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l2SToRM
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l2SToRM
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# Dynamic MR Image Reconstruction using SToRM # Publication: Sunrita Poddar and Mathews Jacob. "Dynamic MRI using smooThness regularization on manifolds (SToRM)." IEEE transactions on medical imaging 35.4 (2016): 1106-1115. Please cite the above paper if you use the developed code. # Code: ## main.m This code generates the result in result.avi, by solving the optimization problem:  where the notation is described in the SToRM paper. In brief,  is the series of MR image frames,  is the Fourier under-sampling operator,  is the matrix of data samples,  is the regularization parameter and  is the estimated manifold Laplacian. ## Functions called by main.m: ### AhAX.m Code to compute  for the conjugate gradient algorithm ### Ahb.m Code to compute  for the conjugate gradient algorithm ### computeWeights.m Code to compute the weight matrix from navigator data ### XL.m Code to compute  for the conjugate gradient algorithm # Data: Available at: https://iowa-my.sharepoint.com/personal/jcb_uiowa_edu/_layouts/15/guestaccess.aspx?folderid=0a72e5307778348e9a18a820d9fa3624d&authkey=ARlI1w22gv4KBG9EJ3p-OQo The cardiac data was acquired in the ungated mode on a free-breathing subject using a FLASH sequence. The sampling pattern is a mix of golden angle radial lines and uniform radial navigators (refer to paper). Each frame has 10 radial lines, out of which 6 are golden angle lines and 4 are uniform radial navigator lines. Data for 1000 frames was acquired in around 40 s. 1. S.mat: Sampling pattern saved in a cell of size {1 x number_of_frames}. Example: S{1} contains k-space locations sampled for frame 1. These Cartesian locations are obtained by gridding the original non-cartesian locations. 2. b.mat: Acquired data saved in a cell of size {number_of_frames x number_of_coils}. Example: b{10, 3} contains k-space data acquired by the 3rd coil at sampling locations in S{10}. This Cartesian data is obtained by gridding the original non-cartesian data. 3. csm.mat: Estimated coil sensitivity maps of size {image_size x number_of_coils}. Maps are obtained using ESPIRIT algorithm 4. bCom.mat: Navigator lines (in original non-cartesian co-ordinates). The data corresponds to a coil that is close to the heart. Size of the bCom is {(number_of_readouts x number_of_navigator_lines) x number_of_frames}. # Results: A few frames of the result are saved in the video file result.avi.
