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Imaging Evidence Path

Literature map

The intellectual / navigation layer over the literature. Zotero remains the canonical PDF/bibliography store; this map lets you jump to any topic and read intelligently now — one click from the source. It imposes no curriculum and no global reading cap.

Roles per item: Core · Worth reading · Go deeper · Watch · Historical. All PMIDs/DOIs verified 2026-08-10 unless marked UNVERIFIED; peer-reviewed vs preprint is marked on each. No scientific claim in the chapters depends on an UNVERIFIED item.

CT & DICOM (Ch. 1–2) — tools/standards (no papers)

Geometry / SEG / resampling (Ch. 2)

  • SimpleITK fundamentals — simpleitk.org (read the “fundamental concepts” page) — Core tool. · dcmqi / highdicom — SEG encode/decode — Go deeper.

3D Slicer (companion + Ch. 2–3)

  • Jan Witowski — 3D Slicer Tutorial (11-video playlist) — YouTube playlist — Core course (see learning/companion-3d-slicer.md).
  • 3D Slicer Training Compendium — training.slicer.org — Core (official tutorials + data).
  • SlicerRadiomics extension — Worth reading (interactive radiomics).

Quantitative imaging & radiomics (Ch. 3)

Statistical learning & validation (Ch. 4)

  • Tran et al. — DL ACL-tear knee MRI, multi-continental external validation, Eur Radiol 2022;32(12):8394-8403 — PMID 35726103 / doi — Core (Ch. 7 worked example). Full-text lesson: the abstract’s headline external AUCs (0.962/0.922) are retrained/fine-tuned; the frozen-travel AUCs are 0.941/0.860, and thresholds were re-estimated per dataset — so “external validation” ≠ “frozen model travels.”
  • PROBAST+AI — Moons et al., BMJ 2025;388:e082505 — doi — Core framework (prediction-model risk of bias).
  • TRIPOD+AI — Collins et al. 2024 — tripod-statement.org — Core framework (prediction-model reporting).
  • ISLP / StanfordOnline Statistical Learning with Python — Core course (Ch. 4 is the imaging companion).

Imaging informatics (Ch. 5)

Medical-imaging AI (Ch. 6)

  • TotalSegmentator CT — Wasserthal et al., Radiol AI 2023;5(5):e230024 — PMID 37795137 / doi — Core (operational segmentation tool; built on nnU-Net).
  • TotalSegmentator MRI — Akinci D’Antonoli et al., Radiology 2025;314(2):e241613 — PMID 39964271 / doi — Worth reading (MRI seg behaviour).
  • MRSegmentator (whole-body MRI/CT) — Häntze et al., Radiol Artif Intell 2025;7(6):e240777 — PMID 40767616 / doi — Worth reading (cross-modality 40-structure seg; external testing on NAKO/AMOS/TotalSeg-MRI).
  • nnU-Net — Isensee et al. (MICCAI 2021) — Go deeper (self-configuring baseline).
  • MONAI — monai.io — Worth reading (tool/framework).
  • CLAIM 2024 — Tejani et al., Radiol AI 2024;6(4):e240300 — doi — Core framework (imaging-AI model studies; not radiomics).
  • Merlin — CT VLM foundation model, Nature 2026 — PMID 41781626 / doi (preprint PMID 38978576) — Worth reading (3D VLM; foundation-model evaluation at scale).
  • a2z-1 — Rajpurkar et al. — arXiv:2412.12629 — Worth reading (preprint; generalist abd/pelvis CT, strong external validation).
  • OmniMRI — He et al. — arXiv:2508.17524 — Watch (preprint; unified MRI VLM: recon/seg/detect/diagnose/report).
  • Foundation-model reviews — Akinci D’Antonoli et al., Diagn Interv Radiol 2026;32(3):259-272 — PMID 40626693 / doi — Worth reading (fundamentals/applications/risks; distinct from Paschali). · Paschali et al., Radiology 2025;314(2):e240597 — PMID 39903075 / doi — Worth reading (“what/how/why/why-not” orientation).
  • GRAI (generalist radiology AI) — Dogra et al., Radiology 2025;316(3) — doi — Watch (review/perspective proposing GRAI; financial/operational/clinical framing).
  • GPT-4 protocol selection — Gertz et al., Radiology 2023;307(5):e230877 — PMID 37310247 / doi — Watch (feasibility of LLM study/protocol determination from request forms; on a protocol-routing task).

Synthetic imaging (Ch. 6 Part E; Ch. 8 Part D) — verified representative set

  • MR→synthetic CT (review) — Spadea et al., “Deep learning based synthetic-CT generation in radiotherapy and PET: A review,” Med Phys 2021;48(11):6537-6566 — PMID 34407209 / doi — Worth reading (problem family B: MR→CT for electron density/dose; clinical context).
  • Reconstruction / denoising — Sim et al. (shoulder MRI DL-recon), Cureus 2025;17(10):e94561 — PMID 41246632 / doi — Worth reading (problem C; shoulder, not elbow). · Park et al. (lumbar CT DL denoising), Eur Radiol 2025;35(12):7867-7876 — PMID 40483292 / doi — Worth reading (problem C). · Faster Elbow MRI DL recon — Herrmann et al., Diagnostics 2023;13(17):2747 — PMID 37685285 / doi — Worth reading (distinct from PMID 41246632).
  • Task-based evaluation (perceptual realism insufficient) — “RaD: A Metric for Medical Image Distribution Comparison,” arXiv:2412.01496 — Worth reading (preprint; problem D — shows upstream perceptual metrics like FID do not track downstream segmentation performance; T1/T2 MRI translation).
  • Lecture example unresolved — the specific webinar T2→T1/STIR sequence-synthesis paper could not be reliably identified; do not conflate it with another paper. The method family (sequence-to-sequence translation) is represented by the MR→CT review and the RaD paper above.

Clinical deployment / lifecycle (Ch. 9)

  • Herpe et al. — AI assistance in radiologist knee-MRI interpretation, Eur Radiol 2026;36(2):1294-1305 — PMID 40745051 / doi — Worth reading (Ch. 9 reader-study example; abstract level). Lesson: 6 readers × 165 cases, AI trained on a separate 23k-study set, consensus reference; AI improved accuracy and inter-reader agreement (κ 0.54→0.78) — a retrospective reader+AI demonstration, not deployment evidence.
  • Yang et al. — AI boost to MRI lumbar spine reporting, Eur J Radiol 2024;179:111636 — PMID 39133990 / doi — Watch (reporting/workflow informatics).
  • ReCo (agentic/MCP framework; Orthanc+Merlin+TotalSegmentator+DosimeTron) — Tzanis & Klontzas — medRxiv 2026.07.14.26358025 / github.com/eltzanis/ReCo — Watch (preprint; compounding-error/provenance risks).

Research methodology (Ch. 7)

  • CLAIM 2024 (above) · TRIPOD+AI (above) · PROBAST+AI (above) · CLEAR (above) · IBSI 1/2 (above) — choose by study type (Ch. 7 table).
  • Tran et al. (above) — worked dissection vehicle.

EuSoMII 2026 MSK / webinar — historical/landscape

  • Ziegelmayer et al. — IMAT/lean muscle MRI in chronic back pain, Lancet Reg Health Eur 2025;54:101323 — PMID 40487774 / doi — Worth reading (segmentation→measurement→phenotype→clinical-question chain).
  • Ruitenbeek et al. — AI in MSK imaging: realistic applications, Skeletal Radiol 2024;53(9):1849-1868 — PMID 38902420 / doi — Worth reading (~1 h landscape map).
  • Guermazi et al. — Radiology 2024;310(1):e230764 — PMID 38165245 (erratum PMID 38289220) — Historical (MSK-AI trajectory; partly superseded).
  • Fritz & Fritz — Skeletal Radiol 2022;51(2):315-329 — PMID 34467424 · Gorelik et al. — Semin Musculoskelet Radiol 2020;24(1):38-49 — PMID 31991451 — Historical (older scoping/narrative reviews).

Unresolved

  • “Segmenting Whole-Body MRI and CT” webinar item — resolved as MRSegmentator (Häntze et al., PMID 40767616, above).

How to use this map

  • Jump anywhere. Interested in Merlin? Open its row, then read a Paschali/Akinci D’Antonoli review alongside. No queue, no gate.
  • “Saved” ≠ “must read.” Zotero preservation is not an obligation.
  • Framework by study type (Ch. 7): prediction → TRIPOD+AI/PROBAST+AI; imaging-AI model → CLAIM 2024; radiomics → CLEAR; feature computation → IBSI 1/2.
  • When a paper moves to active reading, open it with the role/question above, not a blank read.