Measured values for image noise variance in x-ray computed tomography were found to be less than half of the values predicted by published formulas. This phenomenon had previously been attributed qualitatively to the use of linear interpolation of the filtered projections in the back projection process. An analysis of the reconstruction process has allowed the derivation of a formula for image noise variance which incorporates the effect of this interpolation, giving results which are less than 50% of the previously predicted values. This formula has been tested with images of a perspex rod produced by an x-ray microtomography scanner and with a similar image derived from mathematically modeled projection data. The predicted noise variance was within 1% of the measured values for both the simulated and the experimental data.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/XST-1993-4303 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Higher Aircraft Noise Exposure Is Linked to Worse Heart Structure and Function by Cardiovascular MRI.
J Am Coll Cardiol
December 2024
UCL MRC Unit for Lifelong Health and Ageing, University College London, London, United Kingdom; UCL Institute of Cardiovascular Science, University College London, London, United Kingdom; Centre for Inherited Heart Muscle Conditions, Cardiology Department, Royal Free Hospital, London, United Kingdom. Electronic address:
Background: Aircraft noise is a growing concern for communities living near airports.
Objectives: This study aimed to explore the impact of aircraft noise on heart structure and function.
Methods: Nighttime aircraft noise levels (L) and weighted 24-hour day-evening-night aircraft noise levels (L) were provided by the UK Civil Aviation Authority for 2011.
Self-Supervised and Zero-Shot Learning in Multi-Modal Raman Light Sheet Microscopy.
Sensors (Basel)
December 2024
CeMOS Research and Transfer Center, Mannheim University of Applied Sciences, 68163 Mannheim, Germany.
Advancements in Raman light sheet microscopy have provided a powerful, non-invasive, marker-free method for imaging complex 3D biological structures, such as cell cultures and spheroids. By combining 3D tomograms made by Rayleigh scattering, Raman scattering, and fluorescence detection, this modality captures complementary spatial and molecular data, critical for biomedical research, histology, and drug discovery. Despite its capabilities, Raman light sheet microscopy faces inherent limitations, including low signal intensity, high noise levels, and restricted spatial resolution, which impede the visualization of fine subcellular structures.
View Article and Find Full Text PDFThe Design of a Low-Noise CMOS Image Sensor Using a Hybrid Single-Slope Analog-to-Digital Converter.
Sensors (Basel)
December 2024
Department of System Semiconductor, Dongguk University, Seoul 04620, Republic of Korea.
In this study, we describe a low-noise complementary metal-oxide semiconductor (CMOS) image sensor (CIS) with a 10/11-bit hybrid single-slope analog-to-digital converter (SS-ADC). The proposed hybrid SS-ADC provides a resolution of 11 bits in low-light and 10 bits in high-light. To this end, in the low-light section, the digital-correlated double sampling method using a double data rate structure was used to obtain a noise performance similar to that of the 11-bit SS-ADC under low-light conditions, while maintaining linear in-out characteristics.
View Article and Find Full Text PDFMIRA-CAP: Memory-Integrated Retrieval-Augmented Captioning for State-of-the-Art Image and Video Captioning.
Sensors (Basel)
December 2024
Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
Generating accurate and contextually rich captions for images and videos is essential for various applications, from assistive technology to content recommendation. However, challenges such as maintaining temporal coherence in videos, reducing noise in large-scale datasets, and enabling real-time captioning remain significant. We introduce MIRA-CAP (Memory-Integrated Retrieval-Augmented Captioning), a novel framework designed to address these issues through three core innovations: a cross-modal memory bank, adaptive dataset pruning, and a streaming decoder.
View Article and Find Full Text PDFA Time-Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen.
Sensors (Basel)
December 2024
Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA.
Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!