A computer simulation method for low-dose CT images by use of real high-dose images: a phantom study

A practical method to simulate realistic reduced-exposure CT images by the addition of computationally generated noise

Computed tomography (CT) scanning protocols should be optimized to minimize the radiation dose necessary for imaging. The addition of computationally generated noise to the CT images facilitates dose reduction. The objective of this study was to develop a noise addition method that reproduces the complexity of the noise texture present in clinical images with directionality that varies over images according to the underlying anatomy, requiring only Digital Imaging and Communications in Medicine (DICOM) images as input data and commonly available phantoms for calibration. The developed method is based on the estimation of projection data by forward projection from images, the addition of Poisson noise, and the reconstruction of new images. The method was validated by applying it to images acquired from cylindrical and thoracic phantoms using source images with exposures up to 49 mAs and target images between 39 and 5 mAs. 2D noise spectra were derived for regions of interest in the generated low-dose images and compared with those from the scanner-acquired low-dose images. The root mean square difference between the standard deviations of noise was 4%, except for very low exposures in peripheral regions of the cylindrical phantom. The noise spectra from the corresponding regions of interest exhibited remarkable agreement, indicating that the complex nature of the noise was reproduced. A practical method for adding noise to CT images was presented, and the magnitudes of noise and spectral content were validated. This method may be used to optimize CT imaging.

An AI-Based Colonic Polyp Classifier for Colorectal Cancer Screening Using Low-Dose Abdominal CT

Among the non-invasive Colorectal cancer (CRC) screening approaches, Computed Tomography Colonography (CTC) and Virtual Colonoscopy (VC), are much more accurate. This work proposes an AI-based polyp detection framework for virtual colonoscopy (VC). Two main steps are addressed in this work: automatic segmentation to isolate the colon region from its background, and automatic polyp detection. Moreover, we evaluate the performance of the proposed framework on low-dose Computed Tomography (CT) scans. We build on our visualization approach, Fly-In (FI), which provides "filet"-like projections of the internal surface of the colon. The performance of the Fly-In approach confirms its ability with helping gastroenterologists, and it holds a great promise for combating CRC. In this work, these 2D projections of FI are fused with the 3D colon representation to generate new synthetic images. The synthetic images are used to train a RetinaNet model to detect polyps. The trained model has a 94% f1-score and 97% sensitivity. Furthermore, we study the effect of dose variation in CT scans on the performance of the the FI approach in polyp visualization. A simulation platform is developed for CTC visualization using FI, for regular CTC and low-dose CTC. This is accomplished using a novel AI restoration algorithm that enhances the Low-Dose CT images so that a 3D colon can be successfully reconstructed and visualized using the FI approach. Three senior board-certified radiologists evaluated the framework for the peak voltages of 30 KV, and the average relative sensitivities of the platform were 92%, whereas the 60 KV peak voltage produced average relative sensitivities of 99.5%.

Method to calculate frequency characteristics of reconstruction filter kernel in X-ray computed tomography

A computed tomography (CT) image is generally reconstructed by a filtered back projection (FBP) algorithm. In an FBP algorithm, the image quality primarily depends on a reconstruction filter kernel. Although the details of the filter kernel are not disclosed to users, the frequency response of the filter kernel can theoretically be calculated using the relational formula of the filter kernel and the modulation transfer function (MTF) of the reconstruction algorithm (MTFA). In this study, we proposed a method to determine the frequency response of a filter kernel and verify its validity. Two clinical CT scanners were used to derive the filter kernel. The MTF was obtained and subsequently separated to the MTF of the scanner system and MTFA. Using the relational formula of the filter kernel and MTFA, we calculated the frequency response of the filter kernel. To verify the calculated result, we measured the noise power spectrum (NPS). Additionally, the filter kernel was calculated using the relational formula of the filter kernel and NPS. In both CT scanners, the filter kernels calculated by the two methods showed good agreement, and we confirmed the validity of the results and the effectiveness of the proposed method. Furthermore, the inherent image quality performance of the CT scanner could be clarified by the reconstruction filter kernel.

[Image Evaluation with Paired Comparison Method Using Automatic Analysis Software: Comparison of CT Images with Simulated Levels of Exposure Dose]

To simplify a procedure of the observer study with Ura's method of Scheffé's paired comparison and to improve experimental accuracy, we developed a software package to automatically analyze observer study data obtained by using a computer interface developed specially for the ROC observer study. Simulated low-dose CT images were used to demonstrate practical utility of this proposed method with a software package, in terms of a statistical analysis of the change of noise property due to the change of exposure dose. Six radiological technologists were participated in this observer study and compared each of six sample images selected at lower lung and liver slices with dose levels of 100, 80, 60, 40, 20, 10% per case. In the statistical analysis, the average psychological measures were highly correlated with the dose levels (lower lungs: R=0.95, liver: R=0.99). In addition, there were statistically significant differences in all combination of dose levels in liver slices. In conclusion, we demonstrated practical utility of this proposed method in terms of simplification of experimental procedure and the consistency of the analytic results.

Reproducibility of an airway tapering measurement in computed tomography with application to bronchiectasis

We propose a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on computed tomography (CT). We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. We generate a spline from the centerline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analyzing different radiation doses, voxel sizes, and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effect of airway bifurcations. Using 74 airways from 10 CT scans, we show a statistical difference, p = 3.4 × 10 - 4 , in tapering between healthy airways ( n = 35 ) and those affected by bronchiectasis ( n = 39 ). The difference between the mean of the two populations is 0.011    mm - 1 , and the difference between the medians of the two populations was 0.006    mm - 1 . The tapering measurement retained a 95% confidence interval of ± 0.005    mm - 1 in a simulated 25 mAs scan and retained a 95% confidence of ± 0.005    mm - 1 on simulated CTs up to 1.5 times the original voxel size. We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of the simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose, and reconstruction algorithm that are to be used in any quantitative studies.

Lung cancer screening CT-based coronary artery calcification in predicting cardiovascular events: A systematic review and meta-analysis

BACKGROUND

Coronary artery calcificition (CAC) is a well-established predictor of cardiovascular events (CVEs). We aimed to evaluate whether lung cancer screening computed tomography (CT)-based CAC score has a good cost-effectiveness for predicting CVEs in heavy smokers.

METHODS

A literature search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Pubmed, EMBASE, and Cochrane library databases were systematically searched for relevant studies that investigated the association between lung cancer screening CT-based CAC and CVEs up to December 31, 2017. We selected fixed-effect model for analysis of data heterogeneity. Statistical analyses were performed by using Review Manager Version 5.3 for Windows.

RESULTS

Four randomized controlled trials with 5504 participants were included. Our results demonstrated that CVEs were significantly associated with the presence of CAC (relative risk [RR] 2.85, 95% confidence interval [CI] 2.02-4.02, P < .00001). Moreover, higher CAC score (defined as CAC score >400 or >1000) was associated with a significant increased CVE count (RR 3.47, 95% CI 2.65-4.53, P < .00001). However, the prevalence of CVEs was not different between male and female groups (RR 2.46, 95% CI 0.44-13.66, P = .30).

CONCLUSION

CAC Agatston score evaluated by lung cancer screening CT had potential in predicting the likelihood of CVEs in the early stage without sexual difference. Thus, it may guide clinicians to intervene those heavy smokers with increased risk of CVEs earlier by CAC score through lung cancer screening CT.