Diffusion in realistic biophysical systems can lead to aliasing effects in diffusion spectrum imaging

Area of Science:

• Neuroimaging
• Biophysics
• Computational Neuroscience

Background:

• Diffusion spectrum imaging (DSI) is a key technique for mapping white matter tracts in the brain.
• However, DSI's accuracy in complex biological environments, such as those with pathological tissues, remains poorly understood.
• Existing methods struggle with aliasing artifacts caused by fast diffusion components.

Purpose of the Study:

• To evaluate Diffusion Spectrum Imaging (DSI) performance under realistic biophysical conditions.
• To investigate the impact of different tissue configurations, sampling schemes, and processing steps on DSI accuracy.
• To develop and validate a novel method for computing the orientation distribution function (ODF) that incorporates biophysical constraints.

Main Methods:

• Simulated various tissue microstructures and diffusion imaging acquisition parameters.
• Developed a new orientation distribution function (ODF) computation approach integrating biophysical constraints.
• Evaluated DSI performance and artifact presence across different simulation scenarios.

Main Results:

• Simulations revealed aliasing artifacts in several DSI configurations, particularly with fast diffusion components.
• These artifacts can lead to inaccurate white matter tract reconstructions, especially in complex or pathological tissues.
• The novel ODF computation method demonstrated reduced aliasing artifacts and improved resolution of white matter crossings.

Conclusions:

• Current DSI implementations are susceptible to aliasing artifacts from fast diffusion, potentially causing erroneous fiber reconstructions.
• A new ODF computation method effectively minimizes these artifacts and enhances angular resolution.
• This improved method offers more reliable white matter tractography in complex neuroimaging scenarios.