  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.
