{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:44:15Z","timestamp":1782920655190,"version":"3.54.5"},"reference-count":12,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005699","name":"Universit\u00e0 degli Studi del Piemonte Orientale","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005699","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol Exp"],"abstract":"<jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:p>\n                      Integrating vision-language models (VLMs) into clinical radiology workflows requires exporting two-dimensional images that preserve diagnostic viewing context, including imaging plane, slice position, window\/level, slab parameters, and overlays. Existing approaches typically rely on\n                      <jats:italic>ad hoc<\/jats:italic>\n                      screenshots and external uploads, which discard this context, reduce reproducibility, and introduce workflow friction through task switching. We describe a radiology viewer architecture that maintains model interaction entirely within the diagnostic environment by serializing explicit viewer state into replayable descriptors. Requested views are re-rendered offscreen using shared in-memory voxel data, enabling repeatable, context-faithful evidence generation without disrupting interactive interpretation. The user interface employs a split workspace, combining a diagnostic grid for image review with an integrated chat sidebar for evidence staging and model interaction. Capture actions generate batches of serialized viewer states that are executed by an offscreen renderer; the resulting frames are staged for user curation prior to model submission. Under controlled conditions at a 1,000\u2009\u00d7\u20091,000 pixel output resolution, benchmarks demonstrated perfect reproducibility: repeated captures from identical serialized states produced pixel-identical images, with zero hash mismatches across 50 frames. Capture latency ranged from a median of 4.49 to 16.69\u2009ms per frame, depending on view type, and median output size was 27.9\u2009kB per frame using a high-efficiency image container. These results indicate that the proposed architecture supports reproducible, low-latency, and storage-efficient multislice and multiseries evidence assembly at interactive speeds, enabling seamless integration of VLMs into diagnostic radiology workflows.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>Offscreen re-rendering driven by serialized viewing state enables context-preserving and reproducible export of two-dimensional evidence from volumetric radiology viewing, supporting integrated vision-language model workflows with millisecond-scale capture latency from hot cache.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>A split-layout DICOM viewer with an integrated chat interface enables radiologists to capture, stage, and submit visual evidence without leaving the diagnostic environment.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Offscreen re-rendering from serialized viewing state produces pixel-identical exports across repeated captures, supporting traceable, auditable, and reproducible evidence generation.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Batch capture efficiently assembles multislice, multiplane, and multiseries evidence with minimal manual effort, providing models with richer and more representative study context.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Capture latency was millisecond-scale, and high-efficiency image container (HEIC) encoding reduced file size by approximately 2.8-fold compared with the joint photographic experts group (JPEG) format, supporting responsive multi-frame workflows.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-026-00769-0","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T14:48:53Z","timestamp":1782917333000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Technical architecture for integrating vision-language models with DICOM viewers"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1054-2552","authenticated-orcid":false,"given":"Marco","family":"Albera","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Colarieti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irene","family":"Albera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Carriero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"769_CR1","doi-asserted-by":"publisher","unstructured":"Yang Z, Li L, Lin K et al (2023) The dawn of LMMs: preliminary explorations with GPT-4V(ision). Preprint at https:\/\/doi.org\/10.48550\/arXiv.2309.17421","DOI":"10.48550\/arXiv.2309.17421"},{"key":"769_CR2","doi-asserted-by":"publisher","unstructured":"Liu H, Li C, Wu Q, Lee YJ (2023) Visual instruction tuning. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2304.08485","DOI":"10.48550\/arXiv.2304.08485"},{"key":"769_CR3","doi-asserted-by":"publisher","DOI":"10.1148\/rg.240103","volume":"45","author":"AN Wu","year":"2025","unstructured":"Wu AN, Kulbay M, Cheng PM et al (2025) Deep learning models connecting images and text: a primer for radiologists. Radiographics 45:e240103. https:\/\/doi.org\/10.1148\/rg.240103","journal-title":"Radiographics"},{"key":"769_CR4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.250617","volume":"316","author":"B Hou","year":"2025","unstructured":"Hou B, Mukherjee P, Batheja V et al (2025) One year on: assessing progress of multimodal large language model performance on RSNA 2024 case of the day questions. 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The remaining authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"103"}}