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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)
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)
Invasive infections caused by methicillin-resistant Staphylococcus aureus (MRSA), particularly those involving persistent bacteraemia, necrotizing pneumonia, osteomyelitis and other deep-seated sites of infections, are associated with high mortality and are often difficult to treat. The response to treatment of severe MRSA infection with currently available(More)
Traditionally, physicians have not used cefepime (a fourth-generation cephalosporin with greater stability against β-lactamases) or β-lactam/β-lactamase inhibitors (BLBLIs) for infections caused by bacteria (generally Escherichia coli and Klebsiella species) that produce an extended-spectrum β-lactamase (ESBL). Many microbiology laboratories have(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)
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)
Given current challenges in antimicrobial resistance and drug development, infectious diseases clinicians must rely on their own ingenuity to effectively treat infections while preserving the current antimicrobial armamentarium. An understanding of pharmacokinetics (PK), pharmacodynamics (PD), antimicrobial susceptibility testing (AST), and how these(More)