期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 68, 期 5, 页码 1737-1747出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2020.3024826
关键词
Biomarkers; Imaging; Tumors; Ultrasonic imaging; Rabbits; Acoustics; Machine learning; ablation therapy; deformable image registration; convolutional neural network
资金
- National Institutes of Health [5R37CA224141, S10OD018482, 1R03EB026132]
A novel noncontrast multiparametric MR biomarker was introduced for intratreatment assessment, outperforming current contrast-based techniques by accurately predicting MRgFUS treatment outcomes. The study utilized a deep convolutional neural network trained on noncontrast MR images and validated the biomarker using a volume-conserving registration algorithm.
Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses the nonperfused volume (NPV) biomarker immediately after treatment from contrast-enhanced MRI. The NPV has variable accuracy, and the use of contrast agent prevents continuing MRgFUS treatment if tumor coverage is inadequate. This work presents a novel, noncontrast, learned multiparametric MR biomarker that can be used during treatment for intratreatment assessment, validated in a VX2 rabbit tumor model. A deep convolutional neural network was trained on noncontrast multiparametric MR images using the NPV biomarker from follow-up MR imaging (3-5 days after MRgFUS treatment) as the accurate label of nonviable tissue. A novel volume-conserving registration algorithm yielded a voxel-wise correlation between treatment and follow-up NPV, providing a rigorous validation of the biomarker. The learned noncontrast multiparametric MR biomarker predicted the follow-up NPV with an average DICE coefficient of 0.71, substantially outperforming the current clinical standard (DICE coefficient = 0.53). Noncontrast multiparametric MR imaging integrated with a deep convolutional neural network provides a more accurate prediction of MRgFUS treatment outcome than current contrast-based techniques.
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