Purpose: Amplified MRI (aMRI) has been introduced as a new method of detecting and visualizing pulsatile brain motion in 2D. Here, we improve aMRI by introducing a novel 3D aMRI approach.Methods: 3D aMRI was developed and tested for its ability to amplify sub‐voxel motion in all three directions. In addition, 3D aMRI was qualitatively compared to 2D aMRI on multi‐slice and 3D (volumetric) balanced steady‐state free precession cine data and phase contrast (PC‐MRI) acquired on healthy volunteers at 3T. Optical flow maps and 4D animations were produced from volumetric 3D aMRI data.Results: 3D aMRI exhibits better image quality and fewer motion artifacts compared to 2D aMRI. The tissue motion was seen to match that of PC‐MRI, with the predominant brain tissue displacement occurring in the cranial‐caudal direction. Optical flow maps capture the brain tissue motion and display the physical change in shape of the ventricles by the relative movement of the surrounding tissues. The 4D animations show the complete brain tissue and cerebrospinal fluid (CSF) motion, helping to highlight the “piston‐like” motion of the ventricles.Conclusions: Here, we introduce a novel 3D aMRI approach that enables one to visualize amplified cardiac‐ and CSF‐induced brain motion in striking detail. 3D aMRI captures brain motion with better image quality than 2D aMRI and supports a larger amplification factor. The optical flow maps and 4D animations of 3D aMRI may open up exciting applications for neurological diseases that affect the biomechanics of the brain and brain fluids.

Increasing evidence for the cumulative effects of head trauma on structural integrity of the brain has emphasized the need to understand the relationship between tissue mechanic properties and injury susceptibility. Here, diffusion tensor imaging, helmet accelerometers and amplified magnetic resonance imaging were combined to gather insight about the region-specific vulnerability of the corpus callosum to microstructural changes in white-matter integrity upon exposure to sub-concussive impacts. A total of 33 male Canadian football players (mean_age = 20.3 ± 1.4 years) were assessed at three time points during a football season (baseline pre-season, mid-season and post-season). The athletes were split into a LOW (N = 16) and HIGH (N = 17) exposure group based on the frequency of sub-concussive impacts sustained on a per-session basis, measured using the helmet-mounted accelerometers. Longitudinal decreases in fractional anisotropy were observed in anterior and posterior regions of the corpus callosum (average cluster size = 40.0 ± 4.4 voxels; P < 0.05, corrected) for athletes from the HIGH exposure group. These results suggest that the white-matter tract may be vulnerable to repetitive sub-concussive collisions sustained over the course of a football season. Using these findings as a basis for further investigation, a novel exploratory analysis of strain derived from sub-voxel motion of brain tissues in response to cardiac impulses was developed using amplified magnetic resonance imaging. This approach revealed specific differences in strain (and thus possibly stiffness) along the white-matter tract (P < 0.0001) suggesting a possible signature relationship between changes in white-matter integrity and tissue mechanical properties. In light of these findings, additional information about the viscoelastic behavior of white-matter tissues may be imperative in elucidating the mechanisms responsible for region-specific differences in injury susceptibility observed, for instance, through changes in micro-structural integrity following exposure to sub-concussive head impacts.

Amplified MRI (aMRI) is a promising new technique that can visualize pulsatile brain tissue motion by amplifying sub-voxel motion in cine MRI data, but it lacks the ability to quantify the sub-voxel motion field in physical units. Here, we introduce a novel post-processing algorithm called 3D quantitative amplified MRI (3D q-aMRI). This algorithm enables the visualization and quantification of pulsatile brain motion. 3D q-aMRI was validated and optimized on a 3D digital phantom and was applied in vivo on healthy volunteers for its ability to accurately measure brain parenchyma and CSF voxel displacement. Simulation results show that 3D q-aMRI can accurately quantify sub-voxel motions in the order of 0.01 of a voxel size. The algorithm hyperparameters were optimized and tested on in vivo data. The repeatability and reproducibility of 3D q-aMRI were shown on six healthy volunteers. The voxel displacement field extracted by 3D q-aMRI is highly correlated with the displacement measurements estimated by phase contrast (PC) MRI. In addition, the voxel displacement profile through the cerebral aqueduct resembled the CSF flow profile reported in previous literature. Differences in brain motion was observed in patients with dementia compared with age-matched healthy controls. In summary, 3D q-aMRI is a promising new technique that can both visualize and quantify pulsatile brain motion. Its ability to accurately quantify sub-voxel motion in physical units holds potential for the assessment of pulsatile brain motion as well as the indirect assessment of CSF homeostasis. While further research is warranted, 3D q-aMRI may provide important diagnostic information for neurological disorders such as Alzheimer’s disease.

Histological haematoxylin and eosin–stained (H&E) tissue sections are used as the gold standard for pathologic detection of cancer, tumour margin detection, and disease diagnosis ¹ . Producing H&E sections, however, is invasive and time-consuming. Non-invasive optical imaging modalities, such as optical coherence tomography (OCT), permit label-free, micron-scale 3D imaging of biological tissue microstructure with significant depth (up to 1mm) and large fields-of-view ² , but are difficult to interpret and correlate with clinical ground truth without specialized training ³ . Here we introduce the concept of a virtual biopsy, using generative neural networks to synthesize virtual H&E sections from OCT images. To do so we have developed a novel technique, “optical barcoding”, which has allowed us to repeatedly extract the 2D OCT slice from a 3D OCT volume that corresponds to a given H&E tissue section, with very high alignment precision down to 25 microns. Using 1,005 prospectively collected human skin sections from Mohs surgery operations of 71 patients, we constructed the largest dataset of H&E images and their corresponding precisely aligned OCT images, and trained a conditional generative adversarial network ⁴ on these image pairs. Our results demonstrate the ability to use OCT images to generate high-fidelity virtual H&E sections and entire 3D H&E volumes. Applying this trained neural network to in vivo OCT images should enable physicians to readily incorporate OCT imaging into their clinical practice, reducing the number of unnecessary biopsy procedures.

Brain pulsatility offers a compelling application in the study of cerebral biomechanics, particularly for mild traumatic brain injury (mTBI) and elevated intracranial pressure (ICP). In this study, we used amplified MRI to quantify brain tissue pulsations. Dynamic mode decomposition (DMD) processing was then applied to provide a spatio-temporal analysis of motion. Four distinct use cases were examined: (i) resting versus exertion-induced heart rate changes, (ii) pre- and post-lumbar puncture (LP), (iii) baseline versus post-brain injury, and (iv) a test–retest case. Results demonstrate that brain tissue motion varies significantly across conditions, with DMD revealing distinct modes and frequencies corresponding to physiological changes. Notably, mTBI showed an increase in pulsatile motion post-injury, while elevated ICP exhibited altered pulsatility patterns post-LP, indicating a potential biomarker for injury and pressure-related changes. This approach offers new insights into physiological and pathological brain pulsatility; however, the study’s limited sample size, reliance on retrospective gating and assumptions regarding pulsatile motion highlight the need for larger and more diverse cohorts to confirm these findings. Despite these limitations, our results suggest our dynamical analysis approach could become a valuable tool for assessing intracranial dynamics, with applications in clinical diagnostics and research on neurovascular and neurological conditions.

This study investigates intracranial dynamics following the Monro–Kellie doctrine, depicting how brain pulsatility, cerebrospinal fluid (CSF) flow and cerebral blood flow (CBF) interact under resting and exercise conditions. Using quantitative amplified magnetic resonance imaging (q-aMRI) alongside traditional MRI flow metrics, we measured and analysed blood flow, CSF dynamics and brain displacement in a cohort of healthy adults both at rest and during low-intensity handgrip exercise. Exercise was found to reduce pulsatility in CBF while increasing CSF flow and eliminating CSF regurgitation, highlighting a shift towards more sustained forward flow patterns (from cranial to spinal compartments). Displacement analysis using q-aMRI revealed a consistent trend of reduced whole brain motion during exercise, though as the sample of data that met quality control was low ( n = 5), this was not a significant result. There was an observable decrease in the motion of third and fourth ventricles, linking ventricular displacement to CSF flow alterations. These findings suggest that exercise may not only affect the rate and directionality of CSF flow but also modulate brain tissue motion, supporting cerebral homeostasis. This study offers insights into how the brain adapts dynamically under varying conditions, with implications for understanding intracranial pressure regulation in humans and diagnostic contexts.