Current clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For three- dimensional (3D) images, however, these models have been little explored so far. In this work, first, two novel designs of a multislice channelized Hotelling observer (CHO) are proposed for the task of detecting 3D signals in 3D images. The novel designs are then compared and evaluated in a simulation study with five different CHO designs: a single-slice model, three multislice models, and a volumetric model. Four different random background statistics are considered, both Gaussian (noncorrelated and correlated Gaussian noise) and non-Gaussian (lumpy and clustered lumpy backgrounds). Overall, the results show that the volumetric model outperforms the others, while the disparity between the models decreases for greater complexity of the detection task. Among the multislice models, the second proposed CHO could most closely approach the volumetric model, whereas the first new CHO seems to be least affected by the number of training samples.

Standardization bodies recommend various protocols to conduct subjective quality assessment (QA) of imaging systems. While many studies have compared these QA protocols, few have assessed the impact of different approaches for preprocessing and analyzing quality ratings. Furthermore, the effect of large versus small quality differences on the discrimination performance of protocols has not been extensively studied in video QA. This study examines these issues for four QA protocols. H.264 compressed medical videos and denoised natural scene videos were evaluated by expert and naive subjects. Scores were collected with four QA protocols-forced choice (FC), two ratio-scaled paired comparison methods: preference (Pref) and dissimilarity (Dissim), and single stimulus (SS)-and analyzed using combinations of different rating preprocessing approaches, generating a total of 14 methods. Performance metrics-probability and effect size-quantified the ability of the methods to discriminate between quality levels. The Pref and Dissim methods analyzed with classical multidimensional scaling and the SS method with Z-score transformation consistently outperformed the other methods. The type of preprocessing introduced large differences in the performance of individual protocols. Grouping stimuli pairs by small and large quality differences introduced significant differences in the performance rankings of the methods, with Pref and Dissim being most sensitive to small quality differences. For the future, we suggest further validation of the FC method, due to its simplicity and ease of use, and continued investigation into more robust raw scores transformation and statistical analysis methods for both SS and FC ratings.

Medical professionals are viewing an increasing number of images and videos in their clinical routine. However, various types of distortions can affect medical imaging data, and therefore impact the viewers' experienced quality and their clinical practice. Thus it is necessary to quantify this impact and understand how the viewers, i.e., medical experts, perceive the quality of (distorted) images and videos. In this paper, we present an up-to-date review of the methodologies used in the literature for the subjective quality assessment of medical images and videos and discuss their merits and drawbacks depending on the use case.