  Parallel imaging, a fast MRI technique, involves dynamic adjustments based on
the configuration i.e. number, positioning, and sensitivity of the coils with
respect to the anatomy under study. Conventional deep learning-based image
reconstruction models have to be trained or fine-tuned for each configuration,
posing a barrier to clinical translation, given the lack of computational
resources and machine learning expertise for clinicians to train models at
deployment. Joint training on diverse datasets learns a single weight set that
might underfit to deviated configurations. We propose, HyperCoil-Recon, a
hypernetwork-based coil configuration task-switching network for multi-coil MRI
reconstruction that encodes varying configurations of the numbers of coils in a
multi-tasking perspective, posing each configuration as a task. The
hypernetworks infer and embed task-specific weights into the reconstruction
network, 1) effectively utilizing the contextual knowledge of common and
varying image features among the various fields-of-view of the coils, and 2)
enabling generality to unseen configurations at test time. Experiments reveal
that our approach 1) adapts on the fly to various unseen configurations up to
32 coils when trained on lower numbers (i.e. 7 to 11) of randomly varying
coils, and to 120 deviated unseen configurations when trained on 18
configurations in a single model, 2) matches the performance of coil
configuration-specific models, and 3) outperforms configuration-invariant
models with improvement margins of around 1 dB / 0.03 and 0.3 dB / 0.02 in PSNR
/ SSIM for knee and brain data. Our code is available at
https://github.com/sriprabhar/HyperCoil-Recon
