  Lowering radiation dose per view and utilizing sparse views per scan are two
common CT scan modes, albeit often leading to distorted images characterized by
noise and streak artifacts. Blind image quality assessment (BIQA) strives to
evaluate perceptual quality in alignment with what radiologists perceive, which
plays an important role in advancing low-dose CT reconstruction techniques. An
intriguing direction involves developing BIQA methods that mimic the
operational characteristic of the human visual system (HVS). The internal
generative mechanism (IGM) theory reveals that the HVS actively deduces primary
content to enhance comprehension. In this study, we introduce an innovative
BIQA metric that emulates the active inference process of IGM. Initially, an
active inference module, implemented as a denoising diffusion probabilistic
model (DDPM), is constructed to anticipate the primary content. Then, the
dissimilarity map is derived by assessing the interrelation between the
distorted image and its primary content. Subsequently, the distorted image and
dissimilarity map are combined into a multi-channel image, which is inputted
into a transformer-based image quality evaluator. Remarkably, by exclusively
utilizing this transformer-based quality evaluator, we won the second place in
the MICCAI 2023 low-dose computed tomography perceptual image quality
assessment grand challenge. Leveraging the DDPM-derived primary content, our
approach further improves the performance on the challenge dataset.
