ruvector

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rUv 7a79b74d13 feat(sonic_ct): acoustic digital human workbench — Rust/WASM USCT + R3F UI (#595 ) * feat(sonic_ct): acoustic digital human workbench — Rust/WASM USCT + R3F UI Add `sonic_ct`, a research-grade Ultrasound Computed Tomography (USCT) simulator and reconstruction workbench. Core (crates/sonic-ct, pure Rust, zero deps, 17 tests): - procedural z-varying torso phantom (fat/muscle/organ shells, spine, ribs, pelvis, liver/spleen/kidneys/aorta, heart+lungs in thorax) - circular ring acquisition with straight-ray travel-time + attenuation - SART time-of-flight reconstruction (1 sweep == delay backprojection) - transparent speed-band segmentation with per-cell uncertainty - coordinate-ascent threshold training (mean Dice ~0.30 -> ~0.63) - RuVector-style acoustic memory: NSW vector index, longitudinal drift, warm-start, anatomical graph-coherence checks, .rvf-style serialization - 3-D volume sweep (truth / recon / error / confidence channels) - mock Butterfly Embedded acquisition boundary (trait, no hardware SDK) WASM (crates/sonic-ct-wasm): raw C-ABI cdylib (no wasm-bindgen, ~39 KB) exposing the single-slice + progressive volume pipeline. UI (examples/sonic-ct): React Three Fiber "Sonic Chamber" — water chamber, transducer ring(s), holographic torso with internal organ glows and class-tinted contour slices, live HUD (acoustic paths, phantom fidelity, path confidence, body composition), cranio-caudal scrubber. Driven entirely by real reconstruction data. Docs (docs/sonic-ct): 8 ADRs, SOTA research map, market brief, SPARC. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(sonic_ct ui): welcome modal + GLB body-model loader with procedural fallback - WelcomeModal: Simulate/Reconstruct/Analyze/Validate intro, Get Started cards, "show on startup" preference, research-only disclaimer. - BodyModel: loads a supplied GLB anatomy model (GLB_URL) and applies a ghost material override + per-organ tinting from organ_manifest.json; cleanly falls back to the procedural violet ghost (torso + internal organ glows) when no asset is supplied or it fails to load. GLB is a visual prior only — the Rust phantom stays the physics ground truth. - Refined holographic ghost: violet volumetric glow, class-tinted contour slices, twin transducer rings, glowing base, internal organ volumes. - docs/sonic-ct/BODY-MODELS.md: researched model sources (Zygote, BioDigital, SMPL/Meshcapade, Z-Anatomy, BodyParts3D) + GLB integration pipeline. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(sonic_ct ui): load open-source CesiumMan GLB as the ghost body shell - Ship CesiumMan (Khronos glTF Sample Assets, CC-BY 4.0) as public/models/human.glb, loaded via useGLTF, auto-fit to the chamber, and styled with the ghost-material override; procedural internal organ glows render inside it. - GLB_URL now points at the bundled model; missing/broken asset still falls back to the procedural torso shell via the error boundary. - Attribution recorded in organ_manifest.json and docs/sonic-ct/BODY-MODELS.md. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): organ-hypothesis detector, Darwin optimizer, rebrand Rename the app to MetaBioHacker (Acoustic Digital Human Workbench · Sonic Chamber) across HUD, welcome modal, and metadata. Organ inference (ADR-0009/0010): new `crates/sonic-ct/src/organ.rs` detects liver, spleen, kidneys, aorta, heart, and lungs from the reconstructed volume using anatomical priors (zone, side, size, posterior adjacency, slice-consistency) — never from speed alone. Each hypothesis carries a confidence and an evidence bitmask. Exposed via WASM (sct_organ_, sct_quality_flag) and surfaced in a new HUD panel with per-organ confidence bars + quality flags (bone shadowing / sparse coverage / boundary uncertainty / gas). 18 Rust tests pass; clippy clean. Harness optimization (examples/sonic-ct/optimize.mjs): uses @metaharness/darwin ("freeze the model, evolve the harness") with cheap->frontier tiering and Pareto selection over the frozen WASM engine to evolve {elements, fan, iters}; lifts phantom fidelity ~0.53 -> ~0.59. Documented in docs/sonic-ct/OPTIMIZATION.md. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 feat(metabiohacker): faithful Darwin harness evolution + OpenRouter write layer - crates/sonic-ct/src/bin/serve.rs: the frozen acoustic engine as a JSON-over- stdio process (sonic_ct_serve) — the physics truth layer for the evolver. - examples/sonic-ct/src/optimizer/reconstructionEvolution.ts: typed genome (reconstruction/routing/scoring/safety), runFrozenRustEngine (spawns the real binary), cheap->frontier routeReconstruction (augments engine output, never rewrites anatomy), multi-objective scoreCandidate, mutateGenome, and evolveMetaBioHarness using Darwin mapLimit + paretoFront + an archive. - optimize.mjs: OpenRouter LLM "write layer" proposes harness mutations (cheap gpt-4o-mini / frontier gpt-4o), gated by routing policy, bounded budget, key read from env only; archive-based acceptance gate now PASSES (latency -92.8%, no regression). probeDarwin.mjs verifies the export surface. - Tests (npm test, Node type-stripping): mapLimit bounds concurrency; paretoFront keeps accurate+cheap trade-offs and drops dominated; frontier never bypasses the frozen engine. docs/sonic-ct/OPTIMIZATION.md updated. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * docs(metabiohacker): ADRs 0009-0019 — organ inference, harness evolution, multimodal data + governance Add 11 ADRs and an index covering the layers built and the medical-data architecture roadmap: Organ/inference layer (grounded in organ.rs / segmentation.rs / Hud.jsx): - 0009 five acoustic classes canonical (no organ identity from speed alone) - 0010 organ identity inferred from anatomical priors (evidence + confidence) - 0011 organ function requires dynamic/multiparametric channels ("not measured") - 0012 explainability mandatory (evidence bitmask surfaced in the UI) - 0013 no disease labels — research mode only Harness + data architecture: - 0014 freeze the physics engine, evolve the reconstruction harness (Darwin) - 0015 patient data as a graph of typed observations (MedicalObservation, provenance + uncertainty + consent scope) - 0016 adopt DICOM / FHIR / LOINC / SNOMED CT / OMOP + RuVector similarity index - 0017 typed multimodal fusion patterns (monitoring/research, not diagnosis) - 0018 governance & SaMD boundary (FDA GMLP/PCCP, Health Canada, Ontario PHIPA) - 0019 a medical signal operating system, not an AI doctor Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): benchmark harness on real CT data + synthetic corpus - Real-data ingestion: Grid::from_pgm (P5 parser), Phantom::from_intensity_grid (band a grayscale CT slice into the five acoustic classes), and pipeline::run_with_phantom (reconstruct a supplied phantom — engine unchanged). - sonic_ct_serve gains a phantomPgm path: reconstruct a real anatomical slice instead of a procedural one and emit the same score schema. - tools/fetchRealSlice.mjs: fetch a public-domain abdominal CT slice (Wikimedia Commons) and convert to a grayscale PGM (image not committed; fetched on demand, derived PGM gitignored). - benchmark.mjs (npm run benchmark): baseline vs Darwin-evolved harness over 12 reproducible synthetic phantoms + 1 real CT slice; writes docs/sonic-ct/ BENCHMARK.md + benchmark.report.json. Representative: evolved harness ~157% faster at equal Dice; real CT honestly harder (Dice ~0.27). - New integration test exercises the PGM/real-phantom reconstruction path (19 Rust tests pass). Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): scale benchmark — 40 synthetic seeds + multiple real CT slices, 95% CI - fetchRealSlice.mjs fetches several public-domain CT slices (abdomen, thorax, pelvis) resiliently, skipping unavailable ones. - benchmark.mjs now runs N synthetic seeds (default 40) + every fetched real slice, reports mean ± 95% CI, and writes docs/sonic-ct/BENCHMARK.md. Representative: 42 samples, evolved harness ~149% faster at equal Dice (±0.002 CI); real CT slices honestly harder (Dice ~0.30). Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): Multimodal Ingest V0 — observations, graph, fusion, ledger, ruvn evidence gate New package packages/metabiohacker (@metabiohacker/core, TS, 14 tests pass): - ingest/: canonical MedicalObservation + lab (CSV→LOINC), imaging (DICOM sidecar), and pathology adapters with provenance/uncertainty/consent. - graph/: auditable patient state graph + rule-based contradiction detection (low-quality, ≥2x same-test disagreement, unflagged review modalities). - fusion/: prior builder (data shapes priors, never forces conclusions), multimodal scoring (acoustic residual passed through unchanged), contradiction penalty, and a Darwin harness (mapLimit + paretoFront) selecting fusion policy. - evidence/: ruvn as the evidence-intelligence layer (off the hot path) — provider interface, A/B-or-blocked claim gate, deterministic cached provider + optional @ruvnet/ruvn CLI adapter (never a hard dep). Claims ship only on grade A/B with citations; pathology/biopsy/Pap/HPV/cytology force human review. - ledger/ + output/: stable-hash reconstruction run ledger (tamper-evident, verifiable) and the safe UI packet (uncertainty overlay, diagnosis blocked). Benchmark: +10% stability, ~37% uncertainty drop, residual unchanged, ledger verified, clinical-review mode forced by pathology. Docs: ADR-0020 (canonical observation), 0021 (graph+contradictions), 0022 (run ledger), 0023 (ruvn evidence layer); ADR index updated. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): real-slice calibration, domain-gap honesty gate, evidence refresh, CI gates Attacks the synthetic→real Dice gap honestly rather than hiding it. - Engine: sonic_ct_serve emits per-class (region) Dice on real slices. - calibration/: region-level Dice (diceByRegion), domain-gap scoring + honesty gate (classifyRealSliceResult: headline/researchOnly/exclude), centroid registration-error + boundary-complexity proxies. Real CT slices are calibration targets, not USCT. - benchmark.mjs: 3-section report (synthetic / real region-level / governance); headline separates speed from real fidelity. Real slices now classify as exclude/researchOnly and stay out of headline metrics (abdomen~0.30). - evidence:refresh (OpenRouter): grades modality evidence into docs/evidence/.md + a candidate cache; promotion to the curated cache stays a reviewed step. Live run graded acoustic USCT = C (research-only), MRI = B. - CI gates (ciGates.test.ts + .github/workflows/metabiohacker-ci.yml): residual invariant, pathology review forced, A/B-only claims, real-slice honesty gate. 23 metabiohacker tests + 12 Rust integration tests pass. ADR-0024 added. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 feat(sonic_ct): method comparison vs BP/SART/Landweber on Shepp-Logan with RMSE/PSNR/SSIM Bench reconstruction against recognised algorithms on a recognised target: - shepp_logan.rs: standard 10-ellipse Shepp-Logan phantom -> speed map. - reconstruction.rs: Method enum + reconstruct_speed_with; Landweber solver (gradient descent on ‖As−t‖²) alongside backprojection (1 sweep) and SART. - metrics.rs: standard image-quality metrics RMSE, PSNR (dB), SSIM. - sonic_ct_methods bin -> docs/sonic-ct/METHOD-BENCHMARK.md (deterministic). Measured: backprojection < SART < Landweber on every metric for both Shepp-Logan and abdomen (abdomen RMSE 130→99→51 m/s, SSIM 0.22→0.60→0.92) at ~4/28/100 ms. SART stays production default; Landweber is the higher-fidelity option. 2 new tests; 14 integration tests pass; clippy clean. ADR-0025 added. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(metabiohacker): rigid translation registration for real-slice calibration Replace the centroid-only proxy with registerByTranslation — finds the integer offset that maximises predicted/target body-mask overlap Dice, returning the offset, residual misalignment (errorPx), and aligned overlap. Gives the domain-gap honesty gate a real registration estimate (landmark refinement is the next step). +1 test (recovers a known offset; maximises overlap). 24 tests pass. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(sonic_ct): full-waveform inversion (FWI) — forward + adjoint-state gradient The SOTA step beyond straight-ray TOF (ADR-0004 roadmap), as a dependency-free 2-D reference: - fwi.rs: FDTD scalar-wave forward model (∂ₜ²p = κ∇²p + f), CFL-stable, damping sponge; adjoint-state gradient ∂χ/∂κ = Σ_t λ ∇²p; gradient descent with source/receiver-footprint muting, smoothing, and backtracking line search. - Proven by the gold-standard adjoint-vs-finite-difference gradient check (cosine > 0.85) + an inversion that cuts data misfit ≥15% and recovers a centrally-concentrated velocity anomaly. 2 new tests; 23 Rust tests pass; clippy clean. - Honest scope: single-frequency, unregularised — frequency continuation, regularisation, source encoding, and 3-D are the documented next steps; no quantitative clinical recovery claimed. ADR-0026 added. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 * feat(sonic-ct): add FWI frequency continuation (multiscale inversion) Add invert_multiscale + Stage to fwi.rs: chains low->high frequency FWI stages with between-stage model smoothing to avoid cycle-skipping. Low frequencies recover the smooth background first, keeping high-frequency stages out of local minima. Proven by a third FWI test: frequency continuation lowers the inclusion-region error below single-scale FWI at matched iteration count (deterministic). Adjoint-vs-FD gradient check and misfit-reduction tests still pass. Updates ADR-0026. Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7 --------- Co-authored-by: Claude <noreply@anthropic.com>	2026-06-22 09:54:22 -04:00
..
metabiohacker	feat(sonic_ct): acoustic digital human workbench — Rust/WASM USCT + R3F UI (#595 )	2026-06-22 09:54:22 -04:00

feat(sonic_ct): acoustic digital human workbench — Rust/WASM USCT + R3F UI (#595 )

* feat(sonic_ct): acoustic digital human workbench — Rust/WASM USCT + R3F UI

Add `sonic_ct`, a research-grade Ultrasound Computed Tomography (USCT)
simulator and reconstruction workbench.

Core (crates/sonic-ct, pure Rust, zero deps, 17 tests):
- procedural z-varying torso phantom (fat/muscle/organ shells, spine, ribs,
  pelvis, liver/spleen/kidneys/aorta, heart+lungs in thorax)
- circular ring acquisition with straight-ray travel-time + attenuation
- SART time-of-flight reconstruction (1 sweep == delay backprojection)
- transparent speed-band segmentation with per-cell uncertainty
- coordinate-ascent threshold training (mean Dice ~0.30 -> ~0.63)
- RuVector-style acoustic memory: NSW vector index, longitudinal drift,
  warm-start, anatomical graph-coherence checks, .rvf-style serialization
- 3-D volume sweep (truth / recon / error / confidence channels)
- mock Butterfly Embedded acquisition boundary (trait, no hardware SDK)

WASM (crates/sonic-ct-wasm): raw C-ABI cdylib (no wasm-bindgen, ~39 KB)
exposing the single-slice + progressive volume pipeline.

UI (examples/sonic-ct): React Three Fiber "Sonic Chamber" — water chamber,
transducer ring(s), holographic torso with internal organ glows and
class-tinted contour slices, live HUD (acoustic paths, phantom fidelity,
path confidence, body composition), cranio-caudal scrubber. Driven entirely
by real reconstruction data.

Docs (docs/sonic-ct): 8 ADRs, SOTA research map, market brief, SPARC.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(sonic_ct ui): welcome modal + GLB body-model loader with procedural fallback

- WelcomeModal: Simulate/Reconstruct/Analyze/Validate intro, Get Started cards,
  "show on startup" preference, research-only disclaimer.
- BodyModel: loads a supplied GLB anatomy model (GLB_URL) and applies a ghost
  material override + per-organ tinting from organ_manifest.json; cleanly falls
  back to the procedural violet ghost (torso + internal organ glows) when no
  asset is supplied or it fails to load. GLB is a visual prior only — the Rust
  phantom stays the physics ground truth.
- Refined holographic ghost: violet volumetric glow, class-tinted contour
  slices, twin transducer rings, glowing base, internal organ volumes.
- docs/sonic-ct/BODY-MODELS.md: researched model sources (Zygote, BioDigital,
  SMPL/Meshcapade, Z-Anatomy, BodyParts3D) + GLB integration pipeline.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(sonic_ct ui): load open-source CesiumMan GLB as the ghost body shell

- Ship CesiumMan (Khronos glTF Sample Assets, CC-BY 4.0) as public/models/human.glb,
  loaded via useGLTF, auto-fit to the chamber, and styled with the ghost-material
  override; procedural internal organ glows render inside it.
- GLB_URL now points at the bundled model; missing/broken asset still falls back
  to the procedural torso shell via the error boundary.
- Attribution recorded in organ_manifest.json and docs/sonic-ct/BODY-MODELS.md.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(metabiohacker): organ-hypothesis detector, Darwin optimizer, rebrand

Rename the app to MetaBioHacker (Acoustic Digital Human Workbench · Sonic
Chamber) across HUD, welcome modal, and metadata.

Organ inference (ADR-0009/0010): new `crates/sonic-ct/src/organ.rs` detects
liver, spleen, kidneys, aorta, heart, and lungs from the reconstructed
volume using anatomical priors (zone, side, size, posterior adjacency,
slice-consistency) — never from speed alone. Each hypothesis carries a
confidence and an evidence bitmask. Exposed via WASM (sct_organ_*,
sct_quality_flag) and surfaced in a new HUD panel with per-organ confidence
bars + quality flags (bone shadowing / sparse coverage / boundary
uncertainty / gas). 18 Rust tests pass; clippy clean.

Harness optimization (examples/sonic-ct/optimize.mjs): uses
@metaharness/darwin ("freeze the model, evolve the harness") with
cheap->frontier tiering and Pareto selection over the frozen WASM engine to
evolve {elements, fan, iters}; lifts phantom fidelity ~0.53 -> ~0.59.
Documented in docs/sonic-ct/OPTIMIZATION.md.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(metabiohacker): faithful Darwin harness evolution + OpenRouter write layer

- crates/sonic-ct/src/bin/serve.rs: the frozen acoustic engine as a JSON-over-
  stdio process (sonic_ct_serve) — the physics truth layer for the evolver.
- examples/sonic-ct/src/optimizer/reconstructionEvolution.ts: typed genome
  (reconstruction/routing/scoring/safety), runFrozenRustEngine (spawns the real
  binary), cheap->frontier routeReconstruction (augments engine output, never
  rewrites anatomy), multi-objective scoreCandidate, mutateGenome, and
  evolveMetaBioHarness using Darwin mapLimit + paretoFront + an archive.
- optimize.mjs: OpenRouter LLM "write layer" proposes harness mutations (cheap
  gpt-4o-mini / frontier gpt-4o), gated by routing policy, bounded budget, key
  read from env only; archive-based acceptance gate now PASSES (latency -92.8%,
  no regression). probeDarwin.mjs verifies the export surface.
- Tests (npm test, Node type-stripping): mapLimit bounds concurrency; paretoFront
  keeps accurate+cheap trade-offs and drops dominated; frontier never bypasses
  the frozen engine. docs/sonic-ct/OPTIMIZATION.md updated.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* docs(metabiohacker): ADRs 0009-0019 — organ inference, harness evolution, multimodal data + governance

Add 11 ADRs and an index covering the layers built and the medical-data
architecture roadmap:

Organ/inference layer (grounded in organ.rs / segmentation.rs / Hud.jsx):
- 0009 five acoustic classes canonical (no organ identity from speed alone)
- 0010 organ identity inferred from anatomical priors (evidence + confidence)
- 0011 organ function requires dynamic/multiparametric channels ("not measured")
- 0012 explainability mandatory (evidence bitmask surfaced in the UI)
- 0013 no disease labels — research mode only

Harness + data architecture:
- 0014 freeze the physics engine, evolve the reconstruction harness (Darwin)
- 0015 patient data as a graph of typed observations (MedicalObservation,
  provenance + uncertainty + consent scope)
- 0016 adopt DICOM / FHIR / LOINC / SNOMED CT / OMOP + RuVector similarity index
- 0017 typed multimodal fusion patterns (monitoring/research, not diagnosis)
- 0018 governance & SaMD boundary (FDA GMLP/PCCP, Health Canada, Ontario PHIPA)
- 0019 a medical signal operating system, not an AI doctor

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(metabiohacker): benchmark harness on real CT data + synthetic corpus

- Real-data ingestion: Grid::from_pgm (P5 parser), Phantom::from_intensity_grid
  (band a grayscale CT slice into the five acoustic classes), and
  pipeline::run_with_phantom (reconstruct a supplied phantom — engine unchanged).
- sonic_ct_serve gains a phantomPgm path: reconstruct a real anatomical slice
  instead of a procedural one and emit the same score schema.
- tools/fetchRealSlice.mjs: fetch a public-domain abdominal CT slice (Wikimedia
  Commons) and convert to a grayscale PGM (image not committed; fetched on
  demand, derived PGM gitignored).
- benchmark.mjs (npm run benchmark): baseline vs Darwin-evolved harness over 12
  reproducible synthetic phantoms + 1 real CT slice; writes docs/sonic-ct/
  BENCHMARK.md + benchmark.report.json. Representative: evolved harness ~157%
  faster at equal Dice; real CT honestly harder (Dice ~0.27).
- New integration test exercises the PGM/real-phantom reconstruction path
  (19 Rust tests pass).

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(metabiohacker): scale benchmark — 40 synthetic seeds + multiple real CT slices, 95% CI

- fetchRealSlice.mjs fetches several public-domain CT slices (abdomen, thorax,
  pelvis) resiliently, skipping unavailable ones.
- benchmark.mjs now runs N synthetic seeds (default 40) + every fetched real
  slice, reports mean ± 95% CI, and writes docs/sonic-ct/BENCHMARK.md.
  Representative: 42 samples, evolved harness ~149% faster at equal Dice
  (±0.002 CI); real CT slices honestly harder (Dice ~0.30).

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(metabiohacker): Multimodal Ingest V0 — observations, graph, fusion, ledger, ruvn evidence gate

New package packages/metabiohacker (@metabiohacker/core, TS, 14 tests pass):

- ingest/: canonical MedicalObservation + lab (CSV→LOINC), imaging (DICOM
  sidecar), and pathology adapters with provenance/uncertainty/consent.
- graph/: auditable patient state graph + rule-based contradiction detection
  (low-quality, ≥2x same-test disagreement, unflagged review modalities).
- fusion/: prior builder (data shapes priors, never forces conclusions),
  multimodal scoring (acoustic residual passed through unchanged), contradiction
  penalty, and a Darwin harness (mapLimit + paretoFront) selecting fusion policy.
- evidence/: ruvn as the evidence-intelligence layer (off the hot path) — provider
  interface, A/B-or-blocked claim gate, deterministic cached provider + optional
  @ruvnet/ruvn CLI adapter (never a hard dep). Claims ship only on grade A/B with
  citations; pathology/biopsy/Pap/HPV/cytology force human review.
- ledger/ + output/: stable-hash reconstruction run ledger (tamper-evident,
  verifiable) and the safe UI packet (uncertainty overlay, diagnosis blocked).

Benchmark: +10% stability, ~37% uncertainty drop, residual unchanged, ledger
verified, clinical-review mode forced by pathology.

Docs: ADR-0020 (canonical observation), 0021 (graph+contradictions),
0022 (run ledger), 0023 (ruvn evidence layer); ADR index updated.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(metabiohacker): real-slice calibration, domain-gap honesty gate, evidence refresh, CI gates

Attacks the synthetic→real Dice gap honestly rather than hiding it.

- Engine: sonic_ct_serve emits per-class (region) Dice on real slices.
- calibration/: region-level Dice (diceByRegion), domain-gap scoring +
  honesty gate (classifyRealSliceResult: headline/researchOnly/exclude),
  centroid registration-error + boundary-complexity proxies. Real CT slices are
  calibration targets, not USCT.
- benchmark.mjs: 3-section report (synthetic / real region-level / governance);
  headline separates speed from real fidelity. Real slices now classify as
  exclude/researchOnly and stay out of headline metrics (abdomen~0.30).
- evidence:refresh (OpenRouter): grades modality evidence into docs/evidence/*.md
  + a candidate cache; promotion to the curated cache stays a reviewed step.
  Live run graded acoustic USCT = C (research-only), MRI = B.
- CI gates (ciGates.test.ts + .github/workflows/metabiohacker-ci.yml): residual
  invariant, pathology review forced, A/B-only claims, real-slice honesty gate.

23 metabiohacker tests + 12 Rust integration tests pass. ADR-0024 added.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(sonic_ct): method comparison vs BP/SART/Landweber on Shepp-Logan with RMSE/PSNR/SSIM

Bench reconstruction against recognised algorithms on a recognised target:
- shepp_logan.rs: standard 10-ellipse Shepp-Logan phantom -> speed map.
- reconstruction.rs: Method enum + reconstruct_speed_with; Landweber solver
  (gradient descent on ‖As−t‖²) alongside backprojection (1 sweep) and SART.
- metrics.rs: standard image-quality metrics RMSE, PSNR (dB), SSIM.
- sonic_ct_methods bin -> docs/sonic-ct/METHOD-BENCHMARK.md (deterministic).

Measured: backprojection < SART < Landweber on every metric for both Shepp-Logan
and abdomen (abdomen RMSE 130→99→51 m/s, SSIM 0.22→0.60→0.92) at ~4/28/100 ms.
SART stays production default; Landweber is the higher-fidelity option. 2 new
tests; 14 integration tests pass; clippy clean. ADR-0025 added.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(metabiohacker): rigid translation registration for real-slice calibration

Replace the centroid-only proxy with registerByTranslation — finds the integer
offset that maximises predicted/target body-mask overlap Dice, returning the
offset, residual misalignment (errorPx), and aligned overlap. Gives the
domain-gap honesty gate a real registration estimate (landmark refinement is the
next step). +1 test (recovers a known offset; maximises overlap). 24 tests pass.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(sonic_ct): full-waveform inversion (FWI) — forward + adjoint-state gradient

The SOTA step beyond straight-ray TOF (ADR-0004 roadmap), as a dependency-free
2-D reference:
- fwi.rs: FDTD scalar-wave forward model (∂ₜ²p = κ∇²p + f), CFL-stable, damping
  sponge; adjoint-state gradient ∂χ/∂κ = Σ_t λ ∇²p; gradient descent with
  source/receiver-footprint muting, smoothing, and backtracking line search.
- Proven by the gold-standard adjoint-vs-finite-difference gradient check
  (cosine > 0.85) + an inversion that cuts data misfit ≥15% and recovers a
  centrally-concentrated velocity anomaly. 2 new tests; 23 Rust tests pass;
  clippy clean.
- Honest scope: single-frequency, unregularised — frequency continuation,
  regularisation, source encoding, and 3-D are the documented next steps; no
  quantitative clinical recovery claimed. ADR-0026 added.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

* feat(sonic-ct): add FWI frequency continuation (multiscale inversion)

Add invert_multiscale + Stage to fwi.rs: chains low->high frequency FWI
stages with between-stage model smoothing to avoid cycle-skipping. Low
frequencies recover the smooth background first, keeping high-frequency
stages out of local minima.

Proven by a third FWI test: frequency continuation lowers the
inclusion-region error below single-scale FWI at matched iteration count
(deterministic). Adjoint-vs-FD gradient check and misfit-reduction tests
still pass. Updates ADR-0026.

Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01Mx4vKMfvsq5KBQgPRSoxM7

---------

Co-authored-by: Claude <noreply@anthropic.com>

2026-06-22 09:54:22 -04:00

metabiohacker

feat(sonic_ct): acoustic digital human workbench — Rust/WASM USCT + R3F UI (#595 )

2026-06-22 09:54:22 -04:00