发表机构
Phenikaa University; Phenikaa University Hospital; National Economics University（Phenikaa大学; Phenikaa大学医院; 国家经济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GateSPINE通过门控跨视图融合模块融合矢状和轴向MRI体积，提升腰椎MRI报告生成的临床效能召回率，在三个数据集上取得最高CE F1。
AI 中文摘要
自动报告生成可以减轻放射科医生在解读多序列MRI研究时面临的负担。与CT不同，MRI检查包含多个序列和成像平面，每个序列和平面提供互补的诊断信息。现有方法将检查编码为单一体积，并通过固定规则组合多个采集。因此，仅在一个平面中可见的发现被稀释且常常被遗漏，降低了临床效能指标上的召回率，而遗漏异常是最代价高昂的。我们提出GateSPINE，一种视觉-语言框架，它使用无训练操作符融合矢状T1和T2体积，用两个并行3D编码器编码融合的矢状和轴向体积，并将其组合表示解码为报告。其核心机制是门控跨视图融合模块，该模块预测每个特征通道和令牌应接纳每个视图的多少，从而使信息量更大的视图在每个空间位置占主导。我们在三个腰椎MRI数据集上评估GateSPINE，包括两个公共基准和一个从Phenikaa大学医院收集的私人队列，使用自然语言生成（NLG）和临床效能（CE）指标。GateSPINE通过在所有三个数据集上提高召回率实现了最高的CE F1；在缺乏轴向序列的SPIDER上，这反映了矢状融合组件而非门控跨视图机制，后者在两个具有两个成像平面的队列上得到验证。GateSPINE在标准NLG指标上也保持竞争力。
英文摘要
Automated report generation can ease the burden radiolo gists face when interpreting multi-sequence MRI studies. Unlike CT, MRI examinations comprise multiple sequences and imaging planes, each con tributing complementary diagnostic information. Existing methods en code a study as a single volume and combine multiple acquisitions by fixed rules. Findings visible in only one plane are thus diluted and of ten missed, lowering recall on clinical efficacy metrics, where a missed abnormality is most costly. We propose GateSPINE, a vision-language framework that fuses sagittal T1 and T2 volumes with a training-free operator, encodes the fused sagittal and axial volumes with two parallel 3D encoders, and decodes their combined representation into a report. Its core mechanism is a gated cross view fusion module that predicts, per feature channel and token, how much of each view to admit, so the more informative view dominates at each spatial location. We evaluate GateSPINE on three lumbar MRI datasets, comprising two public bench marks and a private cohort collected from Phenikaa University Hospital, using both natural language generation (NLG) and clinical efficacy (CE) metrics. GateSPINE achieves the highest CE F1 through improved re call on all three datasets; on SPIDER, which lacks an axial sequence, this reflects the sagittal fusion component rather than the gated cross-view mechanism, which is validated on the two cohorts with both imaging planes. GateSPINE also remains competitive on standard NLG metrics.
