CS 229 Final Project 1 Classification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging

Subject motion is a significant problem in magnetic resonance imaging (MRI). In this project, unsupervised learning using Kmeans clustering, and supervised learning using logistic regression (LR) and support vector machine (SVM) are implemented and applied to solving (1) classification of MR images with/without motion, and (2) estimation of current motion state based on previous low frequency navigator signals. 105 MR images with labeling of motion/no-motion and 40000 navigator signals are collected as the training and testing data. Over 10 features were designed and manually selected for each learning approach based on principle component analysis (PCA). Results show that, for unsupervised learning, the K-means algorithm achieves an accuracy of ~83% for classification of MR images and a root-mean-squared error (RMSE) of 5.3% in prediction based on navigator signals. For supervised learning, SVM using Gaussian kernels achieves the best accuracy of ~90% for classification of non-motion and motion images, and a RMSE of ~1.7% for estimation of motion states based on navigator signals. These approaches provide a possibility for advanced reconstruction algorithms to perform more accurate motion corrections with or without a large training set, and may improve the quality of clinical images. INTRODUCTION Among all the medical imaging modalities, MRI is one of the best candidates for evaluation and diagnosis of diseases, because of its high spatial-resolution, good softtissue contrast and no ionizing radiation. However, MRI requires relatively long scan times, and therefore is more sensitive to subject motion during exams. Subject motion can create imaging artifacts such as imaging blurring, ghosting, and reduced SNR, and degrade image quality. This limits its wide use in clinical diagnoses. In order to overcome this disadvantage, several approaches have been developed for improving speed and reducing motion artifacts during reconstructions of acquired MRI signals. However, these approaches require classification and estimation of motion, which is usually difficult to achieve. The fundamental goal of this project is to classify the images with/without motion based on DICOM images. The second objective of this project is to estimate the current motion state based on previous information on patient positions. With the help of unsupervised and supervised learning models, hopefully MRI scanners can automatically determine the significance of motion artifacts, and perform appropriate corrections and reconstructions based on prior information of motion. RELATED WORK Both unsupervised learning and supervised learning have been applied to solving MRI problems. However, only unsupervised learning has been used to classify MR images for cardiac perfusion applications[1]. Few attempts have been made to classify abdominal images and mixed images with different body parts. Supervised learning approaches, such as support vector machine (SVM), have been used to classify functional MRI data of brain states[2][3]. However, it has not been applied to prediction of motion states and subject positions, which is the main focus of this project.