Medical Image Noise
Medical Image Noise - Related Videos
08:21
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
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Cited by 4 •
2014
Optical detection of ultrasound is impractical in many imaging scenarios because it often requires stable environmental conditions. We demonstrate an optical technique for ultrasound sensing in volatile environments with miniaturization and sensitivity levels appropriate for optoacoustic imaging in restrictive scenarios, e.g. intravascular applications.
02:35
Continuous Monitoring of Biological Noise in Bacteria Using Time-Lapse Microscopy
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2025
Source: Chaimovitz, E. A., et al. Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy. J. Vis. Exp. (2021)This video demonstrates real-time visualization of biological noise in E. coli by tracking fluctuations in GFP fluorescence during bacterial growth and division using time-lapse microscopy.
09:43
Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores
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Cited by 18 •
2013
A methodology for preparing solid-state nanopores in solution for biomolecular translocation experiments is presented. By applying short pulses of high electric fields, the nanopore diameter can be controllably enlarged with subnanometer precision and its electrical noise characteristics significantly improved. This procedure is performed in situ using standard laboratory equipment under experimental conditions.
08:19
Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics
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Cited by 24 •
2009
Lensfree on-chip imaging and characterization of cells is illustrated. This on-chip cell imaging approach provides a compact and cost-effective tool for medical diagnostics and high-throughput cell biology applications, making it especially suitable for resource poor settings.
09:36
Safety Precautions and Operating Procedures in an (A)BSL-4 Laboratory: 4. Medical Imaging Procedures
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Cited by 8 •
2016
Here, we present an overview of the preparation and animal handling procedures required to safely perform medical imaging in an animal biosafety level 4 laboratory. Computed tomography of a mock-infected guinea pig illustrates these procedures that may be used to evaluate the disease caused by a high consequence pathogen.
FAQs
Noise sources differ by modality: X-ray and CT are affected by photon fluctuations, whereas MRI can show thermal or electronic effects, and ultrasound can display acoustic speckle. These mechanisms produce different image patterns and require modality-aware analysis. Identifying the source helps bioengineers select denoising or reconstruction strategies that address the observed variation rather than treating all images identically.
Reconstruction algorithms and signal-processing methods can either amplify or suppress existing noise patterns. Consequently, a visually similar image may reflect different balances between acquired signal and processing effects. Evaluating the image after reconstruction is important when designing a denoising method, because reducing visible variation alone does not show whether diagnostically important anatomical features have been preserved.
Acquisition conditions influence how strongly noise appears and therefore affect image contrast and interpretability. Bioengineers can use this relationship when developing acquisition protocols, seeking improved image quality without automatically increasing imaging exposure or scan time. The goal is to balance noise reduction with preservation of anatomical detail, so resulting images remain useful for interpretation and quantitative analysis.
Noise reduction is not successful if it removes diagnostically important features along with unwanted variation. A useful method should improve contrast while retaining anatomical detail needed for interpretation and measurement. This balance matters because downstream tasks such as segmentation, computer-aided diagnosis, and treatment planning depend on image features that processing could otherwise distort or obscure.
A bioengineering workflow can connect observed noise patterns with the imaging modality and acquisition conditions, then use that information to guide denoising algorithms, image-reconstruction techniques, or acquisition protocols. Characterization helps evaluate whether a proposed change suppresses unwanted variation while preserving useful image content, supporting improvements in contrast and quantitative reliability without unnecessary increases in exposure or scan time.
Controlling image noise can improve several downstream uses of medical images, including anatomical segmentation, computer-aided diagnosis, treatment planning, and quantitative measurement. In each case, clearer contrast and preserved features can make image information easier to interpret or analyze. These applications also show why noise reduction must be assessed for its effect on diagnostically important structures, not only overall visual appearance.