Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator

TL;DR

This paper proposes a novel blind CT image quality assessment method inspired by human visual system principles, combining a DDPM-based primary content prediction with a transformer evaluator, achieving top results in a MICCAI challenge.

Contribution

It introduces an innovative BIQA metric that emulates active inference of the human visual system using DDPM and transformer models, advancing low-dose CT image quality evaluation.

Findings

Achieved second place in MICCAI 2023 low-dose CT IQA challenge.

Improved performance on challenge dataset using DDPM-derived content.

Demonstrated effectiveness of combining generative models with transformers for BIQA.

Abstract

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.