Convolution

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Convolution

Convolution (/kɒnvəˈluːʃən/), from the Latin convolvere, meaning "to roll together", is a mathematical operation on two functions that produces a third function expressing how the shape of one is modified by the other. In the medical field, it is often used in the context of signal processing and image processing, particularly in the realms of medical imaging and neuroimaging.

Definition

In the simplest terms, convolution is a mathematical operation that combines two functions to produce a third function. It is often symbolized by an asterisk (*). In the context of medical imaging, convolution is used to improve the quality of images, reduce noise, and extract features.

Usage in Medical Field

Convolution is a fundamental concept in the field of medical imaging. It is used in the processing of images obtained from various imaging techniques such as MRI (Magnetic Resonance Imaging), CT scan (Computed Tomography), and PET scan (Positron Emission Tomography).

In neuroimaging, convolution is used to analyze brain activity. For example, in fMRI (Functional Magnetic Resonance Imaging), the convolution of the hemodynamic response function with the neural activity is used to predict the blood oxygen level dependent signal.

Related Terms

  • Signal processing: The analysis, interpretation, and manipulation of signals. In medical imaging, signal processing techniques such as convolution are used to improve the quality of images and extract useful information.
  • Image processing: The manipulation of digital images by means of a digital computer. Convolution is a common technique used in image processing to filter images and extract features.
  • Medical imaging: The technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention.
  • Neuroimaging: The use of various techniques to either directly or indirectly image the structure, function, or pharmacology of the nervous system.

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