Many noise guidelines currently use A-weighted equivalent sound pressure level L(Aeq) as the noise metric and the equal energy hypothesis to assess the risk of occupational noises. Because of the time-averaging effect involved with the procedure, the current guidelines may significantly underestimate the risk associated with complex noises. This study develops and evaluates several new noise metrics for more accurate assessment of exposure risks to complex and impulsive noises. The analytic wavelet transform was used to obtain time-frequency characteristics of the noise. 6 basic, unique metric forms that reflect the time-frequency characteristics were developed, from which 14 noise metrics were derived. The noise metrics were evaluated utilizing existing animal test data that were obtained by exposing 23 groups of chinchillas to, respectively, different types of noise. Correlations of the metrics with the hearing losses observed in chinchillas were compared and the most promising noise metric was identified.
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http://dx.doi.org/10.1121/1.3159587 | DOI Listing |
J Appl Clin Med Phys
January 2025
Medical Physics Section, OHS Department, Hamad Medical Corporation, Doha, Qatar.
Purpose: To evaluate image quality (IQ) of for-processing (raw) and for-presentation (clinical) radiography images, under different exposure conditions and digital image post-processing algorithms, using a phantom that enables multiple detection tasks.
Methods: A modified version of the radiography phantom described in the IAEA Human Health Series No. 39 publication was constructed, incorporating six additional Aluminum (Al) targets of thicknesses both smaller and larger than the standard one.
BMC Med Imaging
January 2025
Department of Magnetic Resonance Imaging, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China.
Background: Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
January 2025
From the Orthopedic Data Innovation Lab (ODIL), Hospital for Special Surgery (A.M.L.S., M.A.F.), Department of Radiology and Imaging, Hospital for Special Surgery Centre (E.E.X, Z.I, E.T.T, D.B.S, J.L.C)and Department of Population Health Sciences, Weill Cornell Medicine (M.A.F), New York, New York, USA.
Background And Purpose: To train and evaluate an open-source generative adversarial networks (GANs) to create synthetic lumbar spine MRI STIR volumes from T1 and T2 sequences, providing a proof-of-concept that could allow for faster MRI examinations.
Materials And Methods: 1817 MRI examinations with sagittal T1, T2, and STIR sequences were accumulated and randomly divided into training, validation, and test sets. GANs were trained to create synthetic STIR volumes using the T1 and T2 volumes as inputs, optimized using the validation set, then applied to the test set.
J Vis
January 2025
Neural Information Processing Group, University of Tübingen, Tübingen, Germany.
Human performance in psychophysical detection and discrimination tasks is limited by inner noise. It is unclear to what extent this inner noise arises from early noise (e.g.
View Article and Find Full Text PDFFront Med (Lausanne)
December 2024
Software Engineering Department, LUT University, Lahti, Finland.
Introduction: Neurodegenerative diseases, including Parkinson's, Alzheimer's, and epilepsy, pose significant diagnostic and treatment challenges due to their complexity and the gradual degeneration of central nervous system structures. This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.
Methods: We propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data.
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