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Application of Region of Interest Compressed Sensing to accelerate magnetic resonance angiography.
Conf Proc IEEE Eng Med Biol Soc 2014; 2014:2428-31CP

Abstract

Magnetic Resonance Angiography (MRA) is a group of techniques based on Magnetic Resonance Imaging (MRI) to image blood vessels. Compressed Sensing (CS) is a mathematical framework to reconstruct MR images from sparse data to minimize the data acquisition time. Image sparsity is the key in CS to reconstruct MR images. CS technique allows reconstruction from significantly fewer k-space samples as compared to full k-space acquisition, which results in reduced MRI data acquisition time. The images resulting from MRA are sparse in native representation, hence yielding themselves well to CS. Recently our group has proposed a novel CS method called Region of Interest Compressed Sensing (ROICS) as a part of Region of Interest (ROI) weighted CS. This work aims at the implementation of ROICS for the first time on MRA data to reduce MR data acquisition time. It has been demonstrated qualitatively and quantitatively that ROICS outperforms CS at higher acceleration factors. ROICS technique has been applied to 3D angiograms of the brain data acquired at 1.5T. It helps to reduce the MRA data acquisition time and improves the visualization of arteries. ROICS technique has been applied on 4 brain angiogram data sets at different acceleration factors from 2× to 10×. Reconstructed images show ROICS technique performs better than conventional CS technique and is quantified by the comparative Signal to Noise Ratio (SNR) in the ROI.

Authors

No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

25570480

Citation

Konar, Amaresha Shridhar, et al. "Application of Region of Interest Compressed Sensing to Accelerate Magnetic Resonance Angiography." Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, vol. 2014, 2014, pp. 2428-31.
Konar AS, Aiholli S, Shashikala HC, et al. Application of Region of Interest Compressed Sensing to accelerate magnetic resonance angiography. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:2428-31.
Konar, A. S., Aiholli, S., Shashikala, H. C., Ramesh Babu, D. R., & Geethanath, S. (2014). Application of Region of Interest Compressed Sensing to accelerate magnetic resonance angiography. Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014, pp. 2428-31. doi:10.1109/EMBC.2014.6944112.
Konar AS, et al. Application of Region of Interest Compressed Sensing to Accelerate Magnetic Resonance Angiography. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:2428-31. PubMed PMID: 25570480.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Application of Region of Interest Compressed Sensing to accelerate magnetic resonance angiography. AU - Konar,Amaresha Shridhar, AU - Aiholli,Shivaraj, AU - Shashikala,H C, AU - Ramesh Babu,D R, AU - Geethanath,Sairam, PY - 2015/1/9/entrez PY - 2015/1/9/pubmed PY - 2015/10/13/medline SP - 2428 EP - 31 JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference JO - Conf Proc IEEE Eng Med Biol Soc VL - 2014 N2 - Magnetic Resonance Angiography (MRA) is a group of techniques based on Magnetic Resonance Imaging (MRI) to image blood vessels. Compressed Sensing (CS) is a mathematical framework to reconstruct MR images from sparse data to minimize the data acquisition time. Image sparsity is the key in CS to reconstruct MR images. CS technique allows reconstruction from significantly fewer k-space samples as compared to full k-space acquisition, which results in reduced MRI data acquisition time. The images resulting from MRA are sparse in native representation, hence yielding themselves well to CS. Recently our group has proposed a novel CS method called Region of Interest Compressed Sensing (ROICS) as a part of Region of Interest (ROI) weighted CS. This work aims at the implementation of ROICS for the first time on MRA data to reduce MR data acquisition time. It has been demonstrated qualitatively and quantitatively that ROICS outperforms CS at higher acceleration factors. ROICS technique has been applied to 3D angiograms of the brain data acquired at 1.5T. It helps to reduce the MRA data acquisition time and improves the visualization of arteries. ROICS technique has been applied on 4 brain angiogram data sets at different acceleration factors from 2× to 10×. Reconstructed images show ROICS technique performs better than conventional CS technique and is quantified by the comparative Signal to Noise Ratio (SNR) in the ROI. SN - 1557-170X UR - https://www.unboundmedicine.com/medline/citation/25570480/Application_of_Region_of_Interest_Compressed_Sensing_to_accelerate_magnetic_resonance_angiography_ L2 - https://dx.doi.org/10.1109/EMBC.2014.6944112 DB - PRIME DP - Unbound Medicine ER -