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Review
. 2023 Jul;96(1147):20220071.
doi: 10.1259/bjr.20220071. Epub 2022 Aug 8.

Retrospective motion correction in foetal MRI for clinical applications: existing methods, applications and integration into clinical practice

Alena U Uus  1 , Alexia Egloff Collado  2 , Thomas A Roberts  1   3 , Joseph V Hajnal  1   2 , Mary A Rutherford  2 , Maria Deprez  1
Affiliations
Review

Retrospective motion correction in foetal MRI for clinical applications: existing methods, applications and integration into clinical practice

Alena U Uus et al. Br J Radiol. 2023 Jul.

Abstract

Foetal MRI is a complementary imaging method to antenatal ultrasound. It provides advanced information for detection and characterisation of foetal brain and body anomalies. Even though modern single shot sequences allow fast acquisition of 2D slices with high in-plane image quality, foetal MRI is intrinsically corrupted by motion. Foetal motion leads to loss of structural continuity and corrupted 3D volumetric information in stacks of slices. Furthermore, the arbitrary and constantly changing position of the foetus requires dynamic readjustment of acquisition planes during scanning.

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Conflict of interest statement

Competing interestsThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1.
Figure 1.
Motion in foetal MRI: examples of T2-weighted SSTSE stacks of 2D slices. The MRI datasets used in this example were acquired at St.Thomas’ Hospital, London.
Figure 2.
Figure 2.
Motion-resistant T2W SSTSE 1.5 and 3 T protocols optimised for 3D SVR-based reconstruction at St.Thomas’ Hospital, London. GA, gestational age; SAR, specific absorption rate.
Figure 3.
Figure 3.
3D SVR reconstruction for foetal brain MRI. This example is based on the MRI dataset acquired at St.Thomas’ Hospital, London and reconstructed using the classical SVR. SVR, slice-to-volume registration
Figure 4.
Figure 4.
3D DSVR reconstruction for foetal body MRI. These examples are based on the MRI datasets acquired at St.Thomas’ Hospital, London. DSVR, deformable SVR; SVR, slice-to-volume registration.
Figure 5.
Figure 5.
Main limitations of the classical SVR-based methods for foetal MRI and structure of AI-based solutions for automated reconstruction. This example is based on the MRI datasets acquired at St.Thomas’ Hospital, London. AI, artificial intelligence; SVR, slice-to-volume registration.
Figure 6.
Figure 6.
Visualisation of 3D reconstructed images for diagnostic purposes. The examples are based on the MRI datas acquired at St.Thomas’ Hospital, London.
Figure 7.
Figure 7.
Examples of existing quantitative applications for 3D MRI reconstructed images. The examples are based on the MRI datasets acquired at St.Thomas’ Hospital, London. SVR, slice-to-volume registration.
Figure 8.
Figure 8.
Comparison of different SVR reconstruction toolboxes for foetal brain MRI. SVR, slice-to-volume registration.
Figure 9.
Figure 9.
An example of integration of 3D SVR/DSVR reconstruction for foetal MRI into clinical settings (Perinatal Imaging Department at St.Thomas’ Hospital, London). SVR, slice-to-volume registration.
Figure 10.
Figure 10.
An example of qualitative assessment of the classical 3D SVR reconstruction for foetal brain MRI on a large (~1000 cases) single-centre cohort. The datasets were acquired and processed at St.Thomas’ Hospital, London. GA, gestational age; SVR, slice-to-volume registration.
Figure 11.
Figure 11.
Examples of 3D MRI-derived foetal development models. GA, gestational age; SVR, slice-to-volume registration.
Figure 12.
Figure 12.
Examples of SVR-based solutions for advanced foetal MRI acquisition methods. SVR, slice-to-volume registration.
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