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In functional MRI, it is often desirable to reduce the readout duration to make the acquired data less prone to T₂* susceptibility artifacts. In addition, a shorter readout length allows for a shorter minimum TE, which is important for optimizing SNR. This can be achieved by undersampling the k-space. However, the conventional Fourier transform-based(More)
Learning from imbalanced data has conventionally been conducted on stationary data sets. Recently, there have been several methods proposed for mining imbalanced data streams, in which training data is read in consecutive data chunks. Each data chunk is considered as a conventional imbalanced data set, making it easy to apply sampling methods to balance(More)
—Traditional classification algorithms, in many times, perform poorly on imbalanced data sets in which some classes are heavily outnumbered by the remaining classes. For this kind of data, minority class instances, which are usually much more of interest, are often misclassified. The paper proposes a method to deal with them by changing class distribution(More)
This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability. Denoising is performed(More)
s t s t s t s t s t s t H s t s t s t s k t s k t s k t s k t s k t s k t C s k t s k t s k t (d) Denoised spatial distribution (b) Denoised spectrum (a) Noisy spectrum Figure 1. Denoising results with in-vivo experimental data: (a),(b) Spectrum from a particular voxel (shown in absolute mode); (c),(d) Spatial distribution from 1.6 ppm to 2 ppm.(More)
Magnetic resonance imaging (MRI) uses applied spatial variations in the magnetic field to encode spatial position. Therefore, nonuniformities in the main magnetic field can cause image distortions. In order to correct the image distortions, it is desirable to simultaneously acquire data with a field map in registration. We propose a joint estimation (JE)(More)
One of the key factors affecting functional MRI image reconstruction is field inhomogeneity. It is desirable to estimate both the distortion-free MRI image and field map simultaneously , thus compensating for image distortions caused by the field inhomogeneity. To solve this problem, which is called Joint Estimation problem, we propose a new non-iterative(More)
Magnetic field inhomogeneity is a long-standing problem in magnetic resonance imaging (MRI) and spectroscopic imaging (MRSI). Specifically, in MRSI, field inhomogeneity, if not corrected, can cause frequency shifts, line broadening, and lineshape distortions in the spectral peaks. This paper addresses the problem of correcting the field inhomogeneity(More)
Sampling is the most popular approach for handling the class imbalance problem in training data. A number of studies have recently adapted sampling techniques for dynamic learning settings in which the training set is not fixed, but gradually grows over time. This paper presents an empirical study to compare over-sampling and under-sampling techniques in(More)