Fast And Quantitative MRI Imaging Methods Of Important Biophysical Markers In Brain

Quantitative brain imaging maps out properties of tissues of biochemical and biophysical significance,such as diffusion coefficient,fractional anisotropy,T1,T2 and proton density(PD),which have been identified as sensitive biomarkers for detecting diseases such as multiple sclerosis,epilepsy and cancer.However,due to the prohibitively long acquisition time with conventional quantitative imaging,these quantification methods are rarely applied at present in clinical practice.Spatial resolution and imaging speed is a pair of paradoxical needs for quantitative MRI techniques.In spite of recent advance in research,rapid and robust acquisition remains a challenge for whole-brain high-resolution quantitative imaging.It also hinders success of multi-center large scale neuroscientific projects of various national and international brain studies.This thesis work aims at tackling several related technical issues,and also applies some preliminary results in a clinical application.In the first part of this work,we present a 3D MR fingerprinting(MRF)acquisition with a hybrid sliding-window(SW)and GRAPPA reconstruction strategy to obtain high-resolution T1,T2 and PD maps with whole brain coverage in a clinically feasible timeframe.Prospectively under-sampled in vivo study showed that whole brain T1,T2 and PD maps with 1 mm3 resolution could be obtained in 7.5 minutes.In the second part of this work,we quantitatively investigated the subtle changes in hippocampal sclerosis(HS)lesions of mesial temporal lobe epilepsy(MTLE)patients,and compared the sensitivity and specificity of the MRF method to the visual inspection with T1-and T2-weighted images.Within 2.5 minutes,MRF identifies suspicious hippocampal sclerosis in MTLE patients with improved accuracy over visual assessment of T1-and T2-weighted MR images.We demonstrated that MRF could potentially aid multi-modal quantitative mapping and aid clinicians in diagnostic decision-making for epilepsy patients.In the third part of this work,a robust reconstruction method utilizing parallel imaging with low rank constraint(LR-SENSE)was proposed to accelerate high resolution multi-shot spiral diffusion imaging.It was shown that with a same acceleration factor,the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps,when evaluated with different acceleration factors(R=2,3,4)and for all the acquired diffusion directions.In the last part of the work,we proposed a virtual coil(VC)acquisition/reconstruction framework to improve highly accelerated single-shot EPI(SS-EPI)and generalized slice dithered enhanced resolution(gSlider)acquisition in high resolution diffusion imaging(DI).The proposed VC reconstruction substantially improves the image quality of both single-shot SMS-EPI and gSlider-EPI,with reduced g-factor noise and reconstruction artifacts when compared to conventional methods.This has enabled high-quality low-noise diffusion imaging to be performed at 8-9 fold acceleration.The ability to now employ such high accelerations will allow DI with EPI at reduced distortion and faster scan time,which should be beneficial for many clinical and neuroscience applications.In summary,this thesis work developed several fast and robust techniques for quantitative brain imaging,and applied some of them to demonstrate their potential clinical applications.