Assessing the efficacy of tomographic reconstruction methods through physical quantication techniques

Conventionally the performance of computed tomography reconstruction
algorithms is assessed by a voxel-to-voxel comparison between the object and the
reconstructed volume, often using digital phantoms. However the real aim in the CT
imaging community is not to develop a reconstruction algorithm to obtain the bestlooking
images, but one that allows us to extract the relevant information to a desired
accuracy. Here, through various case studies, we quantify features of interest for the
test object and use these as measures of the ecacy of the reconstructions. Where
applicable, we compare the assessment technique against commonly used metrics to
measure the quality of a reconstructed solution, and nd that in most cases the
popular metrics have no relation to the accuracy of the features we extract from
a reconstruction. The assessment technique we demonstrate, which we refer to as
physical quantication, is used to determine the shape, contacts and size of beads for
a test dataset made available via the SophiaBeads Dataset Project. Using this image
analysis approach a number of widely used reconstruction methods are evaluated. Our
work shows that it is important to choose the optimal reconstruction strategy based
on the features you want to quantify from the scan. For example, in our case we found
that the shape of the beads could be measured using TV regularization with 8 times
fewer projections than the other methods, or that reconstructions obtained via many
but noisy projections yield as accurate results as those obtained via less noisy but
fewer projections.