MR elastography-based slip interface imaging (SII) for functional assessment of myofascial interfaces: A feasibility study.

PURPOSE: Abnormal adherence at functional myofascial interfaces is hypothesized as an important phenomenon in myofascial pain syndrome. This study aimed to investigate the feasibility of MR elastography (MRE)-based slip interface imaging (SII) to visualize and assess myofascial mobility in healthy volunteers. METHODS: SII was used to assess local shear strain at functional myofascial interfaces in the flexor digitorum profundus (FDP) and thighs. In the FDP, MRE was performed at 90 Hz vibration to each index, middle, ring, and little finger. Two thigh MRE scans were performed at 40 Hz with knees flexed and extended. The normalized octahedral shear strain (NOSS) maps were calculated to visualize myofascial slip interfaces. The entropy of the probability distribution of the gradient NOSS was computed for the two knee positions at the intermuscular interface between vastus lateralis and vastus intermedius, around rectus femoris, and between vastus intermedius and vastus medialis. RESULTS: NOSS map depicted distinct functional slip interfaces in the FDP for each finger. Compared to knee flexion, clearer slip interfaces and larger gradient NOSS entropy at the vastus lateralis-vastus intermedius interface were observed during knee extension, where the quadriceps are not passively stretched. This suggests the optimal position for using SII to visualize myofascial slip interface in skeletal muscles is when muscles are not subjected to any additional force. CONCLUSION: The study demonstrated that MRE-based SII can visualize and assess myofascial interface mobility in extremities. The results provide a foundation for investigating the hypothesis that myofascial pain syndrome is characterized by changes in the mobility of myofascial interfaces.

Improved quantification of tumor adhesion in meningiomas using MR elastography-based slip interface imaging.

Meningiomas, the most prevalent primary benign intracranial tumors, often exhibit complicated levels of adhesion to adjacent normal tissues, significantly influencing resection and causing postoperative complications. Surgery remains the primary therapeutic approach, and when combined with adjuvant radiotherapy, it effectively controls residual tumors and reduces tumor recurrence when complete removal may cause a neurologic deficit. Previous studies have indicated that slip interface imaging (SII) techniques based on MR elastography (MRE) have promise as a method for sensitively determining the presence of tumor-brain adhesion. In this study, we developed and tested an improved algorithm for assessing tumor-brain adhesion, based on recognition of patterns in MRE-derived normalized octahedral shear strain (NOSS) images. The primary goal was to quantify the tumor interfaces at higher risk for adhesion, offering a precise and objective method to assess meningioma adhesions in 52 meningioma patients. We also investigated the predictive value of MRE-assessed tumor adhesion in meningioma recurrence. Our findings highlight the effectiveness of the improved SII technique in distinguishing the adhesion degrees, particularly complete adhesion. Statistical analysis revealed significant differences in adhesion percentages between complete and partial adherent tumors (p = 0.005), and complete and non-adherent tumors (p<0.001). The improved technique demonstrated superior discriminatory ability in identifying tumor adhesion patterns compared to the previously described algorithm, with an AUC of 0.86 vs. 0.72 for distinguishing complete adhesion from others (p = 0.037), and an AUC of 0.72 vs. 0.67 for non-adherent and others. Aggressive tumors exhibiting atypical features showed significantly higher adhesion percentages in recurrence group compared to non-recurrence group (p = 0.042). This study validates the efficacy of the improved SII technique in quantifying meningioma adhesions and demonstrates its potential to affect clinical decision-making. The reliability of the technique, coupled with potential to help predict meningioma recurrence, particularly in aggressive tumor subsets, highlights its promise in guiding treatment strategies.