Lee Cheng-Chun, Chang Kuang-Hsi, Chiu Feng-Mao, Ou Yen-Chuan, Hwang Jen-I, Hsueh Kuan-Chun, Fan Hueng-Chuen
Division of Diagnostic Radiology, Department of Medical Imaging, Tungs' Taichung Metroharbor Hospital, Taichung 43503, Taiwan.
Department of Medical Research, Tungs' Taichung Metroharbor Hospital, Taichung 43503, Taiwan.
Diagnostics (Basel). 2021 Dec 12;11(12):2340. doi: 10.3390/diagnostics11122340.
The intravoxel incoherent motion (IVIM) model may enhance the clinical value of multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer (PCa). However, while past IVIM modeling studies have shown promise, they have also reported inconsistent results and limitations, underscoring the need to further enhance the accuracy of IVIM modeling for PCa detection. Therefore, this study utilized the control point registration toolbox function in MATLAB to fuse T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) MRI images with whole-mount pathology specimen images in order to eliminate potential bias in IVIM calculations. Sixteen PCa patients underwent prostate MRI scans before undergoing radical prostatectomies. The image fusion method was then applied in calculating the patients' IVIM parameters. Furthermore, MRI scans were also performed on 22 healthy young volunteers in order to evaluate the changes in IVIM parameters with aging. Among the full study cohort, the f parameter was significantly increased with age, while the D* parameter was significantly decreased. Among the PCa patients, the D and ADC parameters could differentiate PCa tissue from contralateral normal tissue, while the f and D* parameters could not. The presented image fusion method also provided improved precision when comparing regions of interest side by side. However, further studies with more standardized methods are needed to further clarify the benefits of the presented approach and the different IVIM parameters in PCa characterization.
体素内不相干运动（IVIM）模型可能会提高多参数磁共振成像（mpMRI）在前列腺癌（PCa）检测中的临床价值。然而，尽管过去的IVIM建模研究显示出了前景，但它们也报告了不一致的结果和局限性，这突出表明需要进一步提高用于PCa检测的IVIM建模的准确性。因此，本研究利用MATLAB中的控制点配准工具箱函数，将T2加权成像（T2WI）和扩散加权成像（DWI）MRI图像与全层病理标本图像进行融合，以消除IVIM计算中的潜在偏差。16例PCa患者在接受根治性前列腺切除术之前进行了前列腺MRI扫描。然后将图像融合方法应用于计算患者的IVIM参数。此外，还对22名健康年轻志愿者进行了MRI扫描，以评估IVIM参数随年龄的变化。在整个研究队列中，f参数随年龄显著增加，而D参数显著降低。在PCa患者中，D和ADC参数可以区分PCa组织和对侧正常组织，而f和D参数则不能。当并排比较感兴趣区域时，所提出的图像融合方法也提供了更高的精度。然而，需要用更标准化的方法进行进一步研究，以进一步阐明所提出方法的益处以及不同IVIM参数在PCa特征描述中的作用。
