MR Imaging in the 21st Century: Technical Innovation over the First Two Decades

Abstract

Clinical MRI systems have continually improved over the years since their introduction in the 1980s. In MRI technical development, the developments in each MRI system component, including data acquisition, image reconstruction, and hardware systems, have impacted the others. Progress in each component has induced new technology development opportunities in other components. New technologies outside of the MRI field, for example, computer science, data processing, and semiconductors, have been immediately incorporated into MRI development, which resulted in innovative applications. With high performance computing and MR technology innovations, MRI can now provide large volumes of functional and anatomical image datasets, which are important tools in various research fields. MRI systems are now combined with other modalities, such as positron emission tomography (PET) or therapeutic devices. These hybrid systems provide additional capabilities.In this review, MRI advances in the last two decades will be considered. We will discuss the progress of MRI systems, the enabling technology, established applications, current trends, and the future outlook.

Keywords: image acquisition; image reconstruction; magnetic resonance imaging; magnetic resonance imaging applications; magnetic resonance imaging system.

Fig. 1

MRI technology advances and the interactions between each technical component. Technical advances in basic sciences and engineering impact MRI technologies. Technical development needs in each element drive innovation in other elements. MRgFUS, MR-guided focused ultrasound; PET, positron emission tomography.

Fig. 2

An example of a high resolution 7T Brain image (adapted from Fig. 6 of Nakada et al.⁶). The image was obtained with an FSE sequence with peripheral gating utilizing a single receiver channel of an 8-channel receiver coil. The main parameters were as follows: TR = 6 cardiac cycle, TE = 30.264 ms, trigger delay = 500 ms, FOV = 5 x 5 cm, matrix size = 512 x 512, NEX = 2, and echo train = eight. NEX, numbers of excitations.

Fig. 3

Examples of deep learning-based motion artifact reduction. The motion artifacts in the acquired images (upper row) were reduced (lower row). The residual components are shown in the middle row (adapted from Fig. 10 of Tamada et al.⁵⁰).

Fig. 4

T2-weighted image reconstructed by dDLR. NAQ2 has higher image noise than NAQ5 and dDLR-NAQ2. Identification of the hippocampal layer structure is superior in both NAQ5 and dDLR-NAQ2 compared with NAQ2 (arrows) (adapted from Fig. 7 of Kidoh et al.⁵¹). dDLR, deep learning-based reconstruction; NAQ, number of image acquisition.

Fig. 5

An automated cardiac slice prescription process flow. Mitral valve, apex, tricuspid valve, and left ventricular outflow tract were detected. These reference points were used to prescribe scan slices such as short-axis view, 4-chamber view, 2-chamber view, and 3-chamber view (adapted from Fig. 1 of Yokoyama et al.⁵⁵).

Fig. 6

Susceptibility maps estimated by SS-TVV-NM and SS-TVV from mSPGR and sSPGR, respectively. A slight signal inhomogeneity was observed in the map estimated by SS-TVV from mSPGR (dashed arrows). Moreover, a large shading was observed by the combination of SS-TVV and sSPGR (dashed arrows). mSPGR, multiple spoiled gradient echo sequence; sSPGR, single-echo spoiled gradient echo sequence; SS-TVV-NM, single-step total variation with variable kernel size and norm minimization within the volume of interest (adapted from Fig. 4 of Kan et al.⁷⁸).