Lucy M. Warren

Effect of image quality on calcification detection in digital mammography.

PURPOSE This study aims to investigate if microcalcification detection varies significantly when mammographic images are acquired using different image qualities, including: different detectors, dose levels, and different image processing algorithms. An additional aim was to determine how the standard European method of measuring image quality using threshold gold thickness measured with a CDMAM phantom and the associated limits in current EU guidelines relate to calcification detection. METHODS One hundred and sixty two normal breast images were acquired on an amorphous selenium direct digital (DR) system. Microcalcification clusters extracted from magnified images of slices of mastectomies were electronically inserted into half of the images. The calcification clusters had a subtle appearance. All images were adjusted using a validated mathematical method to simulate the appearance of images from a computed radiography (CR) imaging system at the same dose, from both systems at half this dose, and from the DR system at quarter this dose. The original 162 images were processed with both Hologic and Agfa (Musica-2) image processing. All other image qualities were processed with Agfa (Musica-2) image processing only. Seven experienced observers marked and rated any identified suspicious regions. Free response operating characteristic (FROC) and ROC analyses were performed on the data. The lesion sensitivity at a nonlesion localization fraction (NLF) of 0.1 was also calculated. Images of the CDMAM mammographic test phantom were acquired using the automatic setting on the DR system. These images were modified to the additional image qualities used in the observer study. The images were analyzed using automated software. In order to assess the relationship between threshold gold thickness and calcification detection a power law was fitted to the data. RESULTS There was a significant reduction in calcification detection using CR compared with DR: the alternative FROC (AFROC) area decreased from 0.84 to 0.63 and the ROC area decreased from 0.91 to 0.79 (p < 0.0001). This corresponded to a 30% drop in lesion sensitivity at a NLF equal to 0.1. Detection was also sensitive to the dose used. There was no significant difference in detection between the two image processing algorithms used (p > 0.05). It was additionally found that lower threshold gold thickness from CDMAM analysis implied better cluster detection. The measured threshold gold thickness passed the acceptable limit set in the EU standards for all image qualities except half dose CR. However, calcification detection varied significantly between image qualities. This suggests that the current EU guidelines may need revising. CONCLUSIONS Microcalcification detection was found to be sensitive to detector and dose used. Standard measurements of image quality were a good predictor of microcalcification cluster detection.

Validation of simulation of calcifications for observer studies in digital mammography

Studies using simulated calcifications can be performed to measure the effect of different imaging factors on calcification detection in digital mammography. The simulated calcifications must be inserted into clinical images with realistic contrast and sharpness. MoCa is a program which modifies the contrast and sharpness of simulated calcification clusters extracted from images of mastectomy specimens acquired on a digital specimen cabinet at high magnification for insertion into clinical mammography images. This work determines whether the use of MoCa results in simulated calcifications with the correct contrast and sharpness. Aluminium foils (thickness 0.1-0.4 mm) and 1.60 µm thick gold discs (diameter 0.13-0.8 mm) on 0.5 mm aluminium were imaged with a range of thicknesses of polymethyl methacrylate (PMMA) using an amorphous selenium direct digital (DR) system and a powder phosphor computed radiography (CR) system (real images). Simulated images of the tests objects were also generated using MoCa. The contrast of the aluminium squares and the degradation of the contrast of the gold discs as a function of disc diameter were compared in the real and simulated images. The average ratios of the simulated-to-real aluminium contrasts over all aluminium and PMMA thicknesses were 1.03 ± 0.04 (two standard errors in the mean) and 0.99 ± 0.03 for the DR and CR systems, respectively. The ratio of the simulated-to-real degradations of contrast averaged over all disc diameters and PMMA thicknesses were 1.007 ± 0.008 and 1.002 ± 0.013 for DR and CR, respectively. The use of MoCa was accurate within the experimental errors.

The Effect of Image Processing on the Detection of Cancers in Digital Mammography

OBJECTIVE. The objective of our study was to investigate the effect of image processing on the detection of cancers in digital mammography images. MATERIALS AND METHODS. Two hundred seventy pairs of breast images (both breasts, one view) were collected from eight systems using Hologic amorphous selenium detectors: 80 image pairs showed breasts containing subtle malignant masses; 30 image pairs, biopsy-proven benign lesions; 80 image pairs, simulated calcification clusters; and 80 image pairs, no cancer (normal). The 270 image pairs were processed with three types of image processing: standard (full enhancement), low contrast (intermediate enhancement), and pseudo-film-screen (no enhancement). Seven experienced observers inspected the images, locating and rating regions they suspected to be cancer for likelihood of malignancy. The results were analyzed using a jackknife-alternative free-response receiver operating characteristic (JAFROC) analysis. RESULTS. The detection of calcification clusters was significantly affected by the type of image processing: The JAFROC figure of merit (FOM) decreased from 0.65 with standard image processing to 0.63 with low-contrast image processing (p = 0.04) and from 0.65 with standard image processing to 0.61 with film-screen image processing (p = 0.0005). The detection of noncalcification cancers was not significantly different among the image-processing types investigated (p > 0.40). CONCLUSION. These results suggest that image processing has a significant impact on the detection of calcification clusters in digital mammography. For the three image-processing versions and the system investigated, standard image processing was optimal for the detection of calcification clusters. The effect on cancer detection should be considered when selecting the type of image processing in the future.

The relationship between cancer detection in mammography and image quality measurements

PURPOSE To investigate the relationship between image quality measurements and the clinical performance of digital mammographic systems. METHODS Mammograms containing subtle malignant non-calcification lesions and simulated malignant calcification clusters were adapted to appear as if acquired by four types of detector. Observers searched for suspicious lesions and gave these a malignancy score. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). Images of a CDMAM contrast-detail phantom were adapted to appear as if acquired using the same four detectors as the clinical images. The resultant threshold gold thicknesses were compared to the FoMs using a linear regression model and an F-test was used to find if the gradient of the relationship was significantly non-zero. RESULTS The detectors with the best image quality measurement also had the highest FoM values. The gradient of the inverse relationship between FoMs and threshold gold thickness for the 0.25mm diameter disk was significantly different from zero for calcification clusters (p=0.027), but not for non-calcification lesions (p=0.11). Systems performing just above the minimum image quality level set in the European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis resulted in reduced cancer detection rates compared to systems performing at the achievable level. CONCLUSIONS The clinical effectiveness of mammography for the task of detecting calcification clusters was found to be linked to image quality assessment using the CDMAM phantom. The European Guidelines should be reviewed as the current minimum image quality standards may be too low.

Using image simulation to test the effect of detector type on breast cancer detection

Introduction: The effect that the image quality associated with different image receptors has on cancer detection in mammography was measured using a novel method for changing the appearance of images. Method: A set of 270 mammography cases (one view, both breasts) was acquired using five Hologic Selenia and two Hologic Dimensions X-ray sets: 160 normal cases, 80 cases with subtle real non-calcification malignant lesions and 30 cases with biopsy proven benign lesions. Simulated calcification clusters were inserted into half of the normal cases. The 270 cases (Arm 1) were converted to appear as if they had been acquired on three other imaging systems: caesium iodide detector (Arm 2), needle image plate computed radiography (CR) (Arm 3) and powder phosphor CR (Arm 4). Five experienced mammography readers marked the location of suspected cancers in the images and classified the degree of visibility of the lesions. Statistical analysis was performed using JAFROC. Results: The differences in the visibility of calcification clusters between all pairs of arms were statistically significant (p<0.05), except between Arms 1 and 2. The difference in the visibility of non-calcification lesions was smaller than for calcification clusters, but the differences were still significant except between Arms 1 and 2 and between Arms 3 and 4. Conclusion: Detector type had a significant impact on the visibility of all types of subtle cancers, with the largest impact being on the visibility of calcification clusters.

A modelling framework for evaluation of 2d-mammography and breast tomosynthesis systems

Planar 2D X-ray mammography is the most common screening technique used for breast cancer detection. Digital breast tomosynthesis (DBT) is a new and emerging technology that overcomes some of the limitations of conventional planar imaging. However, it is important to understand the impact of these two modalities on cancer detection rates and patient recall. Since it is difficult to adequately evaluate different modalities clinically, a collection of modeling tools is introduced in this paper that can be used to emulate the image acquisition process for both modalities. In this paper, we discuss image simulation chains that can be used for the evaluation of 2D-mammography and DBT systems in terms of both technical factors and observer studies.

The oncology medical image database (OMI-DB)

Many projects to evaluate or conduct research in medical imaging require the large-scale collection of images (both unprocessed and processed) and associated data. This demand has led us to design and implement a flexible oncology image repository, which prospectively collects images and data from multiple sites throughout the UK. This Oncology Medical Image Database (OMI-DB) has been created to support research involving medical imaging and contains unprocessed and processed medical images, associated annotations and data, and where applicable expert-determined ground truths describing features of interest. The process of collection, annotation and storage is almost fully automated and is extremely adaptable, allowing for quick and easy expansion to disparate imaging sites and situations. Initially the database was developed as part of a large research project in digital mammography (OPTIMAM). Hence the initial focus has been digital mammography; as a result, much of the work described will focus on this field. However, the OMI -DB has been designed to support multiple modalities and is extensible and expandable to store any associated data with full anonymisation. Currently, the majority of associated data is made up of radiological, clinical and pathological annotations extracted from the UK’s National Breast Screening System (NBSS). In addition to the data, software and systems have been created to allow expert radiologists to annotate the images with interesting clinical features and provide descriptors of these features. The data from OMI-DB has been used in several observer studies and more are planned. To date we have collected 34,104 2D mammography images from 2,623 individuals.

Characterisation of Screen Detected and Simulated Calcification Clusters in Digital Mammograms

Simulated microcalcifciation clusters have been used in studies performed to investigate the effect of different imaging conditions on cancer detection in breast screening. This work compares the characteristics of the simulated clusters to screen-detected calcification clusters. Using a database of 271 screen-detected cancers it was found that 67 (25%) presented radiographically as calcification clusters. The characteristics of 1215 microcalcifications from all 67 clusters and 304 microcalcifications from 30 simulated clusters were quantitatively analysed. The diameter of simulated calcifications were within the range of 99% of real calcifications. The cluster diameters of the simulated clusters were within the range of 70% of the real clusters. Our simulated calcifications had similar characteristics to real calcifications but were representative of smaller clusters which represent 17% of screen-detected cancers. Consequently, a significant change in detection of our simulated clusters due to change in imaging condition has a predictable impact on cancer detection in screening.

Effect of image processing version on detection of non-calcification cancers in 2D digital mammography imaging

Image processing (IP) is the last step in the digital mammography imaging chain before interpretation by a radiologist. Each manufacturer has their own IP algorithm(s) and the appearance of an image after IP can vary greatly depending upon the algorithm and version used. It is unclear whether these differences can affect cancer detection. This work investigates the effect of IP on the detection of non-calcification cancers by expert observers. Digital mammography images for 190 patients were collected from two screening sites using Hologic amorphous selenium detectors. Eighty of these cases contained non-calcification cancers. The images were processed using three versions of IP from Hologic – default (full enhancement), low contrast (intermediate enhancement) and pseudo screen-film (no enhancement). Seven experienced observers inspected the images and marked the location of regions suspected to be non-calcification cancers assigning a score for likelihood of malignancy. This data was analysed using JAFROC analysis. The observers also scored the clinical interpretation of the entire case using the BSBR classification scale. This was analysed using ROC analysis. The breast density in the region surrounding each cancer and the number of times each cancer was detected were calculated. IP did not have a significant effect on the radiologists’ judgment of the likelihood of malignancy of individual lesions or their clinical interpretation of the entire case. No correlation was found between number of times each cancer was detected and the density of breast tissue surrounding that cancer.

Mammographic calcification cluster detection and threshold gold thickness measurements

European Guidelines for quality control in digital mammography specify acceptable and achievable standards of image quality (IQ) in terms of threshold gold thickness using the CDMAM test object. However, there is little evidence relating such measurements to cancer detection. This work investigated the relationship between calcification detection and threshold gold thickness. An observer study was performed using a set of 162 amorphous selenium direct digital (DR) detector images (81 no cancer and 81 with 1-3 inserted calcification clusters). From these images four additional IQs were simulated: different digital detectors (computed radiography (CR) and DR) and dose levels. Seven observers marked and rated the locations of suspicious regions. DBM analysis of variances was performed on the JAFROC figure of merit (FoM) yielding 95% confidence intervals for IQ pairs. Automated threshold gold thickness (Tg) analysis was performed for the 0.25mm gold disc diameter on CDMAM images at the same IQs (16 images per IQ). Tg was plotted against FoM and a power law fitted to the data. There was a significant reduction in FoM for calcification detection for CR images compared with DR; FoM decreased from 0.83 to 0.63 (p≤0.0001). Detection was also sensitive to dose. There was a good correlation between FoM and Tg (R2=0.80, p<0.05), consequently threshold gold thickness was a good predictor of calcification detection at the same IQ. Since the majority of threshold gold thicknesses for the various IQs were above the acceptable standard despite large variations in calcification detection by radiologists, current EU guidelines may need revising.