CT colonography interpretation is difficult and time-consuming because fecal residue or fluid can mimic or
obscure polyps, leading to diagnostic errors. To compensate for this, it is normal practice to obtain CT data
with the patient in prone and supine positions. Repositioning redistributes fecal residue and colonic gas; fecal
residue tends to move, while fixed mural pathology does not.
The cornerstone of competent interpretation is the matching of corresponding endoluminal locations between
prone and supine acquisitions. Robust and accurate automated registration between acquisitions should lead
to faster and more accurate detection of colorectal cancer and polyps. Any directional bias when registering
the colonic surfaces could lead to incorrect anatomical correspondence resulting in reader error. We aim to
reduce directional bias and so increase robustness by adapting a cylindrical registration algorithm to penalize
inverse-consistency error, using a symmetric optimization.
Using 17 validation cases, the mean inverse-consistency error was reduced significantly by 86%, from 3.3 mm
to 0.45 mm. Furthermore, we show improved alignment of the prone and supine colonic surfaces, evidenced by a
reduction in the mean-of-squared-differences by 43% overall. Mean registration error, measured at a sparse set
of manually selected reference points, remained at the same level as the non-symmetric method (no significant
differences). Our results suggest that the inverse-consistent symmetric algorithm performs more robustly than
non-symmetric implementation of B-spline registration.
Respiratory motion causes problems of tumour localisation in radiotherapy treatment planning for lung cancer patients. We have developed a novel method of building patient specific motion models, which model the movement and non-rigid deformation of a lung tumour and surrounding lung tissue over the respiratory cycle. Free-breathing (FB) CT scans are acquired in cine mode, using 3 couch positions to acquire contiguous 'slabs' of 16 slices covering the region of interest. For each slab, 20 FB volumes are acquired over approx 20s. A reference volume acquired at Breath Hold (BH) and covering the whole lung, is non-rigidly registered to each of the FB volumes. The FB volumes are assigned a position in the respiratory cycle (PRC) calculated from the displacement of the chest wall. A motion model is then constructed for each slab, by fitting functions that temporally interpolate the registration results over the respiratory cycle. This can produce a prediction of the lung and tumour within the slab at any arbitrary PRC. The predictions for each of the slabs are then combined to produce a volume covering the whole region of interest. Results indicate that the motion modelling method shows considerable promise, offering significant improvement over current clinical practice, and potential advantages over alternative 4D CT imaging techniques. Using this framework, we examined and evaluated several different functions for performing the temporal interpolation. We believe the results of these comparisons will aid future model building for this and other applications.