Concept: Signal-to-noise ratio
Aim: To develop a clinically applicable MRI technique for tracking stem cells in matrix-associated stem-cell implants, using the US FDA-approved iron supplement ferumoxytol. Materials & methods: Ferumoxytol-labeling of adipose-derived stem cells (ADSCs) was optimized in vitro. A total of 11 rats with osteochondral defects of both femurs were implanted with ferumoxytol- or ferumoxides-labeled or unlabeled ADSCs, and underwent MRI up to 4 weeks post matrix-associated stem-cell implant. The signal-to-noise ratio of different matrix-associated stem-cell implant was compared with t-tests and correlated with histopathology. Results: An incubation concentration of 500 µg iron/ml ferumoxytol and 10 µg/ml protamine sulfate led to significant cellular iron uptake, T2 signal effects and unimpaired ADSC viability. In vivo, ferumoxytol- and ferumoxides-labeled ADSCs demonstrated significantly lower signal-to-noise ratio values compared with unlabeled controls (p < 0.01). Histopathology confirmed engraftment of labeled ADSCs, with slow dilution of the iron label over time. Conclusion: Ferumoxytol can be used for in vivo tracking of stem cells with MRI. Original submitted 28 February 2012; Revised submitted 8 November 2012.
Hypersensitivity to DNaseI digestion is a hallmark of open chromatin and DNaseI-seq allows the genome-wide identification of regions of open chromatin. Interpreting these data is challenging, largely because of inherent variation in signal-to-noise ratio (SNR) between datasets. We have developed PeaKDEck, a peak calling program that distinguishes signal from noise by randomly sampling read densities and employing kernel density estimation to generate a dataset-specific probability distribution of random background signal. PeaKDEck uses this probability distribution to select an appropriate read density threshold for peak calling in each dataset. We benchmark PeaKDEck using published ENCODE DNaseI-seq data, and other peak calling programs, and demonstrate superior performance in low SNR datasets.Availability and implementation: PeaKDEck is written in standard Perl and runs on any platform with Perl installed. PeaKDEck is also available as a standalone application written in Perl/Tk, which does not require Perl to be installed. Files, including a user guide can be downloaded at: www.ccmp.ox.ac.uk/peakdeck CONTACT: email@example.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Although most studies of language learning take place in quiet laboratory settings, everyday language learning occurs under noisy conditions. The current research investigated the effects of background speech on word learning. Both younger (22- to 24-month-olds; n = 40) and older (28- to 30-month-olds; n = 40) toddlers successfully learned novel label-object pairings when target speech was 10 dB louder than background speech but not when the signal-to-noise ratio (SNR) was 5 dB. Toddlers (28- to 30-month-olds; n = 26) successfully learned novel words with a 5-dB SNR when they initially heard the labels embedded in fluent speech without background noise, before they were mapped to objects. The results point to both challenges and protective factors that may impact language learning in complex auditory environments.
- Proceedings of the National Academy of Sciences of the United States of America
- Published about 4 years ago
Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.
Imaging the amygdala with functional MRI is confounded by multiple averse factors, notably signal dropouts due to magnetic inhomogeneity and low signal-to-noise ratio, making it difficult to obtain consistent activation patterns in this region. However, even when consistent signal changes are identified, they are likely to be due to nearby vessels, most notably the basal vein of rosenthal (BVR). Using an accelerated fMRI sequence with a high temporal resolution (TR = 333 ms) combined with susceptibility-weighted imaging, we show how signal changes in the amygdala region can be related to a venous origin. This finding is confirmed here in both a conventional fMRI dataset (TR = 2000 ms) as well as in information of meta-analyses, implying that “amygdala activations” reported in typical fMRI studies are likely confounded by signals originating in the BVR rather than in the amygdala itself, thus raising concerns about many conclusions on the functioning of the amygdala that rely on fMRI evidence alone.
The assessment of both geometry and hemodynamics of the intracranial arteries has important diagnostic value in internal carotid occlusion, sickle cell disease, and aneurysm development. Provided that signal to noise ratio (SNR) and resolution are high, these factors can be measured with time-resolved three-dimensional phase contrast MRI. However, within a given scan time duration, an increase in resolution causes a decrease in SNR and vice versa, hampering flow quantification and visualization. To study the benefits of higher SNR at 7 T, three-dimensional phase contrast MRI in the Circle of Willis was performed at 3 T and 7 T in five volunteers. Results showed that the SNR at 7 T was roughly 2.6 times higher than at 3 T. Therefore, segmentation of small vessels such as the anterior and posterior communicating arteries succeeded more frequently at 7 T. Direction of flow and smoothness of streamlines in the anterior and posterior communicating arteries were more pronounced at 7 T. Mean velocity magnitude values in the vessels of the Circle of Willis were higher at 3 T due to noise compared to 7 T. Likewise, areas of the vessels were lower at 3 T. In conclusion, the gain in SNR at 7 T compared to 3 T allows for improved flow visualization and quantification in intracranial arteries. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.
We acquire the first experimental 3-D tomographic images with magnetic particle imaging (MPI) using projection reconstruction methodology, which is similar to algorithms employed in X-ray computed tomography. The primary advantage of projection reconstruction methods is an order of magnitude increase in signal-to-noise ratio (SNR) due to averaging. We first derive the point spread function, resolution, number of projections required, and the SNR gain in projection reconstruction MPI. We then design and construct the first scanner capable of gathering the necessary data for nonaliased projection reconstruction and experimentally verify our mathematical predictions. We demonstrate that filtered backprojection in MPI is experimentally feasible and illustrate the SNR and resolution improvements with projection reconstruction. Finally, we show that MPI is capable of producing three dimensional imaging volumes in both phantoms and postmortem mice.
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Published about 5 years ago
We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.
Recent advances in the field of image processing have shown that level of noise highly affect the quality and accuracy of classification when working with mammographic images. In this paper, we have proposed a method that consists of two major modules: noise detection and noise filtering. For detection purpose, neural network is used which effectively detect the noise from highly corrupted images. Pixel values of the window and some other features are used as feature for the training of neural network. For noise removal, three filters are used. The weighted average value of these three filters is filled on noisy pixels. The proposed technique has been tested on salt & pepper and quantum noise present in mammogram images. Peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) are used for comparison of proposed technique with different existing techniques. Experiments shows that proposed technique produce better results as compare to existing methods.
Non-contact inkjet printing technology is one of the most promising tools for producing microarrays. The quality of the microarray depends on the type of the substrate used for printing biomolecules. Various porous and non-porous substrates have been used in the past, but due to low production cost and easy availability, non-porous substrates like glass and plastic are preferred over porous substrates. On these non-porous substrates, obtaining spot uniformity and a high signal to noise ratio is a big challenge. In our research work, we have modified pristine glass slides using various silanes to produce a range of hydrophobic glass substrates. The hydrophobicities of the slides expressed in the contact angle (θ) of a sessile drop of water were 49°, 61°, 75°, 88° and 103°. Using a non-contact inkjet printer, microarrays of biotinylated biomolecules (BSA and IgG) were produced on these modified glass substrates, pristine (untreated) glass and also on HTA polystyrene slides. The uniformity of the spots, reflecting the distribution of the biomolecules in the spots, was analyzed and compared using confocal laser scanning microscopy (CLSM). The quality of the spots was superior on the glass slide with a contact angle of ∼75°. We also investigated the influence of the hydrophobicity of the substrate on a two-step, real diagnostic antibody assay. This nucleic acid microarray immunoassay (NAMIA) for the detection of Staphylococcus aureus showed that on highly hydrophilic (θ < 10°) and hydrophobic substrates (θ > 100°) the assay signal was low, whereas an excellent signal was obtained on the substrates with intermediate contact angles, θ ∼ 61° and θ ∼ 75°, respectively.