Guides, project breakdowns, and lessons learned from building medical imaging platforms — written by the PYCAD team.
A PROPELLER diffusion sequence with self-supervised reconstruction keeps the prostate’s shape on DWI and ADC next to rectal gas and fiducial markers, at 1.25 mm in-plane. Siemens prototype, no public code. Chen et al., arXiv:2610.05426.
Drop in a 3D T1w brain MRI and get back the most anatomically similar real scans from a reference set of more than 26,000, by whole brain or by region. Code, weights, and embeddings public. Nieto-del-Amor et al., arXiv:2610.06502.
Feed it an on-board radiotherapy CBCT slice and get back one with fewer streaks and CT numbers closer to the planning CT. RefineCBCT learns without paired CBCT and CT. Code public. Lai and He, arXiv:2610.06094.
Give it one report sentence and an abdominal CT; get a 3D mask of that finding back. LocusCT learns from 105K routine PACS arrows and measurement lines matched to report text. Church et al., arXiv:2610.04095.
Hang a segmented CT or MR in; get a real-time cinematic 3D proxy out whose tissue colors, opacity, and visibility you can change without refitting. Gao et al., arXiv:2610.02382.
Hang a reconstructed implant CT in; get a streak-reduced derived series out with no raw projections, vendor kernel, or trained weights. Banerjee et al., arXiv:2610.01512.
We build custom medical imaging platforms — advanced DICOM viewers, AI segmentation, and the clinical systems around them.
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