An observer study was conducted on a randomly selected sampling of 152 digital projection radiographs of varying
body parts obtained from four medical institutions for the purpose of assessing a new workflow-efficient imageprocessing
framework. Five rendering treatments were compared to measure the performance of a new processing
algorithm against the control condition. A key feature of the new image processing is the capability of processing without
specifying the exam. Randomized image pairs were presented at a softcopy workstation equipped with two diagnosticquality
flat-panel monitors. Five board-certified radiologists and one radiology resident independently reviewed each
image pair blinded to the specific processing used and provided a diagnostic-quality rating using a subjective rank-order
scale for each image. In addition, a relative preference rating was used to indicate rendering preference. Aggregate results
indicate that the new fully automated processing is preferred (sign test for median = 0 (α = 0.05): p < 0.0001 preference
in favor of the control).
A new study supports and expands upon a previous reporting that computed radiography (CR) mammography offers as
good, or better, image quality than state-of-the-art screen/film mammography. The suitability of CR mammography is
explored through qualitative and quantitative study components: feature comparison and cancer detection rates of each
modality. Images were collected from 150 normal and 50 biopsy-confirmed subjects representing a range of breast and
pathology types. Comparison views were collected without releasing compression, using automatic exposure control on
Kodak MIN-R films, followed by CR. Digital images were displayed as both softcopy (S/C) and hardcopy (H/C) for the
feature comparison, and S/C for the cancer detection task. The qualitative assessment used preference scores from five
board-certified radiologists obtained while viewing 100 screen/film-CR pairs from the cancer subjects for S/C and H/C
CR output. Fifteen general image-quality features were rated, and up to 12 additional features were rated for each pair,
based on the pathology present. Results demonstrate that CR is equivalent or preferred to conventional mammography
for overall image quality (89% S/C, 95% H/C), image contrast (95% S/C, 98% H/C), sharpness (86% S/C, 93% H/C),
and noise (94% S/C, 91% H/C). The quantitative objective was satisfied by asking 10 board-certified radiologists to
provide a BI-RADSTM score and probability of malignancy per breast for each modality of the 200 cases. At least 28
days passed between observations of the same case. Average sensitivity and specificity was 0.89 and 0.82 for CR and
0.91 and 0.82 for screen/film, respectively.
Digital mammography is advancing into an arena where analog has long been the gold standard. Direct digital systems
may not be the favored solution for a particular site while computed radiography (CR) mammography, remains
unproven worldwide. This pilot study responds to the growing desire to acquire and display digital mammographic
images by exploring the acceptability of CR mammography. Images representing a range of breast tissue types were
collected from 49 subjects (17 screening; 32 diagnostic) at four clinical sites. Comparison views were collected on the
same breast, under the same compression, using automatic exposure control on state-of-the-art film systems followed by
CR. CR images were processed and printed to a mammography printer for hard copy feature comparison. Each image
pair in the study was evaluated according to 13 image quality attributes covering noise, contrast, sharpness, and image
quality in the overall captured images as well as in each of several particular breast regions (periphery and skin-line,
parenchyma and fatty tissue). A rating scale from 1 to 5 was used (strong preference for film=1, strong preference for
CR=5). Twelve experienced mammographers at four clinical sites scored a subset of the 49 cases for a total of 64 image
pair readings. There were 64 ratings for each of 13 image quality attributes for all cases and, an additional series of
scores (four or five attribute ratings) for each abnormality in the category of mass, architectural distortion and
microcalcification, for a total of 1069 scores. Based on the pilot study results, it was suggested that CR was equivalent
or preferred to conventional screen-film for overall image quality (38% scored 3; 46% scored >3), image contrast (27%
scored 3; 59% scored >3) and sharpness (28% scored 3; 50% scored >3). No preference was found when assessing
noise. This pilot study also suggested that diagnostic quality was maintained in assessing abnormalities for attributes
necessary to evaluate masses and microcalcifications as compared to screen-film.
The need to assure the image quality of digital systems for mammography screening applications is now widely recognized. One approach is embodied in Part B of the European Protocol for the Quality Control of the Physical and Technical Aspects of Mammography Screening (EPQCM), which prescribes criteria for several interconnected image quality metrics. The focus of this study is on the "threshold contrast visibility" (TCV) protocol (section 2.4.1 of the EPQCM), in which human observers score images of a CDMAM or similar 4-AFC phantom. This section of the EPQCM currently omits many critical experimental details, which must be gleaned from ancillary documents. Given these, the purpose of this study is to quantify the effects of several remaining experimental variables, including phantom design, and the methods used for scoring and analysis, on the measured results.
Preliminary studies of two CDMAM version 3.4 (CDMAM 3.4) phantoms have revealed a 17% difference in TCV when averaged over all target diameters from 0.1 to 2.0 mm. This indicates phantom variability may affect results at some sites. More importantly, we have shown that the current CDMAM phantom design, methods for scoring, and analysis, substantially limit the ability to measure system performance accurately and precisely. An improved phantom design has been shown to avoid these limitations.
Viewing environment and presentation context affect the performance and efficiency of visual scoring of phantom images. An automated display tool has been developed that isolates individual 4-AFC targets of CDMAM phantom images, automatically optimizes window/level, and automatically records observers' scores. While not substantially changing TCV, the tool has increased scoring efficiency while mitigating several of the limitations associated with unassisted visual scoring. For example, learning bias and navigational issues are completely avoided. Ultimately, software-based ideal observer scoring will likely prove to be a better approach.
Statistical-decision-theory-based (SDT) analysis has been shown to mitigate limitations associated with the current CDMAM phantom and the ad hoc nearest-neighbor correcting (NNC) scoring method. NNC analysis is sensitive to the degree of incomplete scoring (stopping criteria). However, SDT substantially mitigates this problem, using all of the available data to derive thresholds that are more interpretable. Bootstrap sampling was used to provide an estimate of the standard error for SDT analysis.
In conclusion, the current EPQCM section 2.4.1 protocol fails to measure TCV accurately and precisely enough to qualify digital mammography systems. This paper presents a series of recommendations that supplement section 2.4.1 of the EPQCM and that provide a stable and accurate measure of TCV.
The growing importance of the “European Protocol for the Quality Control of the Physical and Technical Aspects of Mammography Screening, Part B: Digital Mammography” dictates the need to understand the prescribed threshold contrast sensitivity test. Observers following a 4-AFC paradigm, report the location of disks varying in contrast and diameter on multiple images of a CDMAM or similar phantom. Analysis provides a contrast threshold for each disk diameter. The goals of this study were to quantify the performance of new observers, compare it to published results, compare visual scoring with software scoring of the same images, and to quantify the major sources of variability. Digital phantom images, visual scorings by four expert readers, and CDCOM software were downloaded from the EUREF website. These images were read on a 3M Barco flat-panel monitor by 13 observers and scored by CDCOM. Scores were analyzed using the published method from the CDMAM-phantom 3.4 manual and a signal detection theory-based method. The average contrast sensitivities of the 13 study observers generally exceeded the published values by ~10%. The 95% confidence limits for the mean of 6 images from the published data vary from ±20.2% to ±41.8% of their respective means, the average being 31.2%. The average confidence limit for selected study observers is ±36%. Comparisons between software and human observer results using the prescribed method of analysis-revealed marked differences, particularly for small diameter targets. These differences are mitigated by signal-detection-theory analysis of both datasets. The large inter-observer variability and the substantial time required for human scoring supports the need to qualify a readily available software solution.
Tumor segmentation from magnetic resonance (MR) images aids in tumor treatment by tracking the progress of tumor growth and/or shrinkage. In this paper we present an automatic segmentation method which separates non-enhancing brain tumors from healthy tissues in MR images. The MR feature images used for the segmentation consist of three weighted images (T1, T2 and proton density) for each axial slice through the head. An initial segmentation is computed using an unsupervised clustering algorithm. Then, integrated domain knowledge and image processing techniques contribute to the final tumor segmentation. The system was trained on two patient volumes and preliminary testing has shown successful tumor segmentations on four patient volumes.
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