PurposeTo evaluate if cardiac magnetic resonance elastography (MRE) can measure increased stiffness in patients with cardiac amyloidosis. Myocardial tissue stiffness plays an important role in cardiac function. A noninvasive quantitative imaging technique capable of measuring myocardial stiffness could aid in disease diagnosis, therapy monitoring, and disease prognostic strategies. We recently developed a high‐frequency cardiac MRE technique capable of making noninvasive stiffness measurements.Materials and MethodsIn all, 16 volunteers and 22 patients with cardiac amyloidosis were enrolled in this study after Institutional Review Board approval and obtaining formal written consent. All subjects were imaged head‐first in the supine position in a 1.5T closed‐bore MR imager. 3D MRE was performed using 5 mm isotropic resolution oblique short‐axis slices and a vibration frequency of 140 Hz to obtain global quantitativein vivoleft ventricular stiffness measurements. The median stiffness was compared between the two cohorts. An octahedral shear strain signal‐to‐noise ratio (OSS‐SNR) threshold of 1.17 was used to exclude exams with insufficient motion amplitude.ResultsFive volunteers and six patients had to be excluded from the study because they fell below the 1.17 OSS‐SNR threshold. The myocardial stiffness of cardiac amyloid patients (median: 11.4 kPa, min: 9.2, max: 15.7) was significantly higher (P = 0.0008) than normal controls (median: 8.2 kPa, min: 7.2, max: 11.8).ConclusionThis study demonstrates the feasibility of 3D high‐frequency cardiac MRE as a contrast‐agent‐free diagnostic imaging technique for cardiac amyloidosis.Level of Evidence:2Technical Efficacy:Stage 2J. Magn. Reson. Imaging 2017;46:1361–1367.
Slip interface imaging based on MR‐elastography preoperatively predicts meningioma–brain adhesion
PurposeTo investigate the ability of slip interface imaging (SII), a recently developed magnetic resonance elastography (MRE)‐based technique, to predict the degree of meningioma–brain adhesion, using findings at surgery as the reference standard.Materials and MethodsWith Institutional Review Board approval and written informed consent, 25 patients with meningiomas >2.5 cm in maximal diameter underwent preoperative SII assessment. Intracranial shear motions were introduced using a soft, pillow‐like head driver and the resulting displacement field was acquired with an MRE pulse sequence on 3T MR scanners. The displacement data were analyzed to determine tumor–brain adhesion by assessing intensities on shear line images and raw as well as normalized octahedral shear strain (OSS) values along the interface. The SII findings of shear line images, OSS, and normalized OSS were independently and blindly correlated with surgical findings of tumor adhesion by using the Cohen's κ coefficient and chi‐squared test.ResultsNeurosurgeons categorized the surgical plane as extrapial (no adhesion) in 15 patients, mixed in four, and subpial (adhesion) in six. Both shear line images and OSS agreed with the surgical findings in 18 (72%) cases (fair agreement, κ = 0.37, 95% confidence interval [CI]: 0.05–0.69), while normalized OSS was concordant with the surgical findings in 23 (92%) cases (good agreement, κ = 0.86, 95% CI: 0.67–1). The correlation between SII predictions (shear line images, OSS, and normalized OSS) and the surgical findings were statistically significant (chi‐squared test, P = 0.02, P = 0.02, and P < 0.0001, respectively).ConclusionSII preoperatively evaluates the degree of meningioma–brain adhesion noninvasively, allowing for improved prediction of surgical risk and tumor resectability.Level of Evidence: 1Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2017;46:1007–1016.
Dual echo Dixon imaging with a constrained phase signal model and graph cuts reconstruction
Abstract Due to engineering limitations, the spatial encoding gradient fields in conventional magnetic resonance imaging cannot be perfectly linear and always contain higher-order, nonlinear components. If ignored during image reconstruction, gradient nonlinearity (GNL) manifests as image geometric distortion. Given an estimate of the GNL field, this distortion can be corrected to a degree proportional to the accuracy of the field estimate. The GNL of a gradient system is typically characterized using a spherical harmonic polynomial model with model coefficients obtained from electromagnetic simulation. Conventional whole-body gradient systems are symmetric in design; typically, only odd-order terms up to the 5th-order are required for GNL modeling. Recently, a high-performance, asymmetric gradient system was developed, which exhibits more complex GNL that requires higher-order terms including both odd- and even-orders for accurate modeling. This work characterizes the GNL of this system using an iterative calibration method and a fiducial phantom used in ADNI (Alzheimer’s Disease Neuroimaging Initiative). The phantom was scanned at different locations inside the 26 cm diameter-spherical-volume of this gradient, and the positions of fiducials in the phantom were estimated. An iterative calibration procedure was utilized to identify the model coefficients that minimize the mean-squared-error between the true fiducial positions and the positions estimated from images corrected using these coefficients. To examine the effect of higher-order and even-order terms, this calibration was performed using spherical harmonic polynomial of different orders up to the 10th-order including even- and odd-order terms, or odd-order only. The results showed that the model coefficients of this gradient can be successfully estimated. The residual root-mean-squared-error after correction using up to the 10th-order coefficients was reduced to 0.36 mm, yielding spatial accuracy comparable to conventional whole-body gradients. The even-order terms were necessary for accurate GNL modeling. In addition, the calibrated coefficients improved image geometric accuracy compared with the simulation-based coefficients.
Gradient pre-emphasis to counteract first-order concomitant fields on asymmetric MRI gradient systems