Mattusch Chiara, Bick Ulrich, Michallek Florian
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Charitéplatz 1, 10117, Berlin, Germany.
Department of Radiology, Mie University Graduate School of Medicine, Tsu, Japan.
Insights Imaging. 2023 Jan 26;14(1):17. doi: 10.1186/s13244-022-01362-w.
Patient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.
After applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman's rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient K as surrogate parameter for motion artifacts. Mean K decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001).
Four-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.
由于减影伪影，患者运动可降低动态对比增强磁共振成像（DCE-MRI）的图像质量。通过客观和主观评估基于主成分分析（PCA）的配准对乳腺癌患者治疗前DCE-MRI的影响，我们旨在验证DCE乳腺MRI的四维配准。
将基于PCA的四维配准算法应用于154例组织病理学描述良好的乳腺癌患者的治疗前DCE-MRI后，我们定量测定了未配准和配准图像的图像质量。为进行主观评估，我们在临床阅片环境中根据四种运动类别（0：无运动，1：轻度运动，2：中度运动，3：严重运动且图像质量无法诊断）对运动严重程度进行分级。配准后，中度或严重运动图像（中位数类别2，四分位距0）的中位数被重新分配到运动类别1（四分位距0）。运动类别与配准后的运动减少相关（Spearman秩相关系数：0.83，p < 0.001）。为进行客观评估，我们使用扩展Tofts模型进行灌注模型拟合，并计算其容积转移系数K作为运动伪影的替代参数。配准前平均K为0.103（±0.077），配准后降至0.097（±0.070）（p < 0.001）。配准后灌注定量的不确定性降低了7.4%（±15.5，p < 0.001）。
基于PCA的四维图像配准通过校正减影图像中的运动伪影提高了乳腺DCE-MRI的图像质量，并降低了定量灌注建模的不确定性。当中度至严重运动伪影存在时，这种改善最为明显。
