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Volume 34 , Issue 4
Showing 1-10 articles out of 10 articles from the selected issue
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Main Topics / Compressed Sensing and Related Technologies in Medical Imaging
  • Yoshio MACHIDA
    Volume 34 (2016) Issue 4 Pages 175-176
    Released: September 25, 2016
    JOURNALS RESTRICTED ACCESS
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  • Tomoya SAKAI
    Volume 34 (2016) Issue 4 Pages 177-185
    Released: September 25, 2016
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    High-dimensional data can be represented as a concise combination of explanatory data ingredients, which is referred to as the nature of sparsity. Compressed sensing is a general paradigm of sparsity-aware data acquisition to improve the functionality and lower the cost of measurement. For the compressed sensing fundamentally formulated as an underdetermined system of linear equations having a sparse solution, there have been provided theoretical underpinnings of random measurement and sparse reconstruction, as well as efficient sparse solvers based on convex relaxation. In imaging applications, reconstructed images are supposed to have sparse features, e.g., edges of objects. One can consistently derive practical algorithms for such image reconstruction by posing it as a linearly constrained convex optimization problem.
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  • Hiroyuki KUDO
    Volume 34 (2016) Issue 4 Pages 186-197
    Released: September 25, 2016
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    Recently, three new designs of CT scanners called low-dose CT, sparse-view CT, and interior CT (local CT) have been proposed, and, image reconstruction algorithms corresponding to each scanner design have been investigated actively. Among them, in this paper, we review image reconstruction in the interior CT, which measures truncated projection data by radiating x-rays only to a small region of interest (ROI) such as heart or breast to reduce patient dose or to simplify measurement. In particular, we explain two exact reconstruction approaches discovered in the late 2000's, which have been considered a breakthrough in this field. The first approach does not rely on the compressed sensing (CS), and the second approach is strongly based on the CS technique.
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  • Yoshio MACHIDA
    Volume 34 (2016) Issue 4 Pages 198-202
    Released: September 25, 2016
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    The purpose of this article is to review the recent development of compressed sensing magnetic resonance imaging (CS-MRI) and related techniques. After the brief review of basics of MR imaging, characteristics of new technologies such as CS-MRI, MR fingerprinting are discussed.
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  • Satoshi ITO
    Volume 34 (2016) Issue 4 Pages 203-208
    Released: September 25, 2016
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    In most MR scanning, image reconstruction is executed by taking the inverse Fourier transform of acquired signal. However, image reconstruction in compressed sensing requires the choice of sparsifying transform function and algorithms for L1 norm minimization of cost function, therefore, the obtained image quality depends on those functions and reconstruction algorithms. In this paper, comparison of iterative shrinkage algorithm and ADMM (alternating direction method of multipliers) in compressed sensing of phase-varied MR images was performed. Simulation studies showed that the highest PSNR was obtained in ADMM combined with multi-scale sparsifying transform function.
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  • Yutaro IWAMOTO, Yen-Wei CHEN
    Volume 34 (2016) Issue 4 Pages 209-216
    Released: September 25, 2016
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    Super resolution (SR) is a technique to estimate a high-resolution (HR) image from one or several low-resolution (LR) images. SR can be broadly classified into two families of methods: (i) The classical multi-frame super-resolution, and (ii) Example-based or learning-based super-resolution, which is also known as single-frame super-resolution. In the classical multi-frame SR, the HR image is reconstructed by combining subpixel-aligned multi-images (LR images). Since the reconstruction of HR image from LR images is often an ill-posed problem, we need to include some a prior knowledge or additional assumptions for reconstruction. If sparsity is used as a prior knowledge, the SR can be considered as one of compressed sensing techniques. In the learning-based single-frame SR, the HR image is estimated by learning correspondence between low- and high-resolution images from a database. In this paper, we introduce a multi-frame SR and a single-frame SR for medical image enhancement. Both of them use the sparsity as a prior knowledge.
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Survey Paper
  • Mikio SUGA
    Volume 34 (2016) Issue 4 Pages 217-226
    Released: September 25, 2016
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    Palpation allows physicians to assess change in the mechanical properties of tissue associated with the presence and development of disease. Magnetic resonance elastography (MRE) is an imaging technique for the noninvasive quantification of the mechanical properties of tissue using magnetic resonance imaging (MRI). Typical MRE system uses an external driver to create shear wave in the tissue, a phase-contrast MRI pulse sequence to take snapshots of temporally sequential shear wave pattern in the tissue and an inversion algorithm to calculate viscoelastic map from the shear wave snapshots. These three components are improved over the last 20 years and strongly dependent on each other. The improvement of MRE system have allowed for imaging various organs and have shown effectiveness for staging and differential diagnosis. This review describes the principles, technique, and clinical applications of MRE.
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