Table 6. Clusters of the top 100 keywords.
| Cluster | Keyword | Counts | Rank |
|---|---|---|---|
| 1 | Super-resolution | 697 | 1 |
| 1 | Deep learning | 205 | 2 |
| 1 | MRI | 197 | 3 |
| 1 | Convolutional neural network | 116 | 4 |
| 1 | Generative adversarial networks | 92 | 5 |
| 1 | Super-resolution reconstruction | 53 | 8 |
| 1 | Medical imaging | 49 | 11 |
| 1 | CT | 44 | 12 |
| 1 | Compressed sensing | 36 | 13 |
| 1 | Sparse representation | 33 | 19 |
| 1 | Image segmentation | 30 | 23 |
| 1 | Image enhancement | 25 | 27 |
| 1 | Reconstruction | 25 | 28 |
| 1 | Machine learning | 24 | 30 |
| 1 | Image processing | 20 | 34 |
| 1 | Interpolation | 19 | 36 |
| 1 | COVID-19 | 18 | 37 |
| 1 | Attention mechanism | 17 | 40 |
| 1 | Image registration | 17 | 42 |
| 1 | Optical coherence tomography | 16 | 46 |
| 1 | Resolution enhancement | 16 | 47 |
| 1 | Denoising | 15 | 48 |
| 1 | Image quality | 15 | 51 |
| 1 | Transfer learning | 15 | 54 |
| 1 | Artificial intelligence | 13 | 59 |
| 1 | Breast cancer | 12 | 63 |
| 1 | Single image super-resolution | 12 | 67 |
| 1 | Image fusion | 11 | 70 |
| 1 | Spatiotemporal encoding | 10 | 83 |
| 1 | Classification | 9 | 85 |
| 1 | Residual learning | 9 | 88 |
| 1 | Residual network | 9 | 89 |
| 1 | Transformer | 9 | 91 |
| 2 | Image reconstruction | 89 | 6 |
| 2 | Image resolution | 31 | 21 |
| 2 | PET | 31 | 22 |
| 2 | Training | 25 | 29 |
| 2 | Dictionary learning | 20 | 32 |
| 2 | Feature extraction | 20 | 33 |
| 2 | Motion correction | 18 | 39 |
| 2 | Image restoration | 17 | 43 |
| 2 | Inverse problems | 17 | 44 |
| 2 | Three-dimensional displays | 17 | 45 |
| 2 | Neural network | 14 | 58 |
| 2 | Image denoising | 13 | 61 |
| 2 | Medical image processing | 12 | 66 |
| 2 | Task analysis | 11 | 75 |
| 2 | Diseases | 10 | 79 |
| 2 | Medical diagnostic imaging | 10 | 80 |
| 2 | Generators | 9 | 86 |
| 2 | Convolution | 8 | 94 |
| 2 | Noise reduction | 8 | 97 |
| 2 | Unsupervised learning | 8 | 100 |
| 3 | Ultrasound imaging | 51 | 9 |
| 3 | Imaging | 50 | 10 |
| 3 | Ultrasound | 36 | 15 |
| 3 | Ultrasound localization microscopy | 35 | 17 |
| 3 | Microbubbles | 32 | 20 |
| 3 | Microscopy | 30 | 24 |
| 3 | Spatial resolution | 27 | 26 |
| 3 | Super-resolution ultrasound | 20 | 35 |
| 3 | Fluorescence | 18 | 38 |
| 3 | Biomedical imaging | 17 | 41 |
| 3 | Microvasculature | 15 | 52 |
| 3 | Resolution | 15 | 53 |
| 3 | Deconvolution | 14 | 55 |
| 3 | Cancer | 11 | 69 |
| 3 | Location awareness | 11 | 71 |
| 3 | Contrast-enhanced ultrasound | 10 | 78 |
| 3 | Optical imaging | 10 | 81 |
| 3 | Acoustics | 9 | 84 |
| 3 | Modulation transfer function | 9 | 87 |
| 3 | Transducers | 8 | 99 |
| 4 | Super-resolution microscopy | 89 | 7 |
| 4 | STED | 36 | 14 |
| 4 | Alzheimer's disease | 35 | 16 |
| 4 | Structured illumination microscopy | 30 | 25 |
| 4 | Fluorescence microscopy | 15 | 50 |
| 4 | Mitochondria | 14 | 57 |
| 4 | DSTORM | 13 | 60 |
| 4 | Electron microscopy | 12 | 64 |
| 4 | Synapse | 12 | 68 |
| 4 | Nanoparticles | 11 | 72 |
| 4 | Neurodegeneration | 11 | 73 |
| 4 | Protein aggregation | 11 | 74 |
| 4 | Alpha-synuclein | 10 | 77 |
| 4 | Parkinson's disease | 10 | 82 |
| 4 | Tau | 9 | 90 |
| 4 | Confocal microscopy | 8 | 93 |
| 4 | Expansion microscopy | 8 | 95 |
| 4 | Super-resolution fluorescence microscopy | 8 | 98 |
| 5 | Diffusion MRI | 34 | 18 |
| 5 | Brain | 23 | 31 |
| 5 | DTI | 15 | 49 |
| 5 | High resolution | 14 | 56 |
| 5 | Tractography | 13 | 62 |
| 5 | Fetal MRI | 12 | 65 |
| 5 | Track density imaging | 11 | 76 |
| 5 | 3D reconstruction | 8 | 92 |
| 5 | Neuroimaging | 8 | 96 |
MRI, magnetic resonance imaging; CT, computed tomography; COVID-19, coronavirus disease 2019; PET, positron emission tomography; STED, stimulated emission depletion microscopy; DSTORM, direct stochastic optical reconstruction microscopy; DTI, diffusion tensor imaging; 3D, three-dimensional.