复值神经网络利用Fourier PD和PDUNet加速热疗中的MR测温
Complex-valued neural networks to speed-up MR Thermometry during Hyperthermia using Fourier PD and PDUNet
AI总结：
本文首次提出基于Fourier Primal-Dual network和Fourier Primal-Dual UNet的深度学习模型，用于重建高度欠采样的MR测温复值图像，有效降低了温度误差并减少了伪影。
摘要：
Hyperthermia (HT)（热疗）结合放疗和/或化疗已成为治疗特定实体瘤的公认癌症疗法。在HT中，肿瘤组织被外源性加热至39至43 $^\circ$C的温度，持续60分钟。可以使用动态磁共振成像 (MRI) 进行非侵入性温度监测。然而，MRI的缓慢特性导致在图像采集过程中由于患者的运动而在图像中产生运动伪影。通过丢弃部分数据，可以提高采集速度，这被称为欠采样。然而，由于Nyquist准则的失效，采集到的图像可能模糊，并可能产生混叠伪影。因此，这项工作的目的是与常规方法相比，以更好的分辨率和更少的伪影重建高度欠采样的MR测温采集。近年来，深度学习在医学领域的应用兴起，多项研究表明，深度学习具有解决MR图像重建等逆问题的潜力。然而，大多数已发表的工作仅关注幅度图像，而忽略了相位图像，而相位图像是MR测温的基本要求。这项工作首次提出了基于深度学习的解决方案，用于重建欠采样的MR测温数据。这里采用了两种不同的深度学习模型，即Fourier Primal-Dual network（Fourier原始-对偶网络）和Fourier Primal-Dual UNet，以重建高度欠采样的MR测温复值图像。对于10的加速因子，该方法将欠采样MRI与全采样MRI之间的温度差在全体积中从1.3 $^\circ$C降至0.6 $^\circ$C，在肿瘤区域从0.49 $^\circ$C降至0.06 $^\circ$C。
英文摘要：
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 $^\circ$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 $^\circ$C to 0.6 $^\circ$C in full volume and 0.49 $^\circ$C to 0.06 $^\circ$C in the tumour region for an acceleration factor of 10.
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