Self-Supervised Motion-Corrected Image Reconstruction Network for 4D Magnetic Resonance Imaging of the Body Trunk

Kuestner T, Pan J, Gilliam C, Qi H, Cruz G, Hammernik K, Blu T, Rueckert D, Botnar R, Prieto C, Gatidis S (2022)

Publication Type: Journal article

Publication year: 2022

Journal

APSIPA Transactions on Signal and Information Processing

Book Volume: 11

Article Number: e12

Journal Issue: 1

DOI: 10.1561/116.00000039

Abstract

Respiratory motion can cause artifacts in magnetic resonance imaging of the body trunk if patients cannot hold their breath or triggered acquisitions are not practical. Retrospective correction strategies usually cope with motion by fast imaging sequences under free-movement conditions followed by motion binning based on motion traces. These acquisitions yield sub-Nyquist sampled and motion-resolved k-space data. Motion states are linked to each other by non-rigid deformation fields. Usually, motion registration is formulated in image space which can however be impaired by aliasing artifacts or by estimation from low-resolution images. Subsequently, any motion-corrected reconstruction can be biased by errors in the deformation fields. In this work, we propose a deep-learning based motion-corrected 4D (3D spatial + time) image reconstruction which combines a non-rigid registration network and a 4D reconstruction network. Non-rigid motion is estimated in k-space and incorporated into the reconstruction network. The proposed method is evaluated on in-vivo 4D motion-resolved magnetic resonance images of patients with suspected liver or lung metastases and healthy subjects. The proposed approach provides 4D motion-corrected images and deformation fields. It enables a ∼ 14× accelerated acquisition with a 25- fold faster reconstruction than comparable approaches under consistent preservation of image quality for changing patients and motion patterns.

Involved external institutions

St Thomas' Hospital GB United Kingdom (GB) ShanghaiTech University / 上海科技大学 CN China (CN) Royal Melbourne Institute of Technology (RMIT) AU Australia (AU) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) The Chinese University of Hong Kong (CUHK) CN China (CN) Universitätsklinikum Tübingen DE Germany (DE) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Kuestner, T., Pan, J., Gilliam, C., Qi, H., Cruz, G., Hammernik, K.,... Gatidis, S. (2022). Self-Supervised Motion-Corrected Image Reconstruction Network for 4D Magnetic Resonance Imaging of the Body Trunk. APSIPA Transactions on Signal and Information Processing, 11(1). https://doi.org/10.1561/116.00000039

MLA:

Kuestner, Thomas, et al. "Self-Supervised Motion-Corrected Image Reconstruction Network for 4D Magnetic Resonance Imaging of the Body Trunk." APSIPA Transactions on Signal and Information Processing 11.1 (2022).

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