Displaying publications 181 - 189 of 189 in total

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  1. Harun HH, Karim MKA, Abbas Z, Sabarudin A, Muniandy SC, Ibahim MJ
    J Xray Sci Technol, 2020;28(5):893-903.
    PMID: 32741801 DOI: 10.3233/XST-200699
    PURPOSE: To evaluate the influence of iterative reconstruction (IR) levels on Computed Tomography (CT) image quality and to establish Figure of Merit (FOM) value for CT Pulmonary Angiography (CTPA) examinations.

    METHODS: Images of 31 adult patients who underwent CTPA examinations in our institution from March to April 2019 were retrospectively collected. Other data, such as scanning parameters, radiation dose and body habitus information from the subjects were also recorded. Six different levels of IR were applied to the volume data of the subjects. Five circles of the region of interest (ROI) were drawn in five different arteries namely, pulmonary trunk, right pulmonary artery, left pulmonary artery, ascending aorta and descending aorta. The mean Signal-to-noise ratio (SNR) was obtained, and the FOM was calculated in a fraction of the SNR2 divided by volume-weighted CT dose index (CTDIvol) and SNR2 divided by the size-specific dose estimates (SSDE).

    RESULTS: Overall, we observed that the mean value of CTDIvol and SSDE were 13.79±7.72 mGy and 17.25±8.92 mGy, respectively. Notably, SNR values significantly increase with increase of the IR level (p 

    Matched MeSH terms: Signal-To-Noise Ratio
  2. Abdullah KA, McEntee MF, Reed W, Kench PL
    J Med Radiat Sci, 2020 Sep;67(3):170-176.
    PMID: 32219989 DOI: 10.1002/jmrs.387
    INTRODUCTION: 3D-printed imaging phantoms are now increasingly available and used for computed tomography (CT) dose optimisation study and image quality analysis. The aim of this study was to evaluate the integrated 3D-printed cardiac insert phantom when evaluating iterative reconstruction (IR) algorithm in coronary CT angiography (CCTA) protocols.

    METHODS: The 3D-printed cardiac insert phantom was positioned into a chest phantom and scanned with a 16-slice CT scanner. Acquisitions were performed with CCTA protocols using 120 kVp at four different tube currents, 300, 200, 100 and 50 mA (protocols A, B, C and D, respectively). The image data sets were reconstructed with a filtered back projection (FBP) and three different IR algorithm strengths. The image quality metrics of image noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR) were calculated for each protocol.

    RESULTS: Decrease in dose levels has significantly increased the image noise, compared to FBP of protocol A (P 

    Matched MeSH terms: Signal-To-Noise Ratio
  3. Usman OL, Muniyandi RC, Omar K, Mohamad M
    PLoS One, 2021;16(2):e0245579.
    PMID: 33630876 DOI: 10.1371/journal.pone.0245579
    Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels' intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.
    Matched MeSH terms: Signal-To-Noise Ratio
  4. Quar TK, Ching TY, Newall P, Sharma M
    Int J Audiol, 2013 May;52(5):322-32.
    PMID: 23570290 DOI: 10.3109/14992027.2012.755740
    The study aims to compare the performance of hearing aids fitted according to the NAL-NL1 and DSL v5 prescriptive procedure for children.
    Matched MeSH terms: Noise/adverse effects
  5. Ibrahim IA, Ting HN, Moghavvemi M
    J Int Adv Otol, 2019 Apr;15(1):87-93.
    PMID: 30924771 DOI: 10.5152/iao.2019.4553
    OBJECTIVES: This study uses a new approach for classifying the human ethnicity according to the auditory brain responses (electroencephalography [EEG] signals) with a high level of accuracy. Moreover, the study presents three different algorithms used to classify the human ethnicity using auditory brain responses. The algorithms were tested on Malays and Chinese as a case study.

    MATERIALS AND METHODS: The EEG signal was used as a brain response signal, which was evoked by two auditory stimuli (Tones and Consonant Vowels stimulus). The study was carried out on Malaysians (Malay and Chinese) with normal hearing and with hearing loss. A ranking process for the subjects' EEG data and the nonlinear features was used to obtain the maximum classification accuracy.

    RESULTS: The study formulated the classification of Normal Hearing Ethnicity Index and Sensorineural Hearing Loss Ethnicity Index. These indices classified the human ethnicity according to brain auditory responses by using numerical values of response signal features. Three classification algorithms were used to verify the human ethnicity. Support Vector Machine (SVM) classified the human ethnicity with an accuracy of 90% in the cases of normal hearing and sensorineural hearing loss (SNHL); the SVM classified with an accuracy of 84%.

    CONCLUSION: The classification indices categorized or separated the human ethnicity in both hearing cases of normal hearing and SNHL with high accuracy. The SVM classifier provided a good accuracy in the classification of the auditory brain responses. The proposed indices might constitute valuable tools for the classification of the brain responses according to the human ethnicity.

    Matched MeSH terms: Noise/adverse effects
  6. Hameed HK, Wan Hasan WZ, Shafie S, Ahmad SA, Jaafar H, Inche Mat LN
    J Med Eng Technol, 2020 Apr;44(3):139-148.
    PMID: 32396756 DOI: 10.1080/03091902.2020.1753838
    To make robotic hand devices controlled by surface electromyography (sEMG) signals feasible and practical tools for assisting patients with hand impairments, the problems that prevent these devices from being widely used have to be overcome. The most significant problem is the involuntary amplitude variation of the sEMG signals due to the movement of electrodes during forearm motion. Moreover, for patients who have had a stroke or another neurological disease, the muscle activity of the impaired hand is weak and has a low signal-to-noise ratio (SNR). Thus, muscle activity detection methods intended for controlling robotic hand devices should not depend mainly on the amplitude characteristics of the sEMG signal in the detection process, and they need to be more reliable for sEMG signals that have a low SNR. Since amplitude-independent muscle activity detection methods meet these requirements, this paper investigates the performance of such a method on people who have had a stroke in terms of the detection of weak muscle activity and resistance to false alarms caused by the involuntary amplitude variation of sEMG signals; these two parameters are very important for achieving the reliable control of robotic hand devices intended for people with disabilities. A comparison between the performance of an amplitude-independent muscle activity detection algorithm and three amplitude-dependent algorithms was conducted by using sEMG signals recorded from six hemiparesis stroke survivors and from six healthy subjects. The results showed that the amplitude-independent algorithm performed better in terms of detecting weak muscle activity and resisting false alarms.
    Matched MeSH terms: Signal-To-Noise Ratio
  7. Katiri R, Hall DA, Buggy N, Hogan N, Horobin A, van de Heyning P, et al.
    Trials, 2020 Mar 04;21(1):238.
    PMID: 32131880 DOI: 10.1186/s13063-020-4094-9
    BACKGROUND: Single-sided deafness (SSD) describes the presence of a unilateral severe to profound sensorineural hearing loss. SSD disrupts spatial hearing and understanding speech in background noise. It has functional, psychological and social consequences. Potential options for rehabilitation include hearing aids and auditory implants. Benefits and harms of these interventions are documented inconsistently in the literature, using a variety of outcomes ranging from tests of speech perception to quality of life questionnaires. It is therefore difficult to compare interventions when rehabilitating SSD. The Core Rehabilitation Outcome Set for Single Sided Deafness (CROSSSD) study is an international initiative that aims to develop a minimum set of core outcomes for use in future trials of SSD interventions.

    METHODS/DESIGN: The CROSSSD study adopts an international two-round online modified Delphi survey followed by a stakeholder consensus meeting to identify a patient-centred core outcome domain set for SSD based on what is considered critical and important for assessing whether an intervention for SSD has worked.

    DISCUSSION: The resulting core outcome domain set will act as a minimum standard for reporting in future clinical trials and could have further applications in guiding the use of outcome measures in clinical practice. Standardisation will facilitate comparison of research findings.

    Matched MeSH terms: Noise
  8. Said MA, Musarudin M, Zulkaffli NF
    Ann Nucl Med, 2020 Dec;34(12):884-891.
    PMID: 33141408 DOI: 10.1007/s12149-020-01543-x
    OBJECTIVE: 18F is the most extensively used radioisotope in current clinical practices of PET imaging. This selection is based on the several criteria of pure PET radioisotopes with an optimum half-life, and low positron energy that contributes to a smaller positron range. In addition to 18F, other radioisotopes such as 68Ga and 124I are currently gained much attention with the increase in interest in new PET tracers entering the clinical trials. This study aims to determine the minimal scan time per bed position (Tmin) for the 124I and 68Ga based on the quantitative differences in PET imaging of 68Ga and 124I relative to 18F.

    METHODS: The European Association of Nuclear Medicine (EANM) procedure guidelines version 2.0 for FDG-PET tumor imaging has adhered for this purpose. A NEMA2012/IEC2008 phantom was filled with tumor to background ratio of 10:1 with the activity concentration of 30 kBq/ml ± 10 and 3 kBq/ml ± 10% for each radioisotope. The phantom was scanned using different acquisition times per bed position (1, 5, 7, 10 and 15 min) to determine the Tmin. The definition of Tmin was performed using an image coefficient of variations (COV) of 15%.

    RESULTS: Tmin obtained for 18F, 68Ga and 124I were 3.08, 3.24 and 32.93 min, respectively. Quantitative analyses among 18F, 68Ga and 124I images were performed. Signal-to-noise ratio (SNR), contrast recovery coefficients (CRC), and visibility (VH) are the image quality parameters analysed in this study. Generally, 68Ga and 18F gave better image quality as compared to 124I for all the parameters studied.

    CONCLUSION: We have defined Tmin for 18F, 68Ga and 124I SPECT CT imaging based on NEMA2012/IEC2008 phantom imaging. Despite the long scanning time suggested by Tmin, improvement in the image quality is acquired especially for 124I. In clinical practice, the long acquisition time, nevertheless, may cause patient discomfort and motion artifact.

    Matched MeSH terms: Signal-To-Noise Ratio
  9. Lim CS, Krishnan G, Sam CK, Ng CC
    Clin Chim Acta, 2013 Jan 16;415:158-61.
    PMID: 23043757 DOI: 10.1016/j.cca.2012.08.031
    Because blocking agent occupies most binding surface of a solid phase, its ability to prevent nonspecific binding determines the signal-to-noise ratio (SNR) and reliability of an enzyme-linked immunosorbent assay (ELISA).
    Matched MeSH terms: Signal-To-Noise Ratio
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