Denoising 3D Magnetic Resonance Images Based on Low-Rank Tensor Approximation With Adaptive Multirank Estimation

Hongli Lv,Renfang Wang

doi:10.1109/access.2019.2924907

Hongli Lv, Renfang Wang

Open Access

https://doi.org/10.1109/access.2019.2924907

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Abstract

The magnetic resonance (MR) imaging technique is widely used in clinical diagnosis. Unfortunately, in practice, the MR images inevitably suffer from noise, which severely degrades image quality and accordingly impacts on the accuracy of clinical diagnosis. By exploiting both the nonlocal similarity over space and the inherent correlation across the slices of the 3D MR images, in this paper, we present a novel Rician noise reduction method for the 3D MR images. Specifically, the 3D nonlocal similar patches are first extracted from the input noisy 3D MR data and then grouped to form a noisy fourth-order tensor. Since 3D patches used to construct the fourth-order tensor share similar structures, a latent noise-free tensor can be approximated by a low-rank tensor. To this end, the higher-order singular value decomposition (HOSVD) is adopted to recover the latent noise-free tensor. Furthermore, the rank of each mode of the tensor is adaptively determined by an enhanced low-rank method. The experimental results on synthetic and real 3D MR images show that the proposed method outperforms several state-of-the-art denoising methods in terms of objective metrics and visual inspection.

Highlights

Being a non-invasive imaging technique, magnetic resonance imaging (MRI) has seen a tremendous development over the past few decades
We present a novel low-rank tensor approximation method for 3D MR images denoising, which consists of two steps: patch grouping and low-rank tensor approximation
To quantitatively evaluate the denoising performance of the proposed method, peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) are adopted to evaluate the denoising results, which are defined as follows

Summary

Introduction

Being a non-invasive imaging technique, magnetic resonance imaging (MRI) has seen a tremendous development over the past few decades. Owing to providing high-resolution 3D images characterizing among tissues and organs of human body, MRI is widely used for the diagnosis of diseases. MR images are known to be degraded by Rician noise due to the limitations in the imaging hardware, movement of the patients and scanning time. The presence of noise degrades image quality, and interferes with other automatic approaches such as segmentation or registration [1]. Rician noise, which can mask the characteristics of the lesion, seriously impacts on the accuracy of clinical diagnosis. Rician noise reduction is critical to applying MR images in medical practice

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Conclusion