  Sparse-view computed tomography (CT) -- using a small number of projections
for tomographic reconstruction -- enables much lower radiation dose to patients
and accelerated data acquisition. The reconstructed images, however, suffer
from strong artifacts, greatly limiting their diagnostic value. Current trends
for sparse-view CT turn to the raw data for better information recovery. The
resultant dual-domain methods, nonetheless, suffer from secondary artifacts,
especially in ultra-sparse view scenarios, and their generalization to other
scanners/protocols is greatly limited. A crucial question arises: have the
image post-processing methods reached the limit? Our answer is not yet. In this
paper, we stick to image post-processing methods due to great flexibility and
propose global representation (GloRe) distillation framework for sparse-view
CT, termed GloReDi. First, we propose to learn GloRe with Fourier convolution,
so each element in GloRe has an image-wide receptive field. Second, unlike
methods that only use the full-view images for supervision, we propose to
distill GloRe from intermediate-view reconstructed images that are readily
available but not explored in previous literature. The success of GloRe
distillation is attributed to two key components: representation directional
distillation to align the GloRe directions, and band-pass-specific contrastive
distillation to gain clinically important details. Extensive experiments
demonstrate the superiority of the proposed GloReDi over the state-of-the-art
methods, including dual-domain ones. The source code is available at
https://github.com/longzilicart/GloReDi.
