Prone to Supine CT Colonography Registration Using a Landmark and Intensity Composite Method

Matching corresponding location between prone and supine acquisitions for CT colonography (CTC) is essential to verify the existence of a polyp, which can be a difficult task due to the considerable deformations that will often occur to the colon during repositioning of the patient. This can induce error and increase interpretation time. We propose a novel method to automatically establish correspondence between the two acquisitions. A first step segments a set of haustral folds in each view and determines correspondence via a labelling process using a Markov Random Field (MRF) model. We show how the landmark correspondences can be used to non-rigidly transform a 2D source image derived from a conformal mapping process on the 3D endoluminal surface mesh to achieve full surface correspondence between prone and supine views. This can be used to initialise an intensity-based non-rigid B-spline registration method which further increases the accuracy. We demonstrate a statistically significant improvement over the intensity based non-rigid B-spline registration by using the composite method

Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography

Purpose: Computed tomographic (CT) colonography is a relatively new technique for detecting bowel cancer or potentially precancerous polyps. CT scanning is combined with three-dimensional (3D) image reconstruction to produce a virtual endoluminal representation similar to optical colonoscopy. Because retained fluid and stool can mimic pathology, CT data are acquired with the bowel cleansed and insufflated with gas and patient in both prone and supine positions. Radiologists then match visually endoluminal locations between the two acquisitions in order to determine whether apparent pathology is real or not. This process is hindered by the fact that the colon, essentially a long tube, can undergo considerable deformation between acquisitions. The authors present a novel approach to automatically establish spatial correspondence between prone and supine endoluminal colonic surfaces after surface parameterization, even in the case of local colon collapse.Methods: The complexity of the registration task was reduced from a 3D to a 2D problem by mapping the surfaces extracted from prone and supine CT colonography onto a cylindrical parameterization. A nonrigid cylindrical registration was then performed to align the full colonic surfaces. The curvature information from the original 3D surfaces was used to determine correspondence. The method can also be applied to cases with regions of local colonic collapse by ignoring the collapsed regions during the registration.Results: Using a development set, suitable parameters were found to constrain the cylindrical registration method. Then, the same registration parameters were applied to a different set of 13 validation cases, consisting of 8 fully distended cases and 5 cases exhibiting multiple colonic collapses. All polyps present were well aligned, with a mean (+/- std. dev.) registration error of 5.7 (+/- 3.4) mm. An additional set of 1175 reference points on haustral folds spread over the full endoluminal colon surfaces resulted in an error of 7.7 (+/- 7.4) mm. Here, 82% of folds were aligned correctly after registration with a further 15% misregistered by just onefold.Conclusions: The proposed method reduces the 3D registration task to a cylindrical registration representing the endoluminal surface of the colon. Our algorithm uses surface curvature information as a similarity measure to drive registration to compensate for the large colorectal deformations that occur between prone and supine data acquisitions. The method has the potential to both enhance polyp detection and decrease the radiologist's interpretation time. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3577603

Endoluminal surface registration for CT colonography using Haustral Fold Matching

Computed Tomographic (CT) colonography is a technique used for the detection of bowel cancer or potentially precancerous polyps. The procedure is performed routinely with the patient both prone and supine to differentiate fixed colonic pathology from mobile faecal residue. Matching corresponding locations is difficult and time consuming for radiologists due to colonic deformations that occur during patient repositioning. We propose a novel method to establish correspondence between the two acquisitions automatically. The problem is first simplified by detecting haustral folds using a graph cut method applied to a curvature-based metric applied to a surface mesh generated from segmentation of the colonic lumen. A virtual camera is used to create a set of images that provide a metric for matching pairs of folds between the prone and supine acquisitions. Image patches are generated at the fold positions using depth map renderings of the endoluminal surface and optimised by performing a virtual camera registration over a restricted set of degrees of freedom. The intensity difference between image pairs, along with additional neighbourhood information to enforce geometric constraints over a 2D parameterisation of the 3D space, are used as unary and pair-wise costs respectively, and included in a Markov Random Field (MRF) model to estimate the maximum a-posteriori fold labelling assignment. The method achieved fold matching accuracy of 96.0% and 96.1% in patient cases with and without local colonic collapse. Moreover, it improved upon an existing surface-based registration algorithm by providing an initialisation. The set of landmark correspondences is used to non-rigidly transform a 2D source image derived from a conformal mapping process on the 3D endoluminal surface mesh. This achieves full surface correspondence between prone and supine views and can be further refined with an intensity based registration showing a statistically significant improvement (p<0.001p<0.001), and decreasing mean error from 11.9mm11.9mm to 6.0mm6.0mm measured at 1743 reference points from 17 CTC datasets

CT colonography: Inverse-consistent symmetric registration of prone and supine inner colon surfaces

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

CT colonography: external clinical validation of an algorithm for computer-assisted prone and supine registration

Purpose To perform external validation of a computer-assisted registration algorithm for prone and supine computed tomographic (CT) colonography and to compare the results with those of an existing centerline method. Materials and Methods All contributing centers had institutional review board approval; participants provided informed consent. A validation sample of CT colonographic examinations of 51 patients with 68 polyps (6–55 mm) was selected from a publicly available, HIPAA compliant, anonymized archive. No patients were excluded because of poor preparation or inadequate distension. Corresponding prone and supine polyp coordinates were recorded, and endoluminal surfaces were registered automatically by using a computer algorithm. Two observers independently scored three-dimensional endoluminal polyp registration success. Results were compared with those obtained by using the normalized distance along the colonic centerline (NDACC) method. Pairwise Wilcoxon signed rank tests were used to compare gross registration error and McNemar tests were used to compare polyp conspicuity. Results Registration was possible in all 51 patients, and 136 paired polyp coordinates were generated (68 polyps) to test the algorithm. Overall mean three-dimensional polyp registration error (mean ± standard deviation, 19.9 mm ± 20.4) was significantly less than that for the NDACC method (mean, 27.4 mm ± 15.1; P = .001). Accuracy was unaffected by colonic segment (P = .76) or luminal collapse (P = .066). During endoluminal review by two observers (272 matching tasks, 68 polyps, prone to supine and supine to prone coordinates), 223 (82%) polyp matches were visible (120° field of view) compared with just 129 (47%) when the NDACC method was used (P < .001). By using multiplanar visualization, 48 (70%) polyps were visible after scrolling ± 15 mm in any multiplanar axis compared with 16 (24%) for NDACC (P < .001). Conclusion Computer-assisted registration is more accurate than the NDACC method for mapping the endoluminal surface and matching the location of polyps in corresponding prone and supine CT colonographic acquisitions

Registration of prone and supine CT colonography images and its clinical application

Computed tomographic (CT) colonography is a technique for detecting bowel cancer and potentially precancerous polyps. CT imaging is performed on the cleansed and insufflated bowel in order to produce a virtual endoluminal representation similar to optical colonoscopy. Because fluids and stool can mimic pathology, images are acquired with the patient in both prone and supine positions. Radiologists then match endoluminal locations visually between the two acquisitions in order to determine whether pathology is real or not. This process is hindered by the fact that the colon can undergo considerable deformation between acquisitions. Robust and accurate automated registration between prone and supine data acquisitions is therefore pivotal for medical interpretation, but a challenging problem. The method proposed in this thesis reduces the complexity of the registration task of aligning the prone and supine CT colonography acquisitions. This is done by utilising cylindrical representations of the colonic surface which reflect the colon's specific anatomy. Automated alignment in the cylindrical domain is achieved by non-rigid image registration using surface curvatures, applicable even when cases exhibit local luminal collapses. It is furthermore shown that landmark matches for initialisation improve the registration's accuracy and robustness. Additional performance improvements are achieved by symmetric and inverse-consistent registration and iteratively deforming the surface in order to compensate for differences in distension and bowel preparation. Manually identified reference points in human data and fiducial markers in a porcine phantom are used to validate the registration accuracy. The potential clinical impact of the method has been evaluated using data that reflects clinical practise. Furthermore, correspondence between follow-up CT colonography acquisitions is established in order to facilitate the clinical need to investigate polyp growth over time. Accurate registration has the potential to both improve the diagnostic process and decrease the radiologist's interpretation time. Furthermore, its result could be integrated into algorithms for improved computer-aided detection of colonic polyps

External Clinical Validation of Prone and Supine CT Colonography Registration

This paper provides an external validation of a prone-supine registration algorithm for CT colonography (CTC). A validation sample of 49 patient cases with 66 polyps (6 to 30 mm) was selected from a publicly available, anonymized CTC archive. To enhance generalizability, no case was excluded due to poor preparation or inadequate distension. Corresponding prone and supine polyp coordinates were recorded and the endoluminal surfaces registered: a Markov Random Field technique was used to find feature matches between prone/supine acquisitions and following mapping of the endoluminal surface to a cylinder, dense surface correspondence was achieved via cylindrical non-rigid registration. The polyp registration error was determined and a subjective assessment of registration made for 2D slice-based and 3D endoluminal data display using a pre-specified scoring system. Results were compared to using “normalized distance along the colon centerline” (NDACC) which approximates to the method currently employed to match colonic positions using proprietary CT colonography interpretation software. Registration was possible in all 49 cases. Overall mean 3D polyp registration error was significantly smaller with 19.9 mm in comparison to 27.7 mm using NDACC (p=0.001). 82.7% of polyp matches were defined as “successful” in comparison to 37.1% using NDACC according to the pre-specified criteria. Similarly, using 2D visualization, 62.1% registrations were “successful” and only 22.7% using NDACC. Full surface-based prone-to-supine registration can successfully map the location of a polyp identified on one acquisition to the corresponding endoluminal surface in the opposing acquisition, greatly facilitating polyp matching and aiding interpretation. Our method compares favorably to using NDACC