Kernel (image reconstruction for CT)
Citation, DOI, disclosures and article data
At the time the article was created Annika Cruickshank had no recorded disclosures.
View Annika Cruickshank's current disclosuresAt the time the article was last revised Mohd Ashyiraff Ilani Bin Ismail had no financial relationships to ineligible companies to disclose.
View Mohd Ashyiraff Ilani Bin Ismail's current disclosures- Kernels (image reconstruction for CT)
- Convolution algorithm (image reconstruction for CT)
The kernel, also known as a convolution algorithm, refers to the process used to modify the frequency contents of projection data prior to back projection during image reconstruction in a CT scanner 1. This process corrects the image by reducing blurring 1. The kernel affects the appearance of image structures by sharpening the image. Different kernels have been developed for specific anatomical applications including soft tissue (standard kernel) and bone (bone kernel) 2. Bone kernels produce a sharper image with higher spatial resolution 1, thus it is commonly used for high resolution CT scan (i.e HRCT thorax).
When this kind of algorithm is used to improve spatial resolution, the process is called deconvolution. The process of deconvolution coupled with back projection results in filtered back projection 2.
References
- 1. Euclid Seeram. Computed Tomography - E-Book. (2015) ISBN: 9780323323017
- 2. Neal C. Dalrymple, Srinivasa R. Prasad, Michael W. Freckleton, Kedar N. Chintapalli. Introduction to the Language of Three-dimensional Imaging with Multidetector CT1. (2005) RadioGraphics. doi:10.1148/rg.255055044#R2
- 3. Jerrold T. Bushberg, John M. Boone. The Essential Physics of Medical Imaging. (2011) ISBN: 9780781780575
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