How to generate a (pseudo-)reference image for diffusion MRI without losing important data

Diffusion magnetic resonance imaging is one of the diagnostic techniques based on the phenomenon of nuclear magnetic resonance, which is particularly useful in brain scans. The technique relies on the diffusion of water particles, which can be isotropic and anisotropic, where the movement of molecules is constrained by natural barriers. For instance, particles diffuse with much greater freedom along the nerve fibre axes than across them. Diffusion imaging for each voxel, that is, the smallest unit in three-dimensional space, facilates the characterisation of this process with quantitative measures. Therefore, unlike traditional magnetic resonance imaging, the images obtained in this way are secondary to the numerical parameters determined during the scan.

Diffusion imaging can be a great tool for detecting cancer and neurodegenerative diseases or to track the natural changes that occur in the brain during the ageing process. Regrettably, the relatively long time required to perform the scan limits the usefulness of this technique in clinical practice. The signals collected by the scanner can be corrupted by noise due to, for example, changes in a patient’s body temperature or heart rate, or interference caused by thermal noise. As a result, to obtain images free from deformation, the acquisition should be repeated multiple times.

Reference or pseudo-reference images?

Current scientific investigations that aim to obtain high-quality images with a reduced number of necessary repetitions focus on the use of artificial intelligence methods to denoise data. Convolutional neural networks can learn to remove distortions by comparing images corrupted by noise with reference images or, more precisely, pseudo-reference images. This is important because a raw signal registered by the scanner is not useful in any way to the person interpreting the results without proper processing of data using mathematical tools and computer algorithms. To do this, we can use models that represent the multidimensional structure of nervous tissue.

Diffusion tensor vs. spherical harmonics

The ability of artificial intelligence to effectively denoise images will not achieve much in clinical practice if an inappropriate set of data is used to train it. In such a situation, relevant diagnostic data might be removed from even good-quality images. A step forward in relation to the currently available methods has been proposed by Julia Machnio, Eng. in her diploma thesis written at the Faculty of Electrical Engineering, Automatics, Computer Science, and Biomedical Engineering under the supervision of Dr Tomasz Pięciak.

The starting point for Julia Machio’s considerations came from a method that had already been proposed in the literature, namely DeepDTI. The authors of the paper generated pseudo-reference data using DTI (diffusion tensor imaging). The neural network they trained learned to denoise images in a relatively short time, which allowed them to obtain visually high-quality images.

The DeepSH method she proposed in her work to generate pseudo-reference data uses an alternative method of spherical harmonics.

A method more reliable than others

The investigation carried out by Julia Machnio provided proof for the high efficiency of the method to denoise images, while simultaneously preserving data in the processed signals, which might carry significant diagnostic information.