Missing data estimation in fMRI dynamic causal modeling.

Dynamic Causal Modeling (DCM) can be used to quantify cognitive function in individuals as effective connectivity. However, ambiguity among subjects in the number and location of discernible active regions prevents all candidate models from being compared in all subjects, precluding the use of DCM as an individual cognitive phenotyping tool. This paper proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various dataset sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.

Registration under topological change for CT colonography.

Computed tomography (CT) colonography is a minimally invasive screening technique for colorectal polyps, in which X-ray CT images of the distended colon are acquired, usually in the prone and supine positions of a single patient. Registration of segmented colon images from both positions will be useful for computer-assisted polyp detection. We have previously presented algorithms for registration of the prone and supine colons when both are well distended and there is a single connected lumen. However, due to inadequate bowel preparation or peristalsis, there may be collapsed segments in one or both of the colon images resulting in a topological change in the images. Such changes make deformable registration of the colon images difficult, and at present, there are no registration algorithms that can accommodate them. In this paper, we present an algorithm that can perform volume registration of prone/supine colon images in the presence of a topological change. For this purpose, 3-D volume images are embedded as a manifold in a 4-D space, and the manifold is evolved for nonrigid registration. Experiments using data from 24 patients show that the proposed method achieves good registration results in both the shape alignment of topologically different colon images from a single patient and the polyp location estimation between supine and prone colon images.

Deformable registration of supine and prone colons for computed tomographic colonography.

Computed tomographic colonography is a minimally invasive technique for detecting colorectal polyps and colon cancer. Most computed tomographic colonography protocols acquire both prone and supine images to improve the visualization of the lumen wall, reduce false-positives, and improve sensitivity. Comparisons between the prone and supine images can be improved by registration between the scans. In this paper, we propose registering colon lumens, segmented from prone and supine images, using feature matching of the colon centerline and nonrigid registration of the lumen shapes represented as distance functions. Experimental registration results (n = 21 subjects) show a correspondence accuracy of 13.77 +/- 6.20 mm for a range of polyp sizes. The overlap in the registered lumen segmentations show an average Jaccard similarity coefficient of 0.915 +/- 0.07.

Segmentation in virtual colonoscopy using a geometric deformable model.

The Geometric Deformable Model is developed for accurate colon lumen segmentation as part of an automatic Virtual Colonoscopy system. The deformable model refines the lumen surface found by an automatic seed location and thresholding procedure. The challenges to applying the deformable model are described, showing the definition of the stopping function as the key to accurate segmentation. The limitations of current stopping criteria are examined and a new definition, tailored to the task of colon segmentation, is given. First, a multiscale edge operator is used to locate high confidence boundaries. These boundaries are then integrated into the stopping function using a distance transform. The hypothesis is that the new stopping function results in a more accurate representation of the lumen surface compared to previous monotonic functions of the gradient magnitude. This hypothesis is tested using observer ratings of colon surface fidelity at 100,000 randomly selected locations in each of four datasets. The results show that the surfaces determined by the modified deformable model better represent the lumen surface overall.

Deformable registration of prone and supine colons for CT colonography.

CT colonography (CTC) is a non-invasive technique for detecting colorectal polyps and colon cancer. Through the addition of the prone scanning with the original supine scanning, the possibility of detecting the polyps is increased. The registration process for this application requires the comparison between the prone and supine colons for diagnosis. A level-set representation of the object boundary using a distance map is presented in this paper as an input to demons registration algorithm for supine and prone CT colonography image data. After first aligning the colon volumes based on the patient's anus position, distances inside and outside the objects' boundary are computed. The level-set from the distance map allows the demons algorithm to decide the moving direction for the initial demons' force between the two colons. We present a result with a 3 dimensional volume of a patient's colon. The results suggest that our method has excellent registration performance with high confidence even with considerable deformation of the colon lumen in 3 dimensional case.

Nonrigid registration of images with different topologies using embedded maps.

Changes in image topology occur in medical images due to normal variation in anatomy, image artifacts, and the presence of pathology. Non-rigid registration of images undergoing topological change for the purpose of atlas-based segmentation or deformation analysis is challenging since non-smooth geometric transformations must be introduced. As most registration methods impose a smoothness constraint on the allowable transformations they either do not model such changes or perform poorly in their presence. In this paper we describe an approach to non-rigid registration treating the images as embedded maps that deform in a Riemannian space. We show that smooth transformations representing topological changes in the original images can be obtained and describe the evolution in terms of a partial differential equation. Two-dimensional examples from brain morphometry are used to illustrate the method.