{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:QUAB3CUMSALSYDLVCID52LUBGS","short_pith_number":"pith:QUAB3CUM","canonical_record":{"source":{"id":"2107.06888","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-07-13T09:04:20Z","cross_cats_sorted":[],"title_canon_sha256":"333b154762ec3e273de28556afa22063eaf044dfacb66bfa7bf5eb14777f5318","abstract_canon_sha256":"083d4f54a40fe4f9c722a785e011d3255137a33551a0f77b965d478ea5257244"},"schema_version":"1.0"},"canonical_sha256":"85001d8a8c90172c0d751207dd2e813496afd5844611370cd6137aa6b68d55d6","source":{"kind":"arxiv","id":"2107.06888","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.06888","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"arxiv_version","alias_value":"2107.06888v1","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.06888","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"pith_short_12","alias_value":"QUAB3CUMSALS","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"pith_short_16","alias_value":"QUAB3CUMSALSYDLV","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"pith_short_8","alias_value":"QUAB3CUM","created_at":"2026-07-05T02:58:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:QUAB3CUMSALSYDLVCID52LUBGS","target":"record","payload":{"canonical_record":{"source":{"id":"2107.06888","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-07-13T09:04:20Z","cross_cats_sorted":[],"title_canon_sha256":"333b154762ec3e273de28556afa22063eaf044dfacb66bfa7bf5eb14777f5318","abstract_canon_sha256":"083d4f54a40fe4f9c722a785e011d3255137a33551a0f77b965d478ea5257244"},"schema_version":"1.0"},"canonical_sha256":"85001d8a8c90172c0d751207dd2e813496afd5844611370cd6137aa6b68d55d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:58:07.941101Z","signature_b64":"df+pmpYOnwlHvs/Jv/ASgCq+RwEkONovbylZwBop0w+9eZ+v/b7jsRu2Pg5ku3vdx23+mutFyGBxrw6RZuAABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85001d8a8c90172c0d751207dd2e813496afd5844611370cd6137aa6b68d55d6","last_reissued_at":"2026-07-05T02:58:07.940765Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:58:07.940765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.06888","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:58:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hBUdn85EiOeOMP65wbCh/SvhQla+mfmdfCoyWR+VqyLLmG4sucCKm5xeO2M6WGhw57bo5mFAbfxnMGld6H6CDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:51:31.779294Z"},"content_sha256":"e790fa0120e4a0cc5db965319c64217fe1bd3e7fc3065f8ba1579e7ba8538a38","schema_version":"1.0","event_id":"sha256:e790fa0120e4a0cc5db965319c64217fe1bd3e7fc3065f8ba1579e7ba8538a38"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:QUAB3CUMSALSYDLVCID52LUBGS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Cheng Li, Chuyu Rong, Hairong Zheng, Haoran Li, Kehan Qi, Shanshan Wang, Yu Gong","submitted_at":"2021-07-13T09:04:20Z","abstract_excerpt":"Image Quality Assessment (IQA) is of great value in the workflow of Magnetic Resonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especially required since high-quality reference MRI images are usually not available. Recently, many efforts have been devoted to developing deep learning-based BIQA approaches. However, the performance of these methods is limited due to the utilization of simple content-non-adaptive network parameters and the waste of the important 3D spatial information of the medical images. To address these issues, we design a 3D content-adaptive hyper-network f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.06888","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.06888/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:58:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RoNKwkBtBsVAoduBUahTZHp/64+Br3OpEF1fFSE6Ngv3Q+jm5h6Vw3TPW6Vy+F2mKcXcn+c0d/DB31s9ds3tDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:51:31.779762Z"},"content_sha256":"2324a50c660a9ac3266f2c9b080ab619123de556afbfd7be303cbe494fb28994","schema_version":"1.0","event_id":"sha256:2324a50c660a9ac3266f2c9b080ab619123de556afbfd7be303cbe494fb28994"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QUAB3CUMSALSYDLVCID52LUBGS/bundle.json","state_url":"https://pith.science/pith/QUAB3CUMSALSYDLVCID52LUBGS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QUAB3CUMSALSYDLVCID52LUBGS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T02:51:31Z","links":{"resolver":"https://pith.science/pith/QUAB3CUMSALSYDLVCID52LUBGS","bundle":"https://pith.science/pith/QUAB3CUMSALSYDLVCID52LUBGS/bundle.json","state":"https://pith.science/pith/QUAB3CUMSALSYDLVCID52LUBGS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QUAB3CUMSALSYDLVCID52LUBGS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:QUAB3CUMSALSYDLVCID52LUBGS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"083d4f54a40fe4f9c722a785e011d3255137a33551a0f77b965d478ea5257244","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-07-13T09:04:20Z","title_canon_sha256":"333b154762ec3e273de28556afa22063eaf044dfacb66bfa7bf5eb14777f5318"},"schema_version":"1.0","source":{"id":"2107.06888","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.06888","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"arxiv_version","alias_value":"2107.06888v1","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.06888","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"pith_short_12","alias_value":"QUAB3CUMSALS","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"pith_short_16","alias_value":"QUAB3CUMSALSYDLV","created_at":"2026-07-05T02:58:07Z"},{"alias_kind":"pith_short_8","alias_value":"QUAB3CUM","created_at":"2026-07-05T02:58:07Z"}],"graph_snapshots":[{"event_id":"sha256:2324a50c660a9ac3266f2c9b080ab619123de556afbfd7be303cbe494fb28994","target":"graph","created_at":"2026-07-05T02:58:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2107.06888/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Image Quality Assessment (IQA) is of great value in the workflow of Magnetic Resonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especially required since high-quality reference MRI images are usually not available. Recently, many efforts have been devoted to developing deep learning-based BIQA approaches. However, the performance of these methods is limited due to the utilization of simple content-non-adaptive network parameters and the waste of the important 3D spatial information of the medical images. To address these issues, we design a 3D content-adaptive hyper-network f","authors_text":"Cheng Li, Chuyu Rong, Hairong Zheng, Haoran Li, Kehan Qi, Shanshan Wang, Yu Gong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-07-13T09:04:20Z","title":"Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.06888","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e790fa0120e4a0cc5db965319c64217fe1bd3e7fc3065f8ba1579e7ba8538a38","target":"record","created_at":"2026-07-05T02:58:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"083d4f54a40fe4f9c722a785e011d3255137a33551a0f77b965d478ea5257244","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2021-07-13T09:04:20Z","title_canon_sha256":"333b154762ec3e273de28556afa22063eaf044dfacb66bfa7bf5eb14777f5318"},"schema_version":"1.0","source":{"id":"2107.06888","kind":"arxiv","version":1}},"canonical_sha256":"85001d8a8c90172c0d751207dd2e813496afd5844611370cd6137aa6b68d55d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85001d8a8c90172c0d751207dd2e813496afd5844611370cd6137aa6b68d55d6","first_computed_at":"2026-07-05T02:58:07.940765Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:58:07.940765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"df+pmpYOnwlHvs/Jv/ASgCq+RwEkONovbylZwBop0w+9eZ+v/b7jsRu2Pg5ku3vdx23+mutFyGBxrw6RZuAABA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:58:07.941101Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.06888","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e790fa0120e4a0cc5db965319c64217fe1bd3e7fc3065f8ba1579e7ba8538a38","sha256:2324a50c660a9ac3266f2c9b080ab619123de556afbfd7be303cbe494fb28994"],"state_sha256":"6b6e2d0edecd78d59bce486b3e3f15b2249c06ce8ef22b4fffccb5356dad93dc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9c405bFZwN+jljOdr8efajymhNuvV45nH1sr/c+/E3OFyCrliaRqqxJdsu53rJXthlaN1gT+PzzOQkBsWqcBBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T02:51:31.782750Z","bundle_sha256":"126b22155a491b8ef6002b547c2e28038ecfa069c74220e1e4c4c64ed791352d"}}