Motion-Resolved, Comprehensive Quantitative Tissue Characterization Using MR Multitasking - PROJECT SUMMARY
Quantitative magnetic resonance imaging (MRI) measures tissue parameters such as T1, T2, T2*, and
diffusion to detect subtle differences in tissue states (such as microstructure, diffuse fibrosis, edema,
hemorrhage, and iron content) from neurological, oncological, and cardiovascular diseases. Because each
parameter offers complementary tissue information, multiparameter mapping is very promising for risk
assessment, early detection, accurate staging, and treatment monitoring of disease. However, quantitative MRI
is typically very time consuming and difficult to perform. Each parameter is typically measured from its own
series of images, so measuring multiple parameters leads to long, inefficient scanning sessions. Furthermore,
cardiac and breathing motion creates misalignment between images, causing additional problems.
The standard approach to motion is to either remove it (e.g., ask the patient to hold their breath) or to
synchronize image acquisition with it (e.g., using electrocardiography (ECG) to monitor cardiac motion). This
approach makes scan times even longer, limits imaging to patients who can repeatedly perform long breath
holds (which is difficult for aging or weak patients) and who have predictable cardiac motion (which is not true
of patients with cardiac arrhythmias). Furthermore, these methods are often unreliable and difficult to perform.
This project is to develop and validate a new technology, MR Multitasking, to perform multiple
simultaneous measurements in a single, push-button scan that is both comfortable for patients and simple for
technologists to perform. MR Multitasking redesigns quantitative MRI around the concept of images as
functions of many time dimensions, each corresponding to a different dynamic process (e.g., motion, T1, T2,
T2*, and diffusion), and then uses mathematical models called low-rank tensors to perform fast,
multidimensional imaging. This allows continuous acquisition of imaging data even while the subject is moving,
providing motion-resolved parameter maps without breath holding or motion synchronization. We will scan
healthy subjects, liver patients, prostate cancer patients, and cardiovascular patients to develop and validate
this technology and use artificial intelligence to quickly reconstruct images from the collected data. The
resulting tool will be applicable to any organ system, offering clinicians and investigators a valuable tool to
answer a wide range of biomedical questions.
