Positron Emission Tomography (PET) image enhancement using a gradient vector orientation based nonlinear diffusion filter (GVOF) for accurate quantitation of radioactivity concentration
Mahbubunnabi Tamal

TL;DR
This paper introduces a novel gradient vector orientation based nonlinear diffusion filter (GVOF) for PET image enhancement, significantly improving SNR, resolution, and quantitation accuracy across varying noise levels and lesion sizes.
Contribution
The paper proposes a new parameter-free GVOF method that outperforms existing filters in PET image enhancement, especially under low SNR and diverse lesion conditions.
Findings
GVOF yields the highest SNR, CNR, and resolution improvements.
GVOF minimizes bias in SUVmax estimation for larger lesions.
GVOF demonstrates robustness across different noise levels, sizes, and contrast conditions.
Abstract
To accurately quantify in vivo radiotracer uptake using Positron Emission Tomography (PET) is a challenging task due to low signal-to-noise ratio (SNR) and poor spatial resolution of PET camera along with the finite image sampling constraint. Furthermore, inter lesion variations of the SNR and contrast along with the variations in size of the lesion make the quantitation even more difficult. One of the ways to improve the quantitation is via post reconstruction filtering with Gaussian Filter (GF). Edge preserving Bilateral Filter (BF) and Nonlinear Diffusion Filter (NDF) are the alternatives to GF that can improve the SNR without degrading the image resolution. However, the performance of these edge preserving methods are only optimum for high count and low noise cases. A novel parameter free gradient vector orientation based nonlinear diffusion filter (GVOF) is proposed in this paper…
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Radiomics and Machine Learning in Medical Imaging · Lung Cancer Diagnosis and Treatment
