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Journal of Medical Imaging

Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI
Author(s): Rapeepan Maitree; Gloria J. G. Perez-Carrillo; Joshua S. Shimony; H. Michael Gach; Anupama Chundury; Michael Roach; H. Harold Li; Deshan Yang
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Paper Abstract

Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i.e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i.e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography.

Paper Details

Date Published: 1 September 2017
PDF: 14 pages
J. Med. Imag. 4(3) 034004 doi: 10.1117/1.JMI.4.3.034004
Published in: Journal of Medical Imaging Volume 4, Issue 3
Show Author Affiliations
Rapeepan Maitree, Washington Univ. School of Medicine in St. Louis (United States)
Gloria J. G. Perez-Carrillo, Mallinckrodt Institute of Radiology (United States)
Univ. of Arizona (United States)
Joshua S. Shimony, Washington Univ. School of Medicine in St. Louis (United States)
H. Michael Gach, Washington Univ. School of Medicine in St. Louis (United States)
Anupama Chundury, Washington Univ. School of Medicine (United States)
Michael Roach, Washington Univ. School of Medicine (United States)
H. Harold Li, Washington Univ. in St. Louis (United States)
Deshan Yang, Washington Univ. in St. Louis (United States)


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