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Claritas iPET is an image processing software intended for use by radiologists and nuclear medicine physicians for noise reduction, sharpening, and resolution improvement of PET images (including PET/CT and PET/MRI) obtained with any kind of radionuclides, e.g. fluorodeoxyglucose (FDG). Enhanced images will be saved in DICOM files and exist in conjunction with original images.

Claritas iPET v1.0 Image Enhancement System is a medical image enhancement software, i.e., a Software as a Medical Device (SaMD), that can be used to increase image quality by implementation of an image processing and image fusion algorithm. Claritas iPET can be used to enhance Positron Emission Tomography (PET) images with optional simultaneous Magnetic Resonance Imaging (MRI) or Computerized Tomography (CT) scans of the same subject. Claritas iPET takes as input DICOM [Digital Imaging and Communications in Medicine] files of PET, MRI, and CT images, and produces an enhanced image of the same file. The objective is to enhance the DICOM files that are obscured and not clearly visible, to be more visible, sharper, and clearer through the Claritas iPET image enhancement process. Claritas iPET is intended to be used by radiologists and nuclear medicine physicians in hospitals, radiology centres and clinics, as well as by medical universities and research intuitions. The image improvement includes noise reduction, sharpening of organ boundaries, and achieving super-resolution. With the help of Claritas iPET software, high quality PET scans can be produced. The Claritas iPET algorithm computes the fusion of functional (from PET) and anatomic (from MR or CT) information, and is based on Non-Local Means filtering. The goal of the software is to process and visualize the content of DICOM files storing 3D voxel arrays, i.e. a uniformly spaced sequence of slices of a PET scan. The processing algorithm may also input another 3D voxel array storing the density values obtained by a CT or MRI scan. The PET and CT/MR volumes should at least partially overlap to exploit the additional anatomic information. The CT or MR volume is expected to have a higher resolution than the PET volume in order for effective improvement. The sharpness. style and the detail of the visualization can be controlled by the user and can be compared to the visualization of the raw image data. During this process, no new feature is introduced that did not exist in the PET data, just the existing features are emphasized if they are also supported by the anatomy or suppressed if they are in the noise region and are not supported by the anatomy.

iRAD Image Enhancement System is an image processing software than can be used for image enhancement of MRI, CT, and X-Ray images. Enhanced images will be sent to PACS server and exist in conjunction to the original images.

iRAD v1.0 Image Enhancement System is a medical image enhancement software, i.e., a Software as a Medical Device (SaMD), that can be used to enhance images of MRI, CT and X-Ray. iRAD takes as input DICOM [Digital Imaging and Communications in Medicine] files of MRI, CT, and X-Ray images, and produces an enhanced output of the same file, in DICOM format that can be sent to a PACS server. The objective is to enhance the DICOM files that are obscured and not clearly visible, to be more visible, sharper, and clearer through the iRAD image enhancement process. The iRAD image enhancement is done by the implementation of an image enhancement algorithm. iRAD is intended to be used by medical doctors, radiologists and clinicians in hospitals, radiology centers and clinics, as well as by medical universities and research intuitions. The system allows selection of input DICOM images from PACS servers. DICOM images are sent to the iRAD image enhancement server, where they are processed and sent back to the PACS server after enhancement. The enhanced and original images exist in conjunction and can be compared. The system provides the user with a set of adjustable parameters through which to control the degree of contrast and strength enhancement and noise suppression. iRAD implements a modified contrast limited adaptive histogram equalization algorithm to improve the visibility of the image and it uses the iRAD guided filter to reduce noise. The original image is deconstructed into overlapping rectangular components. The equalization and matching algorithm is executed in overlapping rectangular regions, resulting in several level-of-detail layers. The enhanced and denoised image is reconstructed using the level-of-detail layers based on user-controlled parameters of noise suppression and detail enhancement.