A Myocardial T1-Mapping Framework with Recurrent and U-Net Convolutional Neural Networks
Haris Jeelani, Yang Yang, Ruixi Zhou, Christopher M. Kramer, Michael Salerno, Daniel S. Weller
Abstract
Noise and aliasing artifacts arise in various accelerated cardiac magnetic resonance (CMR) imaging applications. In accelerated myocardial T1-mapping, the traditional three-parameter based nonlinear regression may not provide accurate estimates due to sensitivity to noise. A deep neural network-based framework is proposed to address this issue. The DeepT1 framework consists of recurrent and U-net convolution networks to produce a single output map from the noisy and incomplete measurements. The results show that DeepT1 provides noise-robust estimates compared to the traditional pixel-wise three parameter fitting.
Topics & Concepts
Noise (video)Computer scienceConvolution (computer science)AliasingConvolutional neural networkSensitivity (control systems)Artificial intelligencePixelNonlinear systemAlgorithmArtificial neural networkPattern recognition (psychology)Net (polyhedron)Image (mathematics)MathematicsElectronic engineeringPhysicsUndersamplingGeometryEngineeringQuantum mechanicsAdvanced MRI Techniques and ApplicationsCardiac Imaging and DiagnosticsMedical Imaging Techniques and Applications