Model observer for assessing digital breast tomosynthesis for multi-lesion detection in the presence of anatomical noise

Comparative study of the methodologies used for subjective medical image quality assessment

Healthcare professionals have been increasingly viewing medical images and videos in their routine clinical practice, and this in a wide variety of environments. Both the perception and interpretation of medical visual information, across all branches of practice or medical specialties (e.g. diagnostic, therapeutic, or surgical medicine), career stages, and practice settings (e.g. emergency care), appear to be critical for patient care. However, medical images and videos are not self-explanatory and, therefore, need to be interpreted by humans, i.e. medical experts. In addition, various types of degradations and artifacts may appear during image acquisition or processing, and consequently affect medical imaging data. Such distortions tend to impact viewers' quality of experience, as well as their clinical practice. It is accordingly essential to better understand how medical experts perceive the quality of visual content. Thankfully, progress has been made in the recent literature towards such understanding. In this article, we present an up-to-date state-of the-art of relatively recent (i.e. not older than ten years old) existing studies on the subjective quality assessment of medical images and videos, as well as research works using task-based approaches. Furthermore, we discuss the merits and drawbacks of the methodologies used, and we provide recommendations about experimental designs and statistical processes to evaluate the perception of medical images and videos for future studies, which could then be used to optimise the visual experience of image readers in real clinical practice. Finally, we tackle the issue of the lack of available annotated medical image and video quality databases, which appear to be indispensable for the development of new dedicated objective metrics.

Systematic approach to a channelized Hotelling model observer implementation for a physical phantom containing mass-like lesions: Application to digital breast tomosynthesis

PURPOSE

to develop a channelized model observer (CHO) that matches human reader (HR) scoring of a physical phantom containing breast simulating structure and mass lesion-like targets for use in quality control of digital breast tomosynthesis (DBT) imaging systems.

METHODS

A total of 108 DBT scans of the phantom was acquired using a Siemens Inspiration DBT system. The detectability of mass-like targets was evaluated by human readers using a 4-alternative forced choice (4-AFC) method. The percentage correct (PC) values were then used as the benchmark for CHO tuning, again using a 4-AFC method. Three different channel functions were considered: Gabor, Laguerre-Gauss and Difference of Gaussian. With regard to the observer template, various methods for generating the expected signal were studied along with the influence of the number of training images used to form the covariance matrix for the observer template. Impact of bias in the training process on the observer template was evaluated next, as well as HR and CHO reproducibility.

RESULTS

HR performance was most closely matched by 8 Gabor channels with tuned phase, orientation and frequency, using an observer template generated from training image data. Just 24 DBT image stacks were required to give robust CHO performance with 0% bias, although a bias of up to 33% in the training images also gave acceptable performance. CHO and HR reproducibility were similar (on average 3.2 PC versus 3.4 PC).

CONCLUSIONS

The CHO algorithm developed matches human reader performance and is therefore a promising candidate for automated readout of phantom studies.