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Found 5 papers, 1 papers with code
BMapOpt: Optimization of Brain Tissue Probability Maps using a Differentiable MRI Simulator
Reconstructing digital brain phantoms in the form of multi-channeled brain tissue probability maps for individual subjects is essential for capturing brain anatomical variability, understanding neurological diseases, as well as for testing image processing methods.
A novel image space formalism of Fourier domain interpolation neural networks for noise propagation analysis
Noise variance maps obtained from the analytical expression correspond with those obtained via Monte-Carlo simulations, as well as via an auto-differentiation approach.
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution
The found radiofrequency pulse train designs generate an optimal signal for the NN to perform the SR task.
Scale-Equivariant Deep Learning for 3D Data
The ability of convolutional neural networks (CNNs) to recognize objects regardless of their position in the image is due to the translation-equivariance of the convolutional operation.
Accelerated and Quantitative 3D Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)
GAN-CEST images from a brain-tumor subject yielded a semi-solid volume fraction and exchange rate NRMSE of 3. 8$\pm$1. 3% and 4. 6$\pm$1. 3%, respectively, and SSIM of 96. 3$\pm$1. 6% and 95. 0$\pm$2. 4%, respectively.
Generative Adversarial Network
Magnetic Resonance Fingerprinting
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