Contrast-to-noise ratio (CNR) in blood oxygenation level-dependent (BOLD) based functional MRI (fMRI) studies is a fundamental parameter to determine statistical significance and therefore to map functional activation in the brain. The CNR is defined here as BOLD contrast with respect to temporal fluctuation. In this study, a theoretical noise model based on oxygenation-sensitive MRI signal formation is proposed. No matter what the noise sources may be in the signal acquired by a gradient-echo echo-planar imaging pulse sequence, there are only three noise elements: apparent spin density fluctuations, S(0)(t); transverse relaxation rate fluctuations, R(2) (*)(t); and thermal noise, n(t). The noise contributions from S(0)(t), R(2) (*)(t), and n(t) to voxel time course fluctuations were evaluated as a function of echo time (TE) at 3 T. Both noise contributions caused by S(0)(t) and R(2) (*)(t) are significantly larger than that of thermal noise when TE = 30 ms. In addition, the fluctuations between S(0)(t) and R(2) (*)(t) are cross-correlated and become a noise factor that is large enough and cannot be ignored. The experimentally measured TE dependences of noise, temporal signal-to-noise ratio, and BOLD CNR in finger-tapping activation regions were consistent with the proposed model. Furthermore, the proposed theoretical models not only unified previously proposed BOLD CNR models, but also provided mechanisms for interpreting apparent controversies and limitations that exist in the literature.
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http://dx.doi.org/10.1002/mrm.20451 | DOI Listing |
J Occup Med Toxicol
January 2025
School of Health Sciences, Department of Audiology, University of the Pacific, San Francisco, California, USA.
Background: Hazardous noise exposure is an important health concern in many workplaces and is one of the most common work-related injuries in the United States. Dental professionals are frequently exposed to high levels of occupational noise in their daily work environment. This noise is generated by various dental handpieces such as drills, suctions, and ultrasonic scalers.
View Article and Find Full Text PDFJ Neurodev Disord
January 2025
Graduate Neuroscience Program, University of California, Riverside, CA, USA.
Background: Fragile X syndrome (FXS) is a leading known genetic cause of intellectual disability and autism spectrum disorders (ASD)-associated behaviors. A consistent and debilitating phenotype of FXS is auditory hypersensitivity that may lead to delayed language and high anxiety. Consistent with findings in FXS human studies, the mouse model of FXS, the Fmr1 knock out (KO) mouse, shows auditory hypersensitivity and temporal processing deficits.
View Article and Find Full Text PDFSci Rep
January 2025
Hannover Centre for Optical Technologies (HOT), Leibniz University Hannover, Hannover, Germany.
Hyperspectral imaging (HSI) systems acquire images with spectral information over a wide range of wavelengths but are often affected by chromatic and other optical aberrations that degrade image quality. Deconvolution algorithms can improve the spatial resolution of HSI systems, yet retrieving the point spread function (PSF) is a crucial and challenging step. To address this challenge, we have developed a method for PSF estimation in HSI systems based on computed wavefronts.
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January 2025
Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok, Republic of Korea.
Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scanning process. Manually separating BTs from several MRI images gathered in medical practice for cancer analysis is challenging and time-consuming.
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January 2025
College of Computer Sciences, Anhui University, Hefei, 230039, China.
Decoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. Utilizing the BING objectness measure, we efficiently localize objects or their details across varying scales within scenes.
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