Basics#
The course starts from the data a scanner delivers, multichannel k-space, and reconstructs an image from it. The first lesson relates PyTorch tensors to BART’s arrays and commands, simulates an acquisition with BART’s analytical phantom and verifies the centring and scaling of BART’s FFT against NumPy. The second reconstructs an undersampled Cartesian acquisition through the three steps every later section builds on: coil compression, calibration of the coil sensitivity maps by ESPIRiT, and a regularized SENSE reconstruction. The signal model is derived in The MRI encoding operator, and the array conventions are stated in Data layout and conventions.
