{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T04:31:39Z","timestamp":1786422699440,"version":"build-2736575974"},"reference-count":23,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan","doi-asserted-by":"crossref","award":["20KK0234"],"award-info":[{"award-number":["20KK0234"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan","doi-asserted-by":"crossref","award":["21H03470"],"award-info":[{"award-number":["21H03470"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan","doi-asserted-by":"crossref","award":["20K21821"],"award-info":[{"award-number":["20K21821"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"crossref"}]},{"award":["20KK0234"],"award-info":[{"award-number":["20KK0234"]}],"id":[{"id":"https:\/\/ror.org\/048rj2z13","id-type":"ROR","asserted-by":"publisher"}]},{"award":["21H03470"],"award-info":[{"award-number":["21H03470"]}],"id":[{"id":"https:\/\/ror.org\/048rj2z13","id-type":"ROR","asserted-by":"publisher"}]},{"award":["20K21821"],"award-info":[{"award-number":["20K21821"]}],"id":[{"id":"https:\/\/ror.org\/048rj2z13","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Synthesizing multi-phase contrast-enhanced CT images from non-contrast CT (NCCT) may provide complementary phase-specific cues for preliminary assessment. After rigorous clinical validation, such images could provide clinical decision support or aid diagnostic triage by identifying cases that warrant further acquired contrast-enhanced CT (CECT) work-up; they are not intended to replace acquired CECT. Arterial-phase (ART) and portal-venous-phase (PV) images share anatomical structures but exhibit distinct enhancement patterns and intensity distributions, which makes simultaneous multi-phase synthesis challenging for conventional single-decoder models. We propose a task-prompt-guided dual-decoder framework that combines a shared Swin Transformer encoder, two phase-specific decoders, learnable task prompts initialized from Qwen3-8B semantic representations, two phase-specific adversarial discriminators, and an independent ART\/PV domain classifier. The shared encoder extracts phase-invariant anatomical features, whereas the two decoders independently model ART- and PV-specific enhancement. The Qwen3-derived prompt vectors provide phase-aware initialization and subsequently adapt through prompt\u2013feature interaction at the bottleneck. Experiments on a single-center dataset of 86 patients show improved whole-image and lesion-focused PSNR, SSIM, MSE, and PCC relative to Pix2pix and MedGAN. Controlled ablation experiments indicate the benefits of Qwen3-based prompt initialization, subsequent prompt adaptation, and correct prompt\u2013decoder correspondence; a reduced baseline additionally assesses the combined removal of LLTP and ART\/PV domain classification. In a downstream slice-level four-class focal liver lesion classification experiment, synthetic multiphase input improved accuracy from 71.65% with NCCT alone to 85.04%, compared with 91.34% for acquired multiphase CT. These findings provide a proof of concept for LLM-guided multi-phase CT synthesis, although external validation, clinically oriented safety assessment, and reader studies remain necessary before clinical use for decision support or diagnostic triage.<\/jats:p>","DOI":"10.3390\/jimaging12080365","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T08:08:53Z","timestamp":1786349333000},"page":"365","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Task-Prompt-Guided Dual-Decoder Framework with Large Language Models for Multi-Phase Contrast CT Synthesis from Non-Contrast CT"],"prefix":"10.3390","volume":"12","author":[{"given":"Liang","family":"Lyu","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Engineering, Ritsumeikan University, Ibaraki 567-8570, Osaka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8094-1991","authenticated-orcid":false,"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Engineering, Ritsumeikan University, Ibaraki 567-8570, Osaka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Engineering, Ritsumeikan University, Ibaraki 567-8570, Osaka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, SRRSH of School of Medicine, Zhejiang University, Hangzhou 310016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4098-588X","authenticated-orcid":false,"given":"Lanfen","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5952-0188","authenticated-orcid":false,"given":"Yen-Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Engineering, Ritsumeikan University, Ibaraki 567-8570, Osaka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20390","DOI":"10.1038\/s41598-021-99896-4","article-title":"The Feasibility of Deep Learning-Based Synthetic Contrast-Enhanced CT from Nonenhanced CT in Emergency Department Patients with Acute Abdominal Pain","volume":"11","author":"Kim","year":"2021","journal-title":"Sci. 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