Validated novel software to measure the conspicuity index of lesions in DICOM images

Description of purpose A novel software programme and associated Excel spreadsheet has been developed to provide an objective measure of the expected visual detectability of focal abnormalities within DICOM images. Methodology ROIs are drawn around the abnormality, the software then fits the lesion using a least squares method to recognise the edges of the lesion based on the full width half maximum. 180 line profiles are then plotted around the lesion, giving 360 edge profiles. The co-ordinates show in Figure 1 are captured, as well the standard deviation of the pixel values within the background and lesion (representing anatomical noise and lesion noise respectively). An Excel spreadsheet has been developed to allow variables to be calculated, including SNR and CNR. A conspicuity index has also been developed: Results The software has been validated using the GAMMEX ACR CT accreditation phantom, varying mA, kVp and slice thickness (ST) and the results have been found to give a linear response: Conclusion A novel software programme has been validated to allow calculation of many physical properties of lesions. Additionally, a new measure of conspicuity index has been developed for focal lesions. The analysis could be further developed to incorporate reader decision-analysis data and eye-tracking data allowing correlations between physical and perception measures to be made beyond basic CNR calculations. It could also be used as a tool to distinguish between perceptual and cognitive error. Further refinements could lead to measures of the detectability of more diffuse disease features

Electronic cleansing for 24-h limited bowel preparation CT colonography using principal curvature flow

CT colonography (CTC) is one of the recommended methods for colorectal cancer screening. The subject's preparation is one of the most burdensome aspects of CTC with a cathartic bowel preparation. Tagging of the bowel content with an oral contrast medium facilitates CTC with limited bowel preparation. Unfortunately, such preparations adversely affect the 3-D image quality. Thus far, data acquired after very limited bowel preparation were evaluated with a 2-D reading strategy only. Existing cleansing algorithms do not work sufficiently well to allow a primary 3-D reading strategy. We developed an electronic cleansing algorithm, aimed to realize optimal 3-D image quality for low-dose CTC with 24-h limited bowel preparation. The method employs a principal curvature flow algorithm to remove heterogeneities within poorly tagged fecal residue. In addition, a pattern recognition-based approach is used to prevent polyp-like protrusions on the colon surface from being removed by the method. Two experts independently evaluated 40 CTC cases by means of a primary 2-D approach without involvement of electronic cleansing as well as by a primary 3-D method after electronic cleansing. The data contained four variations of 24-h limited bowel preparation and was based on a low radiation dose scanning protocol. The sensitivity for lesions ≥ 6 mm was significantly higher for the primary 3-D reading strategy (84%) than for the primary 2-D reading strategy (68%) (p = 0.031). The reading time was increased from 5:39 min (2-D) to 7:09 min (3-D) (p = 0.005); the readers' confidence was reduced from 2.3 (2-D) to 2.1 (3-D) ( p = 0.013) on a three-point Likert scale. Polyp conspicuity for cleansed submerged lesions was similar to not submerged lesions (p = 0.06). To our knowledge, this study is the first to describe and clinically validate an electronic cleansing algorithm that facilitates low-dose CTC with 24-h limited bowel preparatio

The impact of simulated motion blur on breast cancer detection performance in full field digital mammography (FFDM)

Objective: Full-field Digital Mammography (FFDM) is employed in breast screening for the early detection of breast cancer. High quality, artefact free, diagnostic images are crucial to the accuracy of this process. Unwanted motion during the image acquisition phase and subsequent image blurring is an unfortunate occurrence in some FFDM images. The research detailed in this thesis seeks to understand the impact of motion blur on cancer detection performance in FFDM images using novel software to perform simulation of motion, an observer study to measure the lesion detection performance and physical measures to assess the impact of simulated motion blur on image characteristics of the lesions. Method: Seven observers (15±5 years’ reporting experience) evaluated 248 cases (62 containing malignant masses, 62 containing malignant microcalcifications and 124 normal cases) for three conditions: no motion blur (0.0 mm) and two magnitudes of simulated motion blur (0.7 mm and 1.5 mm). Abnormal cases were biopsy proven. A free-response observer study was conducted to compare lesion detection performance for the three conditions. Equally weighted jackknife alternative free-response receiver operating characteristic (wJAFROC) was used as the figure of merit. A secondary analysis of data was deemed important to simulate ‘double reporting’. In this secondary analysis, six of the observers are combined with the seventh observer to evaluate the impact of combined free-response data for lesion detection and to assess if combined two observers data could reduce the impact of simulated motion blur on detection performance. To compliment this, the physical characteristics of the lesions were obtained under the three conditions in order to assess any change in characteristics of the lesions when blur is present in the image. The impact of simulated motion blur on physical characteristics of malignant masses was assessed using a conspicuity index; for microcalcifications, a new novel metric, known as dispersion index, was used. Results: wJAFROC analysis found a statistically significant difference in lesion detection performance for both masses (F (2,22) = 6.01, P=0.0084) and microcalcifications (F(2,49) = 23.14, P<0.0001). For both lesion types, the figure of merit reduced as the magnitude of simulated motion blur increased. Statistical differences were found between some of the pairs investigated for the detection of masses (0.0mm v 0.7mm, and 0.0mm v 1.5mm) and all pairs for microcalcifications (0.0 mm v 0.7 mm, 0.0 mm v 1.5 mm, and 0.7 mm v 1.5 mm). No difference was detected between 0.7 mm and 1.5 mm for masses. For combined two observers’ data of masses, there was no statistically significant difference between single and combined free-response data for masses (F(1,6) = 4.04, p=0.1001, -0.031 (-0.070, 0.008) [treatment difference (95% CI)]. For combined data of microcalcifications, there was a statistically significant difference between single and combined free-response data (F(1,6) = 12.28, p=0.0122, -0.056 (-0.095, -0.017) [treatment difference (95% CI)]. Regarding the physical measures of masses, conspicuity index increases as the magnitude of simulated motion blur increases. Statistically significant differences were demonstrated for 0.0–0.7 mm t(22)=-6.158 (p<0.000); 0.0–1.5 mm t(22)=-6.273 (p<0.000); and 0.7–1.5 mm (t(22)=-6.231 (p<0.000). Lesion edge angle decreases as the magnitude of simulated motion blur increases. Statistically significant differences were demonstrated for 0.0–0.7 mm t(22)=3.232 (p<0.004); for 0.0–1.5 mm t(22)=6.592 (p<0.000); and 0.7–1.5mm t(22)=2.234 (p<0.036). For the grey level change there was no statistically significant difference as simulated motion blur increases to 0.7 and then to 1.5mm. For image noise there was a statistically significant difference, where noise reduced as simulated motion blur increased: 0.0–0.7 mm t(22)=22.95 (p<0.000); 0.0–1.5mm t(22)=24.66 (p<0.000); 0.7–1.5 mm t(22)=18.11 (p<0.000). For microcalcifications, simulated motion blur had a negative impact on the ‘dispersion index’. Conclusion: Mathematical simulations of motion blur resulted in a statistically significant reduction in lesion detection performance. This reduction in performance could have implications for clinical practice. Simulated motion blur has a negative impact on the edge angle of breast masses and a negative impact on the image characteristics of microcalcifications. These changes in the image lesion characteristics appear to have a negative effect on the visual identification of breast cancer