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JWE Model Engineer Scans Disc 1 Of 2 _TOP_

OCT BasicsWhile the earliest time-domain (TD) OCT technology could only acquire 400 scans per second, current SD-OCT models, depending on the manufacturer, can capture between 26,000 and 70,000 axial-scans per second (Table 1).1,2 This improvement is beneficial to the clinician because it minimizes image artifacts, makes 3D imaging possible and increases image resolution.1,3

JWE Model Engineer Scans Disc 1 Of 2

Glaucoma is characterized by loss of retinal ganglion cells and their axons, and by the remodeling of the optic nerve head, which manifests as neuroretinal rim narrowing, optic disc excavation and displacement of lamina cribrosa. The ONH is formed by the axons of the retinal ganglion cells, blood vessels and glial tissue. The axons exit the eye through the neural canal opening and are supported by the lamina cribrosa (essentially a connective tissue structure), which comprises the floor of the physiologic cup. In 1979, Harry Quigley, MD, and William R. Green, MD, demonstrated that the increased optic disc cup size was caused by the loss of retinal ganglion cells and their axons.1 In addition, the connective tissue in the ONH undergoes profound remodeling in glaucoma, leading to posterior deformation of the lamina cribrosa as well as expansion of anterior and posterior neural canal openings, as illustrated in an experimental monkey model of glaucoma.2 In addition to cupping of the optic nerve, glaucoma patients often show sectorial loss of nerve fiber layer, which can be visualized with a red-free light.

Geomagic Design X converts 3D scan data into high-quality, feature-based CAD models. The software combines automatic and guided solid model extraction in a unique way while being incredibly accurate. On top of this it offers you exact surface fitting to organic 3D scans; mesh editing; and point-cloud processing.

Matterport is the standard for 3D space capture. Our all-in-one platform transforms real-life spaces into immersive digital twin models. So much more than panoramic scans, Matterport empowers people to capture and connect rooms to create truly interactive 3D models of spaces.

Sit back and let Matterport's Cortex AI platform turn your scan into an interactive 3D model. Cortex can identify objects within rooms, stitch all of your scans together, and reconstruct your space into an immersive virtual tour.

Reverse engineering involves taking an existing part, assembly or machine and creating an as-built CAD model to be used for reproduction or to make modifications. We have a wide variety of tools at our disposal making no item too big or too small. Our extreme accuracy and detailed precision provide confidence in the results.

Looking at the dimensional precision of the scans, the average offset of the vertices between the scanned model and the original STL (which is what we want to emulate) was just 0.005mm with a standard deviation of 0.075mm. Excellent results.

As for the actual quality of the scans: fantastic. The average offset between the scanned models and the original STL file was just 0.061mm with a standard deviation of 0.158mm. Our 1mm resolution scan was particularly impressive, as the peel 2-S managed to capture the entire model within 0.20mm of the intended geometry.

Dynamic MRI is another important MR technique to monitor dynamic processes such as brain hemodynamics and cardiac motion. Among the various forms of dy- namic MR modeling, here we mainly focus on the k-t formulation. Specifically, consider a discrete imaging equation for cartesian trajectory for simplicity. Because the samples along the readout direction are fully sampled, most of the dynamic MR formulation is applied separably after taking the Fourier transform along the read- out direction. More specifically, let γ(s, t) denote the unknown image content (for example, proton density, T1/T2 weighted image, etc.) on the spatial coordinate s along the phase encoding line at time instance t. Then, the k-t space measurement b(k, t) at time t is given by

We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. We also compare and contrast the accuracy of the proposed algorithm in segmenting a FIB-SEM dataset against the accuracy of segmentations from a machine learning program called Ilastik and discuss the advantages and disadvantages that these two approaches have.


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