Enhancing Radiation Oncology Imaging with a Novel Variational Model Decomposition, Radon Transformation, and Kohonen Self -Organizing Map Denoising Framework

Document Type

Conference Proceeding

Publication Date

9-30-2025

Publication Title

Med Phys

Keywords

cone beam computed tomography, conference abstract, controlled study, decomposition, diagnostic value, Gaussian noise, human, image guided radiotherapy, male, MRI scanner, noise, noise reduction, nuclear magnetic resonance imaging, organs at risk, radiation oncology, Radon transform, signal noise ratio, treatment planning

Abstract

Purpose: Reduction of noise in medical images critically enables improved accuracy in delineating tumors and organs at risk, leading to more precise treatment planning and safer image-guided radiation therapy. This study proposes a novel, modality agnostic denoising framework that integrates Variational-Model-Decomposition (VMD), Radon- Transformation (RDT), and Kohonen-Self-Organizing-Map (KSOM) to suppress and regulate high-frequency noise components of images while preserving structural details to enhance diagnostic reliability and accuracy. Methods: T1- 3D-VIBE-DIXON-IN Magnetic Resonance (MR) Images (n=4) and Cone Beam Computed Tomography (CBCT) images (n=7) acquired using a Siemens-Magnetom-Free.Max 0.55T and HyperSight-Ethos, respectively, were selected for this study. This framework used RDT with the optimal number of projections (hr∼π /Δ, where D is the reconstruction diameter with Δx-resolution) to ensure adequate angular sampling, minimize aliasing, and satisfy the Nyquist-criterion. The resampled projections were decomposed into a series of intrinsic-modes (IM=25) using VMD. The KSOM (Topologysize: 5X5=25) was then applied to VMD results to regularize the decomposition by selecting the optimal number of modes, effectively suppressing noise by regularizing high-frequency components. The denoised images were reconstructed by applying the inverse-RDT to the projections reconstructed from the retained modes and signal-to-noise ratios (SNRs) of the images were compared. Results: The framework reduced noise while preserving anatomical details. Visual and quantitative evaluation confirmed enhanced clarity, and SNRs improved significantly (31.75±1.40dB to 36.80±1.05dB for MR; 26.23±4.28dB to 31.18±3.14dB for CBCT). KSOM selected optimal modes (IM 19, 21) at central-frequencybandwidths of 0.041, 0.043 cycles/mm (∼7%, ∼9% of Nyquist frequencies), balancing noise reduction and detail preservation. This was confirmed by targeted high-frequency removal affecting primarily Rician/Gaussian noise, preserving anatomical details. Conclusion: This VMD-RDT-KSOM framework significantly enhanced SNR for low-field MRI and CBCT. It is modality-agnostic, offering potential for broader applications in radiation oncology and beyond. Future research on larger datasets and additional modalities will validate its versatility.

Volume

52

Issue

10

First Page

307

Last Page

308

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