Citations

PurposeTo introduce newly developed MR elastography (MRE)‐based dual‐saturation imaging and dual‐sensitivity motion encoding schemes to directly measure in vivo skull–brain motion, and to study the skull–brain coupling in volunteers with these approaches.MethodsSix volunteers were scanned with a high‐performance compact 3T‐MRI scanner. The skull–brain MRE images were obtained with a dual‐saturation imaging where the skull and brain motion were acquired with fat‐ and water‐suppression scans, respectively. A dual‐sensitivity motion encoding scheme was applied to estimate the heavily wrapped phase in skull by the simultaneous acquisition of both low‐ and high‐sensitivity phase during a single MRE exam. The low‐sensitivity phase was used to guide unwrapping of the high‐sensitivity phase. The amplitude and temporal phase delay of the rigid‐body motion between the skull and brain was measured, and the skull–brain interface was visualized by slip interface imaging (SII).ResultsBoth skull and brain motion can be successfully acquired and unwrapped. The skull–brain motion analysis demonstrated the motion transmission from the skull to the brain is attenuated in amplitude and delayed. However, this attenuation (%) and delay (rad) were considerably greater with rotation (59 ± 7%, 0.68 ± 0.14 rad) than with translation (92 ± 5%, 0.04 ± 0.02 rad). With SII the skull–brain slip interface was not completely evident, and the slip pattern was spatially heterogeneous.ConclusionThis study provides a framework for acquiring in vivo voxel‐based skull and brain displacement using MRE that can be used to characterize the skull–brain coupling system for understanding of mechanical brain protection mechanisms, which has potential to facilitate risk management for future injury.

Magnetic resonance elastography (MRE) is a phase contrast based MRI imaging technique that can quantitatively and non-invasively measure full 3D vector displacement data from propagating acoustic waves in vivo. The data acquired allow the calculation of local values of complex shear modulus and the generation of images that depict the viscoelastic properties of tissue. Acquisitions at different frequencies can capture the dispersive behavior of these quantities, and advanced approaches attempt to map anisotropic and poroelastic properties of materials. We will discuss current approaches for the inversion of MRE data, focusing on the issues and assumptions involved, and highlight some of the challenges faced in such inversions, particularly in thin or small structures in which wave propagation is dominated by waveguide effects. We will also present results based on neural network inversions, which may have lower repeatability error and be more resistant to noise than some current approaches.

PurposeTo implement a reduced field of view (rFOV) technique for cardiac MR elastography (MRE) and to demonstrate the improvement in image quality of both magnitude images and post‐processed MRE stiffness maps compared to the conventional full field of view (full‐FOV) acquisition.MethodsWith Institutional Review Board approval, 17 healthy volunteers underwent both full‐FOV and rFOV cardiac MRE scans using 140‐Hz vibrations. Two cardiac radiologists blindly compared the magnitude images and stiffness maps and graded the images based on several image quality attributes using a 5‐point ordinal scale. Fisher's combined probability test was performed to assess the overall evaluation. The octahedral shear strain‐based signal‐to‐noise ratio (OSS‐SNR) and median stiffness over the left ventricular myocardium were also compared.ResultsOne volunteer was excluded because of an inconsistent imaging resolution during the exam. In the remaining 16 volunteers (9 males, 7 females), the rFOV scans outperformed the full‐FOV scans in terms of subjective image quality and ghosting artifacts in the magnitude images and stiffness maps, as well as the overall preference. The quantitative measurements showed that rFOV had significantly higher OSS‐SNR (median: 1.4 [95% confidence interval (CI): 1.2–1.5] vs. 2.1 [95% CI: 1.8–2.4]), P < 0.05) compared to full‐FOV. Although no significant change was found in the median myocardial stiffness between the 2 scans, we observed a decrease in the stiffness variation within the myocardium from 2.1 kPa (95% CI: [1.9, 2.3]) to 1.9 kPa (95% CI: [1.7, 2.0]) for full‐FOV and rFOV, respectively (P < 0.05) in a subgroup of 7 subjects with ghosting present in the myocardium.ConclusionThis pilot volunteer study demonstrated that rFOV cardiac MRE has the capability to reduce ghosting and to improve image quality in both MRE magnitude images and stiffness maps. Magn Reson Med 80:231–238, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

PurposeTo investigate the feasibility of using artificial neural networks to estimate stiffness from MR elastography (MRE) data.MethodsArtificial neural networks were fit using model‐based training patterns to estimate stiffness from images of displacement using a patch size of ∼1 cm in each dimension. These neural network inversions (NNIs) were then evaluated in a set of simulation experiments designed to investigate the effects of wave interference and noise on NNI accuracy. NNI was also tested in vivo, comparing NNI results against currently used methods.ResultsIn 4 simulation experiments, NNI performed as well or better than direct inversion (DI) for predicting the known stiffness of the data. Summary NNI results were also shown to be significantly correlated with DI results in the liver (R2 = 0.974) and in the brain (R2 = 0.915), and also correlated with established biological effects including fibrosis stage in the liver and age in the brain. Finally, repeatability error was lower in the brain using NNI compared to DI, and voxel‐wise modeling using NNI stiffness maps detected larger effects than using DI maps with similar levels of smoothing.ConclusionArtificial neural networks represent a new approach to inversion of MRE data. Summary results from NNI and DI are highly correlated and both are capable of detecting biologically relevant signals. Preliminary evidence suggests that NNI stiffness estimates may be more resistant to noise than an algebraic DI approach. Taken together, these results merit future investigation into NNIs to improve the estimation of stiffness in small regions. Magn Reson Med 80:351–360, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

PurposeThe homeostasis of intracranial pressure (ICP) is of paramount importance for maintaining normal brain function. A noninvasive technique capable of making direct measurements of ICP currently does not exist. MR elastography (MRE) is capable of noninvasively measuring brain tissue stiffness in vivo, and may act as a surrogate to measure ICP. The objective of this study was to investigate the impact of changing ICP on brain stiffness using MRE in a swine model.MethodsBaseline MRE measurements were obtained, and then catheters were surgically placed into the left and right lateral ventricles of three animals. ICP was systematically increased over the range of 0 to 55 millimeters mercury (mmHg), and stiffness measurements were made using brain MRE at vibration frequencies of 60 hertz (Hz), 90 Hz, 120 Hz, and 150 Hz.ResultsA significant linear correlation between stiffness and ICP in the cross‐subject comparison was observed for all tested vibrational frequencies (P ≤ 0.01). The 120 Hz (0.030 ± 0.004 kilopascal (kPa)/mmHg, P < 0.0001) and 150 Hz (0.031 ± 0.008 kPa/mmHg, P = 0.01) vibrational frequencies had nearly identical slopes, which were approximately two‐ to three‐fold higher than the 90 Hz (0.017 ± 0.002 kPa/mmHg, P < 0.0001) and 60 Hz (0.009 ± 0.002 kPa/mmHg, P = 0.001) slopes, respectively.ConclusionIn this study, MRE demonstrated the potential for noninvasive measurement of changes in ICP. Magn Reson Med 79:1043–1051, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Purpose The stiffness of a myocardial infarct affects the left ventricular pump function and remodeling. Magnetic resonance elastography (MRE) is a noninvasive imaging technique for measuring soft‐tissue stiffness in vivo. The purpose of this study was to investigate the feasibility of assessing in vivo regional myocardial stiffness with high‐frequency 3D cardiac MRE in a porcine model of myocardial infarction, and compare the results with ex vivo uniaxial tensile testing. Methods Myocardial infarct was induced in a porcine model by embolizing the left circumflex artery. Fourteen days postinfarction, MRE imaging was performed in diastole using an echocardiogram‐gated spin‐echo echo‐planar‐imaging sequence with 140‐Hz vibrations and 3D MRE processing. The MRE stiffness and tensile modulus from uniaxial testing were compared between the remote and infarcted myocardium. Results Myocardial infarcts showed increased in vivo MRE stiffness compared with remote myocardium (4.6 ± 0.7 kPa versus 3.0 ± 0.6 kPa, P = 0.02) within the same pig. Ex vivo uniaxial mechanical testing confirmed the in vivo MRE results, showing that myocardial infarcts were stiffer than remote myocardium (650 ± 80 kPa versus 110 ± 20 kPa, P = 0.01). Conclusions These results demonstrate the feasibility of assessing in vivo regional myocardial stiffness with high‐frequency 3D cardiac MRE. Magn Reson Med 79:361–369, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

PurposeTo develop and evaluate an image‐domain noise reduction method for multi‐energy CT (MECT) data.MethodsMulti‐Energy Non‐Local Means (MENLM) is a technique that uses the redundant information in MECT images to achieve noise reduction. In this method, spatio‐spectral features are used to determine the similarity between pixels, making the similarity evaluation more robust to image noise. The performance of this MENLM filter was tested on images acquired on a whole‐body research photon counting CT system. The impact of filtering on image quality was quantitatively evaluated in phantom studies in terms of image noise level (standard deviation of pixel values), noise power spectrum (NPS), in‐plane and cross‐plane spatial resolution, CT number accuracy, material decomposition performance, and subjective low‐contrast spatial resolution using the American College of Radiology (ACR) CT accreditation phantom. Clinical feasibility was assessed by performing MENLM on contrast‐enhanced swine images and unenhanced cadaver head images using clinically relevant doses and dose rates.ResultsThe phantom studies demonstrated that the MENLM filter reduced noise substantially and still preserved the shape and peak frequency of the NPS. With 80% noise reduction, MENLM filtering caused no degradation of high‐contrast spatial resolution, as illustrated by the modulation transfer function (MTF) and slice sensitivity profile (SSP). CT number accuracy was also maintained for all energy channels, demonstrating that energy resolution was not affected by filtering. Material decomposition performance was improved with MENLM filtering. The subjective evaluation using the ACR phantom demonstrated an improvement in low‐contrast performance. MENLM achieved effective noise reduction in both contrast‐enhanced swine images and unenhanced cadaver head images, resulting in improved detection of subtle vascular structures and the differentiation of white/gray matter.ConclusionIn MECT, MENLM achieved around 80% noise reduction and greatly improved material decomposition performance and the detection of subtle anatomical/low‐contrast features while maintaining spatial and energy resolution. MENLM filtering may improve diagnostic or functional analysis accuracy and facilitate radiation dose and contrast media reduction for MECT.

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.

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.