Noninvasive virtual biopsy using micro-registered optical coherence tomography (OCT) in human subjects.

Histological hematoxylin and eosin-stained (H&E) tissue sections are used as the gold standard for pathologic detection of cancer, tumor margin detection, and disease diagnosis. Producing H&E sections, however, is invasive and time-consuming. While deep learning has shown promise in virtual staining of unstained tissue slides, true virtual biopsy requires staining of images taken from intact tissue. In this work, we developed a micron-accuracy coregistration method [micro-registered optical coherence tomography (OCT)] that can take a two-dimensional (2D) H&E slide and find the exact corresponding section in a 3D OCT image taken from the original fresh tissue. We trained a conditional generative adversarial network using the paired dataset and showed high-fidelity conversion of noninvasive OCT images to virtually stained H&E slices in both 2D and 3D. Applying these trained neural networks to in vivo OCT images should enable physicians to readily incorporate OCT imaging into their clinical practice, reducing the number of unnecessary biopsy procedures.

Intravital 3D visualization and segmentation of murine neural networks at micron resolution.

Optical coherence tomography (OCT) allows label-free, micron-scale 3D imaging of biological tissues' fine structures with significant depth and large field-of-view. Here we introduce a novel OCT-based neuroimaging setting, accompanied by a feature segmentation algorithm, which enables rapid, accurate, and high-resolution in vivo imaging of 700 µm depth across the mouse cortex. Using a commercial OCT device, we demonstrate 3D reconstruction of microarchitectural elements through a cortical column. Our system is sensitive to structural and cellular changes at micron-scale resolution in vivo, such as those from injury or disease. Therefore, it can serve as a tool to visualize and quantify spatiotemporal brain elasticity patterns. This highly transformative and versatile platform allows accurate investigation of brain cellular architectural changes by quantifying features such as brain cell bodies' density, volume, and average distance to the nearest cell. Hence, it may assist in longitudinal studies of microstructural tissue alteration in aging, injury, or disease in a living rodent brain.

3D amplified MRI (aMRI).

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.

Amplified Flow Imaging (aFlow): A Novel MRI-Based Tool to Unravel the Coupled Dynamics Between the Human Brain and Cerebrovasculature.

With each heartbeat, periodic variations in arterial blood pressure are transmitted along the vasculature, resulting in localized deformations of the arterial wall and its surrounding tissue. Quantification of such motions may help understand various cerebrovascular conditions, yet it has proven technically challenging thus far. We introduce a new image processing algorithm called amplified Flow (aFlow) which allows to study the coupled brain-blood flow motion by combining the amplification of cine and 4D flow MRI. By incorporating a modal analysis technique known as dynamic mode decomposition into the algorithm, aFlow is able to capture the characteristics of transient events present in the brain and arterial wall deformation. Validating aFlow, we tested it on phantom simulations mimicking arterial walls motion and observed that aFlow displays almost twice higher SNR than its predecessor amplified MRI (aMRI). We then applied aFlow to 4D flow and cine MRI datasets of 5 healthy subjects, finding high correlations between blood flow velocity and tissue deformation in selected brain regions, with correlation values r = 0.61 , 0.59, 0.52 for the pons, frontal and occipital lobe ( ). Finally, we explored the potential diagnostic applicability of aFlow by studying intracranial aneurysm dynamics, which seems to be indicative of rupture risk. In two patients, aFlow successfully visualized the imperceptible aneurysm wall motion, additionally quantifying the increase in the high frequency wall displacement after a one-year follow-up period (20%, 76%). These preliminary data suggest that aFlow may provide a novel imaging biomarker for the assessment of aneurysms evolution, with important potential diagnostic implications.

Novel strain analysis informs about injury susceptibility of the corpus callosum to repeated impacts.

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 (meanage = 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 behaviour 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 microstructural integrity following exposure to sub-concussive head impacts.

Revealing sub-voxel motions of brain tissue using phase-based amplified MRI (aMRI).

PURPOSE: Amplified magnetic resonance imaging (aMRI) was recently introduced as a new brain motion detection and visualization method. The original aMRI approach used a video-processing algorithm, Eulerian video magnification (EVM), to amplify cardio-ballistic motion in retrospectively cardiac-gated MRI data. Here, we strive to improve aMRI by incorporating a phase-based motion amplification algorithm. METHODS: Phase-based aMRI was developed and tested for correct implementation and ability to amplify sub-voxel motions using digital phantom simulations. The image quality of phase-based aMRI was compared with EVM-based aMRI in healthy volunteers at 3T, and its amplified motion characteristics were compared with phase-contrast MRI. Data were also acquired on a patient with Chiari I malformation, and qualitative displacement maps were produced using free form deformation (FFD) of the aMRI output. RESULTS: Phantom simulations showed that phase-based aMRI has a linear dependence of amplified displacement on true displacement. Amplification was independent of temporal frequency, varying phantom intensity, Rician noise, and partial volume effect. Phase-based aMRI supported larger amplification factors than EVM-based aMRI and was less sensitive to noise and artifacts. Abnormal biomechanics were seen on FFD maps of the Chiari I malformation patient. CONCLUSION: Phase-based aMRI might be used in the future for quantitative analysis of minute changes in brain motion and may reveal subtle physiological variations of the brain as a result of pathology using processing of the fundamental harmonic or by selectively varying temporal harmonics. Preliminary data shows the potential of phase-based aMRI to qualitatively assess abnormal biomechanics in Chiari I malformation.