arXiv subjects

Qiuyi Shen

Publications and source records attributed to Qiuyi Shen.

2 recordsLinked to original sources

arXiv2025

Quantitative Macromolecular Proton Fraction Imaging using Pulsed Spin-Lock

Purpose: Recent studies have shown that spin-lock MRI can simplify quantitative magnetization transfer (MT) by eliminating its dependency on water pool parameters, removing the need for a T1 map in macromolecular proton fraction (MPF) quantification. However, its application is often limited by the requirement for long radiofrequency (RF) pulse durations, which are constrained by RF hardware capabilities despite remaining within specific absorption rate (SAR) safety limits. Methods: To address this challenge, we propose a novel method, MPF mapping using pulsed spin-lock (MPF-PSL). MPF-PSL employs a pulsed spin-lock train with intermittent free precession periods, enabling extended total spin-lock durations without exceeding hardware and specific absorption rate limits. A comprehensive analytical framework was developed to model the magnetization dynamics of the two-pool MT system under pulsed spin-lock, demonstrating that MPF-PSL achieves MT-specific quantification while minimizing confounding effects from the water pool. The proposed method is validated with Bloch-McConnell simulations, phantoms, and in vivo studies at 3T. Results: Both Bloch-McConnell simulations and phantom validation demonstrated that MPF-PSL exhibits robust insensitivity to water pool parameters while enabling high-SNR MPF quantification. In vivo validation studies confirmed the method's clinical utility in detecting collagen deposition in patients with liver fibrosis. Conclusion: MPF-PSL presents a practical solution for quantitative MT imaging, with strong potential for clinical applications.

physics.med-ph↗

arXiv2024

Chemical Shift Encoding based Double Bonds Quantification in Triglycerides using Deep Image Prior

Fatty acid can potentially serve as biomarker for evaluating metabolic disorder and inflammation condition, and quantifying the double bonds is the key for revealing fatty acid information. This study presents an assessment of a deep learning approach utilizing Deep Image Prior (DIP) for the quantification of double bonds and methylene-interrupted double bonds of triglyceride derived from chemical-shift encoded multi-echo gradient echo images, all achieved without the necessity for network training. The methodology implemented a cost function grounded in signal constraints to continually refine the neural network's parameters on a single slice of images through iterative processes. Validation procedures encompassed both phantom experiments and in-vivo scans. The outcomes evidenced a concordance between the quantified values and the established reference standards, notably exemplified by a Pearson correlation coefficient of 0.96 (p = 0.0005) derived from the phantom experiments. The results in water-oil phantom also demonstrate the quantification reliability of the DIP method under the condition of having a relatively low-fat signal. Furthermore, the in-vivo assessments showcased the method's competency by showcasing consistent quantification results that closely mirrored previously published findings concerning subcutaneous fat. In summary, the study underscores the potential of Deep Image Prior in enabling the quantification of double bonds and methylene-interrupted double bonds from chemical-shift encoded multi-echo magnetic resonance imaging (MRI) data, suggesting potential avenues for future research and clinical applications in the field.

physics.med-ph↗