Clinical image quality criteria for full field digital mammography: a first practical application

Clinical image quality assessment and mean glandular dose for full field digital mammography

This study aims to assess the image quality (IQ) of 12 mammographic units and to identify units with potential optimisation needs. Data for 350 mammography examinations meeting inclusion criteria were collected retrospectively from April 2021 to April 2022. They were categorised based on the medical reports into 10 normal cases, 10 cases displaying calcifications and 10 cases presenting lesions. Two radiologists assessed the IQ of 1400 mammograms, evaluating system performance per Boitaet al's study and positioning performance following European guidelines. To measure agreement between the two radiologists, the Cohen's Kappa coefficient (κ) was computed, quantifying the excess of agreement beyond chance. The visual grading analysis score (VGAS) was computed to compare system and positioning performance assessments across different categories and facilities. Median average glandular dose (AGD) values for cranio caudal and medio lateral oblique views were calculated for each category and facility and compared to the national diagnostic reference levels. The health facilities were categorised by considering both IQ VGAS and AGD levels. Inter-rater agreement between radiologists ranged from poor (κ< 0.20) to moderate (0.41 <κ< 0.60), likely influenced by inherent biases and distinct IQ expectations. 50% of the facilities were classified as needing corrective actions for their system performance as they had IQ or high AGD that could increase recall rate and radiation risk and 50% of the health facilities exhibited insufficient positioning performance that could mask tumour masses and microcalcifications. The study's findings emphasise the importance of implementing quality assurance programs to ensure optimal IQ for accurate diagnoses while adhering to radiation exposure guidelines. Additionally, comprehensive training for technologists is essential to address positioning challenges. These initiatives collectively aim to enhance the overall quality of breast imaging services, contributing to improved patient care.

Development and content validity evaluation of a candidate instrument to assess image quality in digital mammography: A mixed-method study

PURPOSE

To develop a candidate instrument to assess image quality in digital mammography, by identifying clinically relevant features in images that are affected by lower image quality.

METHODS

Interviews with fifteen expert breast radiologists from five countries were conducted and analysed by using adapted directed content analysis. During these interviews, 45 mammographic cases, containing 44 lesions (30 cancers, 14 benign findings), and 5 normal cases, were shown with varying image quality. The interviews were performed to identify the structures from breast tissue and lesions relevant for image interpretation, and to investigate how image quality affected the visibility of those structures. The interview findings were used to develop tentative items, which were evaluated in terms of wording, understandability, and ambiguity with expert breast radiologists. The relevance of the tentative items was evaluated using the content validity index (CVI) and modified kappa index (k*).

RESULTS

Twelve content areas, representing the content of image quality in digital mammography, emerged from the interviews and were converted into 29 tentative items. Fourteen of these items demonstrated excellent CVI ≥ 0.78 (k* > 0.74), one showed good CVI < 0.78 (0.60 ≤ k* ≤ 0.74), while fourteen were of fair or poor CVI < 0.78 (k* ≤ 0.59). In total, nine items were deleted and five were revised or combined resulting in 18 items.

CONCLUSIONS

By following a mixed-method methodology, a candidate instrument was developed that may be used to characterise the clinically-relevant impact that image quality variations can have on digital mammography.

Can the non-pre-whitening model observer, including aspects of the human visual system, predict human observer performance in mammography?

PURPOSE

In mammography, images are processed prior to display. Current methodologies based on physical image quality measurements are however not designed for the evaluation of processed images. Model observers (MO) might be suitable for this evaluation. The aim of this study was to investigate whether the non-pre-whitening (NPW) MO can be used to predict human observer performance in mammography-like images by including different aspects of the human visual system (HVS).

METHODS

The correlation between human and NPW MO performance has been investigated for the detection of disk shaped objects in simulated white noise (WN) and clustered lumpy backgrounds (CLB), representing quantum noise limited and mammography-like images respectively. The images were scored by the MO and five human observers in a 2-alternative forced choice experiment.

RESULTS

For WN images it was found that the log likelihood ratio (RLR2), which expresses the goodness of fit, was highest (0.44) for the NPW MO without addition of HVS aspects. For CLB the RLR2 improved from 0.46 to 0.65 with addition of HVS aspects. The correlation was affected by object size and background.

CONCLUSIONS

This study shows that by including aspects of the HVS, the performance of the NPW MO can be improved to better predict human observer performance. This demonstrates that the NPW MO has potential for image quality assessment. However, due to the dependencies found in the correlation, the NPW MO can only be used for image quality assessment for a limited range of object sizes and background variability.

Comparison of visual grading and free-response ROC analyses for assessment of image-processing algorithms in digital mammography

OBJECTIVE

To compare two methods for assessment of image-processing algorithms in digital mammography: free-response receiver operating characteristic (FROC) for the specific task of microcalcification detection and visual grading analysis (VGA).

METHODS

The FROC study was conducted prior to the VGA study reported here. 200 raw data files of low breast density (Breast Imaging-Reporting and Data System I-II) mammograms (Novation DR, Siemens, Germany)-100 of which abnormal-were processed by four image-processing algorithms: Raffaello (IMS, Bologna, Italy), Sigmoid (Sectra, Linköping, Sweden), and OpView v. 2 and v. 1 (Siemens, Erlangen, Germany). Four radiologists assessed the mammograms for the detection of microcalcifications. 8 months after the FROC study, a subset (200) of the 800 images was reinterpreted by the same radiologists, using the VGA methodology in a side-by-side approach. The VGA grading was based on noise, saturation, contrast, sharpness and confidence with the image in terms of normal structures. Ordinal logistic regression was applied; OpView v. 1 was the reference processing algorithm.

RESULTS

In the FROC study all algorithms performed better than OpView v. 1. From the current VGA study and for confidence with the image, Sigmoid and Raffaello were significantly worse (p<0.001) than OpView v. 1; OpView v. 2 was significantly better (p=0.01). For the image quality criteria, results were mixed; Raffaello and Sigmoid for example were better than OpView v. 1 for sharpness and contrast (although not always significantly).

CONCLUSION

VGA and FROC discordant results should be attributed to the different clinical task addressed.

ADVANCES IN KNOWLEDGE

The method to use for image-processing assessment depends on the clinical task tested.

Increase in perceived case suspiciousness due to local contrast optimisation in digital screening mammography

Objectives

To determine the influence of local contrast optimisation on diagnostic accuracy and perceived suspiciousness of digital screening mammograms.

Methods

Data were collected from a screening region in the Netherlands and consisted of 263 digital screening cases (153 recalled,110 normal). Each case was available twice, once processed with a tissue equalisation (TE) algorithm and once with local contrast optimisation (PV). All cases had digitised previous mammograms. For both algorithms, the probability of malignancy of each finding was scored independently by six screening radiologists. Perceived case suspiciousness was defined as the highest probability of malignancy of all findings of a radiologist within a case. Differences in diagnostic accuracy of the processing algorithms were analysed by comparing the areas under the receiver operating characteristic curves (A_z). Differences in perceived case suspiciousness were analysed using sign tests.

Results

There was no significant difference in A_z (TE: 0.909, PV 0.917, P = 0.46). For all radiologists, perceived case suspiciousness using PV was higher than using TE more often than vice versa (ratio: 1.14–2.12). This was significant (P <0.0083) for four radiologists.

Conclusions

Optimisation of local contrast by image processing may increase perceived case suspiciousness, while diagnostic accuracy may remain similar.

Key Points

• Variations among different image processing algorithms for digital screening mammography are large.

• Current algorithms still aim for optimal local contrast with a low dynamic range.

• Although optimisation of contrast may increase sensitivity, diagnostic accuracy is probably unchanged.

• Increased local contrast may render both normal and abnormal structures more conspicuous.