ARXIV:2603.09945 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

arXiv

k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency. This creates a fundamental mathematical paradox: it attempts to recover high-dimensional pixel arrays (i.e., images) from undersampled k-space,…

METHOD

Full abstract

Conventional clinical CMR pipelines rely on a sequential "reconstruct-then-analyze" paradigm, forcing an ill-posed intermediate step that introduces avoidable artifacts and information bottlenecks. This creates a fundamental mathematical paradox: it attempts to recover high-dimensional pixel arrays (i.e., images) from undersampled k-space, rather than directly extracting the low-dimensional physiological labels actually required for diagnosis. To unlock the direct diagnostic potential of k-space, we propose k-MTR (k-space Multi-Task Representation), a k-space representation learning framework that aligns undersampled k-space data and fully-sampled images into a shared semantic manifold. Leveraging a large-scale controlled simulation of 42,000 subjects, k-MTR forces the k-space encoder to restore anatomical information lost to undersampling directly within the latent space, bypassing the explicit inverse problem for downstream analysis. We demonstrate that this latent alignment enables the dense latent space embedded with high-level physiological semantics directly from undersampled frequencies. Across continuous phenotype regression, disease classification, and anatomical segmentation, k-MTR achieves highly competitive performance against state-of-the-art image-domain baselines. By showcasing that precise spatial geometries and multi-task features can be successfully recovered directly from the k-space representations, k-MTR provides a robust architectural blueprint for task-aware cardiac MRI workflows.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. We demonstrate that this latent alignment enables the dense latent space embedded with high-level physiological semantics directly from undersampled frequencies.

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

Paink-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

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

ARXIV:2603.09945 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

arXiv

k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

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

Paink-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

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

Paper Pack

10.48550/arXiv.2603.09945

No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

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 fallback28 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
k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency. This creates a fundamental mathematical paradox: it attempts to recover high-dimensional pixel arrays (i.e., images) from undersampled k-space, rather than directly extracting the low-dimensional physiological labels actually required for diagnosis.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Conventional clinical CMR pipelines rely on a sequential "reconstruct-then-analyze" paradigm, forcing an ill-posed intermediate step that introduces avoidable artifacts and information bottlenecks. This creates a fundamental mathematical paradox: it attempts to recover high-dimensional pixel arrays (i.e., images) from undersampled k-space, rather than directly extracting the low-dimensional physiological labels actually required for diagnosis.
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. We demonstrate that this latent alignment enables the dense latent space embedded with high-level physiological semantics directly from undersampled frequencies.
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

k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.

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 2603.09945 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(28)

Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond

2025Yundi Zhang, Paul Hager et al.

Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging

2024Wenqi Huang, Veronika Spieker et al.

Classification, Regression and Segmentation directly from k-Space in Cardiac MRI

2024Ruochen Li, Jiazhen Pan et al.

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI

2024Chengyan Wang, Jun Lyu et al.

Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images

2024Yundi Zhang, Chen Chen et al.

Direct Cardiac Segmentation from Undersampled K-Space using Transformers

2024Yundi Zhang, Nil Stolt-Ans'o et al.

Similarity and quality metrics for MR image-to-image translation

2024Melanie Dohmen, Mark Klemens et al.

A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

2024Zelong Liu, Komal Kainth et al.

Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling

2023Jia-Yu Pan, Suprosanna Shit et al.

Self Pre-Training with Masked Autoencoders for Medical Image Classification and Segmentation

2022Lei Zhou, Huidong Liu et al.

Masked Autoencoders Are Scalable Vision Learners

2021Kaiming He, Xinlei Chen et al.

Are We There Yet?: The Road to Routine Rapid CMR Imaging.

2021S. Plein, S. Kozerke

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

2020Fabian Isensee, P. Jaeger et al.

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

2020Alexey Dosovitskiy, Lucas Beyer et al.

OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging

2020C. Chen, Yingmin Liu et al.

Image-Based Cardiac Diagnosis With Machine Learning: A Review

2020Carlos Martín-Isla, Víctor M. Campello et al.

Deep Learning for Cardiac Image Segmentation: A Review

2019Chen Chen, C. Qin et al.

Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning

2018Jo Schlemper, O. Oktay et al.

Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

2018Maximilian Seitzer, Guang Yang et al.

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

2017Wenjia Bai, Matthew Sinclair et al.

Showing 20 of 28 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 WorkNexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI

7.0

Extension

none indexed

Commercially relevant

Higher ViabilityFusionNet: a frame interpolation network for 4D heart models

8.0

Conflicting

Competing ApproachFighting MRI Anisotropy: Learning Multiple Cardiac Shapes From a Single Implicit Neural Representation

6.0

Competing ApproachCineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI

5.0

Competing ApproachAnatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition

7.0

Competing ApproachK-MaT: Knowledge-Anchored Manifold Transport for Cross-Modal Prompt Learning in Medical Imaging

7.0

Competing ApproachCardioDiT: Latent Diffusion Transformers for 4D Cardiac MRI Synthesis

7.0

Competing ApproachInformation-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging

4.0

Competing ApproachSynthetic Cardiac MRI Image Generation using Deep Generative Models

5.0

Competing ApproachPathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction

7.0

Related Resources

What advancements are being made in medical AI?(question)
What advancements are being made in medical AI?(question)
Why is evidence-grounded reasoning important in medical AI?(question)
Medical AI – Use Cases(use_case)

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

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: 2603.09945
Route: /paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space
Active tab: read
Artifact: no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space/paper-pack
REST build passport API/api/v1/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space/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

{
  "contract_version": "paper-r2",
  "paper_id": "9831aff1-bbbd-499c-b0fa-b8258ac57417",
  "arxiv_id": "2603.09945",
  "canonical_route": "/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space",
  "endpoints": {
    "paper_pack": "/api/v1/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space/paper-pack",
    "build_passport": "/api/v1/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space

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

No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

Canonical ID no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space | Route /paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.09945"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space",
  "normalized_query": "2603.09945",
  "route": "/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space",
  "paper_ref": "no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space",
  "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: No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

/buildability/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space

Watchwatch

Subject: No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

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/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space

Paper ref

no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space

arXiv id

2603.09945

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

53b2f7758010236ec76307f02abecb1faacee760b5a9db29b7d32932e1035b17

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.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space#webpage",
      "url": "https://sciencetostartup.com/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space",
      "name": "No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space",
      "description": "k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space#scholarlyArticle",
      "headline": "No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space",
      "description": "k-MTR is a novel framework for direct cardiac analysis from undersampled k-space data, enhancing diagnostic efficiency.",
      "url": "https://sciencetostartup.com/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space",
      "sameAs": "https://arxiv.org/abs/2603.09945",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2603.09945"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-03-10T17:38:38.000Z",
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "4ad2c83de1296b328f8e1305007cab5f6256f393"
          },
          "url": "https://www.semanticscholar.org/paper/4ad2c83de1296b328f8e1305007cab5f6256f393"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "b7eaa3f1ac512cd3339fff1fdda49f932460cdab"
          },
          "url": "https://www.semanticscholar.org/paper/b7eaa3f1ac512cd3339fff1fdda49f932460cdab"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "da10c133085e0e43866531f3d6db1d5fe6b077be"
          },
          "url": "https://www.semanticscholar.org/paper/da10c133085e0e43866531f3d6db1d5fe6b077be"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "be29311e7ac2761b3e83e2d22cd4704a8fe47a26"
          },
          "url": "https://www.semanticscholar.org/paper/be29311e7ac2761b3e83e2d22cd4704a8fe47a26"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1ac9a27296e14bdb05ccbbaa132371ee06aaebda"
          },
          "url": "https://www.semanticscholar.org/paper/1ac9a27296e14bdb05ccbbaa132371ee06aaebda"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "86f559f8a97d71fc801faed663843bb0e18f91c8"
          },
          "url": "https://www.semanticscholar.org/paper/86f559f8a97d71fc801faed663843bb0e18f91c8"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "8f28a724160850bf1edc62d9c3d9df42b297985a"
          },
          "url": "https://www.semanticscholar.org/paper/8f28a724160850bf1edc62d9c3d9df42b297985a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a69c737b3d2a902a9aaa06effe01f991215520b8"
          },
          "url": "https://www.semanticscholar.org/paper/a69c737b3d2a902a9aaa06effe01f991215520b8"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "b301f0dced17a3c8dde6e2d7d4919e0f6192d518"
          },
          "url": "https://www.semanticscholar.org/paper/b301f0dced17a3c8dde6e2d7d4919e0f6192d518"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "0c5ce4edb5656b4ed68c668b9e84f14497f1ef2e"
          },
          "url": "https://www.semanticscholar.org/paper/0c5ce4edb5656b4ed68c668b9e84f14497f1ef2e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "6351ebb4a3287f5f3e1273464b3b91e5df5a16d7"
          },
          "url": "https://www.semanticscholar.org/paper/6351ebb4a3287f5f3e1273464b3b91e5df5a16d7"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a4ee40d8724428dfb8dd7f917124b43c6e2bcc1a"
          },
          "url": "https://www.semanticscholar.org/paper/a4ee40d8724428dfb8dd7f917124b43c6e2bcc1a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f28e387d4229c5f690ce4570a391c0f47e7155c7"
          },
          "url": "https://www.semanticscholar.org/paper/f28e387d4229c5f690ce4570a391c0f47e7155c7"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "268d347e8a55b5eb82fb5e7d2f800e33c75ab18a"
          },
          "url": "https://www.semanticscholar.org/paper/268d347e8a55b5eb82fb5e7d2f800e33c75ab18a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a21260f950b3213b9028b4d42004b7a551c8ea79"
          },
          "url": "https://www.semanticscholar.org/paper/a21260f950b3213b9028b4d42004b7a551c8ea79"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "d8bd3de1772432af978bbefa7bf32fa801afd99e"
          },
          "url": "https://www.semanticscholar.org/paper/d8bd3de1772432af978bbefa7bf32fa801afd99e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "47141ea58f79039c6ab1a51c23dc9c660357ff71"
          },
          "url": "https://www.semanticscholar.org/paper/47141ea58f79039c6ab1a51c23dc9c660357ff71"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "2023355a4592ca98ec9958ea44c30112224563c4"
          },
          "url": "https://www.semanticscholar.org/paper/2023355a4592ca98ec9958ea44c30112224563c4"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "cba6bacdac97b467f149ab71998f6e91c25a0374"
          },
          "url": "https://www.semanticscholar.org/paper/cba6bacdac97b467f149ab71998f6e91c25a0374"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f7385d20ea330e9181c745fefb156aa9e0789f5b"
          },
          "url": "https://www.semanticscholar.org/paper/f7385d20ea330e9181c745fefb156aa9e0789f5b"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 7
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "Medical AI"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Medical AI",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "No Image, No Problem: End-to-End Multi-Task Cardiac Analysis",
          "item": "https://sciencetostartup.com/paper/no-image-no-problem-end-to-end-multi-task-cardiac-analysis-from-undersampled-k-space"
        }
      ]
    }
  ]
}

No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(28)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(28)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline