Optimization of Regularization Parameters in Compressed Sensing of Magnetic Resonance Angiography: Can Statistical Image Metrics Mimic Radiologists' Perception?

In Compressed Sensing (CS) of MRI, optimization of the regularization parameters is not a trivial task. We aimed to establish a method that could determine the optimal weights for regularization parameters in CS of time-of-flight MR angiography (TOF-MRA) by comparing various image metrics with radio...

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Bibliographic Details
Published in:PloS one 2016-01, Vol.11 (1), p.e0146548-e0146548
Main Authors: Akasaka, Thai, Fujimoto, Koji, Yamamoto, Takayuki, Okada, Tomohisa, Fushimi, Yasutaka, Yamamoto, Akira, Tanaka, Toshiyuki, Togashi, Kaori
Format: Article
Language:English
Subjects:
Adult
Algorithms
Angiography
Brain
Brain - blood supply
Brain - physiology
Clinical Decision-Making
Coefficient of variation
Data Compression
Evaluation
Fourier transforms
Hospitalists
Humans
Image Interpretation, Computer-Assisted
Image reconstruction
Magnetic resonance
Magnetic resonance angiography
Magnetic Resonance Angiography - methods
Magnetic resonance imaging
Masks
Medical imaging
Methods
NMR
Nuclear magnetic resonance
Nuclear medicine
Optimization
Parenchyma
Pattern Recognition, Automated
Pattern Recognition, Visual
Practice
Quality
Quality Improvement
Radiographic Image Enhancement
Radiologists
Radiology
Regularization
Signal-To-Noise Ratio
Sparsity
Statistics
Visual flight
Wavelet transforms
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Summary:In Compressed Sensing (CS) of MRI, optimization of the regularization parameters is not a trivial task. We aimed to establish a method that could determine the optimal weights for regularization parameters in CS of time-of-flight MR angiography (TOF-MRA) by comparing various image metrics with radiologists' visual evaluation. TOF-MRA of a healthy volunteer was scanned using a 3T-MR system. Images were reconstructed by CS from retrospectively under-sampled data by varying the weights for the L1 norm of wavelet coefficients and that of total variation. The reconstructed images were evaluated both quantitatively by statistical image metrics including structural similarity (SSIM), scale invariant feature transform (SIFT) and contrast-to-noise ratio (CNR), and qualitatively by radiologists' scoring. The results of quantitative metrics and qualitative scorings were compared. SSIM and SIFT in conjunction with brain masks and CNR of artery-to-parenchyma correlated very well with radiologists' visual evaluation. By carefully selecting a region to measure, we have shown that statistical image metrics can reflect radiologists' visual evaluation, thus enabling an appropriate optimization of regularization parameters for CS.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0146548