In magnetic resonance (MR) images, noise is a common issue which can lead to degraded image
quality and reduced clinical value. The signal-to-noise ratio (SNR) of an image is directly proportional
to specific factors such as the magnetic field strength or the scan acquisition time but increasing those
makes the examination more expensive. Therefore, especially for low-field MR imaging, denoising
techniques can be used to improve the SNR and thus increase the diagnostic value of the resulting
images. The aim of this thesis is to implement a deep-learning-based denoising approach which
operates on reconstructed MR images using corresponding noise maps as supplementary input.
Methods and data
The data for this work is based on internal sources of Siemens Healthineers. There are around 10,000
2-D slice images of 862 studies available which were acquired with 1.5 T or 3 T MRI scanners.
Corresponding noise maps, i.e. spatially resolved image maps showing the standard deviation of
the underlying image noise, were calculated from the image data. To simulate lower field strengths,
synthetic noise will be added to the available image data.
In general, supervised deep learning methods are more straight forward than unsupervised methods, but
good ground truth data (i.e., noise-free images) is often hard to obtain for medical imaging applications.
Metzler et al.  proposed using Stein’s unbiased risk estimator (SURE) to train convolutional neural
networks for image denoising without any ground truth data. They have shown that SURE can be
applied to compute the mean-squared- error loss associated with an estimate of the noiseless ground
truth image under the assumption that the noise is normally distributed. Zhussip et al.  applied
SURE for unsupervised training of image recovery and simultaneous denoising with undersampled
compressed sensing measurements.
The goal of this thesis is to adapt a neural network for denoising using SURE loss and investigate the
benefits of including the noise map of an MR image as supplementary input. This approach will be
compared with standard supervised and unsupervised methods, such as Noise2Void  and Noise2Self
, which require nothing but the noisy data as input. For the supervised approach, the original 3 T
images might be used as ground truth and the images with added, synthetic noise to simulate lower
field strengths as input data.
The following aspects will be evaluated:
Different neural network architectures will be implemented and compared w.r.t their denoising
The SURE-based approach will be compared to other proposed unsupervised or supervised deep
learning methods (e.g., Noise2Void, Noise2Self) as well as conventional denoising algorithms.
An extended evaluation of the network’s performance will be conducted on unseen image data.
 C. Metzler, A. Mousavi, R. Heckel, and R.G. Baraniuk. Unsupervised learning with stein’s unbiased risk
estimator. arXiv:1805.10531, 2020.
 M. Zhussip, S. Soltanayev, and S.Y. Chun. Training deep learning based image denoisers from undersampled
measurements without ground truth and without image prior. CVPR, pages 10255–10264, 2019.
 A. Krull, T.O. Buchholz, and F. Jug. Noise2void – learning denoising from single noisy images. CVPR,
pages 2129–2137, 2019.
 J. Batson and L. Royer. Noise2self: Blind denoising by self-supervision. PLMR, 97:524–533, 2019.