Fold-preserving electronic cleansing using a reconstruction model integrating material fractions and structural responses

A quantitative validation of segmented colon in virtual colonoscopy using image moments

Background

Evaluation of segmented colon is one of the challenges in Computed Tomography Colonography (CTC). The objective of the study was to measure the segmented colon accurately using image processing techniques.

Methods

This was a retrospective study, and the Institutional Ethical clearance was obtained for the secondary dataset. The technique was tested on 85 CTC dataset. The CTC dataset of 100–120 kVp, 100 mA, and ST (Slice Thickness) of 1.25 and 2.5 mm were used for empirical testing. The initial results of the work appear in the conference proceedings. Post colon segmentation, three distance measurement techniques, and one volumetric overlap computation were applied in Euclidian space in which the distances were measured on MPR views of the segmented and unsegmented colons and the volumetric overlap calculation between these two volumes.

Results

The key finding was that the measurements on both the segmented and the unsegmented volumes remain same without much difference noticed. This was statistically proved. The results were validated quantitatively on 2D MPR images. An accuracy of 95.265±0.4551% was achieved through volumetric overlap computation. Through pairedt−test, at α=5%, statistical values were p=0.6769, and t=0.4169 which infer that there was no much significant difference.

Conclusion

The combination of different validation techniques was applied to check the robustness of colon segmentation method, and good results were achieved with this approach. Through quantitative validation, the results were accepted at α=5%.

Electronic cleansing in computed tomography colonography using AT layer identification with integration of gradient directional second derivative and material fraction model

Background

In computed tomography colonography images, electronic cleansing (EC) is applied to remove opacified residual materials, called fecal-tagging materials (FTM), using positive-contrast tagging agents and laxative to facilitate polyp detection.

Methods

The proposed EC, EC_prop, integrates the gradient directional second derivative into material fraction model to preserve submerged soft tissue (ST) under FTM. Three-material fraction model is used to remove FTM and artifacts at air-tagging (AT) layers and T-junctions where air, ST, and FTM material meet simultaneously. Moreover, the proposed AT layer identification is used to distinguish AT layers from air-tissue-tagging (ATT) layers in order to preserve ATT layers during cleansing. The clinical evaluation on 467 3-Dimensional band view images was conducted by the abdominal radiologist using four grading levels of cleansing quality with five causes of low quality EC. The amount of the remaining artifacts at T-junctions was approximated from the results of EC_prop. The results from EC_prop were compared with the results from syngo.via Client 3.0 Software, EC_syngo, and the fast three-material modeling, EC_prev, using the preference of the radiologist. Two-tailed paired Wilcoxon signed rank test is used to indicate statistical significance.

Results

The average grade on cleansing quality is 2.89 out of 4. The artifacts at T-junctions from 86.94% of the test images can be removed, whereas artifacts at T-junctions from only 13.06% of the test images cannot be removed. For 13.06% of the test images, the results from EC_prop are more preferable to the results from EC_syngo (p<0.008). For all the test images, the results from EC_prop are more preferable to the results from EC_prev (p<0.001). Finally, the visual assessment shows that EC_prop can preserve ATT layers, submerged polyps and folds while EC_prev can preserve only submerged folds but fails to preserve ATT layers.

Conclusion

From our implementation, EC_prop can improve the performance of the existing EC, such that it can preserve ST, especially ATT layers and remove the artifacts at T-junctions which have never been proposed by any other methods before.

Measurement of smaller colon polyp in CT colonography images using morphological image processing

PURPOSE

Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques.

METHODS

A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge.

RESULTS

The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6-9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes [Formula: see text] min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, [Formula: see text] and [Formula: see text] were achieved.

CONCLUSIONS

The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at [Formula: see text].

Fast three-material modeling with triple arch projection for electronic cleansing in CTC

In this paper, we propose a fast three-material modeling for electronic cleansing (EC) in computed tomographic colonography. Using a triple arch projection, our three-material modeling provides a very quick estimate of the three-material fractions to remove ridge-shaped artifacts at the T-junctions where air, soft-tissue (ST), and tagged residues (TRs) meet simultaneously. In our approach, colonic components including air, TR, the layer between air and TR, the layer between ST and TR (L(ST/TR)), and the T-junction are first segmented. Subsequently, the material fraction of ST for each voxel in L(ST/TR) and the T-junction is determined. Two-material fractions of the voxels in L(ST/TR) are derived based on a two-material transition model. On the other hand, three-material fractions of the voxels in the T-junction are estimated based on our fast three-material modeling with triple arch projection. Finally, the CT density value of each voxel is updated based on our fold-preserving reconstruction model. Experimental results using ten clinical datasets demonstrate that the proposed three-material modeling successfully removed the T-junction artifacts and clearly reconstructed the whole colon surface while preserving the submerged folds well. Furthermore, compared with the previous three-material transition model, the proposed three-material modeling resulted in about a five-fold increase in speed with the better preservation of submerged folds and the similar level of cleansing quality in T-junction regions.