{"id":"66a2fb74a0bf5dcd5fe94c034189dc535f35fe8bc0da7bfb71ca16535f1a4090","title":"Linrui Dai and coauthors propose pose-aware diffusion priors for sparse-view CT","argument":"The authors present a method for extremely sparse-view computed tomography (xSV-CT), where a scan uses only a few projections because scanning time, motion, or radiation tolerance may be limiting. They focus on settings where those same limits also make pre-reconstructed 3D volumes from dense scans unavailable for training. Their approach learns a pose-aware diffusion prior from one 2D projection per training sample, then guides diffusion sampling using measured-view fidelity, shared-volume reprojection, and projection-physics consistency. The aim is to combine sparse measurements with a learned prior without relying on densely scanned training volumes. On two public CT datasets, across 1-, 4-, and 8-view settings, the authors report the best novel-view synthesis scores and best or competitive reconstruction quality against baselines using dense-volume supervision. The result addresses a specific training-data constraint; the abstract does not establish how performance transfers beyond those datasets or to scans constrained by real-world acquisition conditions.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The supplied abstract reports results on two public CT datasets but provides no numerical scores or information about performance in clinical deployment. The abstract does not specify how the datasets or evaluation settings represent scans constrained by scanning time, motion, or radiation tolerance.","speaker":{"name":"Dai, Linrui, Liang, Jinxiu, Zhou, Chu, Yamaguchi, Mae, Takahashi, Ryuya, Sato, Imari","roleAtSource":"Article author"},"source":{"title":"Extremely Sparse-View Computed Tomography from 2D Projections via Pose-Aware Diffusion Priors","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.38319","publishedAt":"2026-10-28","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors describe xSV-CT as relevant when scanning time, motion, or radiation tolerance limits a scan to only a few projections.","excerpt":"critical when scanning time, motion, or radiation tolerance limits each scan to only a few projections","url":"https://arxiv.org/abs/2610.38319","relationship":"Source-stated"},{"text":"The authors say these acquisition limits can also make pre-reconstructed 3D volumes from dense scans unavailable for dataset construction.","excerpt":"the same acquisition limits also constrain dataset construction, making pre-reconstructed 3D volumes from dense scans unavailable","url":"https://arxiv.org/abs/2610.38319","relationship":"Source-stated"},{"text":"The proposed method learns a pose-aware diffusion prior from one 2D projection per training sample and guides diffusion sampling with three stated forms of consistency or fidelity.","excerpt":"learning a pose-aware diffusion prior from one 2D projection per training sample, then guiding diffusion sampling with measured-view fidelity, shared-volume reprojection, and projection-physics consistency","url":"https://arxiv.org/abs/2610.38319","relationship":"Source-stated"},{"text":"On two public CT datasets across 1-, 4-, and 8-view settings, the authors report the best novel-view synthesis scores and best or competitive reconstruction quality against baselines using dense-volume supervision.","excerpt":"On two public CT datasets across 1-, 4-, and 8-view settings, the proposed method achieves the best novel-view synthesis scores and best or competitive reconstruction quality compared with baselines that use dense-volume supervision.","url":"https://arxiv.org/abs/2610.38319","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.38319","checkedAt":"2026-11-11T05:54:11.746Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How do the two public CT datasets and evaluation settings represent scans constrained by scanning time, motion, or radiation tolerance?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Linrui Dai and coauthors propose pose-aware diffusion priors for sparse-view CT","summary":"The authors present a method for extremely sparse-view computed tomography (xSV-CT), where a scan uses only a few projections because scanning time, motion, or radiation tolerance may be limiting. They focus on settings where those same limits also make pre-reconstructed 3D volumes from dense scans unavailable for training. Their approach learns a pose-aware diffusion prior from one 2D projection per training sample, then guides diffusion sampling using measured-view fidelity, shared-volume reprojection, and projection-physics consistency. The aim is to combine sparse measurements with a learned prior without relying on densely scanned training volumes. On two public CT datasets, across 1-, 4-, and 8-view settings, the authors report the best novel-view synthesis scores and best or competitive reconstruction quality against baselines using dense-volume supervision. The result addresses a specific training-data constraint; the abstract does not establish how performance transfers beyond those datasets or to scans constrained by real-world acquisition conditions.","question":"How do the two public CT datasets and evaluation settings represent scans constrained by scanning time, motion, or radiation tolerance?","claims":[{"text":"The authors describe xSV-CT as relevant when scanning time, motion, or radiation tolerance limits a scan to only a few projections.","excerpt":"critical when scanning time, motion, or radiation tolerance limits each scan to only a few projections","support":"source-stated"},{"text":"The authors say these acquisition limits can also make pre-reconstructed 3D volumes from dense scans unavailable for dataset construction.","excerpt":"the same acquisition limits also constrain dataset construction, making pre-reconstructed 3D volumes from dense scans unavailable","support":"source-stated"},{"text":"The proposed method learns a pose-aware diffusion prior from one 2D projection per training sample and guides diffusion sampling with three stated forms of consistency or fidelity.","excerpt":"learning a pose-aware diffusion prior from one 2D projection per training sample, then guiding diffusion sampling with measured-view fidelity, shared-volume reprojection, and projection-physics consistency","support":"source-stated"},{"text":"On two public CT datasets across 1-, 4-, and 8-view settings, the authors report the best novel-view synthesis scores and best or competitive reconstruction quality against baselines using dense-volume supervision.","excerpt":"On two public CT datasets across 1-, 4-, and 8-view settings, the proposed method achieves the best novel-view synthesis scores and best or competitive reconstruction quality compared with baselines that use dense-volume supervision.","support":"source-stated"}],"limitations":["The supplied abstract reports results on two public CT datasets but provides no numerical scores or information about performance in clinical deployment.","The abstract does not specify how the datasets or evaluation settings represent scans constrained by scanning time, motion, or radiation tolerance."],"mediaId":"8a63f0606eb158a38390788e971b48095ec6928a68ec406a0341c3a3a3f3442b","correctionReason":null,"id":"66a2fb74a0bf5dcd5fe94c034189dc535f35fe8bc0da7bfb71ca16535f1a4090","publicationId":"b83c5bec-e0fe-4fe4-84de-e4a9d3898787","version":1,"sourceUrl":"https://arxiv.org/abs/2610.38319","sourceTitle":"Extremely Sparse-View Computed Tomography from 2D Projections via Pose-Aware Diffusion Priors","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-28","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-30T06:43:04.297Z","createdAt":"2026-11-11T05:39:26.459Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"8a63f0606eb158a38390788e971b48095ec6928a68ec406a0341c3a3a3f3442b","kind":"article","url":"https://arxiv.org/abs/2610.38319","sourceUrl":"https://arxiv.org/abs/2610.38319","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-31T07:16:07.490Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.38319","checkedAt":"2026-11-11T05:54:11.746Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"8a63f0606eb158a38390788e971b48095ec6928a68ec406a0341c3a3a3f3442b","confidence":0.7,"provider":"typesafe","model":"jev-1.13.0","inputHash":"8872da64e1d19ea79c52544a56672c1e8060d843f15ee0840829225cf9c59e57","decidedAt":"2026-10-31T07:16:07.845Z","redecided":{"from":"stream-admission-v5","under":"stream-admission-v5+confidence-0.5"}},"provenance":{"textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observationId":"9805c827621efecb6abcc1be3849be8e14a9613e465e458a26828cd0688e19fb","retrievedAt":"2026-10-31T07:16:07.452Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"cadec408-94a1-42da-8768-1038991791b1"},"reviewer":{"model":"gpt-6-luna","runId":"6641ba87-7320-4a20-85e3-d303b4908bff"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-11T05:39:26.459Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Dai, Linrui","url":null,"kind":"unknown"},{"name":"Liang, Jinxiu","url":null,"kind":"unknown"},{"name":"Zhou, Chu","url":null,"kind":"unknown"},{"name":"Yamaguchi, Mae","url":null,"kind":"unknown"},{"name":"Takahashi, Ryuya","url":null,"kind":"unknown"},{"name":"Sato, Imari","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Dai, Linrui","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Dai, Linrui","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Liang, Jinxiu","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Liang, Jinxiu","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Zhou, Chu","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Zhou, Chu","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Yamaguchi, Mae","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Yamaguchi, Mae","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Takahashi, Ryuya","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Takahashi, Ryuya","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:4","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Sato, Imari","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Sato, Imari","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:5","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}}],"participationCoverage":{"total":6,"returned":6,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.38319","snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z","basis":"publisher-page-metadata","sourceLinks":45,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/66a2fb74a0bf5dcd5fe94c034189dc535f35fe8bc0da7bfb71ca16535f1a4090/source"}},"canonicalUrl":"https://escapemeridian.com/readings/linrui-dai-and-coauthors-propose-pose-aware-diffusion-priors-66a2fb74","formats":{"html":"/readings/linrui-dai-and-coauthors-propose-pose-aware-diffusion-priors-66a2fb74","markdown":"/readings/linrui-dai-and-coauthors-propose-pose-aware-diffusion-priors-66a2fb74.md","json":"/readings/linrui-dai-and-coauthors-propose-pose-aware-diffusion-priors-66a2fb74.json"},"attribution":"Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.","maintainedGuideLinks":[],"readerContext":{"version":"reader-context-v1","attribution":"Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.","terms":[],"entities":[{"id":"arxiv","name":"arXiv","kind":"organization","context":"arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.","referenceUrls":["https://arxiv.org/","https://info.arxiv.org/help/api/index.html"],"path":"/entities/arxiv","json":"/entities/arxiv.json","markdown":"/entities/arxiv.md"},{"id":"time","name":"TIME","kind":"organization","context":"TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.","referenceUrls":["https://time.com/"],"path":"/entities/time","json":"/entities/time.json","markdown":"/entities/time.md"}]},"researchQuestions":[],"withdrawn":false,"references":{"schemaVersion":1,"target":{"type":"reading","id":"66a2fb74a0bf5dcd5fe94c034189dc535f35fe8bc0da7bfb71ca16535f1a4090","revision":"b83c5bec-e0fe-4fe4-84de-e4a9d3898787","revisionKind":"publication-id","title":"Linrui Dai and coauthors propose pose-aware diffusion priors for sparse-view CT","url":"https://escapemeridian.com/readings/linrui-dai-and-coauthors-propose-pose-aware-diffusion-priors-66a2fb74"},"source":{"type":"source","id":"https://arxiv.org/abs/2610.38319","revision":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","revisionKind":"sha256-source-text","title":"Extremely Sparse-View Computed Tomography from 2D Projections via Pose-Aware Diffusion Priors","url":"https://arxiv.org/abs/2610.38319"},"status":"available","items":[],"next":null,"scope":"Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter."},"revisions":[{"id":"66a2fb74a0bf5dcd5fe94c034189dc535f35fe8bc0da7bfb71ca16535f1a4090","title":"Linrui Dai and coauthors propose pose-aware diffusion priors for sparse-view CT","argument":"The authors present a method for extremely sparse-view computed tomography (xSV-CT), where a scan uses only a few projections because scanning time, motion, or radiation tolerance may be limiting. They focus on settings where those same limits also make pre-reconstructed 3D volumes from dense scans unavailable for training. Their approach learns a pose-aware diffusion prior from one 2D projection per training sample, then guides diffusion sampling using measured-view fidelity, shared-volume reprojection, and projection-physics consistency. The aim is to combine sparse measurements with a learned prior without relying on densely scanned training volumes. On two public CT datasets, across 1-, 4-, and 8-view settings, the authors report the best novel-view synthesis scores and best or competitive reconstruction quality against baselines using dense-volume supervision. The result addresses a specific training-data constraint; the abstract does not establish how performance transfers beyond those datasets or to scans constrained by real-world acquisition conditions.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The supplied abstract reports results on two public CT datasets but provides no numerical scores or information about performance in clinical deployment. The abstract does not specify how the datasets or evaluation settings represent scans constrained by scanning time, motion, or radiation tolerance.","speaker":{"name":"Dai, Linrui, Liang, Jinxiu, Zhou, Chu, Yamaguchi, Mae, Takahashi, Ryuya, Sato, Imari","roleAtSource":"Article author"},"source":{"title":"Extremely Sparse-View Computed Tomography from 2D Projections via Pose-Aware Diffusion Priors","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.38319","publishedAt":"2026-10-28","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors describe xSV-CT as relevant when scanning time, motion, or radiation tolerance limits a scan to only a few projections.","excerpt":"critical when scanning time, motion, or radiation tolerance limits each scan to only a few projections","url":"https://arxiv.org/abs/2610.38319","relationship":"Source-stated"},{"text":"The authors say these acquisition limits can also make pre-reconstructed 3D volumes from dense scans unavailable for dataset construction.","excerpt":"the same acquisition limits also constrain dataset construction, making pre-reconstructed 3D volumes from dense scans unavailable","url":"https://arxiv.org/abs/2610.38319","relationship":"Source-stated"},{"text":"The proposed method learns a pose-aware diffusion prior from one 2D projection per training sample and guides diffusion sampling with three stated forms of consistency or fidelity.","excerpt":"learning a pose-aware diffusion prior from one 2D projection per training sample, then guiding diffusion sampling with measured-view fidelity, shared-volume reprojection, and projection-physics consistency","url":"https://arxiv.org/abs/2610.38319","relationship":"Source-stated"},{"text":"On two public CT datasets across 1-, 4-, and 8-view settings, the authors report the best novel-view synthesis scores and best or competitive reconstruction quality against baselines using dense-volume supervision.","excerpt":"On two public CT datasets across 1-, 4-, and 8-view settings, the proposed method achieves the best novel-view synthesis scores and best or competitive reconstruction quality compared with baselines that use dense-volume supervision.","url":"https://arxiv.org/abs/2610.38319","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.38319","checkedAt":"2026-11-11T05:54:11.746Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How do the two public CT datasets and evaluation settings represent scans constrained by scanning time, motion, or radiation tolerance?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Linrui Dai and coauthors propose pose-aware diffusion priors for sparse-view CT","summary":"The authors present a method for extremely sparse-view computed tomography (xSV-CT), where a scan uses only a few projections because scanning time, motion, or radiation tolerance may be limiting. They focus on settings where those same limits also make pre-reconstructed 3D volumes from dense scans unavailable for training. Their approach learns a pose-aware diffusion prior from one 2D projection per training sample, then guides diffusion sampling using measured-view fidelity, shared-volume reprojection, and projection-physics consistency. The aim is to combine sparse measurements with a learned prior without relying on densely scanned training volumes. On two public CT datasets, across 1-, 4-, and 8-view settings, the authors report the best novel-view synthesis scores and best or competitive reconstruction quality against baselines using dense-volume supervision. The result addresses a specific training-data constraint; the abstract does not establish how performance transfers beyond those datasets or to scans constrained by real-world acquisition conditions.","question":"How do the two public CT datasets and evaluation settings represent scans constrained by scanning time, motion, or radiation tolerance?","claims":[{"text":"The authors describe xSV-CT as relevant when scanning time, motion, or radiation tolerance limits a scan to only a few projections.","excerpt":"critical when scanning time, motion, or radiation tolerance limits each scan to only a few projections","support":"source-stated"},{"text":"The authors say these acquisition limits can also make pre-reconstructed 3D volumes from dense scans unavailable for dataset construction.","excerpt":"the same acquisition limits also constrain dataset construction, making pre-reconstructed 3D volumes from dense scans unavailable","support":"source-stated"},{"text":"The proposed method learns a pose-aware diffusion prior from one 2D projection per training sample and guides diffusion sampling with three stated forms of consistency or fidelity.","excerpt":"learning a pose-aware diffusion prior from one 2D projection per training sample, then guiding diffusion sampling with measured-view fidelity, shared-volume reprojection, and projection-physics consistency","support":"source-stated"},{"text":"On two public CT datasets across 1-, 4-, and 8-view settings, the authors report the best novel-view synthesis scores and best or competitive reconstruction quality against baselines using dense-volume supervision.","excerpt":"On two public CT datasets across 1-, 4-, and 8-view settings, the proposed method achieves the best novel-view synthesis scores and best or competitive reconstruction quality compared with baselines that use dense-volume supervision.","support":"source-stated"}],"limitations":["The supplied abstract reports results on two public CT datasets but provides no numerical scores or information about performance in clinical deployment.","The abstract does not specify how the datasets or evaluation settings represent scans constrained by scanning time, motion, or radiation tolerance."],"mediaId":"8a63f0606eb158a38390788e971b48095ec6928a68ec406a0341c3a3a3f3442b","correctionReason":null,"id":"66a2fb74a0bf5dcd5fe94c034189dc535f35fe8bc0da7bfb71ca16535f1a4090","publicationId":"b83c5bec-e0fe-4fe4-84de-e4a9d3898787","version":1,"sourceUrl":"https://arxiv.org/abs/2610.38319","sourceTitle":"Extremely Sparse-View Computed Tomography from 2D Projections via Pose-Aware Diffusion Priors","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-28","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-30T06:43:04.297Z","createdAt":"2026-11-11T05:39:26.459Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"8a63f0606eb158a38390788e971b48095ec6928a68ec406a0341c3a3a3f3442b","kind":"article","url":"https://arxiv.org/abs/2610.38319","sourceUrl":"https://arxiv.org/abs/2610.38319","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-31T07:16:07.490Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.38319","checkedAt":"2026-11-11T05:54:11.746Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"8a63f0606eb158a38390788e971b48095ec6928a68ec406a0341c3a3a3f3442b","confidence":0.7,"provider":"typesafe","model":"jev-1.13.0","inputHash":"8872da64e1d19ea79c52544a56672c1e8060d843f15ee0840829225cf9c59e57","decidedAt":"2026-10-31T07:16:07.845Z","redecided":{"from":"stream-admission-v5","under":"stream-admission-v5+confidence-0.5"}},"provenance":{"textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observationId":"9805c827621efecb6abcc1be3849be8e14a9613e465e458a26828cd0688e19fb","retrievedAt":"2026-10-31T07:16:07.452Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"cadec408-94a1-42da-8768-1038991791b1"},"reviewer":{"model":"gpt-6-luna","runId":"6641ba87-7320-4a20-85e3-d303b4908bff"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-11T05:39:26.459Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Dai, Linrui","url":null,"kind":"unknown"},{"name":"Liang, Jinxiu","url":null,"kind":"unknown"},{"name":"Zhou, Chu","url":null,"kind":"unknown"},{"name":"Yamaguchi, Mae","url":null,"kind":"unknown"},{"name":"Takahashi, Ryuya","url":null,"kind":"unknown"},{"name":"Sato, Imari","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Dai, Linrui","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Dai, Linrui","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Liang, Jinxiu","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Liang, Jinxiu","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Zhou, Chu","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Zhou, Chu","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Yamaguchi, Mae","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Yamaguchi, Mae","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Takahashi, Ryuya","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Takahashi, Ryuya","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:4","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}},{"actor":{"name":"Sato, Imari","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Sato, Imari","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.38319","locator":"head/meta[name=author|citation_author]"},"assertionId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982:participation:5","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-11T05:54:11.746Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","sourceUrl":"https://arxiv.org/abs/2610.38319","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z"}}],"participationCoverage":{"total":6,"returned":6,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.38319","snapshotId":"1687c2db70b92657dd91edccd8a0025aee75ffa2794dfe1d631ee034ad070982","contentSha256":"b6dd92f714810b0bf68ddb7cefed8671d5a905c6d2ac666ef173056b424ecc6d","textHash":"498b60a050395bab941aa55a0668f36d34725528dfcc9cc7a84f11648f7e569c","observedAt":"2026-11-11T05:54:11.746Z","basis":"publisher-page-metadata","sourceLinks":45,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/66a2fb74a0bf5dcd5fe94c034189dc535f35fe8bc0da7bfb71ca16535f1a4090/source"}}}],"audits":[{"publicationId":"b83c5bec-e0fe-4fe4-84de-e4a9d3898787","verdict":"uncertain","findings":[{"dimension":"attribution","status":"pass","explanation":"The reading attributes the method and its results to Linrui Dai and coauthors. This matches the arXiv author list. Performance claims are framed as what the authors report, not as established fact.","excerpt":"Authors:Linrui Dai, Jinxiu Liang, Chu Zhou, Mae Yamaguchi, Ryuya Takahashi, Imari Sato"},{"dimension":"summary","status":"uncertain","explanation":"The summary is faithful, but the paper's central terms are left unexplained for non-specialists: 'projections' (individual X-ray views), 'diffusion prior' (a generative model's learned sense of what plausible images look like), 'pose-aware', and 'novel-view synthesis'. The three guidance terms are also copied verbatim with no plain-language gloss. Smallest repair: briefly define projection, diffusion prior and novel-view synthesis, or attach term-library tooltips. The reading should also state plainly that the practical stake is CT with very few X-ray views, for example less radiation or faster scans.","excerpt":"learning a pose-aware diffusion prior from one 2D projection per training sample"},{"dimension":"date","status":"pass","explanation":"The originalPublished date of 2026-10-28 matches the arXiv v1 submission date. The later feed date of 2026-10-31 is correctly not used.","excerpt":"[v1]\nWed, 28 Oct 2026 06:08:39 UTC (31,238 KB)"},{"dimension":"support","status":"pass","explanation":"All four claims quote the abstract exactly and stay within its scope. The stated aim of avoiding dense training volumes follows from the abstract's train-sparse / infer-sparse framing. The limitations correctly note that the abstract gives no numerical scores and no evidence about real-world transfer, without overclaiming. The question follows from that gap and is not leading.","excerpt":"We study this train-sparse / infer-sparse setting by learning a pose-aware diffusion prior from one 2D projection per training sample"},{"dimension":"media","status":"pass","explanation":"The selected media is the verified direct arXiv abstract page for this paper, which is the most direct way to reach the source.","excerpt":null}],"createdAt":"2026-11-11T05:39:26.459Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}