ArticleRadiological physics and technology2021
Noise power spectrum in compressed sensing magnetic resonance imaging.
Article in Radiological physics and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Improvement in Image Quality and Efficiency of Non-contrast Thoracic MR Angiography: Comparison of a Highly Accelerated Dixon-based Technique with the Conventional Fat-suppressed Technique.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- SuperMRF: deep robust reconstruction for highly accelerated magnetic resonance fingerprinting.Quantitative imaging in medicine and surgery · 2025Article
- Enhancing Diagnostic Precision: A Distribution-Based Compressed Denoising Scheme using Transfer Learning for Noise Reduction in Medical Imaging.Current medical imaging · 2025Article
- Denoising parameter dependence of coronary artery depictability in compressed sensing magnetic resonance angiography.Radiological physics and technology · 2024Article
- Feasibility of accelerated 3D T1-weighted MRI using compressed sensing: application to quantitative volume measurements of human brain structures.Magma (New York, N.Y.) · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
Compressed sensing magnetic resonance imaging (CS-MRI) uses random undersampling and nonlinear iterative reconstruction. This study was conducted to clarify the noise power spectrum (NPS) characteristics of CS-MRI. We measured two-dimensional (2D) NPS of CS-MRI with various acceleration factors (AF) and denoising factors (DF) and compared their appearance to those of conventional parallel MR images. Results showed that the 2D NPS of CS-MRI exhibited the following characteristics: (1) local decrease in the low-frequency region, (2) gradual decrease in the high-frequency region, and (3) a stripe pattern aligned at unequal intervals in the phase-encoding direction. Specifically, the 2D NPS of CS-MRI reflects the random undersampling pattern of k-space data. Additionally, 2D NPS allowed visualization of AF-dependent noise characteristics of CS-MRI. Furthermore, 1D NPS graph shapes clarified the CS-MRI noise characteristic dependence on AF and DF.
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