Whole Brain 3D T1 Mapping in Multiple Sclerosis Using Standard Clinical Images Compared to MP2RAGE and MR Fingerprinting

Quantitative T1 and T2 mapping is a useful tool to assess properties of healthy and diseased tissues. However, clinical diagnostic imaging remains dominated by relaxation-weighted imaging without direct collection of relaxation maps. Dedicated research sequences such as MR fingerprinting can save time and improve resolution over classical gold standard quantitative MRI (qMRI) methods, although they are not widely adopted in clinical studies. We investigate the use of clinical sequences in conjunction with prior knowledge provided by machine learning to elucidate T1 maps of brain in routine imaging studies without the need for specialized sequences. A classification learner was trained on T1w (magnetization prepared rapid gradient echo [MPRAGE]) and T2w (fluid-attenuated inversion recovery [FLAIR]) data (2.6 million voxels) from multiple sclerosis (MS) patients at 3T, compared to gold standard inversion recovery fast spin echo T1 maps in five healthy subjects, and tested on eight MS patients. In the MS patient test, the results of the machine learner-produced T1 maps were compared to MP2RAGE and MR fingerprinting T1 maps in seven tissue regions of the brain: cortical grey matter, white matter, cerebrospinal fluid, caudate, putamen and globus pallidus. Additionally, T1s in lesion-segmented tissue was compared using the three different methods. The machine learner (ML) method had excellent agreement with MP2RAGE, with all average tissue deviations less than 3.2%, with T1 lesion variation of 0.1%-5.3% across the eight patients. The machine learning method provides a valuable and accurate estimation of T1 values in the human brain while using data from standard clinical sequences and allowing retrospective reconstruction from past studies without the need for new quantitative techniques.