MRI-Driven PET Image Optimization for Neurological Applications

Abstract

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.

Keywords: image-derived input function (IDIF); magnetic resonance imaging (MRI); motion correction (MC); multimodal imaging; neuroimaging; partial volume effect (PVE); positron emission tomography (PET).

FIGURE 1

Schematic of MRMC procedures. MRI tracking sequences can be used to monitor the head motion during PET scanning. Each time point when head motion is beyond a default threshold is determined as a symbol of actual movement and a framing boundary for the recombination of the list-mode PET images. By this means, the successive PET raw data are divided into a series of discrete temporal subunits, each of which maintains a specific fixed head pose. Then, the process of MRMC for PET data either within-reconstruction or post-reconstruction is completed by assembling all the framed PET images into a single PET image.

FIGURE 2

Flow chart of MR-based PET data optimization and reconstruction. Corrections for photon attenuation, head motion and partial volume effect of PET data can be achieved by using specific MR sequences, and conducted in the format of either within- or post-reconstruction. Precise image co-registration and segmentation are the prerequisite for various MR-based PET data optimization procedures. MRI-based image-derived input function method could be further applied in the quantitative analysis of dynamic PET imaging.