联合肿瘤分割与配准，让3T和7T乳腺MRI图像对齐并精准识别病灶。
Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths
- 基于变分U-Net设计局部对齐机制，同步完成图像配准与肿瘤分割。
- 在9名患者数据上实现Dice系数62.9%~95.3%，配准相关性达96.4%~99.3%。
- 适合多场强乳腺MRI影像融合分析，尤其用于肿瘤诊断与治疗规划。
多参数乳腺MRI可提升肿瘤诊断、表征与治疗规划效果，但不同场强（如3T与7T）获取的图像对齐与分割仍具挑战。本文提出一种变分U-Net结合局部对齐的联合方法（VALOR-Net），旨在解决跨场强图像配准与一致性肿瘤分割问题。回顾性研究纳入9名女性患者，其中6例为组织学确诊浸润性导管癌（IDC），3例为纤维腺瘤。使用3T与7T扫描仪采集对比增强T1加权三维时间分辨血管造影（TWIST）序列数据。通过峰值信噪比（PSNR）、结构相似性指数（SSIM）、归一化互相关（NCC）、Dice系数、F1分数及相对均方差（rel SSD）等定量指标评估性能。结果显示，个体受试者平均PSNR为27.5–34.5 dB，SSIM为82.6–92.8%，NCC为96.4–99.3%，Dice系数为62.9–95.3%，F1分数为55.4–93.2%，rel SSD为2.0–7.5%。分割指标（Dice与F1）高度相关（r=0.995），配准指标（NCC与SSIM）中度相关（r=0.681）。初步结果表明，该方法在跨场强乳腺MRI图像联合分割与配准中具有可行性。
原文摘要 · Abstract (English)
Background: Multiparametric breast MRI data might improve tumor diagnostics, characterization, and treatment planning. Accurate alignment and delineation of images acquired at different field strengths such as 3T and 7T, remain challenging research tasks. Purpose: To address alignment challenges and enable consistent tumor segmentation across different MRI field strengths. Study type: Retrospective. Subjects: Nine female subjects with breast tumors were involved: six histologically proven invasive ductal carcinomas (IDC) and three fibroadenomas. Field strength/sequence: Imaging was performed at 3T and 7T scanners using post-contrast T1-weighted three-dimensional time-resolved angiography with stochastic trajectories (TWIST) sequence. Assessments: The method's performance for joint image registration and tumor segmentation was evaluated using several quantitative metrics, including signal-to-noise ratio (PSNR), structural similarity index (SSIM), normalized cross-correlation (NCC), Dice coefficient, F1 score, and relative sum of squared differences (rel SSD). Statistical tests: The Pearson correlation coefficient was used to test the relationship between the registration and segmentation metrics. Results: When calculated for each subject individually, the PSNR was in a range from 27.5 to 34.5 dB, and the SSIM was from 82.6 to 92.8%. The model achieved an NCC from 96.4 to 99.3% and a Dice coefficient of 62.9 to 95.3%. The F1 score was between 55.4 and 93.2% and the rel SSD was in the range of 2.0 and 7.5%. The segmentation metrics Dice and F1 Score are highly correlated (0.995), while a moderate correlation between NCC and SSIM (0.681) was found for registration. Data conclusion: Initial results demonstrate that the proposed method may be feasible in providing joint tumor segmentation and registration of MRI data acquired at different field strengths.
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