Deep Spatial-Temporal Convolutional Neural Networks for Medical Image Restoration

Abstract

Computed tomography perfusion (CTP) facilitates low-cost diagnosis and treatment of acute stroke. Cine scanning allows users to visualize brain anatomy and blood flow in virtually live time. However, effective visualization exposes patients to radiocontrast pharmaceuticals and extended scan times. Higher radiation dosage exposes patients to potential risks including hair loss, cataract formation, and cancer. To alleviate these risks, radiation dosage can be reduced along with tube current and/or X-ray radiation exposure time. However, resulting images may lack sufficient information or be affected by noise and/or artifacts. In this chapter, we propose a deep spatial-temporal convolutional neural network to preserve CTP image quality at reduced tube current, low spatial resolution, and shorter exposure time. This network structure extracts multi-directional features from low-dose and low-resolution patches at different cross sections of the spatial-temporal data and reconstructs high-quality CT volumes. We assess the performance of the network concerning image restoration at different tube currents and multiple resolution scales. The results indicate the ability of our network in restoring high-quality scans from data captured at as low as 21% of the standard radiation dose. The proposed network achieves an average improvement of 7% in perfusion maps compared to the state-of-the-art method.