Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics
Hajime Sagawa, Yasutaka Fushimi, Satoshi Nakajima, Koji Fujimoto, Kanae K. Miyake, Hitomi Numamoto, Koji Koizumi, Masahito Nambu, Hiroharu Kataoka, Yuji Nakamoto, Tsuneo Saga
Abstract
To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5.
Topics & Concepts
MedicineFractional anisotropyDiffusion MRINoise reductionNoise (video)Anisotropic diffusionReduction (mathematics)DiffusionReliability (semiconductor)Image noiseArtificial intelligenceRadiologyImage (mathematics)Magnetic resonance imagingComputer sciencePhysicsMathematicsGeometryQuantum mechanicsThermodynamicsPower (physics)Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsMRI in cancer diagnosis