Context-specific method for detection of soft-tissue lesions in non-cathartic low-dose dual-energy CT colonography

In computed tomographic colonography (CTC), orally administered fecal-tagging agents can be used to indicate residual feces and fluid that could otherwise hide or imitate lesions on CTC images of the colon. Although the use of fecal tagging improves the detection accuracy of CTC, it can introduce image artifacts that may cause lesions that are covered by fecal tagging to have a different visual appearance than those not covered by fecal tagging. This can distort the values of image-based computational features, thereby reducing the accuracy of computer-aided detection (CADe). We developed a context-specific method that performs the detection of lesions separately on lumen regions covered by air and on those covered by fecal tagging, thereby facilitating the optimization of detection parameters separately for these regions and their detected lesion candidates to improve the detection accuracy of CADe. For pilot evaluation, the method was integrated into a dual-energy CADe (DE-CADe) scheme and evaluated by use of leave-one-patient-out evaluation on 66 clinical non-cathartic low-dose dual-energy CTC (DE-CTC) cases that were acquired at a low effective radiation dose and reconstructed by use of iterative image reconstruction. There were 22 colonoscopy-confirmed lesions ≥6 mm in size in 21 patients. The DE-CADe scheme detected 96% of the lesions at a median of 6 FP detections per patient. These preliminary results indicate that the use of context-specific detection can yield high detection accuracy of CADe in non-cathartic low-dose DE-CTC examinations

Computer-aided Detection in Computed Tomography Colonography with Full Fecal Tagging: Comparison of Standalone Performance of 3 Automated Polyp Detection Systems

AbstractPurposeWe sought to compare the performance of 3 computer-aided detection (CAD) polyp algorithms in computed tomography colonography (CTC) with fecal tagging.MethodsCTC data sets of 33 patients were retrospectively analysed by 3 different CAD systems: system 1, MedicSight; system 2, Colon CAD; and system 3, Polyp Enhanced View. The polyp database comprised 53 lesions, including 6 cases of colorectal cancer, and was established by consensus reading and comparison with colonoscopy. Lesions ranged from 6-40 mm, with 25 lesions larger than 10 mm in size. Detection and false-positive (FP) rates were calculated.ResultsCAD systems 1 and 2 could be set to have varying sensitivities with higher FP rates for higher sensitivity levels. Sensitivities for system 1 ranged from 73%–94% for all lesions (78%–100% for lesions ≥10 mm) and, for system 2, from 64%–94% (78%–100% for lesions ≥10 mm). System 3 reached an overall sensitivity of 76% (100% for lesions ≥10 mm). The mean FP rate per patient ranged from 8–32 for system 1, from 1–8 for system 2, and was 5 for system 3. At the highest sensitivity level for all polyps (94%), system 2 showed a statistically significant lower FP rate compared with system 1 (P = .001). When analysing lesions ≥10 mm, system 3 had significantly fewer FPs than systems 1 and 2 (P < .012).ConclusionsStandalone CTC-CAD analysis in the selected patient collective showed the 3 systems tested to have a variable but overall promising performance with respect to sensitivity and the FP rate

Fast pseudo-enhancement correction in CT colonography using linear shift-invariant filters

This paper presents a novel method to approximate shift-variant Gaussian filtering of an image using a set of shift-invariant Gaussian filters. This approximation affords filtering of the image using fast convolution techniques that rely on the FFT, while achieving a result that closely matches the shift-variant result. We demonstrate the method in a CT colonography application that reduces the pseudo-enhancement effect, which is a local brightening artifact in CT imaging that can result from the use of oral contrast agents. Experimental results demonstrate the effectiveness of the method and emphasize its computational efficiency

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

Computer-aided detection of polyps in CT colonography

Master'sMASTER OF ENGINEERIN

Diseases of the Abdomen and Pelvis 2018-2021: Diagnostic Imaging - IDKD Book

Gastrointestinal disease; PET/CT; Radiology; X-ray; IDKD; Davo