Undertaking observer studies to compare imaging technology using clinical radiological images is challenging due to patient variability. To achieve a significant result, a large number of patients would be required to compare cancer detection rates for different image detectors and systems. The aim of this work was to create a methodology where only one set of images is collected on one particular imagingsystem. These images are then converted to appear as if they had been acquired on a different detector and x-ray system. Therefore, the effect of a wide range of digital detectors on cancer detection or diagnosis can be examined without the need for multiple patient exposures.Methods:
Three detectors and x-ray systems [Hologic Selenia (ASE), GE Essential (CSI), Carestream CR (CR)] were characterized in terms of signal transfer properties, noise power spectra (NPS), modulation transfer function, and grid properties. The contributions of the three noise sources (electronic, quantum, and structure noise) to the NPS were calculated by fitting a quadratic polynomial at each spatial frequency of the NPS against air kerma. A methodology was developed to degrade the images to have the characteristics of a different (target) imagingsystem. The simulated images were created by first linearizing the original images such that the pixel values were equivalent to the air kerma incident at the detector. The linearized image was then blurred to match the sharpness characteristics of the target detector.Noise was then added to the blurred image to correct for differences between the detectors and any required change in dose. The electronic, quantum, and structure noise were added appropriate to the air kerma selected for the simulated image and thus ensuring that the noise in the simulated image had the same magnitude and correlation as the target image. A correction was also made for differences in primary grid transmission, scatter, and veiling glare. The method was validated by acquiring images of a CDMAM contrast detail test object (Artinis, The Netherlands) at five different doses for the three systems. The ASE CDMAM images were then converted to appear with the imaging characteristics of target CR and CSI detectors.Results:
The measured threshold gold thicknesses of the simulated and target CDMAM images were closely matched at normal dose level and the average differences across the range of detail diameters were −4% and 0% for the CR and CSI systems, respectively. The conversion was successful for images acquired over a wide dose range. The average difference between simulated and target images for a given dose was a maximum of 11%.Conclusions:
The validation shows that the image quality of a digital mammography image obtained with a particular system can be degraded, in terms of noise magnitude and color, sharpness, and contrast to account for differences in the detector and antiscatter grid. Potentially, this is a powerful tool for observer studies, as a range of image qualities can be examined by modifying an image set obtained at a single (better) image quality thus removing the patient variability when comparing systems.
This work is part of the OPTIMAM project and is supported by Cancer research-UK and EPSRC Cancer Imaging Programme in Surrey, in association with the MRC and Department of Health (England). The authors are grateful for the help and support of staff from St. George’s Hospital, London, and Jarvis Breast Screening Unit, Guildford. The authors acknowledge Hologic, Inc., MIS Healthcare, and Carestream Healthcare for their help in accessing images. The authors thank our NCCPM colleagues Lucy Warren, Jenny Oduko, Lebina Shrestha, and Faith Green who have helped with the collection of images and data analysis. The authors thank our colleagues at Katholieke Universiteit Leuven for helpful discussion of this work.
I. INTRODUCTION II. METHODS II.A. Theory II.A.1. Development of image conversion model II.A.2. Linearizing the image II.A.3. Blurring the image II.A.4. Calculating the contributions of electronic, quantum, and structure noise to the NPS II.A.5. Changing noise coefficients to match the blurred original image II.A.6. Change in NPS associated with changes in dose and detector II.A.7. Addition of noise to degrade the image quality II.B. Correcting for x-ray system II.B.1. Flat field correction II.B.2. Primary transmission factor of grid and breast support II.B.3. Scatter-to-primary and glare-to-primary ratios II.B.4. Accounting for differences in veiling glare between systems II.B.5. Accounting for differences in scatter between systems II.B.6. Correction for negative values of noise coefficients II.C. Characterization of detectors and x-ray systems II.C.1. Imagingsystems used II.C.2. Signal transfer properties and noise power spectra II.C.3. Presampled modulation transfer function II.C.4. Noise fitting II.C.5. Flat field correction II.C.6. Primary transmission factor of grid and breast support II.C.7. Measuring scatter-to-primary and glare-to-primary ratios II.D. Validation of model II.D.1. Validation using images of edge and flat field images II.D.2. Validation using CDMAM test object II.E. Practical issues associated with the validation II.E.1. Differences in pixel pitch II.E.2. Conversion methodology III. RESULTS III.A. Characterization of the detector and x-ray systems III.A.1. Signal transfer properties III.A.2. Modulation transfer function III.A.3. Noise coefficients (electronic, quantum, and structure) III.A.4. Characterization of the imagingsystems III.B. Validation of conversion methodology III.B.1. Conversion of edge and flat field images III.B.2. Conversion of images of CDMAM test object III.B.3. Uncertainties of threshold thickness for CDMAM images IV. DISCUSSION IV.A. Accuracy of conversion methodology IV.B. Assumptions in the conversion methodology IV.C. Correction of noise IV.D. MTF and veiling glare IV.E. Use of the conversion model V. CONCLUSIONS
- Medical imaging
- Image sensors
- Medical image noise
- Image detection systems
- Medical image quality
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