We have located links that may give you full text access.
Evaluation Studies
Journal Article
Research Support, Non-U.S. Gov't
Exploitation of temporal redundancy in compressed sensing reconstruction of fMRI studies with a prior-based algorithm (PICCS).
Medical Physics 2015 July
PURPOSE: Compressed sensing is a technique used to accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. While it has proven particularly useful in dynamic imaging procedures such as cardiac cine, very few authors have applied it to functional magnetic resonance imaging (fMRI). The purpose of the present study was to check whether the prior image constrained compressed sensing (PICCS) algorithm, which is based on an available prior image, can improve the statistical maps in fMRI better than other strategies that also exploit temporal redundancy.
METHODS: PICCS was compared to spatiotemporal total variation (TTV) and k-t FASTER, since they have already demonstrated high performance and robustness in other MRI applications, such as cardiac cine MRI and resting state fMRI, respectively. The prior image for PICCS was the average of all undersampled data. Both PICCS and TTV were solved using the split Bregman formulation. K-t FASTER algorithm relies on matrix completion to reconstruct the undersampled k-spaces. The three algorithms were evaluated using two datasets with high and low signal-to-noise ratio (SNR)-BOLD contrast-acquired in a 7 T preclinical MRI scanner and retrospectively undersampled at various rates (i.e., acceleration factors). The authors evaluated their performance in terms of the sensitivity/specificity of BOLD detection through receiver operating characteristic curves and by visual inspection of the statistical maps.
RESULTS: With high SNR studies, PICCS performed similarly to the state-of-the-art algorithms TTV and k-t FASTER and provided consistent BOLD signal at the ROI. In scenarios with low SNR and high acceleration factors, PICCS still provided consistent maps and higher sensitivity/specificity than TTV, whereas k-t FASTER failed to provide significant maps.
CONCLUSIONS: The authors performed a comparison between three reconstructions (PICCS, TTV, and k-t FASTER) that exploit temporal redundancy in fMRI. The prior-based algorithm, PICCS, preserved BOLD activation and sensitivity/specificity better than TTV and k-t FASTER in noisy scenarios. The PICCS algorithm can potentially reach an acceleration factor of ×8 and still provide BOLD contrast in the ROI with an area under the curve over 0.99.
METHODS: PICCS was compared to spatiotemporal total variation (TTV) and k-t FASTER, since they have already demonstrated high performance and robustness in other MRI applications, such as cardiac cine MRI and resting state fMRI, respectively. The prior image for PICCS was the average of all undersampled data. Both PICCS and TTV were solved using the split Bregman formulation. K-t FASTER algorithm relies on matrix completion to reconstruct the undersampled k-spaces. The three algorithms were evaluated using two datasets with high and low signal-to-noise ratio (SNR)-BOLD contrast-acquired in a 7 T preclinical MRI scanner and retrospectively undersampled at various rates (i.e., acceleration factors). The authors evaluated their performance in terms of the sensitivity/specificity of BOLD detection through receiver operating characteristic curves and by visual inspection of the statistical maps.
RESULTS: With high SNR studies, PICCS performed similarly to the state-of-the-art algorithms TTV and k-t FASTER and provided consistent BOLD signal at the ROI. In scenarios with low SNR and high acceleration factors, PICCS still provided consistent maps and higher sensitivity/specificity than TTV, whereas k-t FASTER failed to provide significant maps.
CONCLUSIONS: The authors performed a comparison between three reconstructions (PICCS, TTV, and k-t FASTER) that exploit temporal redundancy in fMRI. The prior-based algorithm, PICCS, preserved BOLD activation and sensitivity/specificity better than TTV and k-t FASTER in noisy scenarios. The PICCS algorithm can potentially reach an acceleration factor of ×8 and still provide BOLD contrast in the ROI with an area under the curve over 0.99.
Full text links
Related Resources
Trending Papers
Challenges in Septic Shock: From New Hemodynamics to Blood Purification Therapies.Journal of Personalized Medicine 2024 Februrary 4
Molecular Targets of Novel Therapeutics for Diabetic Kidney Disease: A New Era of Nephroprotection.International Journal of Molecular Sciences 2024 April 4
The 'Ten Commandments' for the 2023 European Society of Cardiology guidelines for the management of endocarditis.European Heart Journal 2024 April 18
A Guide to the Use of Vasopressors and Inotropes for Patients in Shock.Journal of Intensive Care Medicine 2024 April 14
Diagnosis and Management of Cardiac Sarcoidosis: A Scientific Statement From the American Heart Association.Circulation 2024 April 19
Essential thrombocythaemia: A contemporary approach with new drugs on the horizon.British Journal of Haematology 2024 April 9
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app