  Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g.,
artifacts and noise), which severely compromise the exact diagnosis of
cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality
Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error
diagnosis. The primary challenges of CCTA VIQA are that the local part of
coronary that determines final quality is hard to locate. To tackle the
challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL)
problem, and exploit Transformer-based MIL backbone (termed as T-MIL) to
aggregate the multiple instances along the coronary centerline into the final
quality. However, not all instances are informative for final quality. There
are some quality-irrelevant/negative instances intervening the exact quality
assessment(e.g., instances covering only background or the coronary in
instances is not identifiable). Therefore, we propose a Progressive
Reinforcement learning based Instance Discarding module (termed as PRID) to
progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based
on the above two modules, we propose a Reinforced Transformer Network (RTN) for
automatic CCTA VIQA based on end-to-end optimization. Extensive experimental
results demonstrate that our proposed method achieves the state-of-the-art
performance on the real-world CCTA dataset, exceeding previous MIL methods by a
large margin.

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