Research progress of quantitative multimodal brain imaging technology

Abstract

Advanced medical imaging technology facilitates human brain recognition and its disease diagnosis research like positron emission tomography (PET) and magnetic resonance imaging (MRI). The changes of structure, function, metabolism, and signaling pathways yield richer multimodal image data for disease diagnosis research. Traditional clinical imaging techniques are mostly based on qualitative interpretation. The signal intensity of acquired images are differentiated for normal tissues, resulting in uncertainty in image contrast due to the microscopic scaled structure and function changes in tissue disease pathology. It is an effective way to obtain accurate and reliable detection of lesion features while the tissue contrast changes intensively higher than the noise level. In comparison to qualitative medical imaging, the current measurement focuses on physiology and physics related parameters to generate its quantitative parameter map. Quantitative parameters have their own physical units in common and their quantitative values reflect the physiological and physical information of the object mathematically. Quantitative measurement of tissue is essential to physiopathological modeling. The relationship between image nuances and pathology, realize in-depth clinical data mining for accurate diagnosis based on the integration of effective model analysis. The quantitative integration of cross-modalities and multiple imaging mechanisms medical imaging has been developed in brain tumors and neuropsychiatric diseases. While quantitative imaging technology is challenging in clinical settings, no matter due to its long acquisition time or its different image presentation. The common quantitative PET and MRI measurements are based on data fitting from multiple measurement. The multiple measurements are time consuming and costly. The modeling and simulation of micro-physiological systems still need to be continuously developed and improved, including the development from static models to dynamic models. Our research review and discuss the key technical issues and development of existing quantitative imaging technologies for human brain microstructure and physiological function indicators detection through PET and MRI methods. The clinical applications and future directions are introduced as well. Specifically, we focus on the establishment of quantitative models, the measurement of quantitative parameters and imaging methods, the influencing factors in the measurement, and the clinical application of related technologies. First, the review of quantitative MRI is based on the current situation and deficiencies of single-parameter quantification and the development trendency of simultaneous multi-parameter quantification. Then, it introduces two methods of myelin imaging based on the quantification of microscopic parameters, including multicomponent T2 quantification and ultrashort echo based myelin imaging. An introduction to the comparability and reproducibility of magnetic resonance quantitative imaging is followed on, especially magnetic resonance diffusion imaging. Second, the review of quantitative PET is based on the most extensive metabolic kinetic model-the compartment model. To extend quantitative error sources like model option, image quality, and input functions, the relationship between physiological parameters and tracer uptake is clarified and three aspects of measurement error are analyzed in detail. The latest development is reviewed based on hardware equipment, image reconstruction methods and quantitative analysis methods. The future MRI quantification, PET quantification and PET/MRI quantification are briefly predicted further.