Chen, Jun

AbstractPurposeWhile standard MR elastography (MRE) uses harmonic mechanical waves, there are some applications in which imaging and analysis of transient mechanical motion are of interest. Wavelet MRE has been developed for detecting broadband motion from transient excitation. The goal of this study was to evaluate the accuracy and efficiency of wavelet MRE for transient displacement detection in brain MRE applications.MethodsTransient motion was induced in a gel phantom, while MRE images were acquired using bipolar motion‐encoding gradient (MEG) at multiple frequencies (20–200 Hz). Displacements were estimated using (1) combinations of multiple MEGs forming the wavelet MRE and (2) deconvolution from a single MEG. These estimated displacements were used to calculate the MRE phase for each MEG. Correlation (r2) between the calculated and acquired phases was evaluated.Three healthy volunteers were scanned twice in a clinical scanner using wavelet MRE with an occipital impact. Time‐resolved brain translation, rotation, and maximal principal strain were calculated. Repeatability was assessed both qualitatively and through Pearson correlation.ResultsWavelet MRE outperformed standard MRE in displacement estimation, showing higher correlations between calculated and acquired phase, even with fewer phase offsets. In the volunteer study, consistent temporal motion dynamics and spatial maximal principal strain distributions across repeated scans demonstrated the repeatability of wavelet MRE.ConclusionThis study validated the accuracy and efficiency of wavelet MRE for broadband motion detection and demonstrated its feasibility and repeatability in vivo. This technique shows promise for advancing our understanding of the injury risks and mechanisms associated with sports‐related head trauma.

Abstract Purpose To evaluate the test–retest repeatability of a rapid, free‐breathing two‐dimensional (2D) MR elastography (MRE) technique and to assess the reliability of the liver‐stiffness measurements compared with conventional breath‐hold MREs. Methods Fifteen and 115 participants were enrolled in the technical repeatability and measurement equivalence assessment cohorts, respectively. All participants underwent rapid free‐breathing and conventional breath‐hold 2D MRE (twice in repeatability cohort) on 1.5T scanners. Both methods have four phase offsets over one harmonic motion cycle at 60 Hz; one and 10 cycles were collected and processed in breath‐hold and free‐breathing MREs, respectively. The liver stiffness measurements of free‐breathing (LS F ) and breath‐hold MRE (LS B ) were calculated from manually drawn regions of interest. The repeatability coefficients and Spearman correlation were used to assess technical repeatability and measurement agreement between LS F and LS B . Univariable and multivariable linear regressions were performed to identify potential influencing factors, including age, sex, body mass index, and fat fraction, in the measurement agreement between LS B and LS F . Results The repeatability coefficient of free‐breathing 2D MRE is comparable to breath‐hold MRE (LS F : 20.8%, LS B : 20.4%). LS F showed a strong agreement and significant correlation with LS B (LS F = 1.01 × LS B , ρ = 0.94, p < 0.001). The measurement agreement between LS F and LS B was only significantly affected by sex ( p = 0.047) after adjusting for confounding factors of age, body mass index, and fat fraction. Conclusions The nongated, free‐breathing, multislice 2D MRE technique can provide reliable liver stiffness measurements compared with conventional breath‐hold 2D MRE. It could provide a comfortable alternative method with reliable liver stiffness measurements for patients who have difficulty in suspending respiration.

Abstract Purpose:Quantify in vivo biomechanical tissue properties in various breast densities and in normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk.Methods: Patients with normal risk or high risk of breast cancer underent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (normal versus high). A multivariable logistic regression model was used to evaluate whether the MRE parameters (separately for stiffness, elasticity, and viscosity) predicted risk status after controlling for clinical factors.Results: 50 normal risk and 86 high risk patients were included. Risk groups were similar on age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients (N = 55) compared to normal risk patients (N = 34; all p < 0.001). Stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity.Conclusion: A structurally-based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.

10541 Background: Biomechanical tissue properties may vary in the breasts of patients at elevated risk for breast cancer. We aim to quantify in vivo biomechanical tissue properties in various breast densities and in both normal risk and high risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association of biomechanical properties of the breast with cancer risk. Methods: In this IRB–approved prospective single-institution study, we recruited two groups of women differing by breast cancer risk to undergo a 3.0 T dynamic contrast enhanced MRI/MRE of the breast. Low-average risk women were defined as having no personal or significant family history of breast cancer, no prior high risk breast biopsies and a negative mammography within 12 months. High-risk breast cancer patients were recruited from those patients who underwent standard of care breast MR. Within each breast density group (non-dense versus dense), two-sample t-tests were used to compare breast stiffness, elasticity, and viscosity across risk groups (low-average vs high). Results: There were 50 low-average risk and 86 high-risk patients recruited to the study. The risk groups were similar on age (mean age = 55.6 and 53.6 years), density (68% vs. 64% dense breasts) and menopausal status (66.0% vs. 69.8%). Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high risk patients ( N = 55) compared to low-average risk patients ( N = 34; all p < 0.001). In the multivariate logistic regression model, breast stiffness remained a significant predictor of risk status (OR=4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, MRI BPE, age, and menopausal status. Similar results were seen for breast elasticity (OR=4.88, 95% CI [2.08, 11.43]) and viscosity (OR=11.49, 95% CI [1.15, 114.89]). Conclusions: Structurally-based, quantitative biomarker of tissue stiffness obtained from global 3D breast MRE is associated with differences in breast cancer risk in dense breasts. As such, tissue stiffness could provide a novel prognostic marker to help identify the subset of high-risk women with dense breasts who would benefit from increased surveillance.[Table: see text]