Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA.
NMR Biomed. 2012 Apr;25(4):607-19. doi: 10.1002/nbm.1777. Epub 2011 Sep 30.
Recently, both Magnetic Resonance (MR) Imaging (MRI) and Spectroscopy (MRS) have emerged as promising tools for detection of prostate cancer (CaP). However, due to the inherent dimensionality differences in MR imaging and spectral information, quantitative integration of T(2) weighted MRI (T(2)w MRI) and MRS for improved CaP detection has been a major challenge. In this paper, we present a novel computerized decision support system called multimodal wavelet embedding representation for data combination (MaWERiC) that employs, (i) wavelet theory to extract 171 Haar wavelet features from MRS and 54 Gabor features from T(2)w MRI, (ii) dimensionality reduction to individually project wavelet features from MRS and T(2)w MRI into a common reduced Eigen vector space, and (iii), a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 T in vivo MRI and MRS. A total of 36 1.5 T endorectal in vivo T(2)w MRI and MRS patient studies were evaluated per voxel by MaWERiC using a three-fold cross validation approach over 25 iterations. Ground truth for evaluation of results was obtained by an expert radiologist annotations of prostate cancer on a per voxel basis who compared each MRI section with corresponding ex vivo wholemount histology sections with the disease extent mapped out on histology. Results suggest that MaWERiC based MRS T(2)w meta-classifier (mean AUC, μ = 0.89 ± 0.02) significantly outperformed (i) a T(2)w MRI (using wavelet texture features) classifier (μ = 0.55 ± 0.02), (ii) a MRS (using metabolite ratios) classifier (μ = 0.77 ± 0.03), (iii) a decision fusion classifier obtained by combining individual T(2)w MRI and MRS classifier outputs (μ = 0.85 ± 0.03), and (iv) a data combination method involving a combination of metabolic MRS and MR signal intensity features (μ = 0.66 ± 0.02).
最近，磁共振成像（MRI）和波谱（MRS）都已成为检测前列腺癌（CaP）的有前途的工具。然而，由于 MRI 成像和光谱信息在固有维度上的差异，定量整合 T2 加权 MRI（T2w MRI）和 MRS 以提高 CaP 的检测一直是一个主要挑战。在本文中，我们提出了一种称为多模态小波嵌入表示数据组合的新型计算机决策支持系统（MaWERiC），该系统采用：（i）小波理论从 MRS 中提取 171 个 Haar 小波特征，从 T2w MRI 中提取 54 个 Gabor 特征；（ii）降维，将 MRS 和 T2w MRI 中的小波特征分别投影到公共的降维特征向量空间中；（iii）随机森林分类器，用于在体内 1.5 T MRI 和 MRS 上逐体素自动检测前列腺癌。使用 MaWERiC 对总共 36 个 1.5 T 直肠内活体 T2w MRI 和 MRS 患者研究进行了逐体素评估，使用 25 次迭代的三折交叉验证方法。通过专家放射科医生对前列腺癌的逐体素注释来获得结果评估的真实信息，该注释将每个 MRI 切片与具有映射在组织学上的疾病范围的相应离体全组织学切片进行比较。结果表明，基于 MaWERiC 的 MRS T2w 元分类器（平均 AUC，μ=0.89±0.02）明显优于：（i）T2w MRI（使用小波纹理特征）分类器（μ=0.55±0.02）；（ii）MRS（使用代谢物比率）分类器（μ=0.77±0.03）；（iii）通过组合个体 T2w MRI 和 MRS 分类器输出获得的决策融合分类器（μ=0.85±0.03）；（iv）涉及代谢 MRS 和 MR 信号强度特征组合的数据组合方法（μ=0.66±0.02）。
