  Super-resolving medical images can help physicians in providing more accurate
diagnostics. In many situations, computed tomography (CT) or magnetic resonance
imaging (MRI) techniques capture several scans (modes) during a single
investigation, which can jointly be used (in a multimodal fashion) to further
boost the quality of super-resolution results. To this end, we propose a novel
multimodal multi-head convolutional attention module to super-resolve CT and
MRI scans. Our attention module uses the convolution operation to perform joint
spatial-channel attention on multiple concatenated input tensors, where the
kernel (receptive field) size controls the reduction rate of the spatial
attention, and the number of convolutional filters controls the reduction rate
of the channel attention, respectively. We introduce multiple attention heads,
each head having a distinct receptive field size corresponding to a particular
reduction rate for the spatial attention. We integrate our multimodal
multi-head convolutional attention (MMHCA) into two deep neural architectures
for super-resolution and conduct experiments on three data sets. Our empirical
results show the superiority of our attention module over the state-of-the-art
attention mechanisms used in super-resolution. Moreover, we conduct an ablation
study to assess the impact of the components involved in our attention module,
e.g. the number of inputs or the number of heads. Our code is freely available
at https://github.com/lilygeorgescu/MHCA.
