Moghari M, Goddu, Kissinger K, Goepfert, Manning W, Nezafat. Estimation of respiratory tracking factor between pulmonary vein and right hemi-diaphragm for free-breathing PV LGE. Proc Nineteenth Scientific Meeting of Int Soc Magn Reson Med. 2011.
Publications
2011
Nam, Basha T, Akcakaya, Stehning, Manning W, Tarokh, Nezafat. A GPU implementation of compressed sensing reconstruction of 3D radial (kooshball) acquisition for high-resolution cardiac MRI. Proc Nineteenth Scientific Meeting of Int Soc Magn Reson Med. 2011.
Basha T, Akcakaya, Moghari M, Kissinger K, Goddu, Goepfert, Manning W, Nezafat. Minimization of imaging artifacts from profile ordering of randomly selected ky-kz lines for prospective compressed-sensing acqusition in 3D segmented SSFP and GRE imaging. Proc Nineteenth Scientific Meeting of Int Soc Magn Reson Med. 2011.
Akcakaya, Nam, Basha T, Tarokh, Manning W, Nezafat. Iterative compressed sensing reconstruction for 3D non-Cartesiantrajectories without gridding/regridding at every iteration. Proc Nineteenth Scientific Meeting of Int Soc Magn Reson Med 2011 (* denotes co-first authorship). 2011.
Akcakaya, Basha T, Kissinger K, Goddu, Goepfert, Manning W, Nezafat. Accelerated contrast-enhanced whole heart coronary MRI using low-dimensional-structure self-learning and thresholding (LOST) an improved compressed sensing reconstruction. Proc Nineteenth Scientific Meeting of Int Soc Magn Reson Med 2011 (* denotes co-first authorship). 2011.
Akcakaya, Basha T, Goddu, Goepfert, Kissinger K, Tarokh, Manning W, Nezafat. Low-dimensional-structure self-learning and thresholding (LOST) regularization beyond compressed sensing for MRI reconstruction. Proc Nineteenth Scientific Meeting of Int Soc Magn Reson Med. 2011.
Srinivasan, Hu, Kissinger K, Goddu, Geopfert, Schmidt E, Kozerke, Nezafat. Free-Breathing 3D Whole Heart Black Blood Imaging with Motion Sensitized Driven Equilibrium. Proc Nineteenth Scientific Meeting of Int Soc Magn Reson Med. 2011.
Hu, Hong, Hedjazi M, Goddu, Goepfert, Kissinger K, Hauser T, Manning W, Nezafat. Motion correction using coil arrays for cardiac cine MRI. Proc Nineteenth Scientific Meeting of Int Soc Magn Reson Med. 2011.
Stanton, Dobhal, Moghari M, Casanova, Jain, Manzke, Manning W, Hall, Nezafat. Design and Evaluation of an MR Compatible Pneumatic Non-rigid Moving Heart Phantom for Simulating Respiratory and Cardiac Motion. Proc Nineteenth Scientific Meeting of Int Soc Magn Reson Med. 2011.
Akçakaya M, Basha T, Goddu B, Goepfert L, Kissinger K, Tarokh V, Manning W, Nezafat R. Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction. Magn Reson Med. 2011;66(3):756–67.
An improved image reconstruction method from undersampled k-space data, low-dimensional-structure self-learning and thresholding (LOST), which utilizes the structure from the underlying image is presented. A low-resolution image from the fully sampled k-space center is reconstructed to learn image patches of similar anatomical characteristics. These patches are arranged into "similarity clusters," which are subsequently processed for dealiasing and artifact removal, using underlying low-dimensional properties. The efficacy of the proposed method in scan time reduction was assessed in a pilot coronary MRI study. Initially, in a retrospective study on 10 healthy adult subjects, we evaluated retrospective undersampling and reconstruction using LOST, wavelet-based l(1)-norm minimization, and total variation compressed sensing. Quantitative measures of vessel sharpness and mean square error, and qualitative image scores were used to compare reconstruction for rates of 2, 3, and 4. Subsequently, in a prospective study, coronary MRI data were acquired using these rates, and LOST-reconstructed images were compared with an accelerated data acquisition using uniform undersampling and sensitivity encoding reconstruction. Subjective image quality and sharpness data indicate that LOST outperforms the alternative techniques for all rates. The prospective LOST yields images with superior quality compared with sensitivity encoding or l(1)-minimization compressed sensing. The proposed LOST technique greatly improves image reconstruction for accelerated coronary MRI acquisitions.