A 3D Multi-task Regression and Ordinal Regression Deep Neural Network for Collateral Imaging from Dynamic Susceptibility Contrast-Enhanced MR perfusion in Acute Ischemic Stroke. (October 2022) - Le, Hoang Long; Roh, Hong Gee; Kim, Hyun Jeong; Kwak, Jin Tae
Highlights: We propose a multi-task 3D deep learning approach for collateral imaging that take advantages of both regression and ordinal regression. We introduce a novel discretization strategy, network architecture, and loss functions that are specifically designed for the generation of collateral maps. We achieve the state-of-the-art performance on a dataset with >800 subjects as compared to 8 other competing methods. Abstract: Background and Objective: Cerebral collaterals have been identified as one of the primary determinants for treatment options in acute ischemic stroke. Several works have been proposed, but these have not been adopted for a routine clinical usage due to their manual and heuristic nature as well as inconsistency and instability of the assessment. Herein, we present an advanced deep learning-based method that can automatically generate a multiphase collateral imaging (collateral map) derived from dynamic susceptibility contrast-enhanced MR perfusion (DSC-MRP) in an accurate and robust manner. Methods: We develop a 3D multi-task regression and ordinal regression deep neural network for generating collateral maps from DSC-MRP, which formulates the prediction of collateral maps as both a regression task and an ordinal regression task. For an ordinal regression task, we introduce a spacing-decreasing discretization (SDD) strategy to represent the intensity of the collateral status on a discrete, ordinal scale. We also devise loss functions to achieve effective and efficient multi-task learning. Results: We systematically evaluated the performance of the proposed network using DSC-MRP from 802 patients. On average, the proposed network achieved ≥0.900 squared correlation coefficient (R-Squared), ≥0.916 Tanimoto measure (TM), ≥0.0913 structural similarity index measure (SSIM), and ≤0.564 × 10 −1 mean absolute error (MAE), outperforming eight competing models that have been recently developed in medical imaging and computer vision. We also found that the proposed network could provide an improved contrast between the low and high intensity regions in the collateral maps, which is a key to an accurate evaluation of the collateral status. Conclusions: The experimental results demonstrate that the proposed network is able to generate collateral maps with high accuracy, facilitating a timely and prompt assessment of the collateral status in clinlcs. The future study will entail the optimization of the proposed network and its clinical evalution in a prospective manner.