Complex-valued neural networks to speed-up MR Thermometry during Hyperthermia using Fourier PD and PDUNet
arxiv(2023)
摘要
Hyperthermia (HT) in combination with radio- and/or chemotherapy has become
an accepted cancer treatment for distinct solid tumour entities. In HT, tumour
tissue is exogenously heated to temperatures between 39 and 43 ^∘C for 60
minutes. Temperature monitoring can be performed non-invasively using dynamic
magnetic resonance imaging (MRI). However, the slow nature of MRI leads to
motion artefacts in the images due to the movements of patients during image
acquisition. By discarding parts of the data, the speed of the acquisition can
be increased - known as undersampling. However, due to the invalidation of the
Nyquist criterion, the acquired images might be blurry and can also produce
aliasing artefacts. The aim of this work was, therefore, to reconstruct highly
undersampled MR thermometry acquisitions with better resolution and with fewer
artefacts compared to conventional methods. The use of deep learning in the
medical field has emerged in recent times, and various studies have shown that
deep learning has the potential to solve inverse problems such as MR image
reconstruction. However, most of the published work only focuses on the
magnitude images, while the phase images are ignored, which are fundamental
requirements for MR thermometry. This work, for the first time, presents deep
learning-based solutions for reconstructing undersampled MR thermometry data.
Two different deep learning models have been employed here, the Fourier
Primal-Dual network and the Fourier Primal-Dual UNet, to reconstruct highly
undersampled complex images of MR thermometry. The method reduced the
temperature difference between the undersampled MRIs and the fully sampled MRIs
from 1.3 ^∘C to 0.6 ^∘C in full volume and 0.49 ^∘C to 0.06
^∘C in the tumour region for an acceleration factor of 10.
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