The assignee ranking (built from all 1,164 families in this corpus) is dominated by a small group of MRI hardware vendors and academic medical centres, with the largest portfolios belonging to established device makers rather than newer AI-only entrants. Several of these leading assignees show sharply reduced filing in the most recent year, including multiple filers at -100% year-over-year, which suggests the current leaders built their position earlier in the 2017-2022 filing run rather than in the last cycle. Checking the full ranking table alongside the momentum data gives a clearer picture than the raw count alone, since a large historical portfolio and current filing activity are not the same signal.

Filing volume grew from 26 families in 2017 to a peak of 162 in 2022, but subsequent years sit at or below that midpoint, which points to flat-to-declining growth for this specific technique rather than continued acceleration. Because publication typically lags filing by around 18 months, the 2025 and 2026 counts in the dataset are understated and should not be read as a real drop-off yet. The more reliable read is that the core reconstruction technique — deep learning applied directly to MRI signal or k-space data — has moved past its most active filing period.

The IPC composition shows heavy concentration in G01R (measurement, 880 records) and A61B (diagnosis, 554), with comparatively thinner activity in G06V (150), G06K (127) and G06F (51). That gap suggests recognition-layer and general data-processing claims tied to MRI reconstruction are less contested than core acquisition and signal-processing claims. Specific combinations worth checking include k-space undersampling with attention models, multi-coil calibration methods, and cross-vendor image standardization approaches, since these sit adjacent to dense claim areas without being fully occupied themselves.

US20230097417A1, filed by Shenzhen Technology University and published in March 2023, covers a multi-slice MRI reconstruction method that uses a long-distance attention model: it takes simultaneously acquired multi-slice MRI images or k-space data, adds learnable positional embedding and imaging parameter embedding, and feeds all of that into a deep learning reconstruction model to produce the final image. The claim value sits specifically in the combination of multi-slice simultaneous acquisition with positional and parameter embedding fed into an attention-based model, not in deep learning MRI reconstruction generally. Anyone building a multi-slice reconstruction pipeline with a similar embedding scheme should review this filing's claim scope directly rather than assuming it only covers generic denoising.

The United States leads by a wide margin with 593 records in this dataset, followed by the European Patent Office at 181 and India at 169, with WIPO PCT filings at 113 and China at 52. This distribution means US-issued claims carry the broadest scope in this corpus and are the first place to check for freedom-to-operate risk. The relatively high India count alongside lower China volume is worth noting for teams assuming China is automatically the largest filing jurisdiction in medical AI — in this specific corpus it is not.