Read packages/nanovirdx/src/nanovirdx/main.py as a conceptual schema spanning histology embedding, MRI simulation, and inversion.
Identified the central path WSI -> DomainRepresentation -> synthetic MRI -> recovered tissue/clinical state, with reconstruction or task loss closing the cycle.
Distinguished the four parameter families and their local optimization scopes: embedding, tissue calibration, acquisition sequence fitting, and inverse training.
Added an editable SVG at packages/nanovirdx/docs/research-loop.svg and interpretation notes at packages/nanovirdx/docs/research-loop.md in the mono workspace.
Validated the SVG as well-formed XML.
Lessons Learned: Pitfalls
The source calls the sequence loop “optimization,” but its sketched loss only matches a real MRI acquired with fixed settings. This is acquisition fitting, not yet task- or information-optimal sequence design.
The comments state an optimization order (EmbeddingParams -> TissueParams -> SequenceParams -> InverseParams), but this should not be presented as a causal dependency graph.
The pseudo-code contains unresolved names (_params_tissue, _mri_acq_params) and an unimported Any; these were treated as sketch-level inconsistencies rather than encoded into the figure.
Lessons Learned: Improvements
A durable NanoVirDx design note should define the boundary between DomainRepresentation and TissueParams, the actual loss/registration spaces, and whether inversion targets WSI reconstruction or clinical endpoints.
Sequence design needs an explicit downstream utility or information objective plus physical acquisition constraints before it can be shown as true sequence optimization.
Paired WSI/MRI evidence, WSI-only synthetic training, and real-MRI supervised training should stay visually separate because they support different loops and carry different assumptions.
