These patent-pending optical contrast agents and molecular detection technologies were developed to enhance the ability of optical coherence tomography (OCT) to...
These patent-pending optical contrast agents and molecular detection technologies were developed to enhance the ability of optical coherence tomography (OCT) to non-invasively map molecules in living specimens and diagnose disease where it starts. OCT utilizes low-coherence interferometry to measure the intensity of reflected or backscattered light to form images with micrometer resolution, is readily integrated with existing optical instrumentation and has application across a wide range of biological, medical, surgical, and non-biological specialties.
While the majority of contrast agents are engineered to alter the intensity of backscattered light, this class of near-infrared dyes was designed to transform spectral wavelength, making in situ and in vivo three-dimensional imaging a reality
CT scanners are gathering more data than ever, far exceeding the ability of the hardware and software to process and analyze the data and consequently slowing...
CT scanners are gathering more data than ever, far exceeding the ability of the hardware and software to process and analyze the data and consequently slowing down diagnosis. This is becoming a more serious issue as the field moves from fan-beam (2-D and spiral) to cone-beam (fast volumetric or 3-D) acquisition. These algorithms were developed to address this problem. This suite of patented and patent-pending algorithms reconstructs tomographic images for standard (i.e., 2-D) and volumetric (i.e., 3-D) CT scans 10 to 100 times faster than conventional methods for typical image sizes, lowering scanning costs, increasing throughput, enabling improved image quality, and freeing up precious computer resources.
Fast Hierarchical Backprojection Method for Imaging
This method involves backprojecting a sinogram to a tomographic image by subdividing it into subsinograms corresponding to subimages as small as a single pixel. The subsinograms are backprojected to produce corresponding subimages, and the subimages then are aggregated to create the full tomographic image. As with several of the algorithms described above, speed is greatly enhanced through the use of an approximate decomposition algorithm.
Fast Hierarchical Backprojection for 3-D Radon Transform
With this method, data from a 3-D sinogram are backprojected to form a 3-D volume. An input sinogram is subdivided into subsinograms, which are further subdivided until they represent volumes as small as a single voxel. The subvolumes then are aggregated to form a final volume. Again, this algorithm combines an accurate but slow subdivision algorithm with a faster but less accurate subdivision algorithm, reaching an accurate result quickly.
Fast Hierarchical Native Fan-Beam Tomographic Reconstruction Algorithms
This family of native divergent beam algorithms can be used to reconstruct all divergent-beam tomographic data, including single- and multi-slice 2-D fan-beam and 3-D cone-beam with arbitrary scan trajectories, including single circle and spiral trajectories for short and long objects. The algorithms operate directly on the data without prior rebinning to parallel beam projections. Both reprojection and backprojection functions are available.
Multilevel Domain Decomposition Method for Fast Reprojection of Images
The method involves decomposing an image into one or more subimages, reprojecting the subimages into sinograms (i.e., arrays of numbers), scaling the sinograms, and aggregating the subimage sinograms into a single sinogram of the entire image.
Fast Hierarchical Reprojection Algorithm for Tomography
This variation on the above reprojection method combines an exact algorithm, which is accurate but slow, with an approximation algorithm, which is less accurate but fast, to create an accurate result in a short time.
Fast Hierarchical Reprojection Algorithm for 3-D Radon Transforms
This algorithm is based on 3-D radon transform, which is a mathematical model used in volumetric imaging. It begins by dividing the 3-D image into subvolumes as small as a single voxel. These subvolumes then are reprojected at various orientations to form subsinograms. The subsinograms are then successively aggregated and processed to form a full sinogram for the initial volume. Like the previous algorithm, this technology combines a highly accurate slow subdivision algorithm with a faster but less accurate subdivision algorithm to quickly obtain an accurate result.
Qualified companies are invited to license the algorithms as well as enter into agreements that will allow evaluation and suitable modifications to the algorithms that may be necessary for use in specific applications.
Industrial Imaging: By reconstructing tomograms faster than do previous methods, these algorithms dramatically increase the number of items that can be scanned per hour (i.e., throughput), eliminating the "image reconstruction bottleneck" and significantly reducing manufacturing/ inspections costs. These algorithms can be used with any industry inspection using CT scans:
Security Imaging: The faster imaging speeds enabled by these algorithms will offer dramatic improvements in 3-D CT inspection of baggage or containers for the detection of weapons, explosives, or other hazardous materials. This will be a tremendous benefit as U.S. airports strive to meet new federal baggage inspection requirements.