  Clinical diffusion imaging requires short acquisition times and good image
quality to permit its use in various medical applications. In turn, these
demands require the development of a robust and efficient post-processing
framework in order to guarantee useful and reliable results. However, multiple
artefacts abound in in vivo measurements; from either subject such as cardiac
pulsation, bulk head motion, respiratory motion and involuntary tics and
tremor, or imaging hardware related problems, such as table vibrations, etc.
These artefacts can severely degrade the resulting images and render diffusion
analysis difficult or impossible. In order to overcome these problems, we
developed a robust and efficient framework enabling the use of initially
corrupted images from a clinical study. At the heart of this framework is an
improved least trimmed squares diffusion tensor estimation algorithm that works
well with severely degraded datasets with low signal-to-noise ratio. This
approach has been compared with other diffusion imaging post-processing
algorithms using simulations and in vivo experiments. Exploiting track-based
spatial statistics analysis, we demonstrate that corrupted datasets can be
restored and reused in further clinical studies rather than being discarded due
to poor quality. The developed robust framework is shown to exhibit a high
efficiency and accuracy and can, in principle, be exploited in other MR studies
where artefact/outlier suppression is needed.
