Medical image quality can be objectively defined in terms of diagnostic decision accuracy in clinically relevant perceptual tasks. Because of the high cost and effort involved in evaluating image quality using clinical studies, especially in early technological developments, there has been an ongoing effort to develop numerical algorithms (model observers) that can be applied to images to predict human accuracy in clinically relevant perceptual tasks. In recent years model observers have transitioned from laboratory investigations to actual tools used in technology development in the industry and for image quality evaluation by manufacturers to seek approval from the Food and Drug Administration. However, the recent increase of the use of 3D medical images (computed tomography, breast tomosynthesis, magnetic resonance) has motivated a need for the development of the next generation of model observers. A fundamental limitation of current model observers is that they disregard that the human brain processes an image with decreasing spatial resolution from the point of fixation. With 3D data-sets, radiologists rarely exhaustively fixate every region of every slice; instead, they process a significant portion of images with their retinal periphery which has drastically different visual processing. Increased computer power and recent advances in the understanding of the computational neuroscience of visual search provide the opportunity to develop the next generation model observers which potentially can more accurately characterize how radiologists scrutinize medical images, as well as their decision accuracy and errors. The current project proposes to develop the 1st model observer to emulate radiologists by processing medical images with varying spatial processing resolution across the human visual field, searching through the image with simulated eye movements, and reaching a decision through integration across fixations. The foveated search model, which makes eye movements unlike any previous model observer in medical imaging, will be the 1st model to emulate radiologists in making two distinct types of errors: search errors ( missed lesions that are not fixated) perceptual errors (missed lesion that are fixated). The decisions and eye movements of over twenty radiologists reading digital breast tomosynthesis (DBT) images will be compared to the newly proposed foveated search model and a comprehensive list of existing non-scanning and scanning model observers in what will represent the most extensive validation study to date of model observers with actual radiologists' decisions. The newly proposed model will be utilized to optimize DBT acquisition geometry and compared to use of current metrics of medical image quality. If successful, the newly proposed foveated search model will allow for more accuracy assessment of medical image quality, could be utilized to accelerate the evaluation of new technology, optimize parameters of current technology and gain a better understanding how radiologists search and reach diagnostic decisions.
Medical images are typically scrutinized by radiologists looking for signs of disease. Improving the image quality of a medical image should translate to an increased ability by radiologists to find disease. This proposal seeks to build on recent advances in the scientific understanding of computations and neuroscience of visual search to develop a new search model for medical images that emulates how radiologists scrutinize images with fixational eye movements. The model developed could potentially lead to more accurate assessment of medical image quality and accelerate technology developments.
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