We present application and developments of qMRI. We reconstructed data from the Prediva Ouderen Extentie (POE) database and calculated quantitative maps. The POE database consists of 29 7 Tesla (T) whole-brain MRI scans, including both male and female participants covering the age group between 80 and 91 years old. We used this data to extend the currently existing Amsterdam Ultra-high field adult lifespan database (AHEAD), covering the entire adult life span (18-80 years). Data was acquired at a submillimeter resolution using a the MP2RAGEME sequence, resulting in complete anatomical alignment of quantitative $R_1$-maps, $R_2^*$-maps, $T_1$-maps, $T_2^*$-maps, and quantitative susceptibility mapping (QSM).

As qMRI acquisitions and reconstructions are slow, we also investigated the potential of deep learning to speed up this process using the same dataset. Conventional MRI acquisitions are already time consuming, scanning the multiple images necessary for qMRI may take even longer. Computing a quantitative susceptibility map requires several processing steps involving phase unwrapping, background phase removal, and solving an ill-posed inverse problem, which all together is computationally expensive. COSMOS is currently seen as gold standard for solving the dipole inversion, although sampling from different orientations makes it a lengthy procedure and not feasible for clinical practice. Iterative methods were developed to make high quality QSM maps out of a single orientation measurement, however they suffer from artifacts and can be computationally expensive. Deep learning may improve the QSM pipeline in terms of time, convenience and quality. Neural networks were developed to learn processing steps like phase unwrapping and dipole inversion. Especially for the dipole inversion step several methods are proposed to calculate a high quality QSM map out of single orientation measurements which could speed up the QSM pipeline. We propose a method which can be useful in accelerated end-to-end QSM calculation and highlight advances and possible points for improvement. We will conclude with a discussion of the challenges that need to be overcome to establish a fast, easy and high quality QSM pipeline.