Speaker
Description
Mediso Medical Imaging Systems is a Hungarian company which develops, manufactures and sells 3D medical imaging tools. Beside Computer Tomography (CT), Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) tools, Single Photon Emission Tomography (SPECT) is the leading technology of the Company.
Since the very beginning of Mediso, SPECT technology has always been improved. Among many goals, development aims at three main targets: Improving 2D scintigraphy image and 3D SPECT reconstruction quality, i.e. improving clinical utility; decreasing dose burden on patients according to ALARA (As Low As Reasonably Achievable) principle; decreasing acquisition times, i.e. improving throughput and economic payback.
In 2018, attention focused on the use of AI for data enhancement. In most cases, clinical SPECT tools are used for acquiring whole-body planar bone scintigraphy and bone SPECT acquisitions. In the first project, an efficient 2D denoising autoencoder neural network tool was developed and optimized for denoising whole-body planar bone scintigraphy images. For that purpose, a complex framework was built, with which a comprehensive optimization study was performed. Different architectures, training strategies and many parameters were optimized. An efficient metrics was developed for evaluating the denoiser performance both with acquisitions of normal or deteriorated statistics. Acquisitions with additional artificial lesions were also investigated. Preliminary validation has also been started with the involvement of clinical doctors who have found our solution exceptionally promising.
A similar development project has also been performed aiming at the enhancement of 3D SPECT acquisition data. In this case, very special solutions must have been applied for reaching adequate convergence in terms of loss and quality of improved data. Metrics was also developed for both 3D sinograms and 3D reconstructed volumes.
In our presentation, the above described developments are presented. Mediso AI group is also introduced and its tasks are briefly summarized.
