# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
###### Abstract  
This study develops a data-driven reduced-order model based on a deep convolutional neural network (CNN) for real-time and accurate prediction of the drug trajectory and concentration field in transarterial chemoembolization therapy to assist in directing the drug to the tumor site. The convolutional and deconvolutional layers are used as the encoder and the decoder, respectively. The input of the network model is designed to contain the information of drug injection location and the blood vessel geometry and the output consists of the drug trajectory and the concentration field. We studied drug delivery in two-dimensional straight, bifurcated blood vessels and the human hepatic artery system and showed that the proposed model can quickly and accurately predict the spatial-temporal drug concentration field. For the human hepatic artery system, the most complex case, the average prediction accuracy was 99.9% compared with the CFD prediction. Further, the prediction time for each concentration field was less than 0.07 s, which is four orders faster than the corresponding CFD simulation. The high performance, accuracy and speed of the CNN model shows the potential for effectively assisting physicians in directing chemoembolization drugs to tumor-bearing segments, thus improving its efficacy in real-time. 24 August 2022415 October 202241  
to evaluate the impact of clinical parameters on the transarterial drug trajectory in interventional treatment with numerical simulations. [PERSON] [9] investigated the effect of the relative position of the catheter spout and the liquid drug injection angle on the drug perfusion rate in vascular interventional treatment. However, only idealized bifurcated blood vessel geometry and limited numbers of injection positions and angles were examined. [PERSON] et al. [10] developed a computational model of a representative hepatic artery system to study the influence of the particle release position and the temporal release window on the velocity field and the particle trajectory for Yttrium-90 (90Y) radioembolization treatment. The three-pulse simulation took approximately 72.87 h. To compare the impact of the microagent quantity and size, catheter-tip position on the trajectory of the microagent, [PERSON] et al. [11] performed numerical simulations to mimic liver radioembolization in terms of delivering particles to tumor-bearing segments and concluded that the catheter-tip position has the most important effect on the trajectories of the microagent. While these studies provide significant insight on the influence of clinical parameters on drug delivery in the human hepatic artery, the computational cost is huge due to the complex geometry and nonlinearity of the flow problem. Over the past decade, a considerable amount of literature has been published on machine learning (ML) and deep learning (DL), due to their ability to derive powerful reduced-order models (ROM) to address large computational and highly nonlinear problems. In particular, convolutional neural networks (CNNs), as one of the representative algorithms of deep learning, can perform stable and efficient learning of lattice-point features (such as pixels) with small computational resources and have been widely used in image processing [12; 13; 14]. In numerical simulation, the physical field often appears in the form of a spatial distribution, similar to a digital image. Therefore, CNNs have the potential to provide accurate ROMs for physical field prediction. Recently, a series of papers demonstrated the above point by investigating a CNN-based ROM to predict the flow field over cylinders and airfoils. [PERSON] et al. [15] proposed a hybrid deep neural network to predict the flow field over a cylinder and an airfoil at future times based on the flow field at previous times with good agreement with numerical simulations. [PERSON] et al. [16] showed that a CNN-based ROM can achieve even greater performance than the network model with a fully connected decoder.