Intrinsic motion of brain parenchyma, spinal cord and arteries can be altered by pathology or natural factors as a result of changing tissue properties, vascular compliance, and pressure and flow dynamics. Conventional MRI has limited ability to image these changes, partly because of its lack of sensitivity to small but macroscopic motion. Here, we introduce a new motion detection and visualization method called amplified Magnetic Resonance Imaging (aMRI). aMRI uses the Eulerian Video Magnification technique (EVM) method [1], originally developed for use in conventional videography, to magnify cardiac-induced brain motion recorded by retrospectively cardiac-gated MRI. The heart is assumed to act as an endogenous mechanical driver, producing subtle changes that can be amplified to reveal useful biomechanical information.Image acquisition: With IRB approval, we scanned 4 healthy volunteers (2M/2F, 29-64 years old) using 3T GE MR750 Discovery MRI system (GE Healthcare, Milwaukee, WI) and an 8-channel head coil. The aMRI method is implemented with the cardiac-gated balanced steady-state free precession (bSSFP) sequence [2] (Fig. 1a) to produce cine MRI images. For all scans, a 2D mid-sagittal slice of the brain was obtained (matrix size = 224², flip angle = 45°, TR/TE = 3.6/1.3 msec; FOV = 24 mm²; slice-thickness = 4 mm, acceleration factor [R]= 2, peripheral cardiac gating, views/segment [N_seg] = 1, retrospective re-binning of data to 150 cardiac phases, scan time = 3:30 min). Phase-contrast cine MR images were acquired as a gold standard [3] (matrix size = 224², 42 slices, flip angle = 15°, TR/TE = 5.5/12 ms; FOV = 24 cm²; slthck = 4 mm, V_enc = 5 cm/sec in A/P and S/I directions, R = 2, N_seg = 10, scan time = 5 min). To test the hypothesis that aMRI reflects tissue compliance, we manipulated intracranial pressure in three volunteers by performing a 20sec physiologic challenge called the Valsalva maneuver [4]. Motion amplification: Fig. 1b describe the EVM method: it takes cine MRI data as ‘video input’, performs spatial decomposition, then temporal filtering and frequency-selective amplification of the spatial components to synthesize a motion-amplified cine data set. For this study, we used: an ideal narrowband filter with a pass band of 0.5-1.5 Hz, corresponding to a cardiac cycle of 30-90 beats per minute; a user-defined amplification parameter, α = 200; and spatial frequency cutoff, λ_c = 200. Normalized variance maps (calculated over the cardiac cycle) and difference maps (amplified frames minus corresponding unamplified/reference frames) were produced. Figure 2 and its associated movie Video 1 illustrate a dataset where output images displayed amplified subtle motion of brain structures arising from cardiac pulsatility. While movement of the brain was barely perceptible in the input data, it was significantly amplified in the output images, especially near the brainstem, cerebellum and spinal cord. This motion is highlighted in a spatiotemporal x-t and y-t slice-time profile through the brain (Fig. 2a-c). Difference maps and variance maps (Fig. 2d-e) also show amplified motion. Figure 3 clearly demonstrates that amplified displacement of aMRI produces better visualization of brain motion than the subtle, unamplified displacement revealed by phase-contrast MRI. For the Valsalva experiment, participants showed considerably reduced brain tissue displacement during the Valsalva maneuver compared with that during free breathing (Figure 4). These findings support the expectation that increased intracranial pressure induced by the Valsalva maneuver reduces brain tissue motion [5]. It is important to note that there is no simple relationship converting values of α and λ_c to a physical amplification factor, as noted in the original EVM paper [1]. In our implementation, our heuristic focused on achieving adequate motion amplification without the introduction of confounding image artifacts and noise, by independently adjusting α and λ_c. Here we introduce a new MR-based method called aMRI, in which near-imperceptible brain motion can be enhanced through spatio-temporal processing of dynamic cardiac-gated MRI acquisitions. Using aMRI, one may observe important biomechanical aspects of the brain and potentially other organs in a non-invasive manner that may shed light on a large number of clinical applications in medical imaging. This approach may enable the characterization of brain tissue integrity in a wide range of neurological diseases in which changes in the biomechanical properties of the brain can lead to dramatic changes in pressure and flow dynamics, and hence tissue motion.