Noise reduction in FLAIR2 images using total generalized variation, Gaussian and Wiener filtering

Purpose: Multiplication of FLAIR and T2-weighted MRI scans results in images (called FLAIR(2)) with an improved contrast-to-noise ratio (CNR) for multiple sclerosis (MS) lesions but with a reduced signal-to-noise ratio (SNR). Denoising of these images may therefore further improve FLAIR(2) image quality. The purpose of this work is to present a systematic investigation of FLAIR(2) image denoising methods using Gaussian, Wiener and Total Generalized Variation (TGV) filtering approaches. Materials and methods: T2-weighted and FLAIR data of four MS patients were used. For CNR and SNR measurements, each scan was performed up to three times. TGV, Gaussian and Wiener filtering was applied to T2, FLAIR and the FLAIR(2) data. FLAIR(2) images were afterwards additionally created using all combinations of input data (native, filtered T2 and filtered FLAIR). SNR and CNR measurements were performed using the subtraction method for all FLAIR(2) approaches (native and filtered input data) and for twenty MS lesions. Additionally, quantitative analysis of filtering based image blurring was performed on all data sets. Results: FLAIR(2) images denoised with TGV showed the highest SNR and CNR, while SNR values were similar for Gaussian and Wiener filtered images. The average CNR over 20 MS lesions within the native FLAIR(2) (32.99) achieved an improvement to 91.17, 82.33 and 56.07 corresponding to TGV Wiener and Gaussian filtering. FLAIR multiplied with T2.denoised showed no improvement, while FLAIR.denoised multiplied with T2 showed an increase by a factor of two to the native, not filtered FLAIR(2). Blurring was most pronounced in Gaussian filtered images and similar in TGV and Wiener filtered images. Conclusion: FLAIR images filtered with Wiener or TGV multiplied with the unfiltered T2 results in FLAIR(2) images with increased SNR and CNR and with minimal edge blurring.