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Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction.

Publication ,  Journal Article
Akçakaya, M; Basha, TA; Goddu, B; Goepfert, LA; Kissinger, KV; Tarokh, V; Manning, WJ; Nezafat, R
Published in: Magnetic resonance in medicine
September 2011

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.

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Published In

Magnetic resonance in medicine

DOI

EISSN

1522-2594

ISSN

0740-3194

Publication Date

September 2011

Volume

66

Issue

3

Start / End Page

756 / 767

Related Subject Headings

  • Retrospective Studies
  • Pilot Projects
  • Nuclear Medicine & Medical Imaging
  • Models, Theoretical
  • Male
  • Magnetic Resonance Imaging
  • Imaging, Three-Dimensional
  • Image Processing, Computer-Assisted
  • Image Enhancement
  • Humans
 

Citation

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Akçakaya, M., Basha, T. A., Goddu, B., Goepfert, L. A., Kissinger, K. V., Tarokh, V., … Nezafat, R. (2011). Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction. Magnetic Resonance in Medicine, 66(3), 756–767. https://doi.org/10.1002/mrm.22841
Akçakaya, Mehmet, Tamer A. Basha, Beth Goddu, Lois A. Goepfert, Kraig V. Kissinger, Vahid Tarokh, Warren J. Manning, and Reza Nezafat. “Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction.Magnetic Resonance in Medicine 66, no. 3 (September 2011): 756–67. https://doi.org/10.1002/mrm.22841.
Akçakaya M, Basha TA, Goddu B, Goepfert LA, Kissinger KV, Tarokh V, et al. Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction. Magnetic resonance in medicine. 2011 Sep;66(3):756–67.
Akçakaya, Mehmet, et al. “Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction.Magnetic Resonance in Medicine, vol. 66, no. 3, Sept. 2011, pp. 756–67. Epmc, doi:10.1002/mrm.22841.
Akçakaya M, Basha TA, Goddu B, Goepfert LA, Kissinger KV, Tarokh V, Manning WJ, Nezafat R. Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction. Magnetic resonance in medicine. 2011 Sep;66(3):756–767.
Journal cover image

Published In

Magnetic resonance in medicine

DOI

EISSN

1522-2594

ISSN

0740-3194

Publication Date

September 2011

Volume

66

Issue

3

Start / End Page

756 / 767

Related Subject Headings

  • Retrospective Studies
  • Pilot Projects
  • Nuclear Medicine & Medical Imaging
  • Models, Theoretical
  • Male
  • Magnetic Resonance Imaging
  • Imaging, Three-Dimensional
  • Image Processing, Computer-Assisted
  • Image Enhancement
  • Humans