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Denoising Intravoxel Incoherent Motion Magnetic Resonance Images Using Non-Local Mean Technique for Oropharyngeal Cancer Study

Y Ding

Y Ding1*, C Fuller1, A Mohamed1, R He1, J Wang1, S Frank1, D Rosenthal1, R Colen1, J Hazle1,(1) UT MD Anderson Cancer Center, Houston, TX


SU-E-QI-5 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) normally shows a low signal to noise ratio (SNR) due to the presence of noise which complicates and biases the estimation of quantitative diffusion parameters. In this study, a Non-local Means (NLM) approach was applied to remove the noise in oropharyngeal cancer IVIM-MRI images.

Methods: Eight male patients with squamous cell carcinoma of the oropharynx were included in this study under an approved IRB protocol. IVIM-MRI was carried out on a 3.0-T GE MRI. NLM denoising technique was performed using the MIPAV (V7, NIH). IVIM parameters (D, pure diffusion coefficient; f, perfusion fraction; D*, pseudodiffusion coefficient) were calculated on a pixel-by-pixel basis using a bi-exponential model implemented within ImageJ (V1.47, NIH) with ten b-values ranging from 0 - 800 s/mm². SNR with and without denoising processing was calculated and compared in tumor regions. The agreements of IVIM-MRI parameters estimated between with and without NLM processing in tumor region were assessed by Bland-Altman plots.

Results: Examples of IVIM images (b=800 s/mm²) and parametric maps (D, D* and f) with and without NLM applied are illustrated in Fig. 1 for an oropharynx cancer patient. Results of SNR and IVIM diffusion parameters between two different processing, as mean and standard deviation on 8 patients, are reported in Table 1. Bland-Altman plots (Fig. 2) between the two approaches show better concordance for D (0.22% ± 1.42%) and f (0.56% ± 3.49%), whereas larger discrepancies were found for D* (0.86% ± 6.25%).

Conclusion: NLM approach gives better SNR and quantitative data quality, also shows a good agreement between the IVIM parameters processed with and without NLM approaches. Therefore, NLM denoising technique can be applied to improve performance in terms of denoising quality and estimation of IVIM parameter as a post-processing step without increasing the scanning time.

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