用深度学习将CBCT辐射剂量降至六分之一，仍保持诊断级图像质量。
Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation
- 三阶段框架结合先验知识，实现仅需1/6剂量的高质量重建
- 12中心4102例数据验证，11位医生评价无差异
- 临床试验中放射科与介入医生均无偏好，适合真实医疗场景
锥形束计算机断层扫描（CBCT）引导穿刺已成为诊治胸段早期至中期肿瘤的常规方法，但其伴随的辐射暴露显著增加继发性恶性肿瘤风险。尽管已有多种低剂量CBCT策略，但均未通过大规模多中心回顾性数据集验证，且缺乏前瞻性临床评估。本文提出DeepPriorCBCT——一个三阶段深度学习框架，在仅使用六分之一常规辐射剂量条件下实现诊断级重建。研究纳入12个中心共4102名患者、8675次CBCT扫描进行模型开发与验证。此外，开展一项前瞻性交叉试验（注册号：NCT07035977），招募138例计划接受经皮胸腔穿刺的患者，评估模型临床适用性。11位医师评估确认重建图像与原始图像无法区分。诊断性能和整体图像质量与标准算法生成结果相当。在前瞻性试验中，五位放射科医生报告模型重建与临床标准在图像质量及病灶评估上无显著差异（所有P>0.05）。25位介入医生对基于模型与全采样图像在手术导航中的表现无偏好（Kappa<0.2）。DeepPriorCBCT将辐射暴露降低至传统方法的约六分之一，结果表明该方法可在稀疏采样条件下实现高质量CBCT重建，显著降低术中辐射风险。
原文摘要 · Abstract (English)
Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating early- to mid-stage thoracic tumours, yet the associated radiation exposure substantially elevates the risk of secondary malignancies. Although multiple low-dose CBCT strategies have been introduced, none have undergone validation using large-scale multicenter retrospective datasets, and prospective clinical evaluation remains lacking. Here, we propose DeepPriorCBCT - a three-stage deep learning framework that achieves diagnostic-grade reconstruction using only one-sixth of the conventional radiation dose. 4102 patients with 8675 CBCT scans from 12 centers were included to develop and validate DeepPriorCBCT. Additionally, a prospective cross-over trial (Registry number: NCT07035977) which recruited 138 patients scheduled for percutaneous thoracic puncture was conducted to assess the model's clinical applicability. Assessment by 11 physicians confirmed that reconstructed images were indistinguishable from original scans. Moreover, diagnostic performance and overall image quality were comparable to those generated by standard reconstruction algorithms. In the prospective trial, five radiologists reported no significant differences in image quality or lesion assessment between DeepPriorCBCT and the clinical standard (all P>0.05). Likewise, 25 interventionalists expressed no preference between model-based and full-sampling images for surgical guidance (Kappa<0.2). Radiation exposure with DeepPriorCBCT was reduced to approximately one-sixth of that with the conventional approach, and collectively, the findings confirm that it enables high-quality CBCT reconstruction under sparse sampling conditions while markedly decreasing intraoperative radiation risk.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。