.5% [31.8%, 32.8%]).ConclusionBone marrow adipose tissue FAC quantification using chemical‐shift encoding is feasible at 7 T. Both the noise performance and accuracy of the technique are superior using a constrained signal model.
Funder
Greta och Johan Kocks stiftelser
H2020 European Research Council
Subject
Radiology, Nuclear Medicine and imaging