BM4D-PC: nonlocal volumetric denoising of principal components of diffusion-weighted MR images

Purpose: Noise in diffusion-weighted MRI (dMRI) is often spatially correlated due to different acquisition and reconstruction strategies, which is not fully accounted for in current denoising strategies. Thus, we propose a novel model-based denoising method for dMRI that effectively accounts for the different noise characteristics of data. Methods: We propose a denoising strategy that incorporates full noise statistics, including the noise power spectral density (PSD), by leveraging the BM4D algorithm. Furthermore, to exploit redundancy across the diffusion MRI dataset, BM4D is applied to principal components (PC) of diffusion-weighted images (DWI) obtained through principal component analysis (PCA) decomposition of the entire DWI dataset, an approach we refer to as BM4D-PC. Importantly, our method also allows for direct estimation of both the noise map and PSD. We evaluated BM4D-PC against four existing state-of-the-art methods using in-silico and in vivo datasets, including high-resolution human and marmoset acquisitions. Results: Overall, BM4D-PC presented the best results for the metrics PSNR, SSIM and RMSE on the in-silico experiments. The in-vivo studies also showed that BM4D-PC dramatically enhanced the image quality of raw DWIs, outperforming existing denoising methods in terms of noise suppression and detail preservation, leading to improved quality of diffusion metrics. Conclusion: The proposed BM4D-PC method demonstrated state-of-the-art denoising results for dMRI using datasets from various acquisition strategies and image resolutions, potentially supporting future advances in neuroscience research.

Data Augmentation for Detection of Architectural Distortion in Digital Mammography using Deep Learning Approach

Early detection of breast cancer can increase treatment efficiency. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. To train a Convolutional Neural Network (CNN), which is a deep neural architecture, is necessary a huge amount of data. To overcome this problem, this paper proposes a data augmentation approach applied to clinical image dataset to properly train a CNN. Results using receiver operating characteristic analysis showed that with a very limited dataset we could train a CNN to detect AD in digital mammography with area under the curve (AUC = 0.74).