Abstract
Computed Tomography Perfusion (CTP) imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows the visualization of anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. In fact, the accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation, and even cancer. Solutions for reducing radiation exposure include reducing the tube current and/or shortening the X-ray radiation exposure time. However, images scanned at lower tube currents are usually accompanied by higher levels of noise and artifacts. On the other hand, shorter X-ray radiation exposure time with longer scanning intervals will lead to image information that is insufficient to capture the blood flow dynamics between frames. Thus, it is critical for us to seek a solution that can preserve the image quality when the tube current and the temporal frequency are both low. We propose STIR-Net in this paper, an end-to-end spatial-temporal convolutional neural network structure, which exploits multi-directional automatic feature extraction and image reconstruction schema to recover high-quality CT slices effectively. With the inputs of low-dose and low-resolution patches at different cross-sections of the spatio-temporal data, STIR-Net blends the features from both spatial and temporal domains to reconstruct high-quality CT volumes. In this study, we finalize extensive experiments to appraise the image restoration performance at different levels of tube current and spatial and temporal resolution scales.The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to the patch-based log likelihood method.
Highlights
Acute stroke has high mortality and severe long-term disability rates worldwide
We show that STIR-Net is a general solution for different tube currents as the peak signal-to-noise ratio (PSNR) improvements for different test cases are all higher than 5 dB. mAs is the unit for tube current-time product
This paper presents a novel deep learning-based multidirectional spatio-temporal framework to recover the low radiation dose Computed Tomography Perfusion (CTP) images of acute stroke patients by addressing both denoising and super-resolution problems simultaneously
Summary
Acute stroke has high mortality and severe long-term disability rates worldwide. In the United States, more than 795,000 people have a stroke annually, and about 140,000 of them lose their lives, accounting for 5% of all deaths [1]. Someone develops a stroke approximately every 40 s, and nearly every 4 min, someone loses he or her life because of stroke. Stroke can occur at any age, and it increases in likelihood with age. In 2009, two-thirds of people who had been hospitalized for stroke were older than 65 years old [2]. The estimated cost related to stroke in the United States is about 34 billion dollars each year [3]
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