26 research outputs found
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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
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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 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
Feature extraction to aid disease detection and assessment of disease progression in CT and MR colonography
Computed tomographic colonography (CTC) is a technique employed to examine the whole colon for cancers and premalignant adenomas (polyps). Oral preparation is taken to fully cleanse the colon, and gas insufflation maximises the attenuation contrast between the enoluminal colon surface and the lumen. The procedure is performed routinely with the patient both prone and supine to redistribute gas and residue. This helps to differentiate fixed colonic pathology from mobile faecal residue and also helps discover pathology occluded by retained fluid or luminal collapse. Matching corresponding endoluminal surface locations with the patient in the prone and supine positions is therefore an essential aspect of interpretation by radiologists; however, interpretation can be difficult and time consuming due to the considerable colonic deformations that occur during repositioning. Hence, a method for automated registration has the potential to improve efficiency and diagnostic accuracy. I propose a novel method to establish correspondence between prone and supine CT colonography acquisitions automatically. The problem is first simplified by detecting haustral folds which are elongated ridgelike endoluminal structures and can be identified by curvature based measurements. These are subsequently matched using appearance based features, and their relative geometric relationships. It is shown that these matches can be used to find correspondence along the full length of the colon, but may also be used in conjunction with other registration methods to achieve a more robust and accurate result, explicitly addressing the problem of colonic collapse. The potential clinical value of this method has been assessed in an external clinical validation, and the application to follow-up CTC surveillance has been investigated. MRI has recently been applied as a tool to quantitatively evaluate the therapeutic response to therapy in patients with Crohn's disease, and is the preferred choice for repeated imaging. A primary biomarker for this evaluation is the measurement of variations of bowel wall thickness on changing from the active phase of the disease to remission; however, a poor level of interobserver agreement of measured thickness is reported and therefore a system for accurate, robust and reproducible measurements is desirable. I propose a novel method which will automatically track sections of colon, by estimating the positions of elliptical cross sections. Subsequently, estimation of the positions of the inner and outer bowel walls are made based on image gradient information and therefore a thickness measurement value can be extracted
Fast colon centreline calculation using optimised 3D topological thinning
Topological thinning can be used to accurately identify the central path through a computer model of the colon generated using computed tomography colonography. The central path can subsequently be used to simplify the task of navigation within the colon model. Unfortunately standard topological thinning is an extremely inefficient process. We present an optimised version of topological thinning that significantly improves the performance of centreline calculation without compromising the accuracy of the result. This is achieved by using lookup tables to reduce the computational burden associated with the thinning process
Enhanced computer assisted detection of polyps in CT colonography
This thesis presents a novel technique for automatically detecting colorectal polyps in computed tomography colonography (CTC). The objective of the documented computer assisted diagnosis (CAD) technique is to deal with the issue of false positive detections without adversely affecting polyp detection sensitivity. The thesis begins with an overview of CTC and a review of the associated research areas, with particular attention given to CAD-CTC. This review identifies excessive false positive detections as a common problem associated with current CAD-CTC techniques. Addressing this problem constitutes the major contribution of this thesis. The documented CAD-CTC technique is trained with, and evaluated using, a series of clinical CTC data sets These data sets contain polyps with a range of different sizes and morphologies. The results presented m this thesis indicate the validity of the developed CAD-CTC technique and demonstrate its effectiveness m accurately detecting colorectal polyps while significantly reducing the number of false positive detections
Eye-tracking the moving medical image: Development and investigation of a novel investigational tool for CT Colonography
Colorectal cancer remains the third most common cancer in the UK but the second leading cause of cancer death with >16,000 dying per year. Many advances have been made in recent years in all areas of investigation for colorectal cancer, one of the more notable being the widespread introduction of CT Colonography (CTC). CTC has rapidly established itself as a cornerstone of diagnosis for colonic neoplasia and much work has been done to standardise and assure quality in practice in both the acquisition and interpretation of the technique. A novel feature of CTC is the presentation of imaging in both traditional 2D and the ‘virtual’ 3D endoluminal formats. This thesis looks at expanding our understanding of and improving our performance in utilizing the endoluminal 3D view. We present and develop novel metrics applicable to eye-tracking the moving image, so that the complex dynamic nature of 3D endoluminal fly-through interpretation can be captured. These metrics are then applied to assess the effect of important elements of image interpretation, namely, reader experience, the effect of the use Computer Aided Detection (CAD) and the influence of the expected prevalence of abnormality. We review our findings with reference to the literature of eye tracking within medical imaging. In the co-registration section we apply our validated computer-assisted registration algorithm to the matching of 3D endoluminal colonic locations between temporally separate datasets, assessing its accuracy as an aid to colonic polyp surveillance with CTC
물질 혼합비율과 구조적 특징의 통합 재구성 모델을 이용한 전자적 장세척 기법
학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 신영길.대장 컴퓨터 단층 촬영 영상에서 조영 처리된 잔여물을 제거하기 위해 전자적 장세척 방법이 이용된다. 본 논문에서는 전자적 장세척 방법에서 결함의 주요 원인이 되는 부분 용적 효과와 가성 상승 효과를 동시에 해결하기 위해 물질 혼합비율과 구조적 특징의 통합 재구성 모델을 이용한 전자적 장청소 기법을 제안한다. 먼저 대장 컴퓨터 단층 촬영 영상에서 공기, 조영 처리된 잔여물, 공기와 조영 처리된 잔여물 사이의 경계 (공기-잔여물 경계), 대장외부의 연조직과 조영 처리된 잔여물 사이의 경계 (연조직-잔여물 경계), 그리고 공기, 연조직, 조영 처리된 잔여물이 만나는 경계 (공기-연조직-잔여물 경계) 영역을 포함한 결장 요소를 분할한다. 분할된 공기와 공기-잔여물 경계 영역에 대해서는 각 복셀의 밀도값을 동일하게 공기의 대표 밀도값으로 대체함으로써 잔여물을 제거한다. 반면에 분할된 연조직-잔여물 경계와 공기-연조직-잔여물 경계 영역에 대해서는 물질 혼합비율과 구조적 특징을 계산한다. 물질 혼합비율은 두 물질간 혹은 세 물질간 전이 모델을 이용하여 예측하고 구조적 특징은 헤시안 행렬의 아이겐 분석에 기반하여 계산한다. 계산된 물질 혼합비율과 구조적 특징을 이용하여 연조직-잔여물 경계와 공기-연조직-잔여물 경계 영역에 속하는 각 복셀의 밀도값이 재구성된다. 물질 혼합비율과 구조적 특징의 통합 재구성 모델은 각 복셀 내의 연조직의 부분 용적을 유지시키는 동시에 조영 처리된 잔여물의 가성 상승 효과로 인해 약화된 잔여물에 잠긴 대장 주름 및 용종이 보존될 수 있도록 한다. 따라서 제안된 전자적 장세척 방법에서는 부분 용적 효과로 인한 연조직-잔여물 경계의 계단무늬 결함과 가성 상승 효과로 인한 잔여물에 잠긴 대장 주름 및 용종의 지나친 세척 결함을 피할 수 있다. 또한 본 논문에서는 기존 세 물질간 전이 모델의 연산 복잡도를 줄이기 위해 단순 세 물질간 전이 모델을 제안한다. 단순 세 물질간 전이 모델에서는 두 물질간 전이 모델을 반복 적용시킴으로써 얻어진 세 쌍의 (공기-연조직, 공기-잔여물, 연조직-잔여물) 두 물질간 혼합비율을 구하고 이를 삼각형을 이용한 무게중심좌표 상에서의 보간방법을 이용해 하나의 세 물질간 혼합비율로 변환한다. 열개의 임상 데이터를 이용하여 제안한 전자적 장세척 방법의 성능을 평가하였다. 방사선 전문의에 의한 장세척 품질 평가에서 제안 방법이 물질 혼합비율을 이용한 기존 방법에 비해 더 높은 점수의 장세척 결과를 보였으며, 특히 잔여물에 잠긴 대장 주름 및 용종이 더 잘 보존되는 것을 확인하였다. 이러한 결과는 잔여물에 잠긴 대장 주름 영역을 수동 분할하여 제안 방법과 기존 방법에 의한 장세척 결과 영상에서 해당 영역의 평균 밀도값과 주름 보존 비율을 비교한 결과에서도 마찬가지로 입증되었다. 또한 기존의 두 물질간 전이 모델로는 잘 해결되지 않았던 공기-연조직-잔여물 경계 영역에서의 산등성이 형태의 결함에 대해서도 제안 방법에서는 단순 세 물질간 전이 모델을 이용하여 공기-연조직-잔여물 경계 영역에서의 결함을 제거하고 전체 대장의 표면이 깨끗하게 재구성되는 것을 확인하였다.Electronic cleansing (EC) is the process of virtually cleansing the colon by removal of the tagged materials (TMs) in computed tomographic colonography (CTC) images and generating electronically cleansed images. We propose an EC method using a novel reconstruction model. To mitigate partial volume (PV) and pseudo-enhancement (PEH) effects simultaneously, material fractions and structural responses are integrated into a single reconstruction model. In our approach, colonic components including air, TM, interface layer between air and TM (air-TM interface) and interface layer between soft-tissue (ST) and TM (ST-TM interface), and T-junction (i.e., locations where air-TM interface with the colon wall) are first segmented. For each voxel in the segmented TM and air-TM interface, CT density value is replaced with the pure material density of air and thus the unexpected ST-like layers at the air-TM interface (caused by PV effect) are simply removed. On the other hand, for each voxel in the segmented ST-TM interface and T-junction, the two- and three-material fractions at the voxel are derived using a two- and three-material transition models, respectively. For each voxel in the segmented ST-TM interface and T-junction, the structural response is also calculated by rut- and cup-enhancement functions based on the eigenvalue signatures of the Hessian matrix. Then, CT density value of each voxel in ST-TM interface and T-junction is reconstructed based on both the material fractions and structural responses to conserve the PV contributions of ST in the voxel and preserve the folds and polyps submerged in TMs. Therefore, in our ST-preserving reconstruction model, the material fractions remove the aliasing artifacts at the ST-TM interface (caused by PV effect) effectively while the structural responses avoid the erroneous cleansing of the submerged folds and polyps (caused by PEH effect). To reduce the computational complexity of solving the orthogonal projection problem in the three-material model, we currently propose a new projection method for the three-material model that provides a very quick estimate of the three-material fractions without the use of code-book, which is pre-generated by uniformly sampling the model representation in material fraction space and used to find the best match with the observed measurements. In our new projection method for the three-material model, three pairs of two-material fractions are calculated by using the two-material model and then simply combined into a single triple of three-material fractions based on the barycentric interpolation in material fraction space. Experimental results using clinical datasets demonstrated that the proposed EC method showed higher cleansing quality and better preservation of submerged folds and polyps than the previous method. In addition, by using the new projection method for the three-material model, the proposed EC method clearly reconstructed the whole colon surface without the T-junction artifacts, which are observed as distracting ridges along the line where the air-TM interface touches the colon surface when the two-material model does not cope with the three-material fractions at T-junctions.Docto
Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images
We present a novel kernel regression framework for smoothing scalar surface
data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel
constructed from the eigenfunctions, we formulate a new bivariate kernel
regression framework as a weighted eigenfunction expansion with the heat kernel
as the weights. The new kernel regression is mathematically equivalent to
isotropic heat diffusion, kernel smoothing and recently popular diffusion
wavelets. Unlike many previous partial differential equation based approaches
involving diffusion, our approach represents the solution of diffusion
analytically, reducing numerical inaccuracy and slow convergence. The numerical
implementation is validated on a unit sphere using spherical harmonics. As an
illustration, we have applied the method in characterizing the localized growth
pattern of mandible surfaces obtained in CT images from subjects between ages 0
and 20 years by regressing the length of displacement vectors with respect to
the template surface.Comment: Accepted in Medical Image Analysi