ARXIV:2603.08305 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation

arXiv

Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency. In contrast, structure-driven methods ensure strong anatomical consistency but typically assume access to…

METHOD

Full abstract

Text-conditioned generative models for volumetric medical imaging provide semantic control but lack explicit anatomical guidance, often resulting in outputs that are spatially ambiguous or anatomically inconsistent. In contrast, structure-driven methods ensure strong anatomical consistency but typically assume access to ground-truth annotations, which are unavailable when the target image is to be synthesized. We propose a retrieval-augmented approach for Text-to-CT generation that integrates semantic and anatomical information under a realistic inference setting. Given a radiology report, our method retrieves a semantically related clinical case using a 3D vision-language encoder and leverages its associated anatomical annotation as a structural proxy. This proxy is injected into a text-conditioned latent diffusion model via a ControlNet branch, providing coarse anatomical guidance while maintaining semantic flexibility. Experiments on the CT-RATE dataset show that retrieval-augmented generation improves image fidelity and clinical consistency compared to text-only baselines, while additionally enabling explicit spatial controllability, a capability inherently absent in such approaches. Further analysis highlights the importance of retrieval quality, with semantically aligned proxies yielding consistent gains across all evaluation axes. This work introduces a principled and scalable mechanism to bridge semantic conditioning and anatomical plausibility in volumetric medical image synthesis. Code will be released.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the CT-RATE dataset show that retrieval-augmented generation improves image fidelity and clinical consistency compared to text-only baselines, while additionally enabling explicit spatial…

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainGenerate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.08305 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation

arXiv

Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainGenerate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.08305

Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation

Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback29 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency. In contrast, structure-driven methods ensure strong anatomical consistency but typically assume access to ground-truth annotations, which are unavailable when the target image is to be synthesized.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Text-conditioned generative models for volumetric medical imaging provide semantic control but lack explicit anatomical guidance, often resulting in outputs that are spatially ambiguous or anatomically inconsistent. In contrast, structure-driven methods ensure strong anatomical consistency but typically assume access to ground-truth annotations, which are unavailable when the target image is to be synthesized.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the CT-RATE dataset show that retrieval-augmented generation improves image fidelity and clinical consistency compared to text-only baselines, while additionally enabling explicit spatial controllability, a capability inherently absent in such approaches.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Medical AI

Competitors

not indexed

Competitive landscape

Generate anatomically consistent CT scans from radiology reports by retrieving and incorporating relevant anatomical annotations, improving image fidelity and clinical consistency.

Segment

Medical AI

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.08305 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(29)

Radiology Report Conditional 3D CT Generation with Multi Encoder Latent diffusion Model

2025S. Amirrajab, Zohaib Salahuddin et al.

Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining

2025Daniele Molino, C. M. Caruso et al.

Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation

2025Daniele Molino, Francesco Di Feola et al.

XGeM: A multi-prompt foundation model for multimodal medical data generation.

2025Daniele Molino, Francesco Di Feola et al.

MAISI: Medical AI for Synthetic Imaging

2024Pengfei Guo, Can Zhao et al.

Generalist foundation models from a multimodal dataset for 3D computed tomography.

2024I. Hamamci, Sezgin Er et al.

MedSyn: Text-Guided Anatomy-Aware Synthesis of High-Fidelity 3-D CT Images

2023Yanwu Xu, Li Sun et al.

Benchmarking Large Language Models in Retrieval-Augmented Generation

2023Jiawei Chen, Hongyu Lin et al.

GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes

2023I. Hamamci, Sezgin Er et al.

Adding Conditional Control to Text-to-Image Diffusion Models

2023Lvmin Zhang, Anyi Rao et al.

Diffusion models in medical imaging: A comprehensive survey

2022A. Kazerouni, Ehsan Khodapanah Aghdam et al.

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

2022Xingchao Liu, Chengyue Gong et al.

TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images.

2022J. Wasserthal, H. Breit et al.

A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and Applications

2022Ms. Aayushi Bansal, Dr. R. P. Sharma et al.

High-Resolution Image Synthesis with Latent Diffusion Models

2021Robin Rombach, A. Blattmann et al.

Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging

2021Nima Tajbakhsh, H. Roth et al.

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

2021Z. Alyasseri, M. Al-Betar et al.

A review of medical image data augmentation techniques for deep learning applications

2021P. Chlap, Hang Min et al.

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

2021Pengfei Guo, Puyang Wang et al.

Learning Transferable Visual Models From Natural Language Supervision

2021Alec Radford, Jong Wook Kim et al.

Showing 20 of 29 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkCross Modality Image Translation In Medical Imaging Using Generative Frameworks

7.0

Extension

Builds On ThisGenerating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation

5.0

Commercially relevant

Higher ViabilityBeyond the Embedding Bottleneck: Adaptive Retrieval-Augmented 3D CT Report Generation

9.0

Higher ViabilityVisually-Guided Controllable Medical Image Generation via Fine-Grained Semantic Disentanglement

8.0

Higher ViabilityMedSteer: Counterfactual Endoscopic Synthesis via Training-Free Activation Steering

8.0

Higher ViabilityHigh-Fidelity Medical Shape Generation via Skeletal Latent Diffusion

8.0

Higher ViabilityConditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays

8.0

Conflicting

Competing ApproachSynthetic Cardiac MRI Image Generation using Deep Generative Models

5.0

Competing ApproachClinically Aware Synthetic Image Generation for Concept Coverage in Chest X-ray Models

7.0

Competing ApproachRepresentation geometry shapes task performance in vision-language modeling for CT enterography

4.0

Related Resources

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Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.08305
Route: /paper/retrieval-augmented-anatomical-guidance-for-text-to-ct-generation
Active tab: read
Artifact: retrieval-augmented-anatomical-guidance-for-text-to-ct-generation

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/retrieval-augmented-anatomical-guidance-for-text-to-ct-generation/paper-pack
REST build passport API/api/v1/paper/retrieval-augmented-anatomical-guidance-for-text-to-ct-generation/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "canonical_route": "/paper/retrieval-augmented-anatomical-guidance-for-text-to-ct-generation",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "retrieval-augmented-anatomical-guidance-for-text-to-ct-generation",
  "endpoints": {
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  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/retrieval-augmented-anatomical-guidance-for-text-to-ct-generation

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation

Canonical ID retrieval-augmented-anatomical-guidance-for-text-to-ct-generation | Route /paper/retrieval-augmented-anatomical-guidance-for-text-to-ct-generation

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/retrieval-augmented-anatomical-guidance-for-text-to-ct-generation

MCP example

{
  "tool": "get_paper",
  "arguments": {
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  }
}

source_context

{
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  "mode": "paper",
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  "paper_ref": "retrieval-augmented-anatomical-guidance-for-text-to-ct-generation",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation

/buildability/retrieval-augmented-anatomical-guidance-for-text-to-ct-generation

Watchwatch

Subject: Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/retrieval-augmented-anatomical-guidance-for-text-to-ct-generation

Paper ref

retrieval-augmented-anatomical-guidance-for-text-to-ct-generation

arXiv id

2603.08305

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

6f151a6d24fcaa5d3bbfac4689c9f042d5ede48e9d3b1117b857bcbfe34a4fc8

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation

Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation

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Brief

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Translational engineer

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GTM lead

Regulatory/clinical advisor

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