Research Terms
This algorithm combines multiple scans from a scanning tunneling microscope to reduce or eliminate errors in individual scans without any hardware upgrades. Scanning tunneling microscopes are electron microscopes that show 3D images of a sample on an atomic level. Available scanning tunneling microscopes are not able to differentiate between two-dimension and 3D conduction or to accurately measure transport properties and small signals in 2D materials and are limited by equipment response time and amount of data acquired when speed and throughput are increased.
Researchers at the University of Florida have developed a computational algorithm within a software package to more rapidly analyze scanning tunneling microscope data and high-throughput measurements. This software creates a solution to the problem that noise and inconsistency in multiple scans cause to the size and complexity of scanning tunneling microscope data and is also applicable to other scanning probe instruments, such as atomic force microscopy.
Software package for image extraction and correction enabling for high-speed, high-throughput scanning tunneling microscopes
Scanning tunneling microscopes show the position of individual atoms with high resolution. This software utilizes algorithms to analyze several scanning tunneling microscope scans to obtain an accurate image of the materials surface on an atomic level. Correcting information from both forward and backward scans eliminates significant image distortions and scan drift that can be present in scans from high-speed scanning tunneling microscopes. The software aligns scan traces and generates weighting factors from the scan alignment based on the smoothness to determine the accurate position of atoms on the surface.
This diagnostic software/hardware tool detects unusual iron deposits in the living human brain and other tissue. These deposits may indicate risk for neurodegenerative diseases, such as Alzheimer's or Parkinson's, before symptoms occur. Early treatment of neurodegenerative diseases is particularly important because areas of the brain may already be irreparably damaged by the time symptoms appear. Alzheimer's disease, for example, is the most common cause of dementia, affecting more than 35 million people worldwide in 2010. The effects of Alzheimer's are both devastating and life-altering for patients and their families, and the global cost of Alzheimer’s disease is estimated at $315 billion annually. The "gold standard" for diagnosis of Alzheimer's disease is post-mortem examination of the brain. It has been shown that the identification of abnormal iron in brain tissue can be an indicator of Alzheimer's risk. Researchers from the University of Florida have developed a tool that uses MRI technology to detect minute abnormal iron deposits in the living brain, leading to early detection of risk for neurodegenerative diseases. This tool could lead to earlier treatment and improved patient outcomes and may have application in other diseases, such as metastatic cancer detection.
Diagnostic tool for risk of neurodegenerative diseases, such as Alzheimer's and Parkinson's disease
Researchers at the University of Florida have developed a tool to detect unusual iron oxide nanoparticles that form in association with neurodegenerative diseases, such as Alzheimer's and Parkinson's. Researchers have found indications that abnormal iron oxides may be formed early in the Alzheimer's disease process, possibly due to a malfunction of the normal iron-storage protein, ferritin. The software/hardware package developed by UF researchers carefully locates very small abnormal signals that are often dismissed in MRI radiology interpretations. By incorporating an uncommon imaging modality with advanced image analysis, the detection of risk for neurodegenerative diseases is maximized. The technology can be incorporated into most MRI platforms, allowing for the fluid transition of the system into various markets.
This software reconstructs high quality Four-Dimensional Cone-Beam Computer Tomography (4D-CBCT) images using limited imaging projections and a standard free breathing CBCT scan. Roughly two–thirds of all cancer patients receive radiotherapy to target and, it is hoped, eliminate tumors. Radiotherapy treatment plans begin with locating the tumor using a CT scan. A 4D-CBCT is a medical device that records a sequence of CT images, formed at successive times, which illustrate moving portions of an object being imaged. Unfortunately, under normal operation, this device produces an unclear image set. The greatest challenge for medical imaging is producing a high-quality image with an insufficient amount of projection data. One solution is to increase the sample number of X-ray projections, but this increases the amount of time a patient spends in a CT scan and the patient’s radiation exposure. Researchers at the University of Florida have developed software called common-mask guided image reconstruction (c-MGIR) that allows the reconstruction of high quality 4D-CBCT images with no more imaging projection than a clinically-used, standard free-breathing CBCT scan. This software has potential for online image-guided radiation therapy.
Highly efficient medical imaging software that reconstructs high quality 4D-CBCT images using limited projection data and standard CT scanning technology.
The software allows the image quality of 4-Dimensional Cone-Beam CT images to be significantly improved by using full projections, rather than only phase-resolved projections, for each single phase reconstruction. Full projections permit more information to be accommodated into the final, enhanced image. This software utilizes the medical imaging protocol called CBCT, which consists of sequencing an image with computed X-rays where the X-rays are divergent, forming a cone. The unknown cone volume of the X-ray was mathematically modeled to predict the next image through computer analysis and draw prediction lines called motion vectors and static vectors. The combination of phase-specific motion vectors and static vectors allows the software to reconstruct the next image in the X-ray sequence. The distinguishing aspect of this software is the common-mask, the matrix that differentiates between the 3D elements that are moving from the stationary ones. Then the moving and static volumes are updated, and a higher quality image using 4D-CBCT is created.
