Saturation transfer MR fingerprinting for magnetization transfer contrast and chemical exchange saturation transfer quantification

• Published in: Magnetic resonance in medicine Vol. 94; no. 3; pp. 993 - 1009
• Main Authors: Singh, Munendra, Kang, Beomgu, Mahmud, Sultan Z., Zijl, Peter, Zhou, Jinyuan, Heo, Hye‐Young
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.09.2025; John Wiley and Sons Inc
• Subjects: Adult; Algorithms; APT; Biophysics; Brain - diagnostic imaging; CEST; Deep Learning; exchange rate; Feasibility studies; Fingerprinting; Humans; Image acquisition; Image Processing, Computer-Assisted - methods; Image reconstruction; Imaging Methodology; Inhomogeneity; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Magnetization; MR fingerprinting; Neural networks; Noise levels; Parameters; Phantoms, Imaging; Protons; Signal-To-Noise Ratio; Substantia alba; Synthesis; Transfer learning; White Matter - diagnostic imaging; MR fingerprinting; deep learning; CEST; exchange rate; APT
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.30532

Purpose The aim of this study was to develop a saturation transfer MR fingerprinting (ST‐MRF) technique using a biophysics model‐driven deep learning approach. Methods A deep learning–based quantitative saturation transfer framework was proposed to estimate water, magnetization transfer contrast, and amide proton transfer (APT) parameters plus B0 field inhomogeneity. This framework incorporated a Bloch‐McConnell simulator during neural network training and enforced consistency between synthesized MRF signals and experimentally acquired ST‐MRF signals. Ground‐truth numerical phantoms were used to assess the accuracy of estimated tissue parameters, and in vivo tissue parameters were validated using synthetic MRI analysis. Results The proposed ST‐MRF reconstruction network achieved a normalized root mean square error (nRMSE) of 9.3% when tested against numerical phantoms with a signal‐to‐noise ratio of 46 dB, which outperformed conventional Bloch‐McConnell fitting (nRMSE of 15.3%) and dictionary‐matching approaches (nRMSE of 19.5%). Synthetic MRI analysis indicated excellent similarity (RMSE = 3.2%) between acquired and synthesized ST‐MRF images, demonstrating high in vivo reconstruction accuracy. In healthy human brains, the APT pool size ratios for gray and white matter were 0.16 ± 0.02% and 0.13 ± 0.02%, respectively, and the exchange rates for gray and white matter were 101 ± 25 Hz and 131 ± 27 Hz, respectively. The reconstruction network processed the eight tissue parameter maps in approximately 27 s for ST‐MRF data sized at 256 × 256 × 9 × 103. Conclusion This study highlights the feasibility of the deep learning–based ST‐MRF imaging for rapid and accurate quantification of free bulk water, magnetization transfer contrast, APT parameters, and B0 field inhomogeneity.