Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter.

Journal: Nuclear medicine communications

PMID: 37272279

Abstract

INTRODUCTION: A DnCNN for image denoising trained with natural images is available in MATLAB. For Tc-99m DMSA images, any loss of clinical details during the denoising process will have serious consequences since denoised image is to be used for diagnosis. The objective of the study was to find whether this pre-trained DnCNN can be used for denoising Tc-99m DMSA images and compare its performance with block matching 3D (BM3D) filter.

Authors

Jagrati Chaudhary

Department of Nuclear Medicine, All India Institute of Medical Sciences.
Ankita Phulia

Maulana Azad Medical College.
Anil Kumar Pandey
Param D Sharma

Department of Computer Science, SGTB Khalsa College, University of Delhi, New Delhi, India.
Chetan Patel

Keywords

Cicatrix Humans Image Processing, Computer-Assisted Imaging, Three-Dimensional Neural Networks, Computer Signal-To-Noise Ratio Technetium Tc 99m Dimercaptosuccinic Acid

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Denoising Tc-99m DMSA images using Denoising Convolutional Neural Network with comparison to a Block Matching Filter.

Abstract

Authors

Keywords

External Resources

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